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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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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 Network (CNN) based algorithm from the chest CT image. Automatic detection of lung nodules of 3 to 20mm in chest CT images. By complying with DICOM standards, this product can be linked with the 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.

Here's a breakdown of the acceptance criteria and study details for the AVIEW Lung Nodule CAD, as derived from the provided document:

Acceptance Criteria and Reported Device Performance

Study Details

1. Acceptance Criteria and Reported Device Performance (as above)

2. Sample size used for the test set and data provenance:

• Test Set Size: 282 cases (140 cases with nodule data and 142 cases without nodule data) for the standalone study.
• Data Provenance:
  * Geographically distinct US clinical sites.
  * All datasets were built with images from the U.S.
  * Anonymized medical data was purchased.
  * Included both incidental and screening populations.
  * For the Multi-Reader Multi-Case (MRMC) study, the dataset consisted of 151 Chest CTs (103 negative controls and 48 cases with one or more lung nodules).

3. Number of experts used to establish the ground truth for the test set and their qualifications:

• Number of Experts: Three (for both the MRMC study and likely for the standalone ground truth, given the consistency in expert involvement).
• Qualifications: Dedicated chest radiologists with at least ten years of experience.

4. Adjudication method for the test set:

• Not explicitly stated for the "standalone study" ground truth establishment.
• For the MRMC study, the three dedicated chest radiologists "determined the ground truth" in a blinded fashion. This implies a consensus or majority vote, but the exact method (e.g., 2+1, 3+1) is not specified. It does state "All lung nodules were segmented in 3D" which implies detailed individual expert review before ground truth finalization.

5. Multi-Reader Multi-Case (MRMC) comparative effectiveness study:

• Yes, an MRMC study was performed.
• Effect size of human readers improving with AI vs. without AI assistance:
  * AUC: The point estimate difference was 0.19 (from 0.73 unassisted to 0.92 aided).
  * Sensitivity: The point estimate difference was 0.23 (from 0.68 unassisted to 0.91 aided).
  * FP/scan: The point estimate difference was 0.24 (from 0.48 unassisted to 0.28 aided), indicating a reduction in false positives.
• Reading Time: "Reading time was decreased when AVIEW Lung Nodule CAD aided radiologists."

6. Standalone (algorithm only without human-in-the-loop performance) study:

• Yes, a standalone study was performed.
• The acceptance criteria and reported performance for this study are detailed in the table above.

7. Type of ground truth used:

• Expert consensus by three dedicated chest radiologists with at least ten years of experience. For the standalone study, it is directly compared against "ground truth," which is established by these experts. For the MRMC study, the experts "determined the ground truth." The phrase "All lung nodules were segmented in 3D" suggests a thorough and detailed ground truth establishment.

8. Sample size for the training set:

• Not explicitly stated in the provided text. The document mentions the lung nodule detection model was "trained by Deep Convolution Network (CNN) based algorithm from the chest CT image," but does not provide details on the training set size.

9. How the ground truth for the training set was established:

• Not explicitly stated in the provided text.

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