Artificial Intelligence for Dementia Research Methods Optimization

TL;DR

This paper reviews how machine learning techniques are applied in dementia research, emphasizing current methods, challenges like reproducibility, and future opportunities for clinical translation and improved understanding of dementia.

Contribution

It critically evaluates existing ML applications in dementia research and discusses innovative methods like transfer and reinforcement learning to enhance clinical impact.

Findings

ML algorithms are widely used in dementia studies

Transfer learning can improve model generalizability

Addressing reproducibility is crucial for clinical adoption

Abstract

Introduction: Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater. Methods: We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research. Results: We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future. Discussion: ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia.