Koios DS

K242130 · Koios Medical, Inc. · POK · Nov 15, 2024 · Radiology

Device Facts

Record IDK242130
Device NameKoios DS
ApplicantKoios Medical, Inc.
Product CodePOK · Radiology
Decision DateNov 15, 2024
DecisionSESE
Submission TypeTraditional
Regulation21 CFR 892.2060
Device ClassClass 2
AttributesAI/ML, Software as a Medical Device

Intended Use

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 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 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. 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 assisting physicians in analyzing breast/thyroid ultrasound images. Input: DICOM ultrasound images; user-selected ROIs (via manual selection, Smart Calipers, or Smart Click). Operation: AI/ML engines classify lesions/nodules, generate cancer risk assessments, and suggest lexicon-based descriptors (BI-RADS/U1-U5 for breast; TI-RADS/ATA for thyroid). Output: Risk assessment, descriptor recommendations, and structured reports. Used in clinical/hospital settings by trained physicians. Features include image registration/matching, OCR for text/measurement extraction, and multi-modality image viewing. Benefits: Improved diagnostic accuracy, reduced inter-physician variability, and streamlined reporting. Clinical decision-making is supported by AI-derived risk modifiers; physicians retain final diagnostic authority.

Clinical Evidence

Evidence includes retrospective MRMC clinical trials and bench testing. Breast: 900-case validation set (AUC 94.5%, Sensitivity 0.976, Specificity 0.632). Thyroid: 650-case MRMC trial (all readers/data) showed AUC improvement of +0.083 [0.066, 0.099] with Koios DS. Secondary analyses demonstrated significant improvements in sensitivity/specificity for FNA and follow-up recommendations. Inter-reader variability reduced (Kendall Tau-B increased from 0.5404 to 0.6797).

Technological Characteristics

ASP.NET web application deployed on Microsoft IIS server. Operates on off-the-shelf hardware. Inputs: DICOM ultrasound images. Processing: AI/ML-based computer vision engines for breast/thyroid classification. Connectivity: Networked, web-based client-server architecture. Software: Version 3.6, includes OCR and image matching engines. Platform-agnostic.

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. Adjunct to diagnostic ultrasound. 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

Related Devices

Submission Summary (Full Text)

{0}------------------------------------------------ November 15, 2024 Image /page/0/Picture/1 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo features the letters 'FDA' in a blue square, followed by the words 'U.S. FOOD & DRUG ADMINISTRATION' in blue text. Koios Medical, Inc. % Michael Bocchinfuso Director of Regulatory Compliance and Quality 242 West 38th Street 14th Floor New York, NY 10018 Re: K242130 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: August 13, 2024 Received: August 19, 2024 Dear Michael Bocchinfuso: 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 (the 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 available 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. {1}------------------------------------------------ Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download). Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review. the OS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181). 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 Part 803) for devices or postmarketing safety reporting (21 CFR Part 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 Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050. All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rue"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system. Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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. {2}------------------------------------------------ For comprehensive regulatory information about medical devices and radiation-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. # YANNA S. KANG -S Yanna Kang, Ph.D. Assistant Director Mammography and Ultrasound Team DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health Enclosure {3}------------------------------------------------ # Indications for Use 510(k) Number (if known) K242130 Device Name Koios DS #### Indications for Use (Describe) 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 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/MLderived cancer risk assessment and selects applicable lexicon-based to improve overall diagnostic accuracy as well as reduce interpreting physician variability. 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 out unto 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. | 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. 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/1 description: The image shows the logo for Koios. The logo consists of a stylized owl head on the left and the word "koios" in lowercase letters on the right. The owl head is dark gray with light blue eyes, and the word "koios" is light blue. # 510(k) Summary of Safety and Effectiveness 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<br>New York, NY 10018 | | Phone: | 732-529-5755 | | Fax: | 732-529-5757 | | Contact: | Michael Bocchinfuso | | Title: | Director of Regulatory Compliance and Quality | | Phone: | 732-529-5755 | | Fax: | 732-529-5757 | | Summary Date: | October 18, 2024 | #### 2. Identification of Product: | Device Name: | Koios DS<br>Version 3.6 | |-----------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------| | Device Common Name:<br>Device Classification: | Radiological Computer-Assisted Diagnostic Software<br>21 CFR 892.2060, Class II, POK (primary)<br>21 CFR 892.2050, Class II, QIH (secondary) | | Classification Name: | Radiological Computer-Assisted Diagnostic Software (CADx) for<br>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: | Predicate device: | Koios DS | |-------------------|---------------------| | Manufacturer: | Koios Medical, Inc. | 510(k) Summary {5}------------------------------------------------ 510(k) Number: K212616 #### 4. Device Description Koios Decision Support (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 (ROIs) 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® Atlas2 or European U1-U5 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) 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 usereditable 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 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 RSSs. The AI 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, respectively. <sup>4</sup> BI-RADS® ATLAS is a registered trademark of American College of Radiology. All Rights Reserved. {6}------------------------------------------------ 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 AI 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 nodule 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 and ACR TI-RADS Atlas for thyroid lexicon classification forms 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 - for streamlining the 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. - . Image Registration and Matching - allows users to select images and regions of interest through their own image viewers when interacting with Koios DS Breast and Koios DS Thyroid, and facilitates a flexible viewer agnostic workflow. When the Image Matching Engine is given a screenshot of a medical image with coordinates for a region of interest, it identifies the original full quality image and translates the coordinates to its frame of reference. - . Automatic Size and Position population using Optical Character Recognition (OCR) - the Koios DS Optical Character Recognition engine uses machine learning and rulebased methods to create a system which is capable of retrieving fast, accurate transcriptions of the text overlaid on ultrasound images. Given an ultrasound image that has been annotated by a radiologist or technician, the OCR function identifies all text in the image and extracts relevant information to the documentation of lesions or nodules. {7}------------------------------------------------ This allows users to quickly interpret and transcribe the locations and measurements of ultrasound findings. - . 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. - Ability to save findings to PACS. - Ability to export findings to reporting software. ● ## 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. ## Use Environment: Koios DS is a software application for use within the healthcare setting (in a clinic or hospital) 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 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. For breast functionality, the user selects or confirms up to two ROIs, from up to two orthogonal views that represent a single breast lesion for processing by the system. For thyroid functionality, two ROIs are required for processing by the system. The first ROI must be drawn on the transverse view, with the second on the {8}------------------------------------------------ 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 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 Koios DS Client is an optional workflow enhancement tool installed as a desktop application on the user workstation that enables a user to draw ROIs natively within their image viewing software. The Koios DS Client captures a screenshot of the ROI selected by the user instead of being directly drawn on and captured with DICOM data. The ROI screenshot is transmitted electronically to the Image Matching Engine within the Koios DS Server. The Image Matching Engine processes the ROI screenshot and data, identifying and matching the correct DICOM image, and overlaying the ROI on that image. Once matched, the ROIs are returned to the user for review in the Koios DS Breast or Thyroid interface. 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 Al/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 thirdparty 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 is storing 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 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. {9}------------------------------------------------ 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 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 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 Al/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 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. 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 postsurgical 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. {10}------------------------------------------------ • The software does not predict the presence of the thyroid nodule margin descriptor, extrathyroidal extension. In the event that this condition is present, the user may select this category manually from the margin descriptor list. {11}------------------------------------------------ ## 6. Substantial Equivalence Chart | Product | Koios DS 3.0<br>(K212616) | Koios DS 3.6<br>(subject device) | |------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Physical<br>Characteristics | Software Package<br>Operates on off-the-shelf hardware | Software Package<br>Operates on off-the-shelf hardware | | Storage | Storage not supported | Storage not supported | | Image Input | DICOM | DICOM | | Characteristics | Decision support device used to assist<br>in the assessment and<br>characterization of breast lesions and<br>thyroid nodules using US image data. | Decision support device used to assist in<br>the assessment and characterization of<br>breast lesions and thyroid nodules using US<br>image data. | | Intended<br>Use/Indications<br>for Use | Koios Decision Support (DS) is an<br>artificial intelligence (AI)/machine<br>learning (ML)-based computer-aided<br>diagnosis (CADx) software device<br>intended for use as an adjunct to<br>diagnostic ultrasound examinations of<br>lesions or nodules suspicious for<br>breast or thyroid cancer. | Koios Decision Support (DS) is an artificial<br>intelligence (AI)/machine learning (ML)- based computer-aided diagnosis (CADx)<br>software device intended for use as an<br>adjunct to diagnostic ultrasound<br>examinations of lesions or nodules<br>suspicious for breast or thyroid cancer.<br><br>Koios DS allows the user to select or<br>confirm regions of interest (ROIs) within an<br>image representing a single lesion or<br>nodule to be analyzed. The software then<br>automatically characterizes the selected<br>image data to generate an AI/ML-derived<br>cancer risk assessment and selects<br>applicable lexicon-based descriptors<br>designed to improve overall diagnostic<br>accuracy as well as reduce interpreting<br>physician variability.<br><br>Koios DS software may also be used as an<br>image viewer of multi-modality digital<br>images, including ultrasound and<br>mammography. The software includes<br>tools that allow users to adjust, measure | | | | | | | includes tools that allow users to adjust, measure and document images, and output into a structured report. | structured report.<br>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. | | Target Population<br>(subset of above<br>for comparison<br>purposes) | 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. | 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<br>(subset of above<br>for comparison<br>purposes) | Limitations:<br>• Patient management decisions should not be made solely on the results of the Koios DS analysis.<br>• 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. | Limitations:<br>• Patient management decisions should not be made solely on the results of the Koios DS analysis.<br>• 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. | | | | | | | for use on portable handheld devices | on portable handheld devices (e.g. | | | (e.g. smartphones or tablets) or as a | smartphones or tablets) or as a primary | | | primary diagnostic viewer of | diagnostic viewer of mammography | | | mammography images. | images. | | | • The software does not predict the | • The software does not predict the | | | presence of the thyroid nodule | presence of the thyroid nodule margin | | | margin descriptor, extra-thyroidal | descriptor, extra-thyroidal extension. In | | | extension. In the event that this | the event that this condition is present, the | | | condition is present, the user may | user may select this category manually | | | select this category manually from the | from the margin descriptor list. | | | margin descriptor list. | | | Modality Used for | Breast Ultrasound Data | Breast Ultrasound Data | | Analysis | Thyroid Ultrasound Data | Thyroid Ultrasound Data | | Input | Medical images provided in a DICOM | Medical images provided in a DICOM | | | format | format | | ROI | Breast | Breast | | Requirements | The software requires a user to select | The software requires a user to select up to | | | up to two ROIs, from up to two | two ROIs, from up to two orthogonal | | | orthogonal views, that represent a | views, that represent a single lesion to be | | | single lesion to be selected and | selected and processed. | | | processed. | | | | | Thyroid | | | Thyroid | Two ROIs that represent a single lesion to | | | Two ROIs that represent a single | be selected and processed are required for | | | lesion to be selected and processed | analysis. | | | are required for analysis. |…
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