Accuracy of Fully Automated and Human-assisted AI-based CT Quantification of Pleural Effusion Changes after Thoracentesis.

Abstract

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Quantifying pleural effusion change on chest CT is important for evaluating disease severity and treatment response. The purpose of this study was to assess the accuracy of artificial intelligence (AI)-based volume quantification of pleural effusion change on CT images, using the volume of drained fluid as the reference standard. Seventy-nine participants (mean age, 65 ± [SD] 13 years; 47 male) undergoing thoracentesis were prospectively enrolled from October 2021 to September 2023. Chest CTs were obtained just before and after thoracentesis. The volume of pleural fluid on each CT scan, with the difference representing the drained fluid volume, was measured by automated segmentation (fully-automated measurement). An expert thoracic radiologist then manually corrected these automated volume measurements (human-assisted measurement). Both fully-automated (median percentage error, 13.1%; maximum estimated 95% error range, 708 mL) and human-assisted measurements (median percentage error, 10.9%; maximum estimated 95% error range, 312 mL) systematically underestimated the volume of drained fluid, beyond the equivalence margin. The magnitude of underestimation increased proportionally to the drainage volume. Agreement between fully-automated and human-assisted measurements (intraclass correlation coefficient [ICC], 0.99), and the test-retest reliability of fully-automated (ICC, 0.995) and human-assisted (ICC, 0.997) measurements were excellent. These results highlight a potential systematic discrepancy between AI segmentation- based CT quantification of pleural effusion volume change and actual volume change. ©RSNA, 2025.