دورية أكاديمية
A deep learning-based automated diagnosis system for SPECT myocardial perfusion imaging
| العنوان: | A deep learning-based automated diagnosis system for SPECT myocardial perfusion imaging |
|---|---|
| المؤلفون: | Dai Kusumoto, Takumi Akiyama, Masahiro Hashimoto, Yu Iwabuchi, Toshiomi Katsuki, Mai Kimura, Yohei Akiba, Hiromune Sawada, Taku Inohara, Shinsuke Yuasa, Keiichi Fukuda, Masahiro Jinzaki, Masaki Ieda |
| المصدر: | Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024) |
| بيانات النشر: | Nature Portfolio, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Medicine, Science |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2045-2322 |
| العلاقة: | https://doaj.org/toc/2045-2322Test |
| DOI: | 10.1038/s41598-024-64445-2 |
| الوصول الحر: | https://doaj.org/article/b7240928821943c98653922da8b7940aTest |
| رقم الانضمام: | edsdoj.b7240928821943c98653922da8b7940a |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20452322 |
|---|---|
| DOI: | 10.1038/s41598-024-64445-2 |