Automated identification of cardiac amyloidosis using cross-modal neural networks on [Formula: see text]-Pyrophosphate SPECT imaging and clinical data.

Authors

Fatih Batı, Nilüfer Bıçakcı, Musa Aydın, Zeki Kuş, Berna Kiraz

Published in

The international journal of cardiovascular imaging. Jul 22, 2026. Epub Jul 22, 2026.

Abstract

Accurate, early identification of transthyretin cardiac amyloidosis (ATTR-CA) is challenging yet critical for effective treatment. In this work, the modeled endpoint is scan positivity on [Formula: see text] scintigraphy, defined by the semi-quantitative Perugini visual grade (Grade 2-3 versus Grade 0-1). Two multimodal deep-learning frameworks, including Late Fusion (LF) and Cross-Modal Fusion Network (CMF-Net), are proposed to combine [Formula: see text] scintigraphy with clinical metadata for automated detection. On a curated cohort of 109 patients (62 positive, 47 negative), fusion models consistently outperformed image-only convolutional neural networks (CNNs): CMF-Net raised average accuracy by 6.9 percentage points and LF by 5.4. EfficientNet CMF-Net achieved peak accuracy 90.9% and F1-score 91.4%. Notable gains included a +30.8 percentage points sensitivity(recall) for ResNet-34 with CMF-Net and ResNet-50 LF sensitivity of 94.9%. These results show that integrating imaging and text/numeric clinical data yields superior, reproducible detection of [Formula: see text] scan positivity and may streamline scan interpretation.

PMID:
42484776
Bibliographic data and abstract were imported from PubMed on 22 Jul 2026.

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