Saturday 27 / April / 2024
Muscat: The number of patients with Alzheimer’s disease is on the rise in Oman, an expert said.
To combat this, a research team from Oman has developed a sophisticated deep learning model designed to predict the risk of Alzheimer’s disease in its early stages.
The research, led by Dr Abraham Varghese, Senior Lecturer, Information Technology Department of University of Technology and Applied Sciences, Muscat, marks a crucial step forward in the early diagnosis of this debilitating disease.
In the past few years, there has been an array of innovative and insightful research projects funded by the Ministry of Higher Education, Research and Innovation.
‘A customised machine learning and deep learning model for predicting the risk of Alzheimer’s disease at an early stage’ by principal investigator Dr. Abraham Varghese, Senior Lecturer at the IT Department, University of Technology and Applied Sciences Muscat, is among the research projects funded by the Block Funding Program of the Ministry of Higher Education, Research and Innovation.
In this research project, Dr. Abraham Varghese explained that Alzheimer’s disease (AD) has become one of the leading causes of death worldwide.
The number of people suffering from AD is expected to rise from 55 million to 139 million by 2050.
A sharp increase in Alzheimers’ disease cases necessitates the development of immediate early diagnostic tools, something that is also true in the Sultanate of Oman, where the number of patients with Alzheimer’s disease is on the rise.
The study therefore aimed to develop an AI-driven diagnostic tool for Alzheimer’s Disease (AD) that leverages both psychological parameters and image features derived from clinical and MRI measurements, integrate Explainable AI (XAI) techniques into the diagnostic tool to enable practitioners and researchers to understand the reasoning behind the model’s decisions, and develop a user-friendly graphical user interface (GUI) for the diagnostic tool, which facilitates model predictions and explanations, thereby supporting informed clinical decision-making.
According to Dr. Abraham, the study emphasised the development of AI-based tools for the early diagnosis of AD, focusing on the stage of Mild Cognitive Impairment (MCI) to prevent the complete neurodegeneration of Alzheimer’s progression.
To understand how AD affects the brain over time, the research team examined MRI scans along with psychological and demographic information from the Alzheime’s Disease Neuroimaging Initiative (ADNI).
Dr. Abraham added that after rigorous preprocessing and application of feature selection algorithms, a set of black box algorithms was applied, and Random Forest was selected as having the highest accuracy of 92%.
Incorporating Explainable AI techniques, the research team enhanced model transparency, enabling medical professionals to gain clear insights into the predictive outcomes.
Dr. Abraham and his team also developed a web application (https://alzheimer-disease-prediction.streamlit.app/) that enables medical experts to use the model effectively. This tool facilitates the provision of personalized care and quality treatment by offering accurate, AI- driven insights into each patient’s condition. Through this initiative, the team aimed to enhance clinical decision-making processes, bridging the gap between advanced AI technologies and their practical application in healthcare settings for Alzheimer’s disease.
Through this research project, Dr. Abraham recommended adopting an integrative approach to AD diagnosis that encompasses a broad spectrum of data, including clinical, genetic, demographic, and particularly neuroimaging and psychological aspects, to provide a holistic view of AD.
This comprehensive strategy is crucial for capturing the multifaceted nature of the disease, enhancing the accuracy of diagnosis, and facilitating the development of personalized treatment plans. He also recommended fostering synergistic collaboration between AI researchers and medical professionals to develop trustworthy AI tools that are aligned with clinical requirements and patient-centered care.
This collaboration is essential to ensure that the integration of AI into healthcare settings is both effective and sensitive to the needs of patients, thereby enhancing the quality of care and the efficacy of medical interventions.
Dr. Abraham stated that the research project has successfully identified key variables and developed both a predictive model and a web application for Alzheimer’s disease. Currently, the input variable scores, crucial for the model, are assessed by psychologists through direct evaluations.
Moving forward, the research team aims to innovate further by developing a virtual psychologist.
This advanced tool will be designed to administer psychological assessments and calculate the necessary scores autonomously.
The development of such a virtual psychologist will significantly broaden the accessibility and applicability of our diagnostic tools, enabling more widespread use and facilitating early detection and intervention for Alzheimer’s disease across diverse settings.
