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Deep Xray is a radiological fully automated image processing software device of either computed (CR) or directly digital (DX) images intended to aid medical professionals in the measurement of minimum joint space width; the assessment of the presence or absence of sclerosis, joint space narrowing, and osteophytes based on OARSI criteria for these parameters; and, the presence or absence of radiographic knee OA based on Kellgren & Lawrence Grading of standing, fixed-flexion radiographs of the knee.

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, radiologists, physicians and medical technicians.

Deep Xray is a standalone software device that utilizes artificial intelligence (AI) and computer vision algorithms to assist clinical professionals in analyzing and measuring radiographic abnormalities of knee osteoarthritis (OA) during review of posterior or anterior-posterior knee radiographs. DeepXray provides automated metric measurements of the joint space width and angular measurements of the femoral-tibial angle. DeepXray also performs assessments of knee osteoarthritis based on the Kellgren-Lawrence Grade (KL Grade), as well as individual radiographic features of osteoarthritis, including joint space narrowing, osteophyte and sclerosis based on the OARSI (Osteoarthritis Research Society International) grading criteria.

The output of DeepXray is rendered as a summary report and can be viewed on a web browser. Using this web interface, the user can verify the AI report side-by-side with the original radiograph using standard DICOM image tools and review each AI analysis result with the help of markup images overlaid with highlighted disease location or reference lines used for automated measurements. The web report also notifies the user for potential data quality issues. The clinical professionals can make modifications to the AI analysis results based on their professional judgement before saving and outputting the report.

Here's a breakdown of the acceptance criteria and the study details for the DeepXray device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" as a set of predefined thresholds that the device must meet in order to be cleared. Instead, it presents the performance metrics obtained during the clinical validation, implying that these results were deemed sufficient for substantial equivalence. I will present the reported performance, which served as the basis for clearance.

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

• Sample Size (Test Set):
  • Patients: 1121
  • DICOM Images: 6125 (after automatic quality control, 6114 DICOM images were analyzed for classification tasks and 5993/5904 for osteophyte/sclerosis, 4432/4377/4310 for mJSW/FTA)
  • Knees: 11816 (11775 knees for classification tasks, 7748/7605/7546 for mJSW/FTA)
• Data Provenance: The data was derived from "one of the site of the Osteoarthritis Initiative (OAI), a multi-center longitudinal study." The OAI is a major public-private partnership focused on osteoarthritis. The text doesn't explicitly state the country of origin, but OAI data generally comes from multiple sites across the United States. It is a retrospective dataset.

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

The document states that the ground truth for the clinical validation was "clinical professionals' labeling provided by the OAI study." The OAI study involved a rigorous process for establishing ground truth, typically involving expert radiologists. However, the exact number of experts and their specific qualifications for the OAI labeling are not explicitly detailed in this document. It generally refers to "clinical professionals."

4. Adjudication Method for the Test Set

The document does not explicitly describe an adjudication method (like 2+1 or 3+1 consensus) for establishing the ground truth of the OAI data used as the test set. It refers to "clinical professionals' labeling." OAI practices typically involve centralized reading by experienced radiologists, often with multiple readers and adjudication processes, but this specific detail is not present in this particular document.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study evaluating human readers with AI assistance versus without AI assistance was not described in this document. The study presented here is a standalone performance study of the DeepXray algorithm against existing ground truth.

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

Yes, a standalone performance study of the algorithm only (without human-in-the-loop performance) was performed. The reported performance metrics (Sensitivity, Specificity, Slopes, Intercepts) are for the DeepXray device operating independently against the established ground truth.

7. The Type of Ground Truth Used

The ground truth used was expert consensus / clinical professionals' labeling obtained from the Osteoarthritis Initiative (OAI) study. This often combines radiological assessment with other clinical data in large research studies, but here it specifically refers to "clinical professionals' labeling" of radiographic findings and measurements.

8. The Sample Size for the Training Set

• Patients: 3387
• DICOM Images: 18406
• Knees: 35217

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

The document states that the training data and testing data were both derived from the OAI study and that the performance data supports that the assessments and measurements are in "good agreement with clinical professionals' labeling provided by the OAI study." This implies that the ground truth for the training set was established through the same or a similar rigorous process of expert clinical professionals' labeling as the test set, within the OAI framework.

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