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A two-stage multimodal learning framework for the automated diagnosis of obstructive coronary artery disease based on
Rong Wang1,2, Haijun Wang2, Chuan Zhou3
1The First School of Clinical Medicine, Lanzhou University, Lanzhou, China.
Insights
This study developed a multimodal AI framework integrating dynamic single-photon emission computed tomography (D-SPECT) images and quantitative gated single-photon emission computed tomography (QGS) data. The novel approach enhances diagnostic accuracy for obstructive coronary artery disease (OCAD).
Area of Science:
- Cardiovascular Imaging
- Artificial Intelligence in Medicine
- Medical Diagnostics
Background:
- Obstructive coronary artery disease (OCAD) poses a significant global health threat.
- Dynamic single-photon emission computed tomography (D-SPECT) visualizes heart perfusion but current OCAD diagnostic models often overlook functional data.
- Quantitative gated single-photon emission computed tomography (QGS) provides functional parameters that can complement imaging data.
Purpose of the Study:
- To develop a two-stage multimodal learning framework for OCAD diagnosis.
- To integrate D-SPECT images and QGS-derived functional data for improved diagnostic accuracy.
- To establish a one-stop, imaging-based diagnostic workflow for OCAD.
Main Methods:
- A two-stage multimodal learning framework was designed for automated OCAD diagnosis.
- Cardiac slices were extracted as regions of interest in Stage I.
- A multimodal network fused D-SPECT images and QGS data in Stage II, utilizing a feature adaptation weighting mechanism (FAWM).
Main Results:
- The multimodal approach achieved an accuracy of 81.67% for OCAD diagnosis.
- This performance surpassed models using only D-SPECT images (75%) or QGS data (70.00%).
- The differences in performance were statistically significant (P<0.05 for QGS-only, P=0.12 for D-SPECT-only).
Conclusions:
- Integrating D-SPECT imaging and QGS functional parameters in a multimodal framework significantly enhances OCAD diagnostic performance.
- This unified approach shows promise for a comprehensive, one-stop diagnostic workflow in clinical settings.
- The study highlights the value of multimodal data integration for improving cardiovascular disease diagnosis.
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 t-test).
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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