Enhanced classification performance using deep learning based segmentation for pulmonary embolism detection in CT angiography.

ABSTRACT:

Purpose

To develop a deep learning-based algorithm that automatically and accurately classifies patients as either having pulmonary emboli or not in CT pulmonary angiography (CTPA) examinations.

Materials and methods

For model development, 700 CTPA examinations from 652 patients performed at a single institution were used, of which 149 examinations contained 1497 PE traced by radiologists. The nnU-Net deep learning-based segmentation framework was trained using 5-fold cross-validation. To enhance classification, we applied logical rules based on PE volume and probability thresholds. External model evaluation was performed in 770 and 34 CTPAs from two independent datasets.

Results

A total of 1483 CTPA examinations were evaluated. In internal cross-validation and test s

SUBMITTER: Kahraman AT 

PROVIDER: S-EPMC11471166 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature