A deep learning-based automated diagnosis system for SPECT myocardial perfusion imaging.
Sci Rep
; 14(1): 13583, 2024 06 12.
Article
in En
| MEDLINE
| ID: mdl-38866884
ABSTRACT
Images 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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Coronary Artery Disease
/
Tomography, Emission-Computed, Single-Photon
/
Myocardial Perfusion Imaging
/
Deep Learning
Limits:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Language:
En
Journal:
Sci Rep
Year:
2024
Type:
Article
Affiliation country:
Japan