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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.

2. Sample Sizes Used for the Test Set and Data Provenance

• Standalone Performance Assessment (Bench Testing):
  • Sample Size: 1,445 radiographs
  • Data Provenance: Collected from more than 35 US sites (retrospective).
• Clinical Testing (MRMC Study):
  • Sample Size: 378 radiographs
  • Data Provenance: Collected from over 25 US locations spread across the country (retrospective).

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

• Standalone Performance Assessment (Bench Testing):
  • Number of Experts: Three
  • Qualifications: US board-certified dentists.
• Clinical Testing (MRMC Study):
  • Number of Experts: Not explicitly stated for the initial labeling, but labels were established by US licensed dentists. One US licensed dentist then adjudicated those labels.
  • Qualifications: US licensed dentists.

4. Adjudication Method for the Test Set

• Standalone Performance Assessment (Bench Testing): The document states the dataset was "ground-truthed by three US board-certified dentists." It doesn't specify a formal adjudication method (e.g., 2+1, 3+1). It implies a consensus or majority approach by multiple experts.
• Clinical Testing (MRMC Study): US licensed dentists labeled the data, and then one US licensed dentist adjudicated those labels to establish a reference standard for the study. This suggests a single adjudicator to resolve discrepancies or confirm final ground truth after initial labeling.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

• Yes, an MRMC comparative effectiveness study was done.
• Effect Size (Average Percentage Improvement in AFROC FOM with VDA):
  • Attrition: 28.5% improvement (0.171 AFROC FOM increase)
  • Broken or Chipped: 15.3% improvement (0.105 AFROC FOM increase)
  • Calculus: 23.0% improvement (0.163 AFROC FOM increase)
  • Caries: 4.3% improvement (0.024 AFROC FOM increase)
  • Dens Invaginatus: 36.8% improvement (0.236 AFROC FOM increase)
  • Furcation: 29.7% improvement (0.199 AFROC FOM increase)
  • Periapical Radiolucency: 11.5% improvement (0.092 AFROC FOM increase)
  • Pulp Stone: 35.4% improvement (0.211 AFROC FOM increase)
  • Restorative Imperfection: 27.9% improvement (0.164 AFROC FOM increase)
  • Widened Periodontal Ligament: 28.4% improvement (0.141 AFROC FOM increase)

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• Yes, a Standalone Performance Assessment was conducted.

7. The Type of Ground Truth Used

• Expert Consensus/Expert Review: For both the standalone performance assessment and the clinical study, the ground truth was established by multiple US board-certified or licensed dentists, either through direct ground-truthing or through labeling followed by adjudication. This falls under expert consensus or expert review.

8. The Sample Size for the Training Set

• The document mentions that "Videa Dental Assist artificial intelligence algorithms were trained with bitewing, periapical and panoramic radiographs" and "trained with that patient population" (referring to pediatric). However, the specific sample size of the training set is NOT provided in the document.

9. How the Ground Truth for the Training Set Was Established

• The document implies that the algorithms were "trained" with data, but does not explicitly describe how the ground truth for the training set was established. It can be inferred that it would also involve expert labeling, similar to the test sets, but no details are given.

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Videa Perio Assist is a radiological semi-automated image processing software device intended to aid dental professionals in the measurements and visualization of mesial and distal bone levels associated with each tooth from bitewing and periapical radiographs. Measurements are made available as linear distances or relative percentages.

It should not be used in-lieu of full patient evaluation or solely relied upon to make or confirm a diagnosis. The system is to be used by trained professionals including, but not limited to, dentists and dental hygienists.

Videa Perio Assist (VPA) software is a cloud-based AI-powered medical device for the automatic measurement of tooth interproximal alveolar bone level in dental radiographs. The device itself is available as an API (Application Programming Interface) behind a firewalled network. The device returns 1) a series of points with connecting lines measuring the mesial and distal alveolar bone levels associated with each tooth 2) this distance expressed in millimeters and/or as a percentage of the root length.

Videa Perio Assist is accessed by the trained professional through their image viewer. From within the image viewer the user can upload a radiograph to Videa Perio Assist and then review the results. The device outputs a line to identify these points which calculate the interproximal bone level.

The device output will show all applicable measurements from one radiograph regardless of the number of teeth present. If no teeth are present the device outputs a clear indication that there are no identifiable teeth to calculate the interproximal bone level.

The intended users of Videa Perio Assist are trained professionals such as dentists and dental hygienists.

The intended patients of Videa Perio Assist are patients 12 years and above with permanent dentition undergoing routine dental visits or suspected of having interproximal bone level concerns. Videa Perio Assist may only be used with patients with permanent dentition present in the radiograph.

Here's a breakdown of the acceptance criteria and the study proving the device's performance, based on the provided FDA 510(k) summary for Videa Perio Assist:

1. Table of Acceptance Criteria & Reported Device Performance

Videa Perio Assist underwent two primary types of testing: Bench Testing (focused on algorithm precision/recall) and Clinical Testing (focused on algorithm sensitivity, specificity, and accuracy for clinical measurements).

Bench Testing Acceptance Criteria & Performance (Per Tooth Landmark Detection)

Note: The document notes that for the periapical CEJ-ABL subgroup, precision was 79.1%, meaning it did not meet the acceptance criteria of >82% precision for this specific subgroup, however, this was attributed to difficulty in estimating obscured points on overlapping teeth.

Clinical Testing Acceptance Criteria & Performance (Per Interproximal Bone Level Measurement)

Note: The document explicitly states for the periapical CEJ-ABL subgroup that specificity "Did not meet acceptance criteria," for the same reason as precision in the bench study—difficulty with obscured points.

2. Sample Sizes Used for the Test Set and Data Provenance

• Bench Testing (Algorithm Standalone Performance):
  • Sample Size: 996 radiographs and 16,131 landmarks.
  • Data Provenance: Not explicitly stated regarding country of origin, but generally, for FDA submissions, data should reflect the US population or be justifiable for generalizability to the US. The document refers to "US licensed dentists" for clinical testing, implying a US context. The data was collected across two phases (retrospective or prospective is not specified, but the context generally suggests retrospective collection for developing and testing an algorithm).
• Clinical Testing (Human-in-the-loop/Algorithm Assistance Effectiveness):
  • Sample Size: 189 radiographs and over 2,350 lines (measurements).
  • Data Provenance: Not explicitly stated regarding country of origin, though "US licensed dentists" and "US licensed periodontists" are mentioned, suggesting US data. The study was conducted in "two phases." The type of study (retrospective vs. prospective) is not specified.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• Bench Testing: "Ground truth labeled across two phases." The specific number or qualifications of the individuals performing this ground truth labeling is not explicitly stated in the provided text.
• Clinical Testing:
  • Initial Labeling: "US licensed dentists labeled data across two phases." The number of dentists is not specified.
  • Adjudication/Reference Standard Establishment: "two US licensed periodontists adjudicated those labels to establish a reference standard for the study." The specific qualifications (e.g., years of experience) for these periodontists are not detailed beyond being "US licensed periodontists."

4. Adjudication Method for the Test Set

• Bench Testing: Adjudication method not explicitly described, only that ground truthing occurred across two phases.
• Clinical Testing: The ground truth was established by "two US licensed periodontists adjudicat[ing] those labels." This implies a consensus process between the two periodontists, or one reviewing the other's work, but the specific consensus or tie-breaking rule (e.g., 2+1, 3+1) is not detailed.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No, an MRMC comparative effectiveness study was NOT mentioned.
• The study primarily focused on the standalone performance of the algorithm against a human-established ground truth, not on how human readers' performance improved with AI assistance. The "Clinical Testing" section describes measuring the algorithm's performance against a reference standard, not a comparative study of human performance with and without the device.

6. Standalone (Algorithm Only Without Human-in-the-Loop) Performance

• Yes, standalone performance was explicitly evaluated. This is what the "Bench Testing" section describes.
  • The Videa Perio Assist output (lines and measurements) was scored directly against the ground-truthed landmarks and measurements.

7. Type of Ground Truth Used

• Expert Consensus/Expert Labeling:
  • For Bench Testing, ground truth was established by "ground truth labeled across two phases." This implies expert review and labeling of landmarks.
  • For Clinical Testing, the reference standard (ground truth) was established by "two US licensed periodontists adjudicat[ing] those labels" initially provided by "US licensed dentists." This is an expert consensus or adjudicated expert labeling ground truth.
• Not Pathology or Outcomes Data.

8. Sample Size for the Training Set

• The document does not explicitly state the sample size for the training set. It only mentions that the "artificial intelligence algorithm was trained with that patient population" (referring to permanent dentition patients).
• However, it does refer to "Bench testing has sensor manufacturer and patient age subgroup analysis for generalizability in a similar method as described in the clinical study generalizability section below. The sensor manufacturer and patient age did not have any outliers in the bench study." This suggests that the training data and evaluation focused on ensuring generalizability across these factors, but the specific size is missing.

9. How the Ground Truth for the Training Set Was Established

• The document does not explicitly describe how the ground truth for the training set was established. It can be inferred that it would follow a similar expert labeling process as the test set (e.g., by dental professionals), but no specifics are provided.

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