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Lung-CAD is a computer-assisted detection (CADe) software device that analyzes chest radiograph studies for lung hyperinflation. The device uses a deep learning algorithm to identify regions of interest (ROIs) with lung hyperinflation and produces boxes around the ROIs.

Lung-CAD is intended for use as a concurrent reading aid for physicians interpreting chest X-rays. The device is not intended for clinical diagnosis of any disease. It does not replace the role of other diagnostic testing in the standard of care for lung parenchymal findings. Lung-CAD is indicated for adults only.

Lung-CAD is computer-assisted detection (CADe) software designed to increase the accurate detection of lung hyperinflation. Lung-CAD's output is available for physicians interpreting chest radiographs as a concurrent reading aid. The device helps physicians more effectively identify lung hyperinflation. Lung-CAD does not replace the role of the physician or of other diagnostic testing in the standard of care and does not provide a diagnosis for any disease. Lung-CAD uses modern deep learning and computer vision techniques to analyze chest radiographs.

For each image within a study, Lung-CAD generates a DICOM Presentation State file (output overlay). If any region of interest (ROI) is detected by Lung-CAD in the study, the output overlay for each image includes "Lung hyperinflation". In addition, if ROI(s) are detected in an image, bounding boxes surrounding each detected ROI are included in the output overlay for that image and are labeled with the radiographic findings: "Lung hyperinflation". If no ROI is detected by Lung-CAD in the study, the output overlay for each image will include the text "No Lung-CAD ROI(s)" and no bounding boxes will be included. Regardless of whether an ROI is detected, the overlay includes text identifying the X-ray study as analyzed by Lung-CAD and a customer configurable message containing a link or instructions, for users, to access labeling documents. The Lung-CAD overlay can be toggled on or off by the physician within their Picture Archiving and Communication System (PACS) viewer, allowing for concurrent review of the X-ray study.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) summary for Lung-CAD:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the reported performance metrics that demonstrate substantial equivalence and effectiveness. While explicit "acceptance criteria" are not listed as pass/fail thresholds in this summary, the strong statistical significance and high performance metrics indicate successful validation.

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

• Test Set Sample Size: 5,000 chest radiograph cases were used for the standalone performance assessment. For the MRMC reader study, clinical readers each evaluated 244 cases.
• Data Provenance: Not explicitly stated regarding country of origin. The summary mentions cases "representative of the intended use population," which typically implies a diverse patient population relevant to the device's market. The studies were retrospective.

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

The document does not explicitly state the number or qualifications of experts used to establish the ground truth for the test set. However, for such studies, it is standard practice to have multiple qualified radiologists involved in establishing ground truth.

4. Adjudication Method for the Test Set

The adjudication method for establishing ground truth is not explicitly described in the provided text. For ground truth establishment in medical imaging AI, common methods include consensus reading by multiple experts (e.g., 2+1, where two experts agree, or a third adjudicates disagreement) or a single expert read with secondary review.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Yes, a fully-crossed MRMC retrospective reader study was done.

• Effect Size of Improvement: The reader AUC improvement for lung hyperinflation was 0.0632 (95% Confidence Interval: 0.0632, 0.0633) when aided by Lung-CAD versus unaided. This represents the absolute improvement in the Area Under the Curve of the Receiver Operating Characteristic curve for readers when assisted by the AI.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

Yes, a standalone performance assessment was conducted on 5,000 chest radiograph cases.

7. The Type of Ground Truth Used

The document does not explicitly state the specific method used to establish ground truth (e.g., expert consensus, pathology, outcomes data). However, considering the device's function (detecting lung hyperinflation on chest radiographs) and the context of FDA clearance, the ground truth would most likely be based on:
* Expert Consensus: A panel of highly qualified radiologists providing a definitive determination of lung hyperinflation for each case.
* Potentially correlated with clinical findings or other diagnostic tests if available and deemed robust enough for ground truth.

8. The Sample Size for the Training Set

The document does not provide the sample size directly for the training set. It only mentions the test set sizes.

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

The document does not describe how the ground truth for the training set was established. However, for deep learning models, particularly in medical imaging, the ground truth for the training set is typically established through a rigorous process involving expert annotation (e.g., by radiologists) based on their clinical expertise, often guided by established diagnostic criteria or reference standards.

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Lung-CAD is a computer-assisted detection (CADe) software device that analyzes chest radiograph studies for interstitial thickening. The device uses a deep learning algorithm to identify regions of interstital thickening and produces boxes around the ROIs.

Lung-CAD is intended for use as a concurrent reading aid for physicians interpreting chest X-rays. The device is not intended for clinical diagnosis of any disease. It does not replace the role of other diagnostic testing in the standard of care for lung parenchymal findings. Lung-CAD is indicated for adults only.

Lung-CAD is computer-assisted detection (CADe) software designed to increase the accurate detection of interstitial thickening. Lung-CAD's output is available for physicians interpreting chest radiographs as a concurrent reading aid. The device helps physicians more effectively identify interstitial thickening. Lung-CAD does not replace the role of the physician or of other diagnostic testing in the standard of care and does not provide a diagnosis for any disease. Lung-CAD uses modern deep learning and computer vision techniques to analyze chest radiographs.

For each image within a study, Lung-CAD generates a DICOM Presentation State file (output overlay). If any ROI is detected by Lung-CAD in the study, the output overlay for each image includes "Interstitial thickening". In addition, if ROI(s) are detected in an image, bounding boxes surrounding each detected ROI are included in the output overlay for that image and are labeled with the radiographic finding: "Interstitial thickening". If no ROI is detected by Lung-CAD in the study, the output overlay for each image will include the text "No Lung-CAD ROI(s)" and no bounding boxes will be included. Regardless of whether an ROI is detected, the overlay includes text identifying the X-ray study as analyzed by Lung-CAD and a customer configurable message containing a link or instructions, for users, to access labeling documents. The Lung-CAD overlay can be toggled on or off by the physician within their Picture Archiving and Communication System (PACS) viewer, allowing for concurrent review of the X-ray study.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Reported Device Performance

Study Details

2. Sample Size and Data Provenance for Test Set

• Sample Size: 5,000 chest radiograph cases for the standalone performance assessment.
• Data Provenance: The text states the cases were "representative of the intended use population" but does not specify country of origin, or if they were retrospective or prospective.

3. Number of Experts and Qualifications for Ground Truth (Test Set)

• The document does not explicitly state the number of experts used to establish the ground truth for the standalone or clinical test sets, nor their specific qualifications (e.g., radiologists with X years of experience).

4. Adjudication Method (Test Set)

• The document does not explicitly state the adjudication method used for establishing the ground truth for the test set.

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

• Yes, an MRMC retrospective reader study was performed.
• Effect Size of Human Readers Improvement with AI vs. without AI assistance: The reader AUC improvement for interstitial thickening when aided by Lung-CAD versus unaided was 0.0797 (95% Confidence Interval: 0.0797, 0.0798). This improvement was statistically significant (p-value < 0.001).

6. Standalone Performance Study

• Yes, a standalone performance assessment was conducted.
• Results:
  • Sensitivity: 0.913 (95% Wilson's CI: 0.850-0.951)
  • Specificity: 0.866 (95% Wilson's CI: 0.856-0.875)
  • AUC: 0.961 (95% Bootstrap CI: 0.948-0.972)

7. Type of Ground Truth Used

• The document implies the ground truth for both standalone and clinical studies was based on expert interpretation, as it refers to "Ground Truth Positive" cases and "accuracy of readers." However, it does not explicitly state the method (e.g., expert consensus, pathology, outcome data).

8. Sample Size for Training Set

• The document does not specify the sample size used for the training set.

9. How Ground Truth for Training Set was Established

• The document does not specify how the ground truth for the training set was established. It only mentions the use of "Supervised Deep Learning," which implies that the training data was labeled.

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