Who is the manufacturer of Koios DS?

Koios Medical, Inc. filed the 510(k) submission for Koios DS (K212616).

What is the FDA product code for Koios DS?

POK. It is classified under 21 CFR 892.2060 as a Class 2 device in the Radiology panel.

What is the regulatory pathway for Koios DS?

510(k) clearance (submission type: Traditional).

Does Koios DS use AI or machine learning?

Yes. Koios DS (K212616) is flagged as an AI/ML-enabled medical device in the FDA's AI/ML device list.

Who can help prepare an FDA 510(k) submission like Koios DS?

Innolitics — a medical-device software consultancy — provides regulatory and engineering support for FDA 510(k) submissions. See https://innolitics.com/services/510ks/ or contact https://innolitics.com/contact.

Can an AI/ML device like Koios DS get a guaranteed FDA 510(k) clearance?

Yes. Innolitics offers a guaranteed FDA 510(k) clearance engagement for AI/ML Software as a Medical Device — combining engineering and regulatory execution under one agreement with a guaranteed clearance outcome. Eligibility: the device must be an AI/ML Software as a Medical Device (SaMD), and its performance must meet the criteria Innolitics determines during an initial regulatory strategy engagement to be necessary for FDA clearance. See https://innolitics.com/services/end-to-end-samd/ or contact https://innolitics.com/contact.

Koios DS

K212616 · Koios Medical, Inc. · POK · Dec 16, 2021 · Radiology

Device Facts

Record ID	K212616
Device Name	Koios DS
Applicant	Koios Medical, Inc.
Product Code	POK · Radiology
Decision Date	Dec 16, 2021
Decision	SESE
Submission Type	Traditional
Regulation	21 CFR 892.2060
Device Class	Class 2
Attributes	AI/ML, Software as a Medical Device

Intended Use

Koios DS is an artificial intelligence (Al)/machine learning (ML)-based computer-aided diagnosis (CADx) software device intended for use as an adjunct to diagnostic ultrasound examinations of lesions or nodules suspicious for breast or thyroid cancer. Koios DS allows the user to select or confirm regions of interest (ROIs) within an image representing a single lesion or nodule to be analyzed. The software then automatically characterizes the selected image data to generate an AI/ML-derived cancer risk assessment and selects applicable lexicon-based descriptors designed to improve overall diagnostic accuracy as well as reduce interpreting physician variability. Koios DS may also be used as an image viewer of multi-modality digital images, including ultrasound and mammography. The software includes tools that allow users to adjust, measure and document images, and output into a structured report. Koios DS software is designed to assist trained interpreting physicians in analyzing the breast ultrasound images of adult (>= 22 years) female patients with soft tissue breast lesions and/or thyroid ultrasounds of all adult (>= 22 years) patients with thyroid nodules suspicious for cancer. When utilized by an interpreting physician who has completed the prescribed training, this device provides information that may be useful in recommending appropriate clinical management.

Device Story

Koios DS is a web-based CADx software for breast and thyroid ultrasound. Input: DICOM ultrasound images. Operation: User selects ROIs (transverse/longitudinal for thyroid; single/orthogonal for breast); software uses AI/ML engines to characterize lesions, generate cancer risk assessments, and recommend lexicon-based descriptors (ACR BI-RADS, U1-U5, ACR TI-RADS, ATA). Output: Categorical risk assessment, confidence level indicator, and structured report. Used in clinical settings by trained interpreting physicians (radiologists, surgeons, endocrinologists). The 'AI Adapter' provides a risk-shift or point-based modification to standard RSS guidelines. Benefits: Improved diagnostic accuracy, reduced inter/intra-operator variability, and streamlined reporting. Integrates with PACS and reporting software via DICOM.

Clinical Evidence

Clinical evidence includes a multi-reader, multi-case (MRMC) retrospective study (CRRS-3) of 650 thyroid cases and bench testing of 900 breast cases. Thyroid study: 15 readers, primary endpoint AUC improvement of 0.083 (all data) and 0.074 (US data). Breast study: 15 readers, 900 cases, AUC improved to 0.929 (from 0.882). Bench testing confirmed performance metrics (sensitivity/specificity) exceed physician averages. No clinical data for normal tissue or post-surgical sites.

Technological Characteristics

Software-only device; web application (ASP.NET/IIS). Operates on off-the-shelf hardware. Inputs: DICOM ultrasound images. Connectivity: Networked (PACS/reporting software). Algorithms: Computer vision/ML-based engines for breast (BI-RADS/U1-U5) and thyroid (TI-RADS/ATA). No specialized hardware required. Platform-agnostic web interface.

Indications for Use

Indicated for adult (>= 22 years) female patients with soft tissue breast lesions and adult (>= 22 years) patients with thyroid nodules suspicious for cancer. Contraindicated for normal tissue, post-surgical excision sites, or images with doppler, elastography, or other overlays.

Regulatory Classification

Identification

A radiological computer-assisted diagnostic software for lesions suspicious of cancer is an image processing prescription device intended to aid in the characterization of lesions as suspicious for cancer identified on acquired medical images such as magnetic resonance, mammography, radiography, or computed tomography. The device characterizes lesions based on features or information extracted from the images and provides information about the lesion(s) to the user. Diagnostic and patient management decisions are made by the clinical user.

Special Controls

A radiological computer-assisted diagnostic (CADx) software for lesions suspicious for cancer must comply with the following special controls: 1. Design verification and validation must include: i. A detailed description of the image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, and algorithm limitations. ii. A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will improve reader performance as intended. iii. Results from performance testing protocols that demonstrate that the device improves reader performance in the intended use population when used in accordance with the instructions for use. The performance assessment must be based on appropriate diagnostic accuracy measures (e.g., receiver operator characteristic plot, sensitivity, specificity, predictive value, and diagnostic likelihood ratio). The test dataset must contain sufficient numbers of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment. iv. Standalone performance testing protocols and results of the device. v. Appropriate software documentation (e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, results, and cybersecurity). 2. Labeling must include: i. A detailed description of the patient population for which the device is indicated for use. ii. A detailed description of the intended reading protocol. iii. A detailed description of the intended user and recommended user training. iv. A detailed description of the device inputs and outputs. v. A detailed description of compatible imaging hardware and imaging protocols. vi. Warnings, precautions, and limitations, including situations in which the device may fail or may not operate at its expected performance level (e.g., poor image quality or for certain subpopulations), as applicable. vii. Detailed instructions for use. viii. A detailed summary of the performance testing, including: test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders (e.g., lesion and organ characteristics, disease stages, and imaging equipment).

*Classification.* Class II (special controls). The special controls for this device are:(1) Design verification and validation must include: (i) A detailed description of the image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, and algorithm limitations. (ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will improve reader performance as intended. (iii) Results from performance testing protocols that demonstrate that the device improves reader performance in the intended use population when used in accordance with the instructions for use. The performance assessment must be based on appropriate diagnostic accuracy measures ( *e.g.,* receiver operator characteristic plot, sensitivity, specificity, predictive value, and diagnostic likelihood ratio). The test dataset must contain sufficient numbers of cases from important cohorts (*e.g.,* subsets defined by clinically relevant confounders, effect modifiers, concomitant diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment.(iv) Standalone performance testing protocols and results of the device. (v) Appropriate software documentation ( *e.g.,* device hazard analysis; software requirements specification document; software design specification document; traceability analysis; and description of verification and validation activities including system level test protocol, pass/fail criteria, results, and cybersecurity).(2) Labeling must include: (i) A detailed description of the patient population for which the device is indicated for use. (ii) A detailed description of the intended reading protocol. (iii) A detailed description of the intended user and recommended user training. (iv) A detailed description of the device inputs and outputs. (v) A detailed description of compatible imaging hardware and imaging protocols. (vi) Warnings, precautions, and limitations, including situations in which the device may fail or may not operate at its expected performance level ( *e.g.,* poor image quality or for certain subpopulations), as applicable.(vii) Detailed instructions for use. (viii) A detailed summary of the performance testing, including: Test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders ( *e.g.,* lesion and organ characteristics, disease stages, and imaging equipment).

Predicate Devices

Koios DS for Breast (K190442)

Related Devices

K242130 — Koios DS · Koios Medical, Inc. · Nov 15, 2024
K190442 — Koios DS for Breast · Koios Medical, Inc. · Jul 3, 2019
K240697 — See-Mode Augmented Reporting Tool, Thyroid (SMART-T) · See-Mode Technologies Pte, Ltd. · Sep 9, 2024
K203502 — MEDO-Thyroid · Medo DX Pte. , Ltd. · Apr 23, 2021
K203555 — AmCAD-UT · Amcad Biomed Corporation · Sep 8, 2021

Submission Summary (Full Text)

{0}------------------------------------------------ Image /page/0/Picture/0 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue. Koios Medical, Inc. % Patricia Setti-Laperch Director of Regulatory Compliance and Quality 242 West 38th Street, 14th Floor NEW YORK NY 10018 December 16, 2021 ## Re: K212616 Trade/Device Name: Koios DS Regulation Number: 21 CFR 892.2060 Regulation Name: Radiological computer-assisted diagnostic software for lesions suspicious of cancer Regulatory Class: Class II Product Code: POK, QIH Dated: November 12, 2021 Received: November 15, 2021 Dear Patricia Setti-Laperch: We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading. If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register. Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for {1}------------------------------------------------ devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050. Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems. For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100). Sincerely. For Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health Enclosure {2}------------------------------------------------ ## Indications for Use 510(k) Number (if known) K212616 Device Name Koios DS #### Indications for Use (Describe) Koios DS is an artificial intelligence (Al)/machine learning (ML)-based computer-aided diagnosis (CADx) software device intended for use as an adjunct to diagnostic ultrasound examinations of lesions or nodules suspicious for breast or thyroid cancer. Kolos DS allows the user to select or confirm regions of interest (ROIs) within an image representing a single lesion or nodule to be analyzed. The software then automatically characterizes the selected image data to generate an AI/MLderived cancer risk assessment and selects applicable lexicon-based descriptors designed to improve overall diagnostic accuracy as well as reduce interpreting physician variability. Koios DS may also be used as an image viewer of multi-modality digital images, including ultrasound and mammography. The software includes tools that allow users to adjust, measure and document images, and output into a structured report. Koios DS software is designed to assist trained interpreting physicians in analyzing the breast ultrasound images of adult (>= 22 years) female patients with soft tissue breast lesions and/or thyroid ultrasounds of all adult (>= 22 years) patients with thyroid nodules suspicious for cancer. When utilized by an interpreting physician who has completed the prescribed training, this device provides information that may be useful in recommending appropriate clinical management. Limitations: · Patient management decisions should not be made solely on the results of the Koios DS analysis. · Koios DS software is not to be used for the evaluation of normal tissue, on sites of post-surgical excision, or images with doppler, elastography, or other overlays present in them. · Koios DS software is not intended for use on portable handheld devices (e.g. smartphones or tablets) or as a primary diagnostic viewer of mammography images. • The software does not predict the thyroid nodule margin descriptor, extra-thyroidal extension. In the event that this condition is present, the user may select this category manually from the margin descriptor list. Type of Use (Select one or both, as applicable) X Prescription Use (Part 21 CFR 801 Subpart D) Over-The-Counter Use (21 CFR 801 Subpart C) #### CONTINUE ON A SEPARATE PAGE IF NEEDED. {3}------------------------------------------------ This section applies only to requirements of the Paperwork Reduction Act of 1995. #### *DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.* The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to: > Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff@fda.hhs.gov "An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number." {4}------------------------------------------------ Image /page/4/Picture/0 description: The image shows the Koios logo. The logo consists of a stylized owl head on the left and the word "koios" in a sans-serif font on the right. The owl head is dark gray with light blue accents in the eyes. The word "koios" is also dark gray, and there is a registered trademark symbol next to the "s". # 510(k) Summary of Safety and Effectiveness ## K212616 This 510(k) summary of safety and effectiveness information is submitted as part of the Premarket Notification in accordance with the requirements of 21 CFR Part 807, Subpart E and Section 807.92. #### 1. Identification of Submitter: | Submitter: | Koios Medical Inc. | |---------------|-----------------------------------------------| | Address: | 242 West 38th Street, 14th Floor | | | New York, NY 10018 | | Phone: | 732-529-5755 | | Fax: | 732-529-5757 | | Contact: | Patricia Setti-Laperch | | Title: | Director of Regulatory Compliance and Quality | | Phone: | 732-529-5755 | | Fax: | 732-529-5757 | | Summary Date: | December 16, 2021 | #### 2. Identification of Product: | Device Name: | Koios DS, Version 3.0 | |-----------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------| | Device Common Name: Device Classification: | Radiological Computer-Assisted Diagnostic Software 21 CFR 892.2060, Class II, POK (primary) 21 CFR 892.2050, Class II, QIH (secondary) | | Classification Name: | Radiological Computer-Assisted Diagnostic Software (CADx) for Lesions Suspicious for Cancer | | Manufacturer: | Koios Medical, Inc. | #### 3. Marketed Devices In terms of safety and performance, this software medical device is substantially equivalent to the devices listed below: | Model: | Koios DS for Breast | |----------------|---------------------| | Manufacturer: | Koios Medical, Inc. | | 510(k) Number: | K190442 | {5}------------------------------------------------ #### 4. Device Description Koios DS is a software application designed to assist trained interpreting physicians in analyzing breast and thyroid ultrasound images. The software device is a web application that is deployed to a Microsoft IIS web server and accessed by a user through a compatible client. Once logged in and granted access to the Koios DS application, the user examines selected breast or thyroid ultrasound DICOM images. The user selects Regions of Interest (ROls) of orthogonal views of a breast lesion or thyroid nodule for processing by Koios DS. The ROI(s) are transmitted electronically to the Koios DS server for image processing and the results are returned to the user for review. #### Breast Functionality: Koios DS software automatically classifies breast lesions suspicious for cancer based on image data into one of four ACR BI-RADS® Atlas32 or European U1-U53 Classification System-aligned categories (Benign, Probably Benign, Suspicious or Indeterminate, or Probably Malignant) and also displays a continuous graphical Confidence Level Indicator depicting where the lesion falls within its respective category and its relation to neighboring categories. The software automatically classifies the shape (Round, Oval, Irregular) and orientation (Parallel, Not Parallel) of the selected lesion. #### Thyroid Functionality: Koios DS is a software medical device used to analyze ultrasound data to classify user-selected regions containing thyroid nodules suspicious for cancer. The software generates a set of user-editable sonographic nodule descriptor recommendations (Composition, Echogenicity, Shape, Margin, Echogenic Foci) along with an optional, deep-learning derived cancer risk assessment of the suspected nodule from two orthogonal views. Nodule descriptor recommendations are subsequently mapped to a categorical assessment and risk level rating via the ACR TI-RADS ATLAS™45 or American Thyroid Association (ATA) risk stratification systems (RSSs) based on user preference. The software's direct, non-descriptor-based cancer risk assessment is presented as the Koios "AI Adapter" that, when used in conjunction with the ACR TI-RADS or ATA guidelines for nodule risk stratification, is shown to improve overall diagnostic performance of both systems. The Al Adapter operates as an optional lexicon-specific input used to modify the final categorization in the ACR TI-RADS and ATA RSS. The Al adapter positively impacts performance through either a point-based modification (either positive or negative) or a risk-shift modification (either positive or negative) for ACR TI-RADS and the ATA systems, 4 BI-RADS® ATLAS is a registered trademark of American College of Radiology. All Rights Reserved. ² ACR BI-RADS Atlas: https://www.acr.org/-/media/ACR/Files/RADS/BI-RADS/US-Reporting.pdf ³ European Guidelines for Quality Assurance in Breast Cancer Screening and Diagnosis (Health & Consumer Protection Directorate – General). Fourth Edition. Editors: N. Perry, M. Broeders, C. de Wolf, S. Törnberg 4 TI-RADS™ ATLAS is a trademark of American College of Radiology. All Rights Reserved. ా ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee. Tessler, Franklin N. et al. Journal of the American College of Radiology, Volume 14, Issue 5, 587 - 595 6 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Cancer. Haugen, Alexander, et al., Thyroid. Jan 2016, 26(1): 1-133. {6}------------------------------------------------ respectively. This process creates an Al-augmented categorization that is meant to be used with no other modifications to the decision-making pathway of either RSS. A trained interpreting physician may choose to incorporate or exclude the Koios Al Adapter from the overall assessment when finalizing their diagnostic interpretation. Koios DS enables the following functionality: - Breast and Thyroid diagnostic core AI engines enabled by state-of-the-art computer vision and machine learning techniques capable of reading, interpreting, analyzing, classifying and generating findings from ultrasound image data resulting in an automated risk assessment for breast lesions and thyroid nodules suspicious for cancer - Automatic classification of thyroid nodules aligned to both TI-RADS and ATA descriptors of: Composition, Echogenicity, Shape, Margin, and Echogenic Foci based on user-selected regions of interest (ROIs) - Automatic classification of breast lesion BI-RADS and U1-U5 Descriptors Shape and Orientation based on user-selected or confirmed regions of interest (ROIs) - Annotation and description of ultrasound images based on ACR BI-RADS Breast Imaging Atlas and U1-U5 for Koios DS Breast - Annotation and description of ultrasound images based on and ACR TI-RADS Atlas and ATA classification guidelines for Koios DS Thyroid - Reporting forms for breast lesion or thyroid nodule identification and tracking in the Electronic Health Record - Smart Calipers - extraction of user-supplied ROI data (alternately referred to as Calipers) embedded in DICOM SR files from the ultrasound modality - . Smart Click (Breast only) - generation of automated ROI based on user-supplied position (click on a lesion) within the image - Ability to save findings to PACS - Ability to export findings to reporting software - Remote analysis interface to generate and view results within compatible software (e.g. ultrasound equipment or PACS workstation software) - Installer and Configuration Wizard - Single Sign-on (SSO) Windows and LDAP Authentication - Operating system and platform-agnostic usage - Zero-footprint web-based HTML5 DICOM image viewer with image manipulation and annotation tools #### User Profile: Koios DS is for use by trained professionals only. Koios DS is not for use by patients. Users must have appropriate medical professional competence, such as trained sonographers and interpreting physicians. {7}------------------------------------------------ #### Use Environment: Koios DS is a software application for use within a healthcare setting for the examination and assessment of breast lesions or thyroid nodules using ultrasound. It is a platform-agnostic web application that queries and accepts DICOM compliant digital medical files from any compliant device subject to the specified DICOM Conformance Statement for Koios DS. Processing of the image(s) occurs in conjunction with a trained interpreting physician's typical diagnostic case read. The output of the system is a digital display to be used as a concurrent read and report input that may be added as an addendum to the DICOM series selected for processing or exported directly into a patient's draft report. #### Operating Principle: Koios DS is an ASP.NET web application deployed to a Microsoft IIS web server inside a Windows operating system environment accessed by a user through a compatible client. The application provides image-derived data via web triggering and remote analysis. Once logged in and granted access to the Koios DS application, the user examines selected breast and thyroid ultrasound DICOM images. The user selects or confirms up to two orthogonal views that represent a single breast lesion for processing by the system. For thyroid functionality, two ROIs are required for analysis by the system. The first ROI must be drawn on the transverse view, with the second on the longitudinal view of the nodule. For breast functionality, bench testing has verified a single ROI does not significantly decrease system AUC performance. The ROI(s) are transmitted electronically to the Koios DS server by the Koios DS Breast or Koios DS Thyroid software for image processing and the results are returned to the user for review in the respective interface. Images and data can be stored, communicated, processed, and displayed within the system and/or across computer networks at distributed locations. The software does not require any specialized hardware to return a diagnostic output, but the time to process ROIs will vary depending on the hardware specifications. Koios DS contains two distinct AI/ML engines to characterize breast lesions and thyroid nodules. Based on the structured data that exists within the DICOM header for a patient study, the Koios DS system calls the corresponding engine for analysis of the identified lesion or nodule. Each system uses computer vision and machine learning techniques embedded within an engine capable of reading, interpreting, and generating findings from ultrasound data. The underlying breast and thyroid engines draw upon knowledge learned from a large database of known cases, tying image features to their eventual diagnosis, to form a predictive model. Koios DS results can be saved or transferred in three separate ways: in-transmission, saving to Picture Archiving and Communication System (PACS), and exporting results to third-party reporting software. In-transit transmission may be utilized when users wish to share analyses across viewing workstations. Results can be stored in in-transit memory for a preset period of time defined by a system administrator. After that preset period of time, all results are wiped from the local memory. Another method of saving a report in the patient study on the PACS. After single or multiple lesion or nodule analyses have been performed and ultimately accepted by a trained interpreting physician, Koios DS can export a summary report to PACS as an {8}------------------------------------------------ addendum to the DICOM study that was selected for processing. This report serves as future reference and aid in the comparison of cases requiring follow up. This functionality is strictly reserved for approved users and must be configured by a site administrator. Koios DS also supports exporting results to third-party reporting software to facilitate the reporting process. Saving or exporting preferences can be configured by the system administrator and user. #### 5. Indications for Use Koios DS is an artificial intelligence (Al)/machine learning (ML)-based computer-aided diagnosis (CADx) software device intended for use as an adjunct to diagnostic ultrasound examinations of lesions or nodules suspicious for breast or thyroid cancer. Koios DS allows the user to select or confirm regions of interest (ROIs) within an image representing a single lesion or nodule to be analyzed. The software then automatically characterizes the selected image data to generate an AI/ML-derived cancer risk assessment and selects applicable lexicon-based descriptors designed to improve overall diagnostic accuracy as well as reduce interpreting physician variability. Koios DS may also be used as an image viewer of multi-modality digital images, including ultrasound and mammography. The software includes tools that allow users to adjust, measure and document images, and output into a structured report. Koios DS software is designed to assist trained interpreting physicians in analyzing the breast ultrasound images of adult (>= 22 years) female patients with soft tissue breast lesions and/or thyroid ultrasounds of all adult (>= 22 years) patients with thyroid nodules suspicious for cancer. When utilized by an interpreting physician who has completed the prescribed training, this device provides information that may be useful in recommending appropriate clinical management. #### Limitations: • Patient management decisions should not be made solely on the results of the Koios DS analysis. • Koios DS software is not to be used for the evaluation of normal tissue, on sites of post-surgical excision, or images with doppler, elastography, or other overlays present in them. • Koios DS software is not intended for use on portable handheld devices (e.g. smartphones or tablets) or as a primary diagnostic viewer of mammography images. • The software does not predict the presence of the thyroid nodule margin descriptor, extra-thyroidal extension. In the event that this condition is present, the user may select this category manually from the margin descriptor list. {9}------------------------------------------------ ## 6. Substantial Equivalence Chart | Product | Koios DS for Breast (K190442) | Koios DS (subject device) | |---------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Physical Characteristics | Software Package Operates on off-the-shelf hardware | Software Package Operates on off-the-shelf hardware | | Storage | Storage not supported | Storage not supported | | Image Input Characteristics | DICOM | DICOM | | Intended Use/Indications for Use | Decision support device used to assist in the assessment and characterization of breast lesions using US image data. | Decision support device used to assist in the assessment and characterization of breast lesions and thyroid nodules using US image data. | | | Koios Decision Support (DS) for Breast is a software application designed to assist trained interpreting physicians in analyzing the breast ultrasound images of patients with soft tissue breast lesions who are being referred for further diagnostic ultrasound examination. | Koios Decision Support (DS) is an artificial intelligence (AI)/machine learning (ML)- based computer-aided diagnosis (CADx) software device intended for use as an adjunct to diagnostic ultrasound examinations of lesions suspicious for breast or thyroid cancer. | | | Koios DS for Breast is a machine learning-based decision support system, indicated as an adjunct to diagnostic ultrasound for breast cancer. | Koios DS allows the user to select or confirm regions of interest (ROIs) within an image representing a single lesion or nodule to be analyzed. The software then automatically characterizes the selected image data to generate an AI/ML-derived cancer risk assessment and selects applicable lexicon-based descriptors designed to improve overall diagnostic accuracy as well as reduce interpreting physician variability. | | | Koios DS for Breast automatically classifies user-selected region(s) of interest (ROIs) containing a breast lesion into four BI-RADS-aligned categories (Benign, Probably Benign, Suspicious, Probably Malignant), and displays a continuous graphical confidence level indicator of where the lesion falls across all categories. Koios DS for Breast also automatically classifies lesion shape and orientation according to BI-RADS descriptors. | Koios DS software may also be used as an image viewer of multi-modality digital images, including ultrasound and mammography. The software includes tools that allow users to adjust, measure and document images, and output into a structured report. | | | The software requires a user to select up to two ROIs, from up to two orthogonal views, that represent a single lesion to be selected and processed. When utilized by an interpreting physician who has completed the prescribed training | Koios DS software is designed to assist trained interpreting physicians in analyzing the breast ultrasound images of adult (>= 22 years) female patients with soft tissue breast lesions and/or thyroid ultrasounds of all adult (>= 22 years) patients with thyroid nodules suspicious for cancer. | | | this device provides information that may be useful in rendering an accurate diagnosis. Patient management decisions should not be made solely on the results of the Koios DS for Breast analysis. This device is intended to help trained interpreting physicians improve their overall accuracy as well as reduce inter- and intra-operator variability. Koios DS for Breast may also be used as an image viewer of multi-modality digital images, including ultrasound and mammography. The software includes tools that allow users to adjust, measure and document images, and output into a structured report. | When utilized by an interpreting physician who has completed the prescribed training, this device provides information that may be useful in recommending appropriate clinical management. | | Target Population (subset of above for comparison purposes) | Koios Decision Support (DS) for Breast is a software application designed to assist trained interpreting physicians in analyzing the breast ultrasound images of patients with soft tissue breast lesions who are being referred for further diagnostic ultrasound examination. | Koios DS software is designed to assist trained interpreting physicians in analyzing the breast ultrasound images of adult (>= 22 years) female patients with soft tissue breast lesions and/or thyroid ultrasounds of all adult (>= 22 years) patients with thyroid nodules suspicious for cancer. | | Limitations for Use (subset of above for comparison purposes) | Limitations: Koios DS for Breast is not to be used on sites of post-surgical excision, or images with doppler, elastography, or other overlays present in them. Koios DS for Breast is not intended for the primary interpretation of digital mammography images. Koios DS for Breast is not intended for use on mobile devices. | Limitations: • Patient management decisions should not be made solely on the results of the Koios DS analysis. • Koios DS software is not to be used for the evaluation of normal tissue, on sites of post-surgical excision, or images with doppler, elastography, or other overlays present in them. • Koios DS software is not intended for use on portable handheld devices (e.g. smartphones or tablets) or as a primary diagnostic viewer of mammography images. • The software does not predict the presence of the thyroid nodule margin descriptor, extra-thyroidal extension. In the event that this condition is present, the user may select this category manually. | | | | from the margin descriptor list. | | Modality Used for Analysis | Breast Ultrasound Data | Breast Ultrasound Data Thyroid Ultrasound Data | | Input | Medical images provided in a DICOM format | Medical images provided in a DICOM format | | ROI Requirements | The software requires a user to select up to two ROIs, from up to two orthogonal views, that represent a single lesion to be selected and processed. | Breast The software requires a user to select up to two ROIs, from up to two orthogonal views, that represent a single lesion to be selected and processed. Thyroid Two ROIs that represent a single lesion to be selected and processed are required for analysis. The first ROI is drawn on the transverse view of the nodule. The second is drawn on the longitudinal view. | | Output (Breast) | Koios defined categorical and continuous outputs (confidence level indicator) that align to BI-RADS and auto-classified shape and orientation | Koios DS Breast defined categorical and continuous outputs (confidence level indicator) that align to BI-RADS, U1-U5, and auto-classified shape and orientation. | | Output (Thyroid) | N/A | Koios DS Thyroid software automatically classifies thyroid nodules suspicious for cancer based on image data generating an output aligned to either the TI-RADS or ATA classification guidelines. The system automatically generates user-modifiable nodule descriptors (Composition, Echogenicity, Shape, Margin, Echogenic Foci) and a direct, image-derived cancer risk assessment that is translated into an optional lexicon-specific modifier. | | Comparative Clinical Performance Testing (Breast) | Metric: AUC Cases: 900 Readers: 15 | Metric: AUC Cases: 900 Readers: 15 | | Comparative Clinical Performance Testing (Thyroid) | N/A | Metric: AUC Cases: 650 Readers: 15 | {10}------------------------------------------------ {11}------------------------------------------------ {12}------------------------------------------------ #### 7. Description of Similarities and/or Differences #### Intended Use/Indications for Use (IFU) Comparing the IFU of the predicate device Koios DS Breast (K190442) and Koios DS, there are several key similarities and differences outlined below: Both devices are intended to be utilized as diagnostic aids that operate on user-supplied Regions of Interest (ROIs). Koios DS Thyroid functionality requires two ROIs representing a single lesion to be selected and processed for analysis. The first ROI is drawn on the transverse view of the second is drawn on the longitudinal view. The Koios DS system also contains the Smart Click (breast only) and Smart Caliper functionalities for streamlining the previously manual ROI selection process. The Smart Click functionality enables the user to click on the center of a lesion in order to activate a system-generated region of interest surrounding the selected lesion for the user. The Smart Calipers functionality ingests caliper data from compatible ultrasound devices with the capability to use a calculation package when drawing calipers on lesions. Koios DS can automatically generate ROIs and resulting analyses for the user based on this caliper data. Both Smart Caliper-generated ROIS can be edited or deleted by the user and have been demonstrated to have no adverse impact on the diagnostic outputs of the Koios DS engines. The intended use and indications for use statements have been updated from those of the predicate device in order to enhance labeling clarity and to detail the additional breast and thyroid cancer decision support functionality and limitations. These differences do not affectiveness of the device when used as a labeled Computer-Assisted Diagnostic software for lesions suspicious for cancer. #### Target Patient Population Both the Koios DS Breast predicate and the subject device are software applications designed to assist trained interpreting physicians in analyzing the ultrasound images of patients with soft tissue lesions who are being referred for further diagnostic ultrasound examination. Additional detail has been included to clarify the Koios DS Breast product indications for females, as well as to exclude pediatric use for Koios DS Breast and Koios DS Thyroid. #### Technological Characteristics #### Modality Koios DS shares the ultrasound modality requirements of Koios DS Breast and provides additional functionality for Thyroid ultrasound images. {13}------------------------------------------------ #### Input Per the respective device descriptions of Koios DS Breast and Koios DS, the input to each consists of medical images provided in a DICOM format. The technical implementation for ingesting images for processing occurs via the same DICOM-based interface. Based on the structured data that exists within the DICOM header for a patient study or a user selection, the Koios DS system calls the appropriate corresponding engine (Breast or Thyroid) for analysis of the identified lesion or nodule. The Koios DS system also contains the Smart Click and Smart Caliper functionalities for streamlining the previously manual ROI selection process. Regarding Region of Interest image input data, Koios DS Thyroid functionality requires two ROIs representing a single lesion to be selected and processed for analysis, whereas this is optional for breast. #### Output When comparing breast functionality, the predicate and subject devices differ only in the additional optional output display in alignment with the European U1-U5 Classification system for enhanced usability in international markets. Direct comparison with the Koios DS Breast v2.0 predicate engine's categorical output performance determined there is a significant increase in AUC (5%), no significant change in sensitivity, and a significant increase (24%) in specificity. Koios DS retains the identical descriptor outputs and performance for the assessment of shape and orientation. Koios DS software contains functionality for automatically classifying thyroid nodules suspicious for cancer. Similar to Koios DS Breast, this is based on image data. The system generates an output aligned to either the TI-RADS or ATA classification guidelines (in comparison with BI-RADS and U1-U5 for breast). The system automatically generates user-modifiable thyroid nodule descriptors (Composition, Echogenicity, Shape, Margin, Echogenic Foci), analogous to the Shape and Orientation descriptors present in the breast functionality, and a direct, image-derived cancer risk assessment that is translated into an optional lexicon-specific (TI-RADS or ATA) modifier. Clinical data demonstrates that this provides a significant improvement in overall reader performance when utilizing Koios DS for the interpretation of thyroid ultrasound studies. #### Performance Testing To compare the performance of the subject device to the predicate device (K190442), a clinical study was conducted in order to assess the performance of the thyroid functionality of the subject device and bench testing was conducted on the updated breast functionality. As in the predicate device, ground truth for all breast analysis was determined by pathology or 1-year follow-up for cases that were not biopsied, whereas for thyroid it was determined exclusively via histo/cyto-pathology and/or surgical excision. The breast and thyroid engine validation sets are composed of 900 lesions from 900 different patients and 650 lesions from 650 different patients, respectively. Each was set aside from the systems' training data for the purpose of validating performance. While operating on identical modalities, but different body regions (breast versus thyroid), similar primary endpoints were utilized in the clinical validation studies of the subject and predicate devices. Both clinical studies evaluated an Area Under the Curve (AUC) shift when comparing the performance of users alone versus users utilizing the respective software platform with a 1-month washout period. The number of cases evaluated in the predicate study was 900, while the subject device study evaluated a total of 650 cases which were both {14}------------------------------------------------ determined via power estimates utilizing pilot study estimates for effect size. The number of interpreting physicians utilized in both studies was 15 readers. Additionally, bench testing was conducted to assess the standalone performance, as measured by AUC, on identical breast validation datasets. The results of the subject device's clinical study evaluating its impact on the diagnostic performance of thyroid lesion classification successfully met all primary endpoints demonstrating a 0.083 (0.066, 0.099 95% Cl) improvement in parametric AUC on the overall dataset along with a stratified analysis of United States (US)based readers on US-based cases demonstrating an improvement of 0.074 (0.051, 0.098 95% Cl) in parametric AUC. The absolute improvement in AUC on the US-only stratification demonstrated a larger mean shift than seen in the predicate device's study (0.074 versus 0.037). Previous clinical study evaluations reported in K190442 have demonstrated significant AUC improvements for readers utilizing Koios DS Breast. The subject device's updated breast classification engine was compared to the predicate device on the same 900 case validation set and demonstrated a statistically significant shift in AUC to 0.929 (0.913, 0.945 95% CI) from 0.882 (0.857, 0.907 95% CI). An additional 50 new cases were added to the set and evaluated to test the subject device for robustness to dataset drift. This additional test generated a resulting AUC of 0.930 [0.914, 0.946 95% Cl], demonstrating there is no degradation in performance attributable to dataset drift. In conclusion, the subject device has demonstrated substantially equivalent performance to the predicate by showing statistically significant results against similar success criteria in both clinical and bench testing comparisons. ## 8. Performance Testing - Bench/Non-Clinical #### Breast Engine Malignancy Risk Classification: Bench testing was performed on the updated breast engine to ascertain the degree of concordance with trained interpreting physicians. Ground truth for malignancy risk classification was determined by pathology or 1-year follow-up for cases that were not biopsied. The system was analyzed on 900 lesions from 900 different patients set aside from the system's training data for the purpose of validating performance. Each lesion was represented by two orthogonal images (e.g. radial and anti-radial), providing a total of 1800 images. System performance on the 900 cases reported an AUC of 92.9%, with a Sensitivity of 0.97 [0.96, 0.99] and a Specificity of 0.61 [0.57, 0.66]. Direct comparison with the prior (Koios DS Breast v2.0 predicate) engine's performance determined there is a significant increase in AUC (5%), no significant change in sensitivity, and a significant increase (24%) in specificity. In summary, a comprehensive evaluation of the breast engines was conducted across key performance metrics and bench testing demonstrated that the system exceeds physician performance measured by AUC, sensitivity, {15}------------------------------------------------ and specificity. The engine's shape and orientation predictions have not been modified from the previously cleared device (which demonstrated the required level of agreement with the subjective categorizations assigned by physicians). Testing characterizes the system's sensitivity to shifts in the selected region of interests (ROI) and transducer frequency. Testing characterizes the system's Positive Value (PPV), Negative Predictive Value (NPV), Positive Likelihood Ratio (PLR) and Negative Likelihood Ratio (NLR) in comparison with physicians. Testing demonstrates that the performance of the engine does not demonstrate degradation when regions of interest are provided by the Smart Click system, as compared to manually drawn regions of interest. In all tests, the Breast engine met or exceeded performance requirements. #### Thyroid Engine Bench testing was performed on the thyroid engine to ascertain the degree of concordance with trained interpreting physicians utilizing both the ACR TI-RADS and ATA classification systems. Ground truth for malignancy risk classification was determined by pathology results only. The system was analyzed on 500 lesions from 500 different patients set aside from the system's training data for the purpose of validating performance. Each lesion was represented by two orthogonal images (e.g. radial and anti-radial), providing a total of 1000 images. When applied to diagnoses made using ACR TI-RADS guidelines, the Al Adapter and descriptor predictors achieved an AUC of 79.8%, demonstrating a significant increase over the average physician AUC. When recommending biopsy, the system's sensitivity is 0.644 [0.545, 0.744] and specificity is 0.612 [0.566, 0.658]. When recommending follow-up, the system's sensitivity and specificity are 0.879 [0.812, 0.946] and 0.495 [0.446, 0.544], respectively. In both scenarios, bench testing of the system demonstrates a non-significant improvement in sensitivity and a significant improvement in specificity over the physician average. Tests demonstrating AI Adapter impact on ATA classifications yielded similarly improved performance. With application of the Al Adapter, physician AUC demonstrates a significant increase of 9.135% [5.975, 12.294]. Sensitivity shows a non-significant increase of 0.511% [-5.182, 6.204], while specificity shows a significant increase of 18.741% [9.885, 27.596]. Bench testing included verification of standalone performance with TI-RADS and ATA outputs, as well as performance when compared to a separate data set including data from independent sites (separate and apart from the sites/data used to train and tune the algorithm). In summary, a comprehensive evaluation of the thyroid engine was conducted across key performance metrics and bench testing demonstrated that application of the Koios DS Al Adapter exceeds physician performance as measured by AUC, sensitivity, and specificity. Descriptor predictions were tested objectively – against ground truth pathology. Testing demonstrated that performance requirements were met under ACR TI-RADS and ATA reporting systems as well as when compared against independent site data. Outputs were additionally tested subjectively and met the requirements for agreement with readers' descriptor categorizations. Testing characterized the sensitivity of the system with respect to shifts in the region of interest and variation in performance between high and low transducer frequencies. System performance on data acquired from {16}------------------------------------------------ independent sites meets performance requirements. In all tests, the Thyroid engine met or exceeded performance requirements. #### 9. Performance Testing - Clinical #### Breast A clinical study was previously executed (K190442) to determine the effect of Koios DS Breast on reader performance. As discussed in the prior section, the prior device's performance has been met or significantly improved across all measured metrics by the subject device. This data continues to apply to the breast functionality within the subject device, with the understanding that its performance is superior, and it would therefore provide an equivalent or greater benefit. The below summary of the clinical study data has been included for ease of reference. The study objective was to determine the impact on Interpreting Physician (Reader) performance as defined by the area under the Receiver Operating Characteristic (ROC) Curve (AUC) when Koios DS Breast and an ultrasound examination are combined (USE + DS), compared to USE Alone in patients that present with a soft tissue breast lesion through any form of imaging or physical examination and are referred for diagnostic ultrasound. The study consisted of 15 readers with varying levels of training and experience providing analysis on a randomized set of 900 patient cases presented with USE + DS and USE Alone in two reading periods separated by a 1-month wash-out, totaling 1800 cases analyzed per reader set and dataset were distributed in accordance with FDA guidance and are explained in detail below: {17}------------------------------------------------ ## Reader Background | Reader ID | Board Certification/ Specialty | Breast Fellowship Trained and/or Dedicated Breast Imager | Years of Experience - Mammography and/or Breast Ultrasound | Academic Institution Affiliation (Yes/No) | MQSA Qualified Interpreting Physician | |--------------|--------------------------------------|-------------------------------------------------------------------------|------------------------------------------------------------------------|----------------------------------------------------|------------------------------------------------| | 1 | Diagnostic Radiology | No | 13 years | No | Yes | | 2 | Diagnostic Radiology | No | 4 years | No | No | | 3 | Diagnostic Radiology | Yes | 7 years | Yes | Yes | | 4 | Breast Surgeon | No | 0 years | No | No | | 5 | OB/GYN | No | 20 years | No | No | | 6 | Diagnostic Radiology | No | 13 years | Yes | No | | 7 | Diagnostic Radiology | No | 3 years | Yes | No | | 8 | OB/GYN | No | 0 years | No | No | | 9 | Diagnostic Radiology | Yes | 15 years | No | Yes | | 10 | Diagnostic Radiology | No | 13 years | No | No | | 11 | Diagnostic Radiology | Yes | 30 years | No | Yes | | 12 | Diagnostic Radiology | Yes | 10 years | Yes | Yes | | 13 | Diagnostic Radiology | No | 0 years | No | No | | 14 | Interventional Radiology | No | 4 years | No | No | | 15 | Breast Surgeon | No | 25 years | Yes | No | {18}------------------------------------------------ #### Dataset Demographic Information The Koios DS Breast engine was tested on images sourced from a wide variety of ultrasound hardware and data with the following patient demographics to ensure the system performance is generalizable to and representative of diverse populations. Patient demographic distribution was based upon data from the Breast Cancer Surveillance Consortium (2006-2009)7. Image /page/18/Figure/2 description: This bar chart shows the count of benign and cancer diagnoses. The x-axis represents the diagnosis type, with 'benign' and 'cancer' as the categories. The y-axis represents the count, with the benign diagnosis having a count of 470 (52.2%) and the cancer diagnosis having a count of 430 (47.8%). The following figures represent the final validation dataset (900 cases): Image /page/18/Figure/4 description: The image is a bar chart that shows the distribution of BI-RADS categories. The x-axis represents the BI-RADS category, and the y-axis represents the count. The bar chart shows that the most frequent category is 4A, with a count of 222 (24.7%), followed by 4C with a count of 187 (20.8%). The least frequent category is 2, with a count of 68 (7.6%). Distribution of Malignancy in Final Validation Set Image /page/18/Figure/6 description: The image is a bar chart showing the distribution of ethnicity. The x-axis represents different ethnicities: white, black, hispanic, asian, and other. The y-axis represents the count, with the highest count for white ethnicity at 592 (65.8%), followed by asian at 133 (14.8%), black at 77 (8.6%), hispanic at 73 (8.1%), and other at 25 (2.8%). Distribution of BI-RADS Category in Final Validation Set Image /page/18/Figure/8 description: The image is a bar chart showing the distribution of ages. The x-axis represents age groups, including 'n/a', '<40', '40-49', '50-59', '60-74', '75-84', and '85+'. The y-axis represents the count, with values ranging from 0 to 250. The age group '60-74' has the highest count at 235 (26.1%), while the age group '85+' has the lowest count at 9 (1.0%). Distribution of Ethnicity in Final Validation Set Distribution of Age in Final Validation Set 7 Data were obtained from the Breast Cancer Surveillance Consortium, funded by the National Cancer Institute (HHSN261201100031C). From the Breast Cancer Surveillance Consortium website, http://www.bcsc-research.org/ {19}------------------------------------------------ Image /page/19/Figure/0 description: The image contains two bar charts. The first bar chart shows the distribution of tumor sizes, with the x-axis representing size in millimeters and the y-axis representing the count. The tumor sizes are categorized as n/a, <10, 10-14, 15-19, and 20+, with corresponding counts of 7, 332, 229, 132, and 200, respectively. The second bar chart shows the distribution of invasive cancer types, with the x-axis representing invasive and dcis/nos, and the y-axis representing the count, with corresponding counts of 369 and 61, respectively. Distribution of Lesion Size in Final Validation Set Distribution of Invasive Cancer in Final Validation Set Image /page/19/Figure/3 description: This bar graph shows the BI-RADS density. The x-axis shows the density, and the y-axis shows the count. The bar graph shows that 343 (38.1%) are scattered fibroglandular, 299 (33.2%) are heterogeneously dense, 143 (15.9%) are extremely dense, and 115 (12.8%) are fatty. Distribution of BI-RADS Density in Final Validation Set {20}------------------------------------------------ Image /page/20/Figure/0 description: The image is a bar chart titled "Distribution of US Machines - Validation Set". The y-axis is labeled "count" and ranges from 0 to 1400. The x-axis lists different machine types, including 'ge', 'ge logiq 7', 'ge logiq e', 'ge logiq e9', 'ge v730', 'philips', 'philips eqiq 5g', 'philips hdi 5000', 'philips iu22', 'siemens', 'siemens antares', 'siemens elegra', 'siemens s2000', 'siemens sequoia', 'supersonic aixplore', 'toshiba', and 'toshiba aplio'. The bar for 'ge logiq e' is the highest, with a count of 1334. Distribution of US Machines in Final Validation Set Per the primary endpoint of the study, ROC curves were generated and analyzed. All AUCs were computed via the trapezoidal approximation. Based on the standard error measurements, the error can be propagated to estimate the mean performance interface and 95% confidence interval. This was found to be 0.0370 (0.030, 0.044) at α = .05, satisfying the success criteria for the primary endpoint. To characterize the effect of Koios DS (USE + DS) system on inter-operator variability, the Kendall Tau-B correlation coefficient was computed in a pairwise manner for all readers. The metric is > 0 for all reader pairs. The standard error for USE + DS and USE Alone was computed to assess if the shifts in the metric were significant. The average Kendall Tau-B of USE Alone was .5404 (.5301, .5507) and the average Kendall Tau-B of USE + DS was .6797 (.6653, .6941) with 95% Cl demonstrating a significant increase in the metric (α = .05). Also assessed was the effect of Koios DS on intra-operator variability leveraging 150 reads that did not switch from USE Alone to USE + DS across the washout session in the reader study (75 each). USE Alone class switching rate was 13.6% and the USE + DS class switching rate was 10.8% (p = 0.042), demonstrating a statistically significant reduction in intra-reader variability when using USE + DS. {21}------------------------------------------------ #### Thyroid An observational case-controlled, Multi-Reader, Multi-Case (MRMC) retrospective clinical trial (CRRS-3) was executed to determine the effect of Koios DS Thyroid on reader performance. Effect on performance was defined by measuring the area under the Receiver Operating Characteristic (ROC) Curve (AUC) when Koios DS and an ultrasound examination were combined (USE + DS), compared to unassisted TI-RADS based Reader performance (USE Alone). All data analysis cases consisted of USE Alone and USE + DS image readings in patients that p…