ArticleMedical & biological engineering & computing2022
GA-MADRID: design and validation of a machine learning tool for the diagnosis of Alzheimer's disease and frontotemporal dementia using genetic algorithms.
Article in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines 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
- Frontotemporal dementia: a systematic review of artificial intelligence approaches in differential diagnosis.Frontiers in aging neuroscience · 2025Pooled it
- Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.Frontiers in neurology · 2024Pooled it
- Protocol for the external validation of machine learning models for risk prediction in early-onset preeclampsia: a multicenter prospective cohort study.BMC pregnancy and childbirth · 2026Article
- A novel diagnosis method based on multimodal-aware bi-objective competitive mechanism for Alzheimer's disease.Scientific reports · 2026Article
- The potential role of machine learning and deep learning in differential diagnosis of Alzheimer's disease and FTD using imaging biomarkers: A review.The neuroradiology journal · 2025Review
- An Evolutionary Federated Learning Approach to Diagnose Alzheimer's Disease Under Uncertainty.Diagnostics (Basel, Switzerland) · 2025Article
- Detecting Alzheimer's Disease Stages and Frontotemporal Dementia in Time Courses of Resting-State fMRI Data Using a Machine Learning Approach.Journal of imaging informatics in medicine · 2024Article
- Explainable artificial intelligence for the automated assessment of the retinal vascular tortuosity.Medical & biological engineering & computing · 2024Article
- An Explainable AI Paradigm for Alzheimer's Diagnosis Using Deep Transfer Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Diagnostic Efficacy and Clinical Relevance of Artificial Intelligence in Detecting Cognitive Decline.Cureus · 2023Review
- XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease.BMC medical informatics and decision making · 2023Article
- Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review.Bioengineering (Basel, Switzerland) · 2022Review
- Predictive modeling of complications arising from early-onset preeclampsia in pregnant women.Women's health (London, England)Observational
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence aids early diagnosis and development of new treatments, which is key to slow down the progress of the diseases, which to date have no cure. The patients' evaluation is carried out through diagnostic techniques such as clinical assessments neuroimaging techniques, which provide high-dimensionality data. In this work, a computational tool is presented that deals with the data provided by the clinical diagnostic techniques. This is a Python-based framework implemented with a modular design and fully extendable. It integrates (i) data processing and management of missing values and outliers; (ii) implementation of an evolutionary feature engineering approach, developed as a Python package, called PyWinEA using Mono-objective and Multi-objetive Genetic Algorithms (NSGAII); (iii) a module for designing predictive models based on a wide range of machine learning algorithms; (iv) a multiclass decision stage based on evolutionary grammars and Bayesian networks. Developed under the eXplainable Artificial Intelligence and open science perspective, this framework provides promising advances and opens the door to the understanding of neurodegenerative diseases from a data-centric point of view. In this work, we have successfully evaluated the potential of the framework for early and automated diagnosis with neuroimages and neurocognitive assessments from patients with Alzheimer's disease (AD) and frontotemporal dementia (FTD).
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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.