Who is the manufacturer of DeepContour (V1.0)?

Wisdom Technologies., Inc. filed the 510(k) submission for DeepContour (V1.0) (K232928).

What is the FDA product code for DeepContour (V1.0)?

QKB. It is classified under 21 CFR 892.2050 as a Class 2 device in the Radiology panel.

What is the regulatory pathway for DeepContour (V1.0)?

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

Does DeepContour (V1.0) use AI or machine learning?

Yes. DeepContour (V1.0) (K232928) 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 DeepContour (V1.0)?

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 DeepContour (V1.0) 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.

DeepContour (V1.0)

Q: What are the predicate devices for DeepContour (V1.0)?

AI-Rad Companion Organs RT (K221305).

K232928 · Wisdom Technologies., Inc. · QKB · May 7, 2024 · Radiology

Device Facts

Record ID	K232928
Device Name	DeepContour (V1.0)
Applicant	Wisdom Technologies., Inc.
Product Code	QKB · Radiology
Decision Date	May 7, 2024
Decision	SESE
Submission Type	Traditional
Regulation	21 CFR 892.2050
Device Class	Class 2
Attributes	AI/ML, Software as a Medical Device, Real-World Evidence

Real-World Evidence

Submission	Device	Sponsor	RWD Sources	RWE Use Summary	Key Tags
K232928 · May 7, 2024	DeepContour (V1.0)	Wisdom Technologies., Inc.	Retrospective clinical CT images from Peking Union Medical College Hospital	Retrospective clinical data was used to validate the performance of the DeepContour auto-segmentation software by comparing its output against ground truth annotations established by radiation oncologists.	Retrospective clinical data; Performance validation; Auto-segmentation; Radiation oncology

Clinical Evidence

Study Design	Population	Comparator	Key Endpoints
Peking Union Medical College Hospital Clinical Dataset; Retrospective performance validation	Patients undergoing CT imaging for radiation therapy planning; Sample Size: 100; Number of Sites: 1	AI-Rad CAI-Rad Companion Organs RT (K221305) and Contour ProtégéAI (K223774)	Dice coefficient, Average Symmetric Surface Distance (ASSD)

AI Performance

Output	Algorithm	Acceptance	Observed	Dev DS	Dev Readers	Test DS	Test Readers
Organ segmentation	Deep learning based auto-segmentation model	—	Mean Dice coefficients ranging from 0.76 to 0.99 across various organs.	800 CT images (200 each for head and neck, chest, abdomen, and pelvic regions) from 35 hospitals across China.	>1 (radiation oncologists) + 1 (internal staff)	100 clinical cases from Peking Union Medical College Hospital; 103 American public datasets (60 LCTSC thoracic CT, 43 Pancreas-CT).	>1 (radiation oncologists) + 1 (internal staff) + >1 (American doctors)
Rigid and elastic registration	—	—	MAE 43.98±10.74 HU; RMSE 117.58±28.22; PSNR 22.23±2.61; SSIM 0.680±0.050.	—	—	Planning CT images (moving) and CBCT images (fixed).	—

Indications for Use

DeepContour is a deep learning based medical imaging software that allows trained healthcare professionals to use DeepContour as a tool to automatically process CT images. In addition, DeepCoutour is suitable for the following conditions: 1. Creation of contours using deep-learning algorithms , support quantitative analysis, organ HU distribution statistics, transfer contour files to TPS, and create management archives for patients. 2. Analyze the anatomical structure at different anatomical positions. 3. Rigid and elastic registration based on CT. 4. 3D reconstruction, editing and other visual tools based on organ contours

Device Story

DeepContour is a deep learning-based software for processing CT images in radiation therapy workflows. It accepts DICOM CT images as input and uses deep learning models to automatically segment organs at risk (OARs) and anatomical structures. The device performs rigid and elastic registration, quantitative analysis, and 3D reconstruction. It produces RTSTRUCT files for export to Treatment Planning Systems (TPS). Used in clinical environments by trained healthcare professionals, the software includes an interactive interface for reviewing and editing auto-generated contours. By automating the time-consuming contouring process, it assists clinicians in radiation therapy planning, potentially improving workflow efficiency and consistency in target delineation.

Clinical Evidence

Bench testing only. Performance validated using 203 CT images: 100 clinical cases from Peking Union Medical College Hospital and 103 public dataset cases (LCTSC and Pancreas-CT). Primary endpoint was Dice similarity coefficient and Average Symmetric Surface Distance (ASSD). Results showed comparable or superior performance to predicate devices across multiple anatomical regions (Head & Neck, Thorax, Abdomen, Pelvis).

Technological Characteristics

Software-based medical image management and processing system. Utilizes deep learning algorithms for automated segmentation. Compatible with DICOM-compliant CT scanners and Treatment Planning Systems. Supports local and cloud-based deployment. Windows-based application. No patient contact.

Indications for Use

Indicated for adult patients requiring CT image processing for radiation therapy planning, including contouring of organs at risk, quantitative analysis, anatomical structure analysis, rigid/elastic registration, and 3D reconstruction.

Regulatory Classification

Identification

A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.

Special Controls

*Classification.* Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).

Predicate Devices

AI-Rad Companion Organs RT (K221305)

Reference Devices

Contour ProtégéAI (K223774)

Submission Summary (Full Text)

{0}------------------------------------------------ May 7, 2024 Image /page/0/Picture/1 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left, there is a seal with an abstract design and the text "DEPARTMENT OF HEALTH & HUMAN SERVICES-USA" arranged around it. To the right, there is a blue square with the letters "FDA" in white, followed by the words "U.S. FOOD & DRUG" in blue, and "ADMINISTRATION" in a smaller font size below it. Wisdom Technologies., Inc. % Wei Wang Regulatory Consultant 11 Longstreet IRVINE, CA 92620 Re: K232928 Trade/Device Name: DeepContour (V1.0) Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QKB Dated: April 5, 2024 Received: April 5, 2024 Dear Wei Wang: 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. 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 {1}------------------------------------------------ product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS 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. 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. 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, Loran Werchner Lora D. Weidner, Ph.D. Assistant Director Radiation Therapy 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 {2}------------------------------------------------ ### Indications for Use Submission Number (if known) K232928 Device Name DeepContour (V1.0) #### Indications for Use (Describe) DeepContour is a deep learning based medical imaging software that allows trained healthcare professionals to use DeepContour as a tool to automatically process CT images. In addition, DeepCoutour is suitable for the following conditions: 1. Creation of contours using deep-learning algorithms , support quantitative analysis, organ HU distribution statistics, transfer contour files to TPS, and create management archives for patients. 2. Analvze the anatomical structure at different anatomical positions. 3. Rigid and elastic registration based on CT. 4. 3D reconstruction, editing and other visual tools based on organ contours Type of Use (Select one or both, as applicable) Prescription Use (Part 21 CFR 801 Subpart D) he-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." {3}------------------------------------------------ ## K232928 Image /page/3/Picture/1 description: The image shows a logo with a stylized human figure in the center, surrounded by radiating lines. The figure is placed within a blue, semi-circular shape. To the right of the figure, there are Chinese characters followed by the words "WISDOM TECH" in blue font. The logo appears to represent a technology company, possibly focused on human-centered solutions. # 510(k) Summary The following information is provided as required by 21 CFR 807.92 #### 1. SUBMITTER Name: Wisdom Technologies., Inc. Address: 4th Floor, Building F2, Phase II, Innovation Industrial Park, Hefei, Anhui, China 230088 Phone: +86-0551-65116387 Email: registration(@wisdom-tech.online Contact Person: Wei Wang, Consultant, Regulatory Affairs Phone: 949-7849283 Date Prepared: August 24, 2023 #### 2. DEVICE Subject Device Name: DeepContour v1.0 Common/Trade Name: DeepContour Product Code and Classification: Medical Image Management And Processing System 21 CFR 892.2050 | QKB | Class II #### 3. PREDICATE DEVICE Primary: AI-Rad CAI-Rad Companion Organs RT (K221305) Siemens Reference: Contour ProtégéAI (K223774) MIM Software #### 4. DEVICE DESCRIPTION DeepContour is a deep learning based medical imaging software that allows trained healthcare professionals to use DeepContour as a tool to automatically process CT images. DeepContour contouring workflow supports CT input data and produces RTSTRUCT outputs. The organ segmentation can also be combined into templates, which can be customized by different hospitals according to their needs. DeepContour provides an interactive contouring application to edit and review the contours automatically generated by DeepContour. #### 5. INDICATIONS FOR USE {4}------------------------------------------------ DeepContour is a deep learning based medical imaging software that allows trained healthcare professionals to use DeepContour as a tool to automatically process CT images. In addition, DeepCoutour is suitable for the following conditions: 1). Creation of contours using deep-learning algorithms , support quantitative analysis, organ HU distribution statistics, transfer contour files to TPS, and create management archives for patients. 2). Analyze the anatomical structure at different anatomical positions. - 3). Rigid and elastic registration based on CT. - 4). 3D reconstruction, editing and other visual tools based on organ contours ### 6. COMPARISON OF TECHNOLOGICAL CHARACTERISTICS WITH PREDICATE DEVICE The primary technological components of DeepContour and its predicate device are to achieve the deep learning based medical imaging software functions that allows trained healthcare professionals to automatically process CT images. Both are software devices that receive inputs related to radiological images; Both generate contours as output that may be used as input for radiation Treatment Planning Systems and interactive contouring applications to review and edit; Both are software devices for prescription use in a professional environment with no patient contact. There are no known differences in technological characteristics between the subject device and the predicate device that raise any questions of safety or effectiveness. The technological characteristics of the subject device are believed to be substantially equivalent to the predicate device. | Area of Comparison | Subject Device- DeepContour | Primary-AI-Rad CAI- Rad Companion Organs RT (K221305) Siemens | Reference-Contour ProtégéAI(K223774) MIM Software | |------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Regulation Number/code | 21 CFR 892.2050 QKB | 22 CFR 892.2050 QKB | 21 CFR 892.2050 QKB | | Regulation Name | Medical Image Management And Processing System | Medical Image Management And Processing System | Medical Image Management And Processing System | | Indications for Use | DeepContour is a deep learning based medical imaging software that allows trained healthcare professionals to use DeepContour as a tool to automatically process CT images. In addition, DeepCoutour is suitable for the following conditions: 1. Creation of contours using deep-learning algorithms , support quantitative analysis, organ HU distribution statistics, transfer contour files to TPS, and create management archives for patients. 2. Analyze the anatomical structure at different anatomical positions. 3. Rigid and elastic registration based on CT. 4. 3D reconstruction, editing and other visual tools based on organ contours | AI-Rad Companion Organs RT is a post-processing software intended to automatically contour DICOM CT imaging data using deep-learning-based algorithms. Contours that are generated by AI-Rad Companion Organs RT may be used as input for clinical workflows including external beam radiation therapy treatment planning. AI-Rad Companion Organs RT must be used in conjunction with appropriate software such as Treatment Planning Systems and Interactive Contouring applications, to review, edit, and accept contours generated by AI-Rad Companion Organs RT. The output of AI-Rad Companion Organs RT in the format of RTSTRUCT objects are intended to be used by trained medical professionals. The software is not intended to automatically detect or contour lesions. Only DICOM images of adult patients are considered to be valid input. | Trained medical professionals use Contour ProtégéAI as a tool to assist in the automated processing of digital medical images of modalities CT and MR, as supported by ACR/NEMA DICOM 3.0. In addition, Contour ProtégéAI supports the following indications: • Creation of contours using machine-learning algorithms for applications including, but not limited to, quantitative analysis, aiding adaptive therapy, transferring contours to radiation therapy treatment planning systems, and archiving contours for patient follow-up and management. • Segmenting anatomical structures across a variety of CT anatomic locations. • And segmenting the prostate, the seminal vesicles, and the urethra within T2-weighted MR images. Appropriate image visualization software must be used to review and, if necessary, edit | | | | | results automatically generated by Contour ProtégéAI. | | Device description | DeepContour is a deep learning based medical imaging software that allows trained healthcare professionals to use DeepContour as a tool to automatically process CT images. DeepContour contouring workflow supports CT input data and produces RTSTRUCT outputs. The organ segmentation can also be combined into templates, which can be customized by different hospitals according to their needs. DeepContour provides an interactive contouring application to edit and review the contours automatically generated by DeepContour. | AI-Rad Companion Organs RT is a post-processing software used to automatically contour DICOM CT imaging data using deep-learning-based algorithms. AI-Rad Companion Organs RT contouring workflow supports CT input data and produces RTSTRUCT outputs. The configuration of the organ database and organ templates defining the organs and structures to be contoured based on the input DICOM data is managed via a configuration interface. Contours that are generated by AI-Rad Companion Organs RT may be used as input for clinical workflows including external beam radiation therapy treatment planning. The output of AI-Rad Companion Organs RT, in the form of RTSTRUCT objects, are intended to be used by trained medical professionals. The output of AI-Rad Companion Organs RT must be used in conjunction with appropriate software such as Treatment Planning Systems and Interactive Contouring applications, to review, edit, and accept contours generated by AI-Rad Companion Organs RT application. At a high-level, AI-Rad Companion Organs RT includes the following functionality: 1. Automated contouring of Organs at Risk (OAR) workflow a. Input -DICOM CT b. Output - DICOM | Contour ProtégéAI is an accessory to MIM software that automatically creates contours on medical images through the use of machine- learning algorithms. It is designed for use in the processing of medical images and operates on Windows, Mac, and Linux computer systems. Contour ProtégéAI is deployed on a remote server using the MIMcloud service for data management and transfer; or locally on the workstation or server running MIM software. | | | | RTSTRUCT 2. Organ Templates configuration (incl. Organ Database) 3. Web-based preview of contouring results to accept or reject the generated contours. | | | Algorithm | Deep Learning | Deep Learning | Machine-learning | | Segmentation of Organ at Risk in the Anatomic Regions | Head & Neck, Thorax, Abdomen & Pelvis (82 OARs) | Head & Neck, Thorax, Abdomen & Pelvis Head & Neck lymph nodes (108 OARs) | Head & Neck, Prostate, Thorax, Abdomen, Lungs & Liver, MRT structures (spleen, pelvic lymph nodes, descending aorta, bone) | | Compatible Modality | CT Images | CT Images | CT & MR | | Compatible Scanner Models | No Limitation on scanner model, DICOM compliance required. | No Limitation on scanner model, DICOM compliance required. | No Limitation on scanner model, DICOM compliance required. | | Compatible Treatment Planning System | No Limitation on TPS model, DICOM compliance required. | No Limitation on TPS model, DICOM compliance required. | No Limitation on TPS model, DICOM compliance required. | | Contraindications | Adult use only | Adult use only | Adult use only | | Target Population | DeepContour is designed for use only in adult populations for whom relevant modality scans , including head and neck, thorax, abdomen, and pelvis, are available . | AI-Rad Companion Organs RT is designed for use only in adult populations. AI-Rad Companion Organs RT is designed for any patient for whom relevant modality scans are available. More specifically, the software is validated on previously acquired CT DICOM volumes for radiation therapy treatment planning, including, head and neck, | No public record found | | Clinical condition the device is intended to diagnose, treat or manage | Limited to patients previously selected for Radiation Therapy. | Limited to patients previously selected for Radiation Therapy. | Limited to patients previously selected for Radiation Therapy. | | Software Architecture | Server-based application supporting Windows and Local deployment on Windows. | AI-Rad Companion (Engine) architecture enabling the deployment of AI Rad Companion Organs RT using Edge and in the Cloud. The UI is provided using a webbased interface. | Server-based application supporting Linux-based OS and Local deployment on Windows or Mac | | Deployment Feature | locally deployed or Cloud- based | Edge & Cloud Deployment | Cloud-based or locally deployed | | Organ Templates | Creating, editing and deletion of organ templates. Customize predefined structure database with mapping to international nomenclature schemes. | Creating, editing and deletion of organ templates. Customize predefined structure database with mapping to international nomenclature schemes. | No public record found | | Automated workflow | DeepContour automatically processes input image data and sends the results as DICOM- RT Structure Sets to a user- configurable target node. | AI-Rad Companion Organs RT automatically processes input image data and sends the results as DICOM-RT Structure Sets to a user- configurable target node. | Automatic contouring working using machine- learning | | Contour visualization and editing feature | DeepContour provides basic result preview of automatic segmentation results, and editing feature of the automatic segmented contour. | AI-Rad Companion Organs RT provides basic result preview of automatic segmentation results, and no editing feature of the automatic segmented contour. | No public record found | #### Table 1. Substantial Equivalence Comparison {5}------------------------------------------------ {6}------------------------------------------------ {7}------------------------------------------------ {8}------------------------------------------------ {9}------------------------------------------------ | Segmentation Performance | The target performance was validated using 100 cases. The mean and standard deviation Dice coefficients, along with the lower 95th percentile confidence bound were calculated. | The target performance was validated using 113 cases distributed to two cohorts. Cohort A is clinical routine treatment planning CT and it is split into two sub-cohort and Cohort B is PET-CT data. To objectively evaluate the target performance, the DICE coefficient, the absolute symmetric surface distance (ASSD) and the fail rate was evaluated. The segmentation performance of the subject and reference device were equivalent as well as the overall performance compared to the predicate device. | 739 CT Images from 12 clinical sites were used for testing. The mean and standard deviation Dice coefficients, along with the lower 95th percentile confidence bound were calculated. | |-------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | User Interface - Results Preview (Confirmation) | Basic visualization functionality of original data and generated contours | Basic visualization functionality of original data and generated contours | Basic visualization functionality of original data and generated contours | | User Interface Configuration | Configuration UI | Configuration UI | Configuration UI | | Automated Workflow to TPS | Results send to Confirmation UI & Optional bypassing of Confirmation UI to TPS | Results send to Confirmation UI & Optional bypassing of Confirmation UI to TPS…

DeepContour (V1.0)

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