A recent study by the Indian Institute of Technology, Indore, identified the early diagnosis of Alzheimer's disease using AI. The study was published in Nature Mental Health Portal. It leverages advanced AI methods to unlock new potentials in Alzheimer's research, significantly advancing understanding and diagnosing the challenging condition.

Alzheimer's disease, which is characterised by a continual deterioration of cognitive abilities in older people, is the most common form of dementia. Neuroimaging data, for example, from magnetic resonance imaging and positron emission tomography, enable the identification of the structural and functional changes caused by Alzheimer's disease in the brain. 

Diagnosing Alzheimer's disease is critical in medical settings, as it supports early intervention and treatment planning and contributes to expanding our knowledge of the dynamics of Alzheimer's disease in the brain. Lately, ensemble deep learning has become popular for enhancing the performance and reliability of Alzheimer's disease diagnosis. These models combine several deep neural networks to increase a prediction's robustness.

In this study, the researchers revisited the key developments of ensemble deep learning, connecting its design—the type of ensemble, its heterogeneity and data modalities—with its application to AD diagnosis using neuroimaging and genetic data. Trends and challenges are discussed thoroughly to assess our knowledge in this area.

Professor M Tanveer of IIT Indore led the study. The research highlights AI's transformative role in advancing medical diagnosis and emphasises the critical role of multidisciplinary expertise and international collaboration in tackling complex healthcare challenges. 

Importance of Early Diagnosis

According to Professor Tanveer, an accurate and early diagnosis of Alzheimer's disease is paramount for effective intervention and treatment planning. Their research enhances diagnostic precision and enriches our understanding of the intricate dynamics underlying AD. 

The researcher also remarked that the early and accurate diagnosis of Alzheimer's is crucial. This allows timely intervention, better management of the condition and aids in planning proper treatments. Moreover, understanding the brain's dynamics through advanced AI techniques can significantly enhance the quality of life of patients and their families.

Other study

A similar study analysing the role of ML in Diagnosing Alzheimer's was published earlier by M. Tanveer in collaboration with researchers from IIT Guwahati, IIT Kharagpur and Birla Institute. The study reviewed 165 papers from 2005-2019 using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. They presented a detailed review of these approaches for Alzheimer's with possible future directions.

This area of study has a broad scope and requires more research. AI can undoubtedly be a big help.

Source:

Nature.com

Research Gate

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