ArticleAJNR. American journal of neuroradiology2025
Deep Learning-Based Algorithm for Automatic Quantification of Nigrosome-1 and Parkinsonism Classification Using Susceptibility Map-Weighted MRI.
Article in AJNR. American journal of neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Machine learning in neuroradiology: Recent developments and applications.World journal of radiology · 2026Review
- Quantitative Assessment of Visible Nigrosome-1 in Patients with Parkinson's Disease.Movement disorders clinical practice · 2026Article
- Deep learning-based susceptibility map-weighted imaging analysis of dorsal nigral hyperintensity as a surrogate for dopamine transporter positron emission tomography in patients with idiopathic rapid eye movement sleep behavior disorder.Frontiers in neurology · 2026Article
- Development of AI-based dopamine transporter (DAT) image generation technique using early phase [18F]-FP-CIT PET imaging.PloS one · 2026Article
- Effect of Deep Learning-Based Artificial Intelligence on Radiologists' Performance in Identifying Nigrosome 1 Abnormalities on Susceptibility Map-Weighted Imaging.Korean journal of radiology · 2025Article
- The combination of theScientific reports · 2025Article
- Economic value of AI-based MRI triage for Parkinson's disease: a cost-benefit study in South Korea and the United States.Frontiers in public health · 2025Article
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13 authors.
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Abstract
background and purposeThe diagnostic performance of deep learning model that simultaneously detecting and quantifying nigrosome-1 abnormality by using susceptibility map-weighted imaging (SMwI) remains unexplored. This study aimed to develop and validate a deep learning-based automatic quantification for nigral hyperintensity and a classification algorithm for neurodegenerative parkinsonism. MATERIALS AND
methodsWe retrospectively collected 450 participants (210 with idiopathic Parkinson disease [IPD] and 240 individuals in the control group) for training data between November 2022 and May 2023, and 237 participants (168 with IPD, 58 with essential tremor, and 11 with drug-induced parkinsonism) for validation data between July 2021 and January 2022. SMwI data were reconstructed from multiecho gradient echo. Diagnostic performance for diagnosing IPD was assessed by using deep learning-based automatic quantification (Heuron NI) and classification (Heuron IPD) models. Reference standard for IPD was based on N-3-fluoropropyl-2-β-carbomethoxy-3-β-(4-iodophenyl) nortropane PET finding. Additionally, the correlation between the Hoehn and Yahr (H&Y) stage and volume of nigral hyperintensity in patients with IPD was assessed.
resultsQuantification of nigral hyperintensity by using Heuron NI showed an area under the curve (AUC) of 0.915 (95% CI, 0.872-0.947) and 0.928 (95% CI, 0.887-0.957) on the left and right, respectively. Classification of nigral hyperintensity abnormality by using Heuron IPD showed area under the curve of 0.967 (95% CI, 0.936-0.986) and 0.976 (95% CI, 0.948-0.992) on the left and right, respectively. H&Y score ≥3 showed smaller nigral hyperintensity volume (1.43 ± 1.19 mm
conclusionsOur deep learning-based model proves rapid, accurate automatic quantification of nigral hyperintensity, facilitating IPD diagnosis, symptom severity prediction, and patient stratification for personalized therapy. Further study is warranted to validate the findings across various clinical settings.
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