ArticleBrain communications2020
Prediction of conversion to Alzheimer's disease using deep survival analysis of MRI images.
Article in Brain communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Deep survival modeling to predict future cognitive impairment in unimpaired adults.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026Article
- SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.Annals of nuclear medicine · 2026Review
- Imaging-genetics-based dementia risk prediction using deep survival neural networks in the Rotterdam Study.Scientific reports · 2026Article
- Assessing the accuracy of survival machine learning and traditional statistical models for Alzheimer's disease prediction over time: a study on the ADNI cohort.BMC medical research methodology · 2025Article
- Imaging-genetics-based dementia risk prediction using deep survival neural networks in the Rotterdam Study.Research square · 2025Article
- Alzheimer's disease risk prediction using machine learning for survival analysis with a comorbidity-based approach.Scientific reports · 2025Article
- Prediction of white matter hyperintensities evolution one-year post-stroke from a single-point brain MRI and stroke lesions information.Scientific reports · 2025Article
- Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling.NPJ digital medicine · 2024Article
- Article
- Sex Differences in Conversion Risk from Mild Cognitive Impairment to Alzheimer's Disease: An Explainable Machine Learning Study with Random Survival Forests and SHAP.Brain sciences · 2024Article
- A multimodal deep learning approach for the prediction of cognitive decline and its effectiveness in clinical trials for Alzheimer's disease.Translational psychiatry · 2024Article
- Predicting Four-Year's Alzheimer's Disease Onset Using Longitudinal Neurocognitive Tests and MRI Data Using Explainable Deep Convolutional Neural Networks.Journal of Alzheimer's disease : JAD · 2024Article
- Explainability of random survival forests in predicting conversion risk from mild cognitive impairment to Alzheimer's disease.Brain informatics · 2023Article
- Identifying underlying patterns in Alzheimer's disease trajectory: a deep learning approach and Mendelian randomization analysis.EClinicalMedicine · 2023Article
- Article
- Neural network on interval-censored data with application to the prediction of Alzheimer's disease.Biometrics · 2023Article
- Multimodal multitask learning for predicting MCI to AD conversion using stacked polynomial attention network and adaptive exponential decay.Scientific reports · 2023Article
- Mapping cerebral atrophic trajectory from amnestic mild cognitive impairment to Alzheimer's disease.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Predicting time-to-conversion for dementia of Alzheimer's type using multi-modal deep survival analysis.Neurobiology of aging · 2023Article
- Predicting Progression to Clinical Alzheimer's Disease Dementia Using the Random Survival Forest.Journal of Alzheimer's disease : JAD · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prediction of the conversion of healthy individuals and those with mild cognitive impairment to the status of active Alzheimer's disease is a challenging task. Recently, a survival analysis based upon deep learning was developed to enable predictions regarding the timing of an event in a dataset containing censored data. Here, we investigated whether a deep survival analysis could similarly predict the conversion to Alzheimer's disease. We selected individuals with mild cognitive impairment and cognitively normal subjects and used the grey matter volumes of brain regions in these subjects as predictive features. We then compared the prediction performances of the traditional standard Cox proportional-hazard model, the DeepHit model and our deep survival model based on a Weibull distribution. Our model achieved a maximum concordance index of 0.835, which was higher than that yielded by the Cox model and comparable to that of the DeepHit model. To our best knowledge, this is the first report to describe the application of a deep survival model to brain magnetic resonance imaging data. Our results demonstrate that this type of analysis could successfully predict the time of an individual's conversion to Alzheimer's disease.
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