# AI-Enhanced Deep Learning Framework for Pulmonary Embolism Detection in CT Angiography

## TL;DR

This paper introduces a new AI method for detecting small blood clots in lung CT scans, improving accuracy for challenging cases.

## Contribution

The novel Consensus Intersection-Optimized Fusion (CIOF) method enhances detection accuracy by combining multiple deep learning models.

## Key findings

- CIOF achieved a mean IoU of 0.569 and Dice score of 0.691 for pulmonary embolism detection.
- CIOF outperformed single models across different clot burdens, including tiny subsegmental emboli.
- The method is accurate but slower, suggesting use for offline analysis or second-reader verification.

## Abstract

Pulmonary embolism (PE) on CT pulmonary angiography (CTPA) demands rapid, accurate assessment, yet small, low-contrast clots in distal arteries remain challenging. We benchmarked ten fully convolutional network (FCN) backbones and introduced Consensus Intersection-Optimized Fusion (CIOF)—a K-of-M, pixel-wise mask fusion with the voting threshold K* selected on training patients to maximize IoU. Using the FUMPE cohort (35 patients; 12,034 slices) with patient-based random splits (18 train, 17 test), we trained five FCN architectures (each with Adam and SGDM) and evaluated segmentation with IoU, Dice, FNR/FPR, and latency. CIOF achieved the best overall performance (mean IoU 0.569; mean Dice 0.691; FNR 0.262), albeit with a higher runtime (~63.7 s per case) because all ten models are executed and fused; the strongest single backbone was Inception-ResNetV2 + SGDM (IoU 0.530; Dice 0.648). Stratified by embolization ratio, CIOF remained superior across <10−4, 10−4–10−3, and >10−3 clot burdens, with mean IoU/Dice = 0.238/0.328, 0.566/0.698, and 0.739/0.846, respectively—demonstrating gains for tiny, subsegmental emboli. These results position CIOF as an accuracy-oriented, interpretable ensemble for offline or second-reader use, while faster single backbones remain candidates for time-critical triage.