(263 days)
AutoContour is intended to assist radiation treatment planners in contouring structures within medical images in preparation for radiation therapy treatment planning.
AutoContour consists of 3 main components:
- An "agent" service designed to run on the Windows Operating System that is configured by the user to monitor a network storage location for new CT datasets that are to be automatically uploaded to:
- A cloud-based AutoContour automatic contouring service that produces initial contours and
- A web application accessed via web browser which allows the user to perform registration with other image sets as well as review, edit, and export the structure set containing the contours.
The provided text describes the acceptance criteria and study proving the device meets those criteria. Here's a breakdown of the requested information:
1. Table of Acceptance Criteria & Reported Device Performance
The document states that formal acceptance criteria and reported device performance are detailed in "Radformation's AutoContour Complete Test Protocol and Report." However, this specific report is not included in the provided text. The summary only generally states that "Nonclinical tests were performed... which demonstrates that AutoContour performs as intended per its indications for use" and "Verification and validation tests were performed to ensure that the software works as intended and pass/fail criteria were used to verify requirements."
Therefore, a table of acceptance criteria and reported device performance cannot be constructed from the provided text.
2. Sample Size Used for the Test Set and Data Provenance
The document mentions that "tests were performed on independent datasets from those included in training and validation sets in order to validate the generalizability of the machine learning model." However, the sample size for the test set is not explicitly stated.
Regarding data provenance:
- The document implies the data used was medical image data (specifically CT, and for registration purposes, MR and PET).
- The country of origin is not specified.
- The terms "training and validation sets" and "independent datasets" suggest these were retrospective datasets used for model development and evaluation. There is no mention of prospective data collection.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
The document does not provide any information about the number of experts used to establish ground truth for the test set or their qualifications.
4. Adjudication Method for the Test Set
The document does not specify any adjudication method (e.g., 2+1, 3+1, none) used for the test set.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance?
The document explicitly states: "As with the Predicate Devices, no clinical trials were performed for AutoContour." This indicates that an MRMC comparative effectiveness study involving human readers and AI assistance was not conducted. Therefore, no effect size for human reader improvement is reported.
6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done
The document mentions "tests were performed on independent datasets from those included in training and validation sets in order to validate the generalizability of the machine learning model." This strongly suggests that standalone performance of the algorithm was evaluated. Although specific metrics for this standalone performance are not detailed in the provided text, the validation of a machine learning model against independent datasets implies a standalone evaluation.
7. The Type of Ground Truth Used
The document mentions that AutoContour is intended to "assist radiation treatment planners in contouring structures within medical images." Given this, the ground truth for the contours would typically be expert consensus or expert-annotated contours. However, the document itself does not explicitly state the type of ground truth used (e.g., expert consensus, pathology, outcomes data).
8. The Sample Size for the Training Set
The document mentions "training and validation sets" but does not provide the sample size for the training set.
9. How the Ground Truth for the Training Set Was Established
The document mentions "training and validation sets" but does not detail how the ground truth for the training set was established. Similar to the test set, it would likely involve expert contouring, but this is not explicitly stated.
§ 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).