Deep Learning Based Emboli Detection Using Ultrasound Doppler Imaging

Abstract

Accurate detection of embolic signals in the bloodstream is crucial for early diagnosis and prevention of cerebrovascular complications, and this work develops and evaluates an artificial intelligence–based system for automatic emboli detection in power Doppler imaging from NeoDoppler, aiming for robust and real-time performance. The study uses a four-stage experimental pipeline built on convolutional neural networks with transfer learning: an initial baseline model (Stage 1), an assessment of spatial generalisation (Stage 2), and a hybrid two-step strategy (Stage 3) that combines conventional High-Intensity Transient Signal (HITS) pre-detection with CNN-based classification, followed by a simplified preprocessing strategy in Stage 4, where single-channel images are replicated into three channels to match pre-trained CNN architectures; all models are trained with 5-fold cross-validation on 523 recordings from 25 patients and evaluated on unseen pilot recordings from the same cohort and additional abdominal surgery data. Across stages, performance improves progressively, with the hybrid two-step framework using the three-channel replication yielding strong results, achieving 96% sensitivity and 98% specificity on the pilot recording and 94% sensitivity and 71% specificity on the abdominal surgery recordings. We estimated 95% confidence intervals (CIs) using Wilson’s score for abdominal surgery recordings, with a CI of 0.730-0.99, demonstrating that the proposed approach is an efficient and interpretable solution for ultrasound-based emboli monitoring.