(136 days)
Videa Caries Assist is a computer-assisted detection (CADe) device that analyzes intraoral radiographs to identify and localize carious lesions. Videa Caries Assist is indicated for use by board licensed dentists for the concurrent review of bitewing (BW) radiographs acquired from adult patients aged 22 years or older.
Videa Caries Assist (VCA) software is a cloud-based AI-powered medical device for the automatic detection of carious lesions in dental radiographs. The device itself is available as a service via an API (Application Programming Interface) behind a firewalled network. Provided proper authentication and a bitewing image, the device returns a set of bounding boxes representing the carious lesions detected. VCA is accessed by the dental practitioner through their Dental Viewer. From within the Dental Viewer the user can upload a radiograph to VCA and then review the results. The device outputs a binary indication to identify the presence or absence of findings are present the device outputs the coordinates of the bounding boxes for each finding. If no findings are present the device outputs a clear indication that there are no carious lesions identified.
Here's a summary of the acceptance criteria and the study details for the Videa Caries Assist device, based on the provided document:
Acceptance Criteria and Device Performance
| Metric | Acceptance Criteria (Implicit) | Reported Device Performance (Standalone Study) | Reported Device Performance (Reader Study - Aided) | Reported Device Performance (Reader Study - Unaided) |
|---|---|---|---|---|
| Overall average AFROC FOM | Improvement over unaided reads | 0.740 (95% CI: 0.721, 0.760) | 0.739 (95% CI: 0.705, 0.773) | 0.667 (95% CI: 0.633, 0.701) |
| Difference in Overall average AFROC FOM (Aided - Unaided) | > 0 | N/A | 0.072 (95% CI: 0.047, 0.097, p |
§ 892.2070 Medical image analyzer.
(a)
Identification. Medical image analyzers, including computer-assisted/aided detection (CADe) devices for mammography breast cancer, ultrasound breast lesions, radiograph lung nodules, and radiograph dental caries detection, is a prescription device that is intended to identify, mark, highlight, or in any other manner direct the clinicians' attention to portions of a radiology image that may reveal abnormalities during interpretation of patient radiology images by the clinicians. This device incorporates pattern recognition and data analysis capabilities and operates on previously acquired medical images. This device is not intended to replace the review by a qualified radiologist, and is not intended to be used for triage, or to recommend diagnosis.(b)
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 a description of the algorithm inputs and outputs, each major component or block, and algorithm limitations.
(ii) A detailed description of pre-specified performance testing methods and dataset(s) used to assess whether the device will improve reader performance as intended and to characterize the standalone device performance. Performance testing includes one or more standalone tests, side-by-side comparisons, or a reader study, as applicable.
(iii) Results from performance testing 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 a sufficient number 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) 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, and results; and cybersecurity).(2) Labeling must include the following:
(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 user training that addresses appropriate reading protocols for the device.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) Discussion of warnings, precautions, and limitations must include 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) Device operating instructions.
(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, such as lesion and organ characteristics, disease stages, and imaging equipment.