ABSTRACT
Pulmonary embolism (PE) remains a significant cause of cardiovascular mortality, with un- treated cases showing mortality rates of up to 30%. The evolution of computer-assisted detec- tion (CAD) for PE has transformed dramatically over the past decades, progressing from simple pattern recognition to sophisticated deep learning approaches. Early CAD systems demonstrat- ed modest performance, with sensitivity around 75% at 2–4 false positives per scan, whereas modern deep learning architectures achieve sensitivities of up to 92.9% at 0.15 false positives per scan. Significantly, the technological progression has evolved from basic patient-level clas- sification to sophisticated voxel-level analysis. This review provides a comprehensive overview of the evolution of PE CAD systems, their clinical value, and future directions.