ArticleQuantitative imaging in medicine and surgery2026
A two-stage multimodal learning framework for the automated diagnosis of obstructive coronary artery disease based on dynamic single-photon emission computed tomography.
Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Obstructive coronary artery disease (OCAD) is among the most life-threatening cardiovascular diseases in the world. Dynamic single-photon emission computed tomography (D-SPECT) offers a noninvasive technique for visualizing the perfusion of the heart and the functional state of the myocardium. However, computer-aided diagnostic models for OCAD mainly focus on analyzing medical images and neglect the potential benefits of integrating functional parameters derived from quantitative gated single-photon emission computed tomography (QGS) obtained within the same imaging system. The objective of this study was to develop a two-stage multimodal learning framework that integrates D-SPECT images and QGS-derived functional data to enhance diagnostic accuracy and support a one-stop, imaging-based diagnostic workflow for OCAD. Methods: We developed a two-stage multimodal learning framework for automated OCAD diagnosis using both D-SPECT images and QGS-derived functional data. In stage I, cardiac slices along multiple axial dimensions were extracted as regions of interest (ROIs). In stage II, a multimodal learning network was constructed to extract, fuse, and classify features from both inputs. Furthermore, a feature adaptation weighting mechanism (FAWM) was introduced to adaptively allocate the contributions of different modalities during training. Finally, the performance of the proposed model was evaluated on a dataset of 298 D-SPECT scans. Results: The multimodal method achieved an accuracy of 81.67%, outperforming models trained with single inputs (D-SPECT images only: 75%, P=0.12; QGS-derived data only: 70.00%, P<0.05; paired Conclusions: The integration of D-SPECT imaging and QGS-derived functional parameters within a unified multimodal framework significantly improves diagnostic performance for OCAD. This approach demonstrates the feasibility of serving as a one-stop, image-based diagnostic workflow for clinical practice.
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