Recent deep learning models for dementia as point-of-care testing: Potential for early detection

Abstract

Deep learning has been intensively researched over the last decade, yielding several new models for natural language processing, images, speech and time series processing that have dramatically improved performance. This wave of technological developments in deep learning is also spreading to medicine. The effective use of deep learning in medicine is concentrated in diagnostic imaging-related applications, but deep learning has the potential to lead to early detection and prevention of diseases. Physical aspects of disease that went unnoticed can now be used in diagnosis with deep learning. In particular, deep learning models for the early detection of dementia have been proposed to predict cognitive function based on various information such as blood test results, speech, and the appearance of the face, where the effects of dementia can be seen. Deep learning is a useful diagnostic tool, as it has the potential to detect diseases early based on trivial aspects before clear signs of disease appear. The ability to easily make a simple diagnosis based on information such as blood test results, voice, pictures of the body, and lifestyle is a method suited to point-of-cate testing, which requires immediate testing at the desired time and place. Over the past few years, the process of predicting disease can now be visualized using deep learning, providing insights into new methods of diagnosis.