Author:
Kusumoto Dai,Akiyama Takumi,Hashimoto Masahiro,Iwabuchi Yu,Katsuki Toshiomi,Kimura Mai,Akiba Yohei,Sawada Hiromune,Inohara Taku,Yuasa Shinsuke,Fukuda Keiichi,Jinzaki Masahiro,Ieda Masaki
Abstract
AbstractImages obtained from single-photon emission computed tomography for myocardial perfusion imaging (MPI SPECT) contain noises and artifacts, making cardiovascular disease diagnosis difficult. We developed a deep learning-based diagnosis support system using MPI SPECT images. Single-center datasets of MPI SPECT images (n = 5443) were obtained and labeled as healthy or coronary artery disease based on diagnosis reports. Three axes of four-dimensional datasets, resting, and stress conditions of three-dimensional reconstruction data, were reconstructed, and an AI model was trained to classify them. The trained convolutional neural network showed high performance [area under the curve (AUC) of the ROC curve: approximately 0.91; area under the recall precision curve: 0.87]. Additionally, using unsupervised learning and the Grad-CAM method, diseased lesions were successfully visualized. The AI-based automated diagnosis system had the highest performance (88%), followed by cardiologists with AI-guided diagnosis (80%) and cardiologists alone (65%). Furthermore, diagnosis time was shorter for AI-guided diagnosis (12 min) than for cardiologists alone (31 min). Our high-quality deep learning-based diagnosis support system may benefit cardiologists by improving diagnostic accuracy and reducing working hours.
Funder
Japan Cardiovascular Research Foundation
Kowa Life Science Foundation
Publisher
Springer Science and Business Media LLC