Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Abstract

Disease prediction from diagnostic reports and pathological images using artificial intelligence (AI) and machine learning (ML) is one of the fastest emerging applications in recent days. Researchers are striving to achieve near-perfect results using advanced hardware technologies in amalgamation with AI and ML based approaches. As a result, a large number of AI and ML based methods are found in the literature. A systematic survey describing the state-of-the-art disease prediction methods, specifically chronic disease prediction algorithms, will provide a clear idea about the recent models developed in this field. This will also help the researchers to identify the research gaps present there. To this end, this paper looks over the approaches in the literature designed for predicting chronic diseases like Breast Cancer, Lung Cancer, Leukemia, Heart Disease, Diabetes, Chronic Kidney Disease and Liver Disease. The advantages and disadvantages of various techniques are thoroughly explained. This paper also presents a detailed performance comparison of different methods. Finally, it concludes the survey by highlighting some future research directions in this field that can be addressed through the forthcoming research attempts.

Kidney Segmentation from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Integrating Deep Convolutional Neural Networks and Level Set Methods

“…In almost all the works reported in Table 1, the number of patients is no larger Related Work: Deep learning is a machine learning technique that imitates the learning process of the human brain and extracts features from data in an unsupervised manner. Convolutional neural networks (ConvNets) are a subset of deep learning that is having an increasingly important role in the segmentation of the human organs from medical scans [2,5]. Several studies have been conducted to accomplish kidney segmentation from CT images.…”

Precognition of mental health and neurogenerative disorders using AI-parsed text and sentiment analysis

“…(2) Neurodegenerative diseases [55,49]: while not directly identifiable through text, subtle changes in typing speed and fine motor skills required for typing might provide early clues of Parkinson's disease [39,54,1,20]; (3) Sleep disorders [73,66]: late-night time stamps and content that indicates restlessness or consistent complaints about lack of sleep may be suggestive of insomnia; (4) Infectious diseases [71,12,72,16]: it is less likely to identify infectious diseases from text messages unless the content explicitly describes symptoms or experiences related to the infectious disease [71]; (5) Chronic diseases [41,43,45]: if someone frequently discusses feelings of tiredness, changes in weight, or other symptomatic experiences, this could indirectly hint at chronic conditions like diabetes or thyroid issues [31]. While text message analysis may provide signals indicative of a health issue [58,75], this method is far from diagnostic so this can be just a preventive supportive tool for specialists (see Figure 1).…”