Cross-institutional validation of a polar map-free 3D deep learning model for obstructive coronary artery disease prediction using myocardial perfusion imaging: insights into generalizability and bias.

Shih, Yu-ChengYu-ChengShihCHI-LUN KOWang, Shan-YingShan-YingWangChang, Chen-YuChen-YuChangLin, Shau-SyuanShau-SyuanLinHuang, Cheng-WenCheng-WenHuangMEI-FANG CHENGYEN-WEN WUChen, Chung-MingChung-MingChen2025-05-092025-05-092025-04-0816197070https://scholars.lib.ntu.edu.tw/handle/123456789/729212Purpose: Deep learning (DL) models for predicting obstructive coronary artery disease (CAD) using myocardial perfusion imaging (MPI) have shown potential for enhancing diagnostic accuracy. However, their ability to maintain consistent performance across institutions and demographics remains uncertain. This study aimed to investigate the generalizability and potential biases of an in-house MPI DL model between two hospital-based cohorts. Methods: We retrospectively included patients from two medical centers in Taiwan who underwent stress/redistribution thallium-201 MPI followed by invasive coronary angiography within 90 days as the reference standard. A polar map-free 3D DL model trained on 928 MPI images from one center to predict obstructive CAD was tested on internal (933 images) and external (3234 images from the other center) validation sets. Diagnostic performance, assessed using area under receiver operating characteristic curves (AUCs), was compared between the internal and external cohorts, demographic groups, and with the performance of stress total perfusion deficit (TPD). Results: The model showed significantly lower performance in the external cohort compared to the internal cohort in both patient-based (AUC: 0.713 vs. 0.813) and vessel-based (AUC: 0.733 vs. 0.782) analyses, but still outperformed stress TPD (all p < 0.001). The performance was lower in patients who underwent treadmill stress MPI in the internal cohort and in patients over 70 years old in the external cohort. Conclusions: This study demonstrated adequate performance but also limitations in the generalizability of the DL-based MPI model, along with biases related to stress type and patient age. Thorough validation is essential before the clinical implementation of DL MPI models.enfalseBiasCoronary artery disease (CAD)Deep learning (DL)GeneralizabilityMyocardial perfusion imaging (MPI)Single-photon emission tomography (SPECT)[SDGs]SDG3Cross-institutional validation of a polar map-free 3D deep learning model for obstructive coronary artery disease prediction using myocardial perfusion imaging: insights into generalizability and bias.journal article10.1007/s00259-025-07243-w401983562-s2.0-105002162855