(주) 뷰노

(주) 뷰노

문의사항 남기기
KR EN
VUNO MED INC.
Publications
BACK TO LIST
Neuroradiology

Data-Driven differentiation of idiopathic Normal-Pressure hydrocephalus and progressive supranuclear palsy via automated volumetric analysis

  • Nov. 2025
  • by Suyoung Yun et. al.

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.

Link

https://link.springer.com/article/10.1007/s00234-025-03830-8

Author

Suyoung Yun, Yujee Song, Chong Hyun Suh, Wooseok Jung, Seung Hyun Lee, Saehyun Kim, Kyu Sung Choi, Hwon Heo, Woo Hyun Shim, Sungyang Jo, Sun Ju Chung, Jae-Sung Lim, Yangsean Choi, Ho Sung Kim, Sang Joon Kim, Jae-Hong Lee & Eung Yeop Kim

Tag in

#medical_image

#VUNO Med®-DeepBrain®

이전글
이전글이 없습니다.
다음글
Improved Deep Learning-Based Computer-Aided Diagnosis for Usual Interstitial Pneumonia Classification in Chest CT scan 2025. 10. 29
List

이 웹사이트는 원활한 서비스 제공을 위하여 당사 개인정보 처리방침에 명시된 쿠키를 사용합니다.

만일 당사의 쿠키 사용 정책에 대해 더 알고 싶으신 경우에는 개인정보 처리방침 을 참고하여 주시기 바랍니다.