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Second Opinion PC is a computer aided detection ("CADe") software to aid dentists in the detection of periapical radiolucencies by drawing bounding polygons to highlight the suspected region of interest.

It is designed to aid dental health professionals to review periapical radiographs of permanent teeth in patients 12 years of age or older as a second reader.

Second Opinion PC (Periapical Radiolucency Contouring) is a radiological, automated, computer-assisted detection (CADe) software intended to aid in the detection of periapical radiolucencies on periapical radiographs using polygonal contours. The device is not intended as a replacement for a complete dentist's review or their clinical judgment which considers other relevant information from the image, patient history, or actual in vivo clinical assessment.

Second Opinion PC consists of three parts:

• Application Programing Interface ("API")
• Machine Learning Modules ("ML Modules")
• Client User Interface ("Client")

The processing sequence for an image is as follows:

1. Images are sent for processing via the API
2. The API routes images to the ML modules
3. The ML modules produce detection output
4. The UI renders the detection output

The API serves as a conduit for passing imagery and metadata between the user interface and the machine learning modules. The API sends imagery to the machine learning modules for processing and subsequently receives metadata generated by the machine learning modules which is passed to the interface for rendering.

Second Opinion PC uses machine learning to detect periapical radiolucencies. Images received by the ML modules are processed yielding detections which are represented as metadata. The final output is made accessible to the API for the purpose of sending to the UI for visualization. Detected periapical radiolucencies are displayed as polygonal overlays atop the original radiograph which indicate to the practitioner which teeth contain which detected periapical radiolucencies that may require clinical review. The clinician can toggle over the image to highlight a potential condition for viewing.

Here's a detailed breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Device Performance Study

The Pear Inc. "Second Opinion Periapical Radiolucency Contours" (Second Opinion PC) device aims to aid dentists in detecting periapical radiolucencies using polygonal contours, functioning as a second reader. The device's performance was evaluated through a standalone clinical study demonstrating non-inferiority to its predicate device, which used bounding boxes.

1. Table of Acceptance Criteria and Reported Device Performance

The submission document primarily focuses on demonstrating non-inferiority to the predicate device rather than explicitly stating pre-defined acceptance criteria with specific thresholds for "passing." However, the implicit acceptance criteria are that the device is non-inferior to its predicate (Second Opinion K210365) in detecting periapical radiolucencies when using polygonal contours.

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

• Sample Size: 500 unique unannotated periapical radiographs.
• Data Provenance: The dataset is characterized by a representative distribution across:
  • Geographical Regions (within the United States):
    • Northwest: 116 radiographs (23.2%)
    • Southwest: 46 radiographs (9.2%)
    • South: 141 radiographs (28.2%)
    • East: 84 radiographs (16.8%)
    • Midwest: 113 radiographs (22.6%)
  • Patient Cohorts (Age Distribution):
    • 12-18 years: 4 radiographs (0.8%)
    • 18-75 years: 209 radiographs (41.8%)
    • 75+ years: 8 radiographs (1.6%)
    • Unknown age: 279 radiographs (55.8%)
  • Imaging Devices: A variety of devices were used, including Carestream-Trophy (RVG6100, RVG5200, RVG6200), DEXIS (DEXIS, DEXIS Platinum, KaVo Dental Technologies DEXIS Titanium), Kodak-Trophy KodakRVG6100, XDR EV71JU213, and unknown devices.
• Retrospective or Prospective: Not explicitly stated, but the description of "representative distribution" and diverse origins suggests a retrospective collection of existing images.

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

• Number of Experts: Four expert readers.
• Qualifications of Experts: Not explicitly stated beyond "expert readers."

4. Adjudication Method for the Test Set

• Adjudication Method: Consensus approach based on agreement among at least three out of four expert readers (3+1 adjudication).

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• Was an MRMC study done? No, a traditional MRMC comparative effectiveness study was not performed for the subject device (Second Opinion PC).
• Effect Size of Human Readers with AI vs. without AI: Not applicable for this specific study of Second Opinion PC. The predicate device (Second Opinion K210365) did undergo MRMC studies, demonstrating statistically significant improvement in aided reader performance for that device. The current study focuses on the standalone non-inferiority of Second Opinion PC compared to its predicate.

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

• Was a standalone study done? Yes, a standalone clinical study was performed. The study compared the performance of Second Opinion PC (polygonal localization) directly with Second Opinion (bounding box localization) in detecting periapical radiolucencies.
• Metrics: wAFROC-FOM and HR-ROC-AUC were used.
• Key Finding: Second Opinion PC was found to be non-inferior to Second Opinion.

7. The Type of Ground Truth Used

• Type of Ground Truth: Expert consensus. The ground truth (GT) was established by the consensus of at least three out of four expert readers who independently marked periapical radiolucencies using the smallest possible polygonal contour.

8. The Sample Size for the Training Set

• Sample Size for Training Set: Not explicitly mentioned in the provided text. The document focuses on the clinical validation (test set).

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

• Ground Truth for Training Set: Not explicitly mentioned in the provided text. It is implied that the device was "developed using machine learning techniques" from "open-source models using supervised machine learning," which typically requires a labeled training set, but specifics on its establishment are absent.

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