(129 days)
Videa Dental Assist is a computer-assisted detection (CADe) device that analyzes intraoral radiographs to identify and localize the following features. Videa Dental Assist is indicated for the review of bitewing, periapical, and panoramic radiographs acquired from patients aged 3 years or older. Suspected Dental Findings: Caries, Attrition, Broken/Chipped Tooth, Restorative Imperfections, Pulp Stones, Dens Invaginatus, Periapical Radiolucency, Widened Periodontal Ligament, Furcation, Calculus. Historical Treatments: Crown, Filling, Bridge, Post and Core, Root Canal, Endosteal Implant, Implant Abutment, Bonded Orthodontic Retainer, Braces. Normal Anatomy: Maxillary Sinus, Maxillary Tuberosity, Mental Foramen, Mandibular Canal, Inferior Border of the Mandible, Mandibular Tori, Mandibular Condyle, Developing Teeth, Erupting Teeth, Non-matured Erupted Teeth, Exfoliating Teeth, Impacted Teeth, Crowding Teeth.
Videa Dental Assist (VDA) software is a cloud-based AI-powered medical device for the automatic detection of the features listed in the Indications For Use statement 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 an eligible bitewing, periapical or panoramic image, the device returns a set of bounding boxes representing the suspect dental finding, historical treatment or normal anatomy detected. VDA is accessed by the dental practitioner through their dental image viewer. From within the dental viewer the user can upload a radiograph to VDA and then review the results. The device outputs a binary indication to identify the presence of findings for each indication. If findings are present the device outputs the number of findings by finding type and the coordinates of the bounding boxes for each finding. If no findings are present the device outputs a clear indication that there are no findings identified for each indication. The device output will show all findings from one radiograph regardless of the number of teeth present.
Here's a breakdown of the acceptance criteria and study details for the Videa Dental Assist device, as provided in the document:
1. Table of Acceptance Criteria and Reported Device Performance
The document states that all listed sensitivity, specificity, and AFROC FOM results met their acceptance criteria, but generally does not explicitly list the specific numerical acceptance criteria. For the purpose of this table, "Met Acceptance Criteria" will be used when the text indicates it.
| Videa Dental Assist Indication | Performance Metric | Acceptance Criteria | Reported Device Performance (Bench Study) | Reported Device Performance (Clinical Study - Human Aided) |
|---|---|---|---|---|
| Suspect Dental Findings | ||||
| Attrition | AFROC FOM | Met Acceptance Criteria | Not specified (Sensitivity/Specificity only for standalone) | 0.171 (28.5% improvement; p-value 3.3e-16) |
| Broken/Chipped Tooth | AFROC FOM | Met Acceptance Criteria | Not specified (Sensitivity/Specificity only for standalone) | 0.105 (15.3% improvement; p-value 1.5e-11) |
| Calculus | AFROC FOM | Met Acceptance Criteria | Not specified (Sensitivity/Specificity only for standalone) | 0.163 (23.0% improvement; p-value e-12) |
| Caries | Sensitivity | Met Acceptance Criteria | Met Acceptance Criteria | 0.024 (4.3% improvement; p-value 0.0085) |
| Caries | Specificity | Not Met, but performed well enough to pass clinical study | Not Met, but performed well enough to pass clinical study | (Implicitly met through AFROC FOM) |
| Dens Invaginatus | AFROC FOM | Met Acceptance Criteria | Not specified (Sensitivity/Specificity only for standalone) | 0.236 (36.8% improvement; p-value 1.9e-9) |
| Furcation | AFROC FOM | Met Acceptance Criteria | Not specified (Sensitivity/Specificity only for standalone) | 0.199 (29.7% improvement; p-value 0.00057) |
| Periapical Radiolucency | AFROC FOM | Met Acceptance Criteria | Not specified (Sensitivity/Specificity only for standalone) | 0.092 (11.5% improvement; p-value 0.0072) |
| Pulp Stone | AFROC FOM | Met Acceptance Criteria | Not specified (Sensitivity/Specificity only for standalone) | 0.211 (35.4% improvement; p-value 2.2e-16) |
| Restorative Imperfection | AFROC FOM | Met Acceptance Criteria | Not specified (Sensitivity/Specificity only for standalone) | 0.164 (27.9% improvement p-value of |
§ 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.