ArticleFrontiers in neuroscience2021
Explainable Deep Learning for Personalized Age Prediction With Brain Morphology.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Generalizable and explainable deep learning for brain MRI: a multi-cohort evaluation of 3D architectures for age and sex prediction.Brain informatics · 2026Article
- Gaining Brain Insights by Tapping into the Black Box: Linking Structural MRI Features to Age and Cognition using Shapley-Based Interpretation Methods.Neuroinformatics · 2025Article
- Brain Age Prediction: Deep Models Need a Hand to Generalize.Human brain mapping · 2025Article
- NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorders.Proceedings of machine learning research · 2025Article
- Explainable AI in Diagnostic Radiology for Neurological Disorders: A Systematic Review, and What Doctors Think About It.Diagnostics (Basel, Switzerland) · 2025Review
- A predictive model of Parkinsonian brain aging based on brain imaging features.Frontiers in neurology · 2025Article
- Explainable brain age prediction: a comparative evaluation of morphometric and deep learning pipelines.Brain informatics · 2024Article
- A novel explainable machine learning-based healthy ageing scale.BMC medical informatics and decision making · 2024Article
- Exploring the correlation between DNA methylation and biological age using an interpretable machine learning framework.Scientific reports · 2024Article
- Personalized identification of autism-related bacteria in the gut microbiome using explainable artificial intelligence.iScience · 2024Article
- Predicted brain age (PBA), promising surrogate indicator for neurodevelopment in preterm baby: how to predict accurately?European radiology · 2024Article
- Older Tissue Age Derived From Abdominal Computed Tomography Biomarkers of Muscle, Fat, and Bone Is Associated With Chronic Conditions and Higher Mortality.Mayo Clinic proceedings · 2024Article
- Machine learning in small sample neuroimaging studies: Novel measures for schizophrenia analysis.Human brain mapping · 2024Article
- Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI.Frontiers in public health · 2024Article
- Explainable artificial intelligence for microbiome data analysis in colorectal cancer biomarker identification.Frontiers in microbiology · 2024Article
- Air pollution and mortality for cancer of the respiratory system in Italy: an explainable artificial intelligence approach.Frontiers in public health · 2024Article
- Article
- Explainable machine learning framework to predict personalized physiological aging.Aging cell · 2023Article
- Assessment of Performance, Interpretability, and Explainability in Artificial Intelligence-Based Health Technologies: What Healthcare Stakeholders Need to Know.Mayo Clinic proceedings. Digital health · 2023Review
- Explainable artificial intelligence incorporated with domain knowledge diagnosing early gastric neoplasms under white light endoscopy.NPJ digital medicine · 2023Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting brain age has become one of the most attractive challenges in computational neuroscience due to the role of the predicted age as an effective biomarker for different brain diseases and conditions. A great variety of machine learning (ML) approaches and deep learning (DL) techniques have been proposed to predict age from brain magnetic resonance imaging scans. If on one hand, DL models could improve performance and reduce model bias compared to other less complex ML methods, on the other hand, they are typically black boxes as do not provide an in-depth understanding of the underlying mechanisms. Explainable Artificial Intelligence (XAI) methods have been recently introduced to provide interpretable decisions of ML and DL algorithms both at local and global level. In this work, we present an explainable DL framework to predict the age of a healthy cohort of subjects from ABIDE I database by using the morphological features extracted from their MRI scans. We embed the two local XAI methods SHAP and LIME to explain the outcomes of the DL models, determine the contribution of each brain morphological descriptor to the final predicted age of each subject and investigate the reliability of the two methods. Our findings indicate that the SHAP method can provide more reliable explanations for the morphological aging mechanisms and be exploited to identify personalized age-related imaging biomarker.
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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 Socratic graph.