RT Journal Article
SR Electronic
T1 Deep learning-based algorithm for automatic quantification of nigrosome-1 and Parkinsonism classification using susceptibility map-weighted MRI
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP ajnr.A8585
DO 10.3174/ajnr.A8585
A1 Suh, Pae Sun
A1 Heo, Hwan
A1 Suh, Chong Hyun
A1 Lee, Myeong Oh
A1 Song, Soohwa
A1 Shin, Dong Hoon
A1 Jo, Sung Yang
A1 Chung, Sun Ju
A1 Heo, Hwon
A1 Shim, Woo Hyun
A1 Kim, Ho Sung
A1 Kim, Sang Joon
A1 Kim, Eung Yeop
YR 2024
UL http://www.ajnr.org/content/early/2024/11/15/ajnr.A8585.abstract
AB BACKGROUND AND PURPOSE: To develop and validate a deep learning-based automatic quantification for nigral hyperintensity and a classification algorithm for neurodegenerative parkinsonism using susceptibility map-weighted imaging (SMwI).MATERIALS AND METHODS: We retrospectively collected 450 participants (210 with idiopathic Parkinson’s 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 multi-echo GRE. Diagnostic performance for diagnosing IPD was assessed using deep learning-based automatic quantification (Heuron NI) and classification (Heuron IPD) model. Reference standard for IPD was based on 18F-FP-CIT PET finding. Additionally, the correlation between the H&Y stage and volume of nigral hyperintensity in patients with IPD was assessed.RESULTS: Quantification of nigral hyperintensity using Heuron NI showed 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 using Heuron IPD showed AUC of 0.967 (95% CI, 0.881–0.991) and 0.976 (95% CI, 0.948–0.992) on the left and right, respectively. H&Y score ≥ 3 showed significant smaller nigral hyperintensity volume (1.43 ± 1.19 mm3) compared to H&Y score 1–2.5 (1.98 ± 1.63 mm3; p = 0.008).CONCLUSIONS: Our 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.ABBREVIATIONS: IPD = Idiopathic Parkinson’s disease; SN = substantia nigra; SMwI = susceptibility map weighted imaging; QSM = quantitative susceptibility mapping; CNN = convolutional neural network; ICV = intracranial volume.