ArticleScientific reports2024
A deep learning-based automated diagnosis system for SPECT myocardial perfusion imaging.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Diagnostic and prognostic comparison of stress electrocardiogram, cardiovascular magnetic resonance, and single photon emission computed tomography, alone and sequentially, in stable chest pain.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Trial
- A Novel Quantitative Analysis in Myocardial Perfusion Imaging: How does it compare to the "Golden Eye"?Zeitschrift fur medizinische Physik · 2026Article
- A Prototype-Guided 3D Deep Learning Framework for Myocardial Perfusion Scintigraphy Segmentation.Journal of clinical medicine · 2026Review
- Review
- Decoding the Myocardium: Tracer-Aware Deep Learning for Patient-Level Classification in Stress-Rest SPECT Myocardial Perfusion Imaging.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence across the cardiovascular diagnostic pathway: a case-based narrative review.Cardiovascular diagnosis and therapy · 2026Review
- A two-stage multimodal learning framework for the automated diagnosis of obstructive coronary artery disease based on dynamic single-photon emission computed tomography.Quantitative imaging in medicine and surgery · 2026Article
- AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.Journal of medical imaging and radiation oncology · 2025Review
- Dual prompt personalized federated learning in foundation models.Scientific reports · 2025Article
- Artificial Intelligence in Nuclear Cardiac Imaging: Novel Advances, Emerging Techniques, and Recent Clinical Trials.Journal of clinical medicine · 2025Review
- The role of deep learning in myocardial perfusion imaging for diagnosis and prognosis: A systematic review.iScience · 2024Review
- Application of the U-Net Deep Learning Model for Segmenting Single-Photon Emission Computed Tomography Myocardial Perfusion Images.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
13 authors.
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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.
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