Pleural effusion, the abnormal accumulation of fluid in the pleural cavity, is a common clinical condition associated with various diseases, including heart failure, pneumonia, and malignancies. Accurate measurement of pleural effusion volume is critical for diagnosis, treatment planning, and monitoring response to therapy. Traditional manual methods for estimating pleural effusion volume from imaging modalities such as chest X-rays or ultrasound are time-consuming, subject to interobserver variability, and often lack precision. This study proposes a semi-automated approach to quantify the pleural effusion index (PEI), combining automated image processing techniques with expert input to improve accuracy, efficiency, and reproducibility.

The semi-automated method employs advanced image segmentation algorithms to delineate pleural fluid boundaries from radiographic images, minimizing manual intervention while allowing clinicians to validate and adjust results. Using a dataset of chest imaging studies from patients with varying degrees of pleural effusion, the algorithm was trained and tested to detect and measure effusion areas, which were then converted into standardized PEI values. The performance of the semi-automated system was evaluated against manual measurements performed by experienced radiologists, using metrics such as Dice similarity coefficient, correlation coefficients, and Bland-Altman analysis.

Results demonstrate that the semi-automated method significantly reduces measurement time by approximately 40%, while achieving a high level of agreement with manual measurements (Dice coefficient > 0.85, correlation coefficient r = 0.92, p < 0.001). The approach also exhibited lower interobserver variability, supporting its potential to standardize pleural effusion quantification across different clinical settings. Furthermore, the system's ability to provide rapid, reliable measurements can enhance clinical workflows, aiding timely decision-making in critical care and pulmonology.

The semi-automated quantification of PEI offers a promising balance between automation and expert oversight, addressing the limitations of fully manual or fully automated methods. This hybrid model ensures both accuracy and flexibility, accommodating complex cases where fully automated systems may struggle due to image artifacts or atypical anatomy. Integration of this technique into routine clinical practice could improve patient management by enabling more precise assessment of pleural fluid dynamics over time.