ArticleBrain informatics2022
A robust framework to investigate the reliability and stability of explainable artificial intelligence markers of Mild Cognitive Impairment and Alzheimer's Disease.
Article in Brain informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- A Flexible-Horizon Clinical Decision Support Model for Dementia Early Detection and Risk Prediction in Secondary Care: A Responsible AI Approach.Journal of medical systems · 2026Article
- RDoC-informed explainable AI as a paradigm for multilevel Alzheimer's disease diagnosis and progression prediction: a systematic review.Brain informatics · 2026Review
- A comprehensive review of explainable artificial intelligence in healthcare methods, evaluation, and clinical integration.iScience · 2026Review
- Use of explainable AI (xAI) in dementia detection and prognosis: a scoping review.BMC medical informatics and decision making · 2026Article
- Enhancing Developmental Language Disorder Identification with Artificial Intelligence: Development of an Explainable Screening App Using Real and Synthetic Data.Journal of autism and developmental disorders · 2025Article
- Gaining Brain Insights by Tapping into the Black Box: Linking Structural MRI Features to Age and Cognition using Shapley-Based Interpretation Methods.Neuroinformatics · 2025Article
- NeuroNet-AD: A Multimodal Deep Learning Framework for Multiclass Alzheimer's Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
- Detecting label noise in longitudinal Alzheimer's data with explainable artificial intelligence.Brain informatics · 2025Article
- Explainable Artificial Intelligence in Neuroimaging of Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2025Review
- TIRESIA and TISBE: Explainable Artificial Intelligence Based Web Platforms for the Transparent Assessment of the Developmental Toxicity of Chemicals and Drugs.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Personalized identification of autism-related bacteria in the gut microbiome using explainable artificial intelligence.iScience · 2024Article
- Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer's disease detection.Brain informatics · 2024Review
- Machine learning in small sample neuroimaging studies: Novel measures for schizophrenia analysis.Human brain mapping · 2024Article
- An explainable machine learning approach for Alzheimer's disease classification.Scientific reports · 2024Article
- Multimodal classification of Alzheimer's disease and mild cognitive impairment using custom MKSCDDL kernel over CNN with transparent decision-making for explainable diagnosis.Scientific reports · 2024Article
- Air pollution and mortality for cancer of the respiratory system in Italy: an explainable artificial intelligence approach.Frontiers in public health · 2024Article
- Explainable artificial intelligence for genotype-to-phenotype prediction in plant breeding: a case study with a dataset from an almond germplasm collection.Frontiers in plant science · 2024Article
- Introduction to Large Language Models (LLMs) for dementia care and research.Frontiers in dementia · 2024Article
- c-Diadem: a constrained dual-input deep learning model to identify novel biomarkers in Alzheimer's disease.BMC medical genomics · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
In clinical practice, several standardized neuropsychological tests have been designed to assess and monitor the neurocognitive status of patients with neurodegenerative diseases such as Alzheimer's disease. Important research efforts have been devoted so far to the development of multivariate machine learning models that combine the different test indexes to predict the diagnosis and prognosis of cognitive decline with remarkable results. However, less attention has been devoted to the explainability of these models. In this work, we present a robust framework to (i) perform a threefold classification between healthy control subjects, individuals with cognitive impairment, and subjects with dementia using different cognitive indexes and (ii) analyze the variability of the explainability SHAP values associated with the decisions taken by the predictive models. We demonstrate that the SHAP values can accurately characterize how each index affects a patient's cognitive status. Furthermore, we show that a longitudinal analysis of SHAP values can provide effective information on Alzheimer's disease progression.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.