Jae-Sung Lim
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Jae-Sung Lim has a total of 16 co-authors. Here are the top co-authors by number of shared publications:
- Chong Hyun Suh
Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
1 shared publication - Eung Yeop Kim
Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
1 shared publication - Ho Sung Kim
Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
1 shared publication - Hwon Heo
Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Republic of Korea
1 shared publication - Jae-Hong Lee
Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
1 shared publication - Kyu Sung Choi
Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
1 shared publication
Latest Publications
- 8 November 2025
- Published by Springer Nature in Neuroradiology
- Vol. 67 (12) , 3555-3563
Data-Driven differentiation of idiopathic Normal-Pressure hydrocephalus and progressive supranuclear palsy via automated volumetric analysis
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.
- 8 November 2025
- Published by Springer Nature in Neuroradiology
- Vol. 67 (12) , 3555-3563
Data-Driven differentiation of idiopathic Normal-Pressure hydrocephalus and progressive supranuclear palsy via automated volumetric analysis
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.