(319 days)
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.
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:
- Automated contouring of Organs at Risk (OAR) workflow
a. Input -DICOM CT
b. Output DICOM RTSTRUCT - Organ Templates configuration (incl. Organ Database)
- Web-based preview of contouring results to accept or reject the generated contours
Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria for the AI-Rad Companion Organs RT are implicitly tied to demonstrating performance comparable to the predicate device, AccuContour, specifically in terms of contouring accuracy.
| Metric | Acceptance Criteria (based on AccuContour) | Reported Device Performance (AI-Rad Companion Organs RT VA20) |
|---|---|---|
| DICE Coefficient | 0.85 – 0.95 | MED: 0.85 |
| 95% Hausdorff Distance | ≤ 3.5 mm | MED: 2.0 mm |
2. Sample Size Used for the Test Set and Data Provenance
- Test Set Sample Size: 113 cases
- Data Provenance: Retrospective CT data previously acquired for RT treatment planning from multiple clinical sites across North America and Europe. The subcohort analysis also included CT data from multiple vendors (GE, Siemens, Phillips).
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
The document states: "Ground truth annotations were established following RTOG and clinical guidelines using manual annotation." It does not specify the number of experts or their specific qualifications (e.g., "radiologist with 10 years of experience"). However, the phrase "following RTOG and clinical guidelines using manual annotation" implies establishment by qualified medical professionals experienced in radiation therapy contouring.
4. Adjudication Method for the Test Set
The document does not explicitly describe an adjudication method (e.g., 2+1, 3+1). It only states that ground truth annotations were established via "manual annotation" following guidelines. This suggests a single expert or a consensus process without a detailed breakdown of the adjudication procedure in the provided text.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done
No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not reported as being done in this document. The study focuses on the standalone performance of the AI algorithm against a manual ground truth and a comparison to a predicate device's reported performance, not on how human readers' performance improves with or without AI assistance.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was Done
Yes, a standalone performance study of the algorithm was done. The reported DICE coefficient and Hausdorff Distance values directly assess the algorithm's output against the ground truth without human intervention in the contouring process itself. The "output of AI-Rad Companion Organs RT... are intended to be used by trained medical professionals" who "review, edit, and accept contours generated by AI-Rad Companion Organs RT," but the performance metrics provided are for the initial automated contouring.
7. The Type of Ground Truth Used
The ground truth used was expert consensus / manual annotation following RTOG and clinical guidelines.
8. The Sample Size for the Training Set
The document does not specify the sample size for the training set. It only discusses the validation set (113 cases).
9. How the Ground Truth for the Training Set Was Established
The document does not specify how the ground truth for the training set was established. It only describes the ground truth establishment for the test/validation set: "Ground truth annotations were established following RTOG and clinical guidelines using manual annotation."
§ 892.2050 Medical image management and processing system.
(a)
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.(b)
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).