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AVIEW Lung Nodule CAD is a Computer-Aided Detection (CAD) software designed to assist radiologists in the detection of pulmonary nodules (with diameter 3-20 mm) during the review of CT examinations of the chest for asymptomatic populations. AVIEW Lung Nodule CAD provides adjunctive information to alert the radiologists to regions of interest with suspected lung nodules that may otherwise be overlooked. AVIEW Lung Nodule CAD may be used as a second reader after the radiologist has completed their initial read. The algorithm has been validated using non-contrast CT images, the majority of which were acquired on Siemens SOMATOM CT series scanners; therefore, limiting device use to use with Siemens SOMATOM CT series is recommended.

The AVIEW Lung Nodule CAD is a software product that detects nodules in the lung. The lung nodule detection model was trained by Deep Convolution Neural Network (CNN) based algorithm from the chest CT image. Automatic detection of lung nodules of 3 to 20mm in diameter CT images. By complying with DICOM standards, this product can be linked with Picture Archiving and Communication System (PACS) and provides a separate user interface to provide functions such as analyzing, identifying, storing, and transmitting quantified values related to lung nodules. The CAD's results could be displayed after the user's first read, and the user could select or de-select the mark provided by the CAD. The device's performance was validated with SIEMENS' SOMATOM series manufacturing. The device is intended to be used with a cleared AVIEW platform.

The provided FDA 510(k) clearance letter for AVIEW Lung Nodule CAD (K251203) does not contain the detailed information necessary to fully answer all aspects of your request regarding acceptance criteria and the study proving device performance.

Specifically, the document states: "This Medical device is not new; therefore, a clinical study was not considered necessary prior to release. Additionally, there was no clinical testing required to support the medical device as the indications for use is equivalent to the predicate device. The substantial equivalence of the device is supported by the non-clinical testing."

This indicates that clinical performance (e.g., accuracy against a medical ground truth) was not a primary focus of this particular submission, but rather a demonstration of equivalence to a predicate device and non-clinical testing (software verification/validation, cybersecurity, OTS testing).

Therefore, based solely on the provided text, I cannot provide details on specific acceptance criteria related to a clinical performance study (like sensitivity, specificity, or FROC scores) or the specifics of a study that proves the device meets those criteria from a clinical perspective.

However, I can extract the available information.

Acceptance Criteria and Study for AVIEW Lung Nodule CAD (K251203)

Based on the provided FDA 510(k) Clearance Letter, the primary "acceptance criteria" for this submission appear to be related to demonstrating substantial equivalence to a predicate device and adherence to non-clinical software and cybersecurity standards, rather than establishing new clinical performance metrics. The document explicitly states that "a clinical study was not considered necessary" and "no clinical testing required."

1. Table of Acceptance Criteria and Reported Device Performance

As specific clinical performance metrics (e.g., sensitivity, specificity, FROC analysis) and their associated acceptance criteria are not detailed in this 510(k) summary, I can only infer the "performance" in terms of equivalence and successful non-clinical testing.

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

The provided document does not specify a sample size for a clinical test set or the provenance of any data used for clinical validation, as it states clinical testing was "not considered necessary" for this submission to establish substantial equivalence. The predicate device (K221592) presumably had a clinical validation, but those details are not in this document.

3. Number of Experts Used to Establish Ground Truth and Qualifications

This information is not provided in the document, as a clinical study involving expert ground truth establishment was not a requirement for this specific submission.

4. Adjudication Method for the Test Set

This information is not provided in the document, as a clinical study involving adjudication was not a requirement for this specific submission.

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

A MRMC comparative effectiveness study was not performed for this submission. The document explicitly states that "clinical testing was not required."

6. Standalone (Algorithm Only) Performance Study

A standalone performance study with clinical metrics (e.g., sensitivity, specificity, FROC curves) was not detailed or required for this particular submission for substantial equivalence. The submission focuses on non-clinical software validation and equivalence.

7. Type of Ground Truth Used

Based on the information provided, a clinical ground truth (e.g., expert consensus, pathology, outcomes data) was not established or utilized within the scope of this specific 510(k) submission, as clinical testing was not required. The "ground truth" for the non-clinical tests would relate to expected software behavior and security standards.

8. Sample Size for the Training Set

The document states, "The lung nodule detection model was trained by Deep Convolution Neural Network (CNN) based algorithm from the chest CT image." However, the sample size for this training set is not provided in the given text.

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

The document mentions that the model was trained using "chest CT image" data; however, the method by which the ground truth for this training set was established (e.g., expert annotations, pathology, outcomes) is not detailed in the provided text.

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