Deep learning algorithm enables lower limb venous thrombosis detection with CT venography.

Introduction: Lower limb computed tomography venography (CTV) has low success rates for deep vein thrombosis (DVT) diagnosis. This study applied deep learning to improve DVT identification. Methods: Our study enrolled 119 positive DVT and 40 negative DVT, 111 positive pulmonary embolism (PE) patients and 20 negative PE. Two algorithms were evaluated: Faster R-CNN trained directly on CTV images, and YOLO11-nano pre-trained on computed tomographic pulmonary angiography (CTPA) then optimized on CTV via transfer learning. Three radiologists (3-5 years' experience) independently interpreted CTV images. Diagnostic performance was compared. Results: The YOLO11-nano model and Faster R-CNN achieved over 97% accuracy and sensitivity in diagnosing PE. Compared to the three radiologists in diagnosing DVT, YOLO also had an excellent accuracy (92.1%) and sensitivity (93.9%), superior to Faster R-CNN (66% accuracy, 68% sensitivity). The YOLO model exceeded 77% accuracy in pelvic and femoral-popliteal segments and 66.7% in detecting calf segment thrombi. Discussion: The CTPA-based transfer learning model significantly improved the feasibility of this method in routine CTV diagnostic performance, offering a promising approach for simultaneous PE and DVT detection.