ArticleEuropean radiology2025
Convolutional neural networks for automatic MR classification of myocardial iron overload in thalassemia major patients.
Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Left ventricular diastolic and systolic functions by cardiac magnetic resonance in beta-thalassemia major: correlation with clinical findings and cardiac complications.The international journal of cardiovascular imaging · 2025Article
- Cardiovascular magnetic resonance in β-thalassemia major: beyond T2.La Radiologia medica · 2024Review
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10 authors.
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Abstract
objectivesTo develop a deep-learning model for supervised classification of myocardial iron overload (MIO) from magnitude T2* multi-echo MR images. MATERIALS AND
methodsEight hundred twenty-three cardiac magnitude T2* multi-slice, multi-echo MR images from 496 thalassemia major patients (285 females, 57%), labeled for MIO level (normal: T2* > 20 ms, moderate: 10 ≤ T2* ≤ 20 ms, severe: T2* < 10 ms), were retrospectively studied. Two 2D convolutional neural networks (CNN) developed for multi-slice (MS-HippoNet) and single-slice (SS-HippoNet) analysis were trained using 5-fold cross-validation. Performance was assessed using micro-average, multi-class accuracy, and single-class accuracy, sensitivity, and specificity. CNN performance was compared with inter-observer agreement between radiologists on 20% of the patients. The agreement between patients' classifications was assessed by the inter-agreement Kappa test.
resultsAmong the 165 images in the test set, a multi-class accuracy of 0.885 and 0.836 was obtained for MS- and SS-Hippo-Net, respectively. Network performances were confirmed on external test set analysis (0.827 and 0.793 multi-class accuracy, 29 patients from the CHMMOTv1 database). The agreement between automatic and ground truth classification was good (MS: κ = 0.771; SS: κ = 0.614), comparable with the inter-observer agreement (MS: κ = 0.872, SS: κ = 0.907) evaluated on the test set.
conclusionThe developed networks performed classification of MIO level from multiecho, bright-blood, and T2* images with good performances. KEY POINTS: Question MRI T2* represents the established clinical tool for MIO assessment. Quality control of the image analysis is a problem in small centers. Findings Deep learning models can perform MIO staging with good accuracy, comparable to inter-observer variability of the standard procedure. Clinical relevance CNN can perform automated staging of cardiac iron overload from multiecho MR sequences facilitating non-invasive evaluation of patients with various hematologic disorders.
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