Key result
A multimodal deep learning framework integrating planar and SPECT imaging achieved 95.5% accuracy for classifying myocardial 99mTc-PYP uptake, which was not significantly superior to conventional H/CL assessment (88.6%, p=0.250).
Why the study?
Objective interpretation of myocardial 99mTc-pyrophosphate uptake is important in suspected cardiac amyloidosis, but automated multimodal classification methods integrating planar and SPECT imaging were not established.
Does a segmentation-informed multimodal deep learning framework improve the classification of myocardial 99mTc-PYP uptake compared to standard assessments in patients with suspected cardiac amyloidosis?
Observational (n=44)
No
Does a segmentation-informed multimodal deep learning framework improve the classification of myocardial 99mTc-PYP uptake compared to standard assessments in patients with suspected cardiac amyloidosis?
Answers are grounded in this paper and cite their sources.
A multimodal deep learning framework for PYP uptake classification is feasible and highly accurate, but it did not demonstrate statistically significant superiority over standard planar or H/CL assessments in this small single-center cohort.
Objective interpretation of myocardial 99mTc-pyrophosphate (PYP) uptake is important in suspected cardiac amyloidosis. We developed a segmentation-informed multimodal framework that classifies myocardial PYP uptake from planar and single-photon emission computed tomography (SPECT) images. The retrospective, class-enriched cohort comprised 49 studies from 44 patients (19 PYP-positive and 25 PYP-negative) with specialist-confirmed image labels. Five consecutive SPECT slices were manually selected under nuclear-medicine-physician supervision; subsequent processing was automated. SPECT Net generated an unthresholded myocardial probability map, which served as a soft, spatially interpretable representation for integration with planar features. Patient-grouped five-fold cross-validation used separate validation and test folds. In PYP-positive patients, cropped nnU-Net achieved a patient-level Dice coefficient of 0.9209±0.0316 and outperformed three segmentation comparators after Holm correction (p<0.001). Transformer-style fusion achieved 95.5% accuracy (95% confidence interval: 84.9–98.7%), 89.5% sensitivity, 100.0% specificity, and a 94.4% F1-score. Improvements over planar-only classification (90.9%; McNemar p=0.500), localized raw SPECT (86.4%; p=0.125), and H/CL assessment (88.6%; p=0.250) were not significant. These single-center internal results demonstrate the feasibility of using a soft myocardial segmentation output as an interpretable bridge for multimodal PYP uptake classification but do not establish superiority; multicenter external validation is required.
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Zhang et al. (2026) conducted an observational in Suspected cardiac amyloidosis (n=44). Multimodal deep learning framework (Fusion Net) vs. Conventional H/CL assessment was evaluated on Classification accuracy of myocardial PYP uptake (95% CI 84.9-98.7, p=0.250). A multimodal deep learning framework integrating planar and SPECT imaging achieved 95.5% accuracy for classifying myocardial 99mTc-PYP uptake, which was not significantly superior to conventional H/CL assessment (88.6%, p=0.250).
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