Alzheimer's Disease Progression Prediction

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This field predicts how Alzheimer’s disease and related cognitive impairment may change, including conversion from mild cognitive impairment to dementia, future cognitive scores, and brain imaging changes. Common approaches combine longitudinal MRI, cognitive tests, biomarkers, and clinical records using recurrent networks or Transformers; continuous-time models such as Neural ODEs address irregular visits, while survival models estimate time-to-conversion and risk. Researchers also use Bayesian methods, disease-staging models, and personalized digital twins to represent uncertainty and individual trajectories. A central challenge is making predictions reliable across cohorts despite missing data and sparse visits, while addressing calibration, fairness, and data leakage.

Latest papers

39 papers2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

Faithful Explanations under Missing Modalities in Multimodal Dementia Prognosis
Oct 26, 2026Firuz KamalovFaithfulness of Language Model Explanations Multimodal Model Interpretability
Canadian University Dubai, City Walk, P.O. Box 117781, Dubai, United Arab Emirates
Multimodal-Conditioned Latent Diffusion Models for Spatiotemporal Brain MRI Forecasting
Oct 18, 2026Antonio Scardace, Silvia Ingala, Sebastiano Battiato +1Brain MRI Alzheimer's Disease Progression Prediction
University of Catania · Copenhagen University Hospital – Rigshospitalet · Copenhagen University Hospital – Herlev and Gentofte +2
Leakage-Controlled Multimodal Learning for Diagnosis and Progression Prediction in Alzheimer's Disease Research
Oct 7, 2026Akeem Temitope Otapo, Ghazaleh Khodabandelou, Zuheng Ming +1Alzheimer's Disease Progression Prediction Alzheimer's Disease Classification
MMAP: Multimodal Missing-Aware Pretraining for Longitudinal Alzheimer's Prediction
Sep 22, 2026Fiona Kekwick, Matthew Baugh, Bernhard Kainz +2Missing-Modality Learning Cross-Modal Alignment
Department of Computing, Imperial College London, London, UK · FAU Erlangen-Nürnberg, Erlangen, Germany · Department of Brain Sciences, Imperial College London, London, UK +1
Reverse Spatio-Temporal Disease Progression Modelling
Sep 13, 2026Ulugbek Shernazarov, Moucheng Xu, Inomjon RamatovDisease Progression Modeling Longitudinal Medical Image Analysis
T´el´ecom SudParis, Institut Polytechnique de Paris, France · University College London, UK · Digital Technologies and Artificial Intelligence Development Research Institute (AIRI), Uzbekistan
A Generalizable Feature Extractor for Alzheimer's-Related Brain MRI Tasks
Sep 4, 2026Reza Rajabli, D. Louis CollinsMedical Imaging Foundation Models Transfer Learning
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University · Department of Biomedical Engineering, McGill University
Longitudinal Bayesian Learning of Continuous Disease Position across the Alzheimer's Disease Continuum
Aug 19, 2026Yingying Zhang, Kun Zhao, Guodong Liu +10Alzheimer's Disease Progression Prediction Longitudinal Medical Image Analysis
Parcel2Progression: An Anatomy-aware Longitudinal Framework for Alzheimer's Disease Diagnosis
Aug 9, 2026Madhumitha Venkatesh, Shanawaj S Madarkar, Konda Reddy MopuriLongitudinal Medical Image Analysis Alzheimer's Disease Classification
Indian Institute of Technology Hyderabad, India
Calibrated Alzheimer's Conversion Risk in Mild Cognitive Impairment: Persistent Homology of Clinical Trajectories with Conformal Guarantees
Jul 20, 2026Navin BondadeClinical Outcome Prediction Alzheimer's Disease Classification
Institute of Health Informatics, University College London
Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts
Jul 13, 2026Christelle Schneuwly Diaz, Narmina Baghirova, Duy-Thanh Vu +4Missing-Modality Learning Learning with Missing Data
Platform of Bioinformatics, Lausanne University Hospital. · Faculty of Biology and Medicine, University of Lausanne. · 3Leenaards Memory Centre, Lausanne University Hospital. +1
Predicting Early Stages Of Alzheimer's Disease And Identifying Key Biomarkers Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies
Jul 2, 2026Debopriya GhoshClinical Prediction Alzheimer's Disease Classification
ENC-ODE: Event-level Neurodegenerative Modeling in Continuous Time with Neural ODEs
Jun 29, 2026Yujee Song, Seunghun Baek, Guorong Wu +1Latent Dynamics Modeling Longitudinal EHR Modeling
Pohang University of Science and Technology, Pohang, South Korea · University of North Carolina at Chapel Hill, Chapel Hill, USA
Neural operator-based digital twins for modeling amyloid- $β$ and tau propagation and treatment optimization in Alzheimer's disease
Jun 23, 2026Xiaofeng Xu, Tingting Dan, Zifan Zhou +3Alzheimer's Disease PDE Operator Learning
Department of Mathematics, Pennsylvania State University, University Park, PA 16802, USA · Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA · School of Electrical Engineering and Computer Science, Pennsylvania State University, University Park, PA 16802, USA +3
Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning
Jun 23, 2026Arya Hariharan, Shreyank N Gowda, Anala M RClinical Outcome Prediction Alzheimer's Disease Classification
Department of Computer Science and Engineering, R.V. College of Engineering, Bengaluru, Karnataka, India · School of Computer Science, University of Nottingham, Nottingham, United Kingdom
SMART: A Flexible, Interpretable, and Scalable Spatio-temporal Brain Atlas from High-Resolution Imaging Data
Jun 17, 2026John Kalkhof, Boris Gutman, Emile d'Angremont +2Disease Progression Modeling Longitudinal Medical Image Analysis
IIT · Inria Center at University Côte d’Azur Sophia Antipolis, France · Illinois Institute of Technology Chicago, IL, USA +4
Bayesian Networks with Latent Time Embedding for Stage-Aware Causal Modeling of Alzheimer's Disease Progression
Jun 14, 2026Nguyen Linh Dan LeCausal Effect Estimation Structural Causal Models
Department of Biomedical Engineering The University of Melbourne, Melbourne, Australia
iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis
Jun 12, 2026Farica Zhuang, Seong Woo Han, Zixuan Wen +3Mixture of Experts Survival Analysis
University of Pennsylvania · Yale University
Transition-Based Digital Twin Modelling for Alzheimer's Disease under Sparse Longitudinal Data
Jun 8, 2026Yinyu Huang, Yilin Zhang, Sofia Michopoulou +2Irregular Time-Series Modeling Clinical Outcome Prediction
School of Electronics and Computer Science, University of Southampton, UK · University Hospital Southampton NHS Foundation Trust, Southampton, UK · Faculty of Medicine, University of Southampton, UK
Reconstructing and forecasting disease trajectories of patients with Alzheimer's disease using routine data in resource-constrained settings
Jun 5, 2026Ratnadeep Das, Atri Chatterjee, Sitikantha RoyAlzheimer's Disease Clinical Outcome Prediction
Yardi School of Artificial Intelligence (ScAI), Indian Institute of Technology Delhi, Hauz Khas, Delhi, 110016, India · Department of Neurology, Vardhman Mahavir Medical College and Safdarjung Hospital, Delhi, 110029, India · Department of Applied Mechanics, Indian Institute of Technology Delhi, Hauz Khas, Delhi, 110016, India
Bayesian meta-learning for modeling Alzheimer's disease progression
Jun 1, 2026Clara Hoffmann, Nadja KleinAlzheimer's Disease Meta-Learning
Scientific Computing Center, Karlsruhe Institute of Technology, Germany
A Two-Scan Deep Learning Model for Predicting Dementia in Mild Cognitive Impairment
May 27, 2026Sara Fin, Alireza Moayedikia, David J. White +2Hybrid CNN-Transformer Architectures Longitudinal Medical Image Analysis
Australian Regenerative Medicine Institute, Monash University, Melbourne, VIC, Australia · School of Business Law and Entrepreneurship, Swinburne University of Technology, Melbourne, VIC, Australia · department of Business Technology and Entrepreneurship of Swinburne University of Technology, John Street, Hawthorn 3122, VIC, Australia +2
Forecasting Medium-Horizon Alzheimer's Disease Progression: Residual Gap-Aware Transformers for 24-Month CDR-SB Change from ADNI Clinical and Biomarker Histories
May 4, 2026Ran Tong, Tong Wang, Lanruo Wang +1Residual Learning Alzheimer's Disease
Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX 75080, United States · Department of Plant Science and Landscape Architecture, University of Connecticut, Storrs, CT, United States · Naveen Jindal School of Management, University of Texas at Dallas, Richardson, TX 75080, United States +1
GeoSAE: Geometric Prior-Guided Layer-Wise Sparse Autoencoder Annotation of Brain MRI Foundation Models
May 3, 2026Favour Nerrise, Lucy Yin, Mohammad H. Abbasi +2Alzheimer's Disease Medical Imaging Foundation Models
Stanford University · Stanford University, CA
PROMISE-AD: Progression-aware Multi-horizon Survival Estimation for Alzheimer's Disease Progression and Dynamic Tracking
Apr 30, 2026Qing Lyu, Jeremy Hudson, Mohammad Kawas +3Alzheimer's Disease Survival Analysis
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510 USA · Department of Radiology Oncology, Wake Forest University School of Medicine, Winston-Salem, NC 27157 USA · Department of Applied Mathematics & Statistics and Department of Computer Science, Stony Brook University, Stony Brook, NY 11794 USA
Toward Personalized Digital Twins for Cognitive Decline Assessment: A Multimodal, Uncertainty-Aware Framework
Apr 29, 2026Bulent Soykan, Gulsah Hancerliogullari Koksalmis, Hsin-Hsiung Huang +1Alzheimer's Disease Clinical Outcome Prediction
Department of Mechanical, Industrial & Manufac. Eng. The University of Toledo Toledo, OH, USA · Department of Industrial Eng. and Mngt. Systems University of Central Florida Orlando, FL, USA · Department of Statistics and Data Science University of Central Florida Orlando, FL, USA +1
Evaluating TabPFN for Mild Cognitive Impairment to Alzheimer's Disease Conversion in Data Limited Settings
Apr 29, 2026Brad Ye, Bulent Soykan, Gulsah Hancerliogullari Koksalmis +2Tabular Foundation Models Alzheimer's Disease
Department of Medicine, University of Central Florida College of Medicine, Orlando, FL, USA · NIMBUS Lab, University of Central Florida · Department of Mechanical, Industrial & Manufacturing Eng., The University of Toledo, Toledo, OH, USA +3
TEMPO: Transformers for Temporal Disease Progression from Cross-Sectional Data
Apr 25, 2026Hongtao Hao, Joseph L. AusterweilAlzheimer's Disease Disease Progression Modeling
University of Wisconsin—Madison, USA · Chiba Institute of Technology, Japan & University of Wisconsin—Madison, USA
CognitiveTwin: Robust Multi-Modal Digital Twins for Predicting Cognitive Decline in Alzheimer's Disease
Apr 24, 2026Bulent Soykan, Gulsah Hancerliogullari Koksalmis, Hsin-Hsiung Huang +1Alzheimer's Disease Multimodal Robustness
Department of Mechanical, Industrial & Manufacturing Eng. The University of Toledo Toledo, OH, USA · Department of Industrial Eng. and Mngt. Systems University of Central Florida Orlando, FL, USA · Department of Statistics and Data Science University of Central Florida Orlando, FL, USA +1
Investigating Trustworthiness of Nonparametric Deep Survival Models for Alzheimer's Disease Progression Analysis
Apr 10, 2026Jacob Thrasher, Kaitlyn Heintzelman, Peter Martone +4Alzheimer's Disease Progression Prediction Algorithmic Fairness
West Virginia University Morgantown, USA · University of Aberdeen Aberdeen, Scotland
Predicting Alzheimer's disease progression using rs-fMRI and a history-aware graph neural network
Apr 7, 2026Mahdi Moghaddami, Mohammad-Reza Siadat, Austin Toma +2Alzheimer's Disease Progression Prediction Temporal GNNs
Oakland University, 502 Meadow Brook Road, Rochester, MI 48309, USA

Oct 26, 2026J/K move · Enter open · S save

Faithful Explanations under Missing Modalities in Multimodal Dementia Prognosis

Firuz Kamalov

Canadian University Dubai, City Walk, P.O. Box 117781, Dubai, United Arab Emirates

Faithfulness of Language Model Explanations Multimodal Model Interpretability Missing-Modality Learning Alzheimer's Disease Progression Prediction

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Large language models are increasingly used to turn the feature attributions of multimodal prognostic models into narratives for clinicians, yet costly modalities such as amyloid PET and cerebrospinal fluid biomarkers are often missing, and whether these explanations stay faithful under missing evidence has not been tested. We separate two failure points: phantom attribution, in which the model credits modalities that were never observed, and unfaithful narration, in which the report omits, cites or misjudges missing modalities. Using 307 participants of the Alzheimer's Disease Neuroimaging Initiative with complete data, we masked every subset of four costly modalities, predicted 36-month conversion from mild cognitive impairment to Alzheimer's disease, and restored the masked data to obtain interventional ground truth. All five missing-data strategies assigned 12% to 23% of attribution to unobserved modalities, and up to 45% with four absent; missingness-aware strategies minimised it at no cost in accuracy. Across 51,200 reports from four language models and four prompting regimes, naming absent modalities in the prompt raised acknowledgement from 48% to 88% and cut phantom evidence twenty-fold, but advice on whether acquiring a missing test would change the prediction was correct in only 27% to 41% of cases until checked against executed restorations, which raised accuracy to 69% for open-weight narrators and 99.9% for a proprietary model. Higher phantom attribution was associated with more phantom evidence and fewer faithful direction claims, and direction fidelity, measured by a calibrated language-model judge, depended on narrator scale rather than prompting. Narrators told to name absent modalities sometimes declared observed ones absent, an error that a structured template eliminated. We release the masking protocol and evaluation code.