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Second Opinion® CS is a computer aided detection ("CADe") software to aid in the detection and segmentation of caries in periapical radiographs.

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 CS detects suspected carious lesions and presents them as an overlay of segmented contours. The software highlights detected caries with an overlay and provides a detailed analysis of the lesion's overlap with dentine and enamel, presented as a percentage. The output of Second Opinion CS is a visual overlay of regions of the input radiograph which have been detected as potentially containing caries. The user can hover over the caries detection to see the segmentation analysis.

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 CS uses machine learning to detect and segment caries. 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 carious lesions are displayed as overlays atop the original radiograph which indicate to the practitioner which teeth contain which detected carious lesions that may require clinical review. The clinician can toggle over the image to highlight a potential condition for viewing. Further, the clinician can hover over the detected caries to show a hover information box containing the segmentation of the caries in the form of percentages.

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 for Second Opinion® CS:

Acceptance Criteria and Reported Device Performance

Study Details

1. Sample Size for Test Set and Data Provenance

• Standalone Test Set: 1250 periapical radiograph images containing 404 overall caries lesions on 286 abnormal images.
  • Provenance: Retrospective. Data was collected from various geographical regions within the United States: Northwest (11.0%), Northeast (18.8%), South (29.2%), West (15.6%), and Midwest (25.5%).
  • Demographics: Includes radiographs from females (50.1%), males (44.6%), and unknown gender (5.3%). Age distribution: 12-18 (12.3%), 18-75 (81.7%), and 75+ (6.0%).
  • Imaging Devices: Carestream-Trophy KodakRVG6100 (25.7%), Carestream-Trophy RVG5200 (3.2%), Carestream-Trophy RVG6200 (27.0%), DEXIS Platinum (19.2%), DEXIS Titanium (18.8%), KodakTrophy KodakRVG6100 (0.8%), and unknown devices (5.3%).
• MRMC Test Set: 330 radiographs with 508 caries lesions across 179 abnormal images.
  • Provenance: Not explicitly stated but inferred to be retrospective, similar to the standalone study, given the focus on existing image characteristics.

2. Number of Experts and Qualifications for Test Set Ground Truth

• Standalone Study: Not explicitly stated for the standalone study. However, the MRMC study description clarifies the method for ground truth establishment, which likely applies to the test set used for standalone evaluation as well.
• MRMC Study: Ground truth was determined by four experienced dentists.
  • Qualifications: "U.S.-certified dentists" and "experienced dentists."

3. Adjudication Method for Test Set

• Standalone Study: Not explicitly stated, but implies expert consensus was used to establish ground truth.
• MRMC Study: Consensus was achieved when the Jaccard index was ≥0.4 amongst the four experienced dentists. This indicates a form of consensus-based adjudication where a certain level of agreement on lesion boundaries was required.

4. MRMC Comparative Effectiveness Study

• Yes, a fully crossed multi-reader multi-case (MRMC) study was done.
• Effect Size (Improvement of human readers with AI vs. without AI assistance):
  • Overall Caries Detection (wAFROC-FOM): Aided readers showed a significant improvement of 0.05 (95% CI: 0.01–0.09) in wAFROC-FOM compared to unaided readers.
  • Lesion-level Sensitivity: Aided readers showed a significant increase of 0.20 (95% CI: 0.16–0.24) in lesion-level sensitivity.
  • False Positives Per Image (FPPI): FPPI increased slightly by 0.16 (95% CI: -0.03–0.36).
  • Surface-level Specificity: Decreased marginally by -0.02 (95% CI: -0.04–0.00).

5. Standalone (Algorithm Only) Performance Study

• Yes, a standalone performance assessment was done to validate the inclusion of a new caries lesion anatomical segmentation.
• Key Results:
  • Sensitivity was > 70%, with an estimated lesion level sensitivity of 0.88 (95% CI), which was statistically significant (p

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