Clinical Research Directory

Retrospective Validation of AccuPulmo CT Portal for Detecting Pulmonary Fibrosis on Chest CT

Sponsor: Taichung Veterans General Hospital

Summary

This retrospective observational study evaluates the diagnostic performance of AccuPulmo CT Portal, an artificial intelligence-assisted medical imaging software, for detecting pulmonary fibrosis on pre-existing chest computed tomography images. A total of 900 chest computed tomography examinations obtained at Taichung Veterans General Hospital between January 1, 2020, and December 31, 2024, will be retrospectively selected. The planned sample includes 300 examinations with pulmonary fibrosis and 600 examinations without pulmonary fibrosis. All study images will be de-identified and coded before evaluation. Three qualified specialists in pulmonology or radiology will independently review each image without access to the original radiology report or the artificial intelligence output. The reference standard will be established by majority agreement of at least two of the three specialists. AccuPulmo CT Portal will retrospectively analyze the coded images. An artificial intelligence-derived pulmonary fibrosis area greater than 10 percent will be classified as positive, and an area of 10 percent or less will be classified as negative. The primary performance measures are sensitivity and specificity. Secondary measures include accuracy, positive predictive value, negative predictive value, and performance across clinically relevant subgroups. The software results will not be returned to treating physicians and will not affect participant diagnosis, treatment, or clinical management.

Official title: Evaluation of the Accuracy and Effectiveness of the AccuPulmo CT Portal AI-Assisted Interpretation System for the Diagnosis of Pulmonary Fibrosis

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