ArticleMagnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine2020
A Novel Deep Learning Approach with a 3D Convolutional Ladder Network for Differential Diagnosis of Idiopathic Normal Pressure Hydrocephalus and Alzheimer's Disease.
Article in Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 34 citations in OpenAlex.
- Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.Neural regeneration research · 2026Article
- Attention-Driven Deep Learning for Hydrocephalus: Preliminary Benchmarking of Multiscale 3D Attention Against Conventional 3D CNN Models Using Ventricular Enlargement on CT.Journal of imaging informatics in medicine · 2026Article
- Data Leakage in Deep Learning for Alzheimer's Disease Diagnosis: A Scoping Review of Methodological Rigor and Performance Inflation.Diagnostics (Basel, Switzerland) · 2025Review
- Automated Idiopathic Normal Pressure Hydrocephalus Diagnosis via Artificial Intelligence-Based 3D T1 MRI Volumetric Analysis.AJNR. American journal of neuroradiology · 2025Article
- Deep Learning for Alzheimer's Disease Prediction: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2024Review
- Structural neuroimaging markers of normal pressure hydrocephalus versus Alzheimer's dementia and Parkinson's disease, and hydrocephalus versus atrophy in chronic TBI-a narrative review.Frontiers in neurology · 2024Review
- Automatic assessment of disproportionately enlarged subarachnoid-space hydrocephalus from 3D MRI using two deep learning models.Frontiers in aging neuroscience · 2024Article
- An interpretable machine learning approach to study the relationship beetwen retrognathia and skull anatomy.Scientific reports · 2023Article
- Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics.Investigative radiology · 2023Review
- Deep Learning Achieves Neuroradiologist-Level Performance in Detecting Hydrocephalus Requiring Treatment.Journal of digital imaging · 2022Article
- Can Shunt Response in Patients with Idiopathic Normal Pressure Hydrocephalus Be Predicted from Preoperative Brain Imaging? A Retrospective Study of the Diagnostic Use of the Normal Pressure Hydrocephalus Radscale in 119 Patients.AJNR. American journal of neuroradiology · 2022Article
- SVM-Based Normal Pressure Hydrocephalus Detection.Clinical neuroradiology · 2021Article
- Artificial Intelligence in Neuroradiology: Current Status and Future Directions.AJNR. American journal of neuroradiology · 2020Article
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Authors and funding
15 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeIdiopathic normal pressure hydrocephalus (iNPH) and Alzheimer's disease (AD) are geriatric diseases and common causes of dementia. Recently, many studies on the segmentation, disease detection, or classification of MRI using deep learning have been conducted. The aim of this study was to differentiate iNPH and AD using a residual extraction approach in the deep learning method.
methodsTwenty-three patients with iNPH, 23 patients with AD and 23 healthy controls were included in this study. All patients and volunteers underwent brain MRI with a 3T unit, and we used only whole-brain three-dimensional (3D) T
resultsTwenty-one out of 23 iNPH cases, 19 out of 23 AD cases and 22 out of 23 controls were correctly diagnosed. The accuracy was 0.90. In the Grad-CAM heat map, brain parenchyma surrounding the lateral ventricle was highlighted in about half of the iNPH cases that were successfully diagnosed. The medial temporal lobe or inferior horn of the lateral ventricle was highlighted in many successfully diagnosed cases of AD. About half of the successful cases showed nonspecific heat maps.
conclusionResidual extraction approach in a deep learning method achieved a high accuracy for the differential diagnosis of iNPH, AD, and healthy controls trained with a small number of cases.
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