Development and validation of a deep-learning-based diagnostic model for drug-induced liver injury using computed tomography images

BACKGROUND Pyrrolizidine-alkaloid induced hepatic sinusoidal obstruction syndrome (PA-HSOS) is a rare and severe drug-induced liver injury with nonspecific manifestations. Its diagnosis currently relies on exclusive strategies and often necessitates invasive examinations, posing significant clinical challenges. The potential role of artificial intelligence algorithms in diagnosing PA-HSOS remains to be established. AIM To develop and validate a deep-learning-based diagnostic model for PA-HSOS using computed tomography images. METHODS This multicenter case-control study compared PA-HSOS patients with Budd-Chiari syndrome and hepatitis B cirrhosis patients as controls. Patients from Zhongshan Hospital, Fudan University were retrospectively assigned to training or internal test cohorts, while those from the First Affiliated Hospital of Zhengzhou University formed an external cohort. We constructed the diagnostic models using multiscale convolutional modules. Model performance was compared with gastroenterologists and radiologists of varying expertise levels. Additionally, diagnostic outcomes and interpretation time with and without model assistance were evaluated. RESULTS Diagnostic models with deep learning methods using computed tomography images for PA-HSOS were developed. In the internal test cohort, models with different input sizes achieved area under the curve ranging from 0.853 to 0.944. Model 96 (96-mm input) demonstrated significantly higher accuracy and specificity than resident physicians (both internal medicine and radiology; P < 0.05) and comparable performance to attending specialists. The area under the curve of model 96 in the external test cohort was 0.873. When assisting clinicians, model 96 significantly improved diagnostic accuracy for internal medicine residents (0.541 to 0.757) and attending gastroenterologists (0.730 to 0.892), while reducing interpretation time across all expertise levels (all P < 0.05). CONCLUSION The deep learning model demonstrates promising diagnostic performance for PA-HSOS and can effectively assist clinicians in improving diagnostic accuracy and efficiency.