Sang Joon Kim

Yang SJ, et al. J Clin Neurol. 2026 Jan;22:e11. https://doi.org/10.3988/jcn.2025.0324

Purpose This study aims to develop automated machine learning methods to differentiate idiopathic normal pressure hydrocephalus (iNPH) from progressive supranuclear palsy (PSP) using advanced imaging features and volumetric analysis. Methods We analyzed T1-weighted 3D brain MRI scans of iNPH and PSP patients using automated component measurement acquisition and deep learning-based automated volumetric analysis. We grouped the MRI features as brainstem subgroup, volumetrics subgroup, midbrain to pons (MP) ratio subgroup (included the midbrain to pons area ratio and volume ratio), and disproportionately enlarged subarachnoid space hydrocephalus (DESH) subgroup (included the callosal angle, Sylvian fissure empty ratio, vertex region crowding ratio, and Evans’ index). Key imaging features were quantified, and statistical comparisons were conducted to identify distinguishing characteristics. Machine learning models were applied to evaluate feature effectiveness and improve diagnostic classification accuracy. Results This study analyzed 192 patients (132 iNPH, 60 PSP) and found significant differences in midbrain volume, midbrain to pons volume ratio, callosal angle, Sylvian fissure empty ratio, vertex region crowding ratio, and Evans’ index. Machine learning models, particularly the linear Support Vector Machine (SVM), achieved high diagnostic accuracy (AUROC = 0.98) in distinguishing iNPH from PSP. Volumetric analysis outperformed other feature subgroups. Conclusion The deep learning-based automated brain volumetric analysis achieved high diagnostic accuracy in distinguishing iNPH from PSP using T1-weighted brain MR images.

Distinguishing between Parkinson’s disease (PD) and essential tremor (ET) can be challenging sometimes. Although positron emission tomography can confirm PD diagnosis, its application is limited by high cost and exposure to radioactive isotopes. Patients with PD exhibit loss of the dorsal nigral hyperintensity on brain magnetic resonance imaging (MRI). Novel MRI-based approaches, including susceptibility map-weighted imaging (SMwI), allow visualization of the dorsal nigral hyperintensity at an increased resolution. Herein, we investigated the diagnostic accuracy of dorsal nigral hyperintensity evaluation on SMwI for distinguishing tremor-dominant PD from ET. Consecutive patients with tremor who underwent SMwI and were diagnosed with tremor-dominant PD or ET between July 2021 and July 2022 were enrolled. The dorsal nigral hyperintensity loss on SMwI was compared between the PD and ET groups. All 143 patients (100%) with tremor-dominant PD showed unilateral or bilateral dorsal nigral hyperintensity loss. Among 136 patients with ET, 131 (96.3%) exhibited an intact dorsal nigral hyperintensity, while 5 (3.7%) showed unilateral/bilateral dorsal nigral hyperintensity loss. SMwI discriminated between tremor-dominant PD and ET with a sensitivity and specificity of 100% and 96.3%, respectively. ¹⁸F-FP-CIT PET revealed normal findings in 4/5 patients with ET who had false-positive results on SMwI. These results indicate that dorsal nigral hyperintensity loss on SMwI could differentiate between tremor-dominant PD and ET with high accuracy.

정소영, et al. J Korean Soc Radiol. 2025 Jan;86(1):17-33. https://doi.org/10.3348/jksr.2024.0140

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.

Background and Purpose: Idiopathic normal pressure hydrocephalus (iNPH) is reversible dementia, that is underdiagnosed. The purpose of this study was to develop an automated diagnostic method for iNPH using artificial intelligence techniques with a T1-weighted MRI scan. Materials and Methods: We quantified iNPH, Parkinson9s disease, Alzheimer9s disease, and healthy control patients on T1-weighted 3D brain MRI scans using 452 scans for training and 110 scans for testing. Automatic component measurement algorithms were developed for Evans9 index, Sylvian fissure enlargement, high-convexity tightness, callosal angle, and normalized lateral ventricle volume. XGBoost models were trained for both automated measurements and manual labels for iNPH prediction. Results: A total of 452 patients (200 men; mean age ± standard deviation, 73.2 ± 6.5 years) were included in the training set. Of the 452 patients, 111 (24.6%) had iNPH. We obtained AUC values of 0.956 for automatically measured high-convexity tightness and 0.830 for Sylvian fissure enlargement. Intra-class correlation values of 0.824 for the callosal angle and 0.924 for Evans9 index were measured. Using the decision tree of the XGBoost model, the model trained on manual labels obtained an average cross-validation AUC of 0.988 on the training set and 0.938 on the unseen test set, while the fully automated model obtained a cross-validation AUC of 0.983 and an unseen test AUC of 0.936. Conclusion: We demonstrated a machine-learning algorithm capable of diagnosing iNPH from a 3D T1-weighted MRI scan that is robust to the failure. We propose a method to scan large numbers of 3D T1-weighted MRI scans with minimal human intervention, making possible large-scale iNPH screening. ABBREVIATIONS: iNPH = idiopathic normal-pressure hydrocephalus; PD = Parkinson9s disease; AD = Alzheimer9s disease; HC = healthy control; CSF = cerebrospinal fluid; DESH = disproportionately enlarged subarachnoid space hydrocephalus; 3D = three-dimensional

Jeong SY, et al. Korean J Radiol. 2024 Aug;25(8):726-741. https://doi.org/10.3348/kjr.2024.0105

To evaluate the diagnostic performance of susceptibility map-weighted imaging (SMwI) taken in different acquisition planes for discriminating patients with neurodegenerative parkinsonism from those without. This retrospective, observational, single-institution study enrolled consecutive patients who visited movement disorder clinics and underwent brain MRI and ¹⁸F-FP-CIT PET between September 2021 and December 2021. SMwI images were acquired in both the oblique (perpendicular to the midbrain) and the anterior commissure-posterior commissure (AC-PC) planes. Hyperintensity in the substantia nigra was determined by two neuroradiologists. ¹⁸F-FP-CIT PET was used as the reference standard. Inter-rater agreement was assessed using Cohen's kappa coefficient. The diagnostic performance of SMwI in the two planes was analyzed separately for the right and left substantia nigra. Multivariable logistic regression analysis with generalized estimating equations was applied to compare the diagnostic performance of the two planes. In total, 194 patients were included, of whom 105 and 103 had positive results on ¹⁸F-FP-CIT PET in the left and right substantia nigra, respectively. Good inter-rater agreement in the oblique (κ = 0.772/0.658 for left/right) and AC-PC planes (0.730/0.741 for left/right) was confirmed. The pooled sensitivities for two readers were 86.4% (178/206, left) and 83.3% (175/210, right) in the oblique plane and 87.4% (180/206, left) and 87.6% (184/210, right) in the AC-PC plane. The pooled specificities for two readers were 83.5% (152/182, left) and 82.0% (146/178, right) in the oblique plane, and 83.5% (152/182, left) and 86.0% (153/178, right) in the AC-PC plane. There were no significant differences in the diagnostic performance between the two planes (P > 0.05). There are no significant difference in the diagnostic performance of SMwI performed in the oblique and AC-PC plane in discriminating patients with parkinsonism from those without. This finding affirms that each institution may choose the imaging plane for SMwI according to their clinical settings.

Introduction: The number of brain MRI with contrast media performed in patients with cognitive impairment has increased without universal agreement. We aimed to evaluate the detection rate of contrast-enhanced brain MRI in patients with cognitive impairment. Materials and methods: This single-institution, retrospective study included 4,838 patients who attended outpatient clinics for cognitive impairment evaluation and underwent brain MRI with or without contrast enhancement from December 2015 to February 2020. Patients who tested positive for cognitive impairment were followed-up to confirm whether the result was true-positive and provide follow-up management. Detection rate was defined as the proportion of patients with true-positive results and was compared between groups with and without contrast enhancement. Individual matching in a 1:2 ratio according to age, sex, and year of test was performed. Results: The overall detection rates of brain MRI with and without contrast media were 4.7% (57/1,203; 95% CI: 3.6%–6.1%) and 1.8% (65/3,635; 95% CI: 1.4%–2.3%), respectively (P<0.001); individual matching demonstrated similar results (4.7% and 1.9%). Among 1,203 patients with contrast media, 3.6% was only detectable with the aid of contrast media. The proportion of patients who underwent follow-up imaging or treatment for the detected lesions were significantly higher in the group with contrast media (2.0% and 0.6%, P < .001). Conclusions: Detection rate of brain MRI for lesions only detectable with contrast media in patients with cognitive impairment was not high enough and further study is needed to identify whom would truly benefit with contrast media.