Search Results

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.

Ask a specific question about this device

AVIEW LCS is intended for the review and analysis and reporting of thoracic CT images for the purpose of characterizing nodules in the lung in a single study, or over the time course of several thoracic studies. Characterizations include nodule type, location of the nodule and measurements such as size (major axis), estimated effective diameter from the volume of the volume of the nodule, Mean HU (the average value of the CT pixel inside the nodule in HU), Minimum HU, Max HU, mass (mass calculated from the CT pixel value), and volumetric measures (Solid Major, length of the longest diameter measured in 3D for a solid portion of the nodule. Solid 2nd Major: The length of the longest diameter of the solid part, measured in sections perpendicular to the Major axis of the nodule), VDT (Volume doubling time), Lung-RADS (classification proposed to aid with findings) and CAC score and LAA analysis. The system automatically performs the measurement, allowing lung nodules and measurements to be displayed and, also integrate with FDA certified Mevis CAD (Computer-aided detection) (K043617).

AVIEW LCS is intended for use as diagnostic patient imaging which is intended for the review and analysis of thoracic CT images. Provides following features as semi-automatic nodule measurement (segmentation), maximal plane measure, 3D measure and volumetric measures, automatic nodules detection by integration with 3th party CAD. Also provides cancer risk based on PANCAN risk model which calculates the malignancy score based on numerical or Boolean inputs. Follow up support with automated nodule matching and automatically categorize Lung-RADS score which is a quality assurance tool designed to standardize lung cancer screening CT reporting and management recommendations that is based on type, size, size change and other findings that is reported.

The provided text does not contain detailed acceptance criteria for specific performance metrics of the AVIEW LCS device, nor does it describe a study proving the device meets particular acceptance criteria with quantitative results.

The document is a 510(k) premarket notification summary, which focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed performance study like a clinical trial.

However, based on the information provided, here's what can be extracted and inferred regarding performance and testing:

1. Table of Acceptance Criteria and Reported Device Performance

As specific quantitative acceptance criteria and detailed performance metrics are not explicitly stated in the provided text for AVIEW LCS, I cannot create a table of acceptance criteria and reported device performance. The document generally states that "the modified device passed all of the tests based on pre-determined Pass/Fail criteria" for software validation.

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

The document does not specify the sample size used for any test set or the data provenance (e.g., country of origin, retrospective/prospective). The described "Unit Test" and "System Test" are internal software validation tests rather than clinical performance studies involving patient data.

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

The document does not mention using experts to establish ground truth for a test set. This type of information would typically be found in a clinical performance study.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method for a test set. This is relevant for clinical studies where multiple readers assess cases.

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

The document does not indicate that a multi-reader multi-case (MRMC) comparative effectiveness study was performed. Therefore, no effect size of human readers improving with AI vs. without AI assistance is mentioned.

6. Standalone (Algorithm Only) Performance Study

The document does not explicitly state that a standalone (algorithm only without human-in-the-loop performance) study was conducted. The "Performance Test" section refers to DICOM, integration, and thin client server compatibility reports, which are software performance tests, not clinical efficacy or diagnostic accuracy studies for the algorithm itself. The device description mentions "automatic nodules detection by integration with 3rd party CAD (Mevis Visia FDA 510k Cleared)", suggesting it leverages an already cleared CAD system for detection rather than having a new, independently evaluated detection algorithm as part of this submission.

7. Type of Ground Truth Used

The document does not specify the type of ground truth used for any performance evaluation. Again, this would be characteristic of a clinical performance study.

8. Sample Size for the Training Set

The document does not provide the sample size for any training set. This is typically relevant for AI/ML-based algorithms. The mention of "deep-learning algorithms" for lung and lobe segmentation suggests a training set was used, but its size is not disclosed.

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

The document does not explain how ground truth for any potential training set was established.

Summary of available information regarding testing:

The "Performance Data" section (8) of the 510(k) summary focuses on nonclinical performance testing and software verification and validation activities.

• Nonclinical Performance Testing: 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 nonclinical testing." This indicates the submission relies on the substantial equivalence argument and internal software testing, not new clinical performance data for efficacy.
• Software Verification and Validation:
  • Unit Test: Conducted using Google C++ Unit Test Framework on major software components for functional, performance, and algorithm analysis.
  • System Test: Conducted based on "integration Test Cases" and "Exploratory Test" to identify defects.
    • Acceptance Criteria for System Test: "Success standard of System Test is not finding 'Major', 'Moderate' defect."
    • Defect Classification:
      • Major: Impacting intended use, no workaround.
      • Moderate: UI/general quality, workaround available.
      • Minor: Not impacting intended use, not significant.
  • Performance Test Reports: DICOM Test Report, Performance Test Report, Integration Test Report, Thin Client Server Compatibility Test Report.

In conclusion, the provided 510(k) summary primarily addresses software validation and verification to demonstrate substantial equivalence, rather than a clinical performance study with specific acceptance criteria related to diagnostic accuracy, reader performance, or a detailed description of ground truth establishment.

Ask a specific question about this device