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InferRead Lung CT.AI is comprised of computer assisted reading tools designed to aid the radiologist in the detection of pulmonary nodules during the review of CT examinations of the chest on an asymptomatic population. InferRead Lung CT.AI requires that both lungs be in the field of view. InferRead Lung CT.AI provides adjunctive information and is not intended to be used without the original CT series.

InferRead Lung CT.AI uses the Browser/Server architecture, and is provided as Service (SaaS) via a URL. The system integrates algorithm logic and database in the same the simplicity of the system and the convenience of system maintenance. The server is able to accept chest CT images from a PACS system, Radiological Information System) or directly from a CT scanner, analyze the images and provide output annotations regarding lung nodules. Users are an existing PACS system to view the annotations. Dedicated servers can be located at hospitals and are directly connected to the hospital networks. The software consists of 4 modules which are Image reception (Docking Toolbox), Image predictive processing (DLServer), Image storage (RePACS) and Image display (NeoViewer).

Here's a breakdown of the acceptance criteria and study details for InferRead Lung CT.AI, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document primarily focuses on a comparative effectiveness study and discusses the device's performance in comparison to unaided human reading. It doesn't explicitly list "acceptance criteria" with numerical targets in the same way a standalone performance study might. However, the objective of the clinical study serves as the de facto acceptance criteria.

2. Sample Size and Data Provenance for Test Set

• Sample Size (Test Set): 249 CT scans.
• Data Provenance: The document does not explicitly state the country of origin. It specifies that the data included "chest CT scans from patients who underwent lung cancer screening," implying it's clinical data, and the study was "retrospective." This suggests the data was collected from existing patient records.

3. Number of Experts and Qualifications for Ground Truth

• The document mentions that a "pivotal reader study" was conducted, involving "10 board-certified radiologists." These radiologists were part of the MRMC study, where their consensus or interpretations would contribute to the ground truth.
• However, it does not explicitly state how many of these, or other, experts were specifically used to establish the definitive ground truth for the test set independent of the reader study itself. The ground truth for the reader study is the consensus of the readers, or a reference standard against which their performance is measured (see point 7).

4. Adjudication Method for the Test Set

The document describes a "fully crossed, multi-reader multi-case (MRMC) study." In such studies, all readers review all cases. While it doesn't explicitly state an adjudication method like "2+1" for establishing a separate ground truth, the MRMC setup inherently uses the collective performance of the expert readers (in both aided and unaided modes) to evaluate the device's impact. The ground truth for nodule presence/absence in the cases would have been established prior to the reader study.

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

Yes, a MRMC comparative effectiveness study was done.

• Effect Size of Human Readers Improvement with AI vs. without AI:
  • Nodule Detection Performance (AUC): The AUC increased by 0.073 (Aided - Unaided), with a 95% confidence interval of (0.020, 0.125). This indicates a statistically significant improvement in detection performance when radiologists used the InferRead Lung CT.AI device.
  • Reading Time: Reading times decreased by 23 seconds (Aided - Unaided), with a 95% confidence interval of (-42, -3). This indicates a statistically significant reduction in reading time.

6. Standalone Performance Study (Algorithm Only)

Yes, a standalone performance study was done.

• The document states: "Standalone performance testing which included chest CT scans from patients who underwent lung cancer screening was performed to validate detection accuracy of InferRead Lung CT.AI. Results showed that InferRead Lung CT.AI had similar nodule detection sensitivity and FP/scan compared to those of the predicate device."
• This suggests comparison against the predicate device's standalone performance, which also implies a form of quantitative performance metric (sensitivity, false positives per scan) for the algorithm in isolation.

7. Type of Ground Truth Used

• For the standalone performance study, the document mentions "detection accuracy" based on scans from lung cancer screening, but doesn't explicitly state whether the ground truth was expert consensus, pathology, or outcomes data. However, for nodule detection, expert consensus on the presence and location of nodules from expert radiologists is a common ground truth, often verified or refined.
• For the MRMC study, the ground truth for evaluating individual reader performance (from which the AUC is derived) would typically be an established reference standard (often expert consensus, sometimes supplemented by follow-up or pathology if available for some cases) created prior to the readers' evaluations.

8. Sample Size for the Training Set

The document does not provide the sample size of the training set used for developing the InferRead Lung CT.AI algorithm.

9. How Ground Truth for Training Set Was Established

The document does not provide information on how the ground truth for the training set was established.

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