Smart health systems are bringing a revolution in the healthcare sector by providing an opportunity to monitor and evaluate the health data of patients 24 × 7, thus making proactive care possible. Smart health technologies have combined the word “smart technology” with “health.” Advanced automated physical sensors are being used to register and record health data from surroundings, integrating health data of patients through wearable sensors and smart health data from within the hospital. In this chapter, we have tried to explain how the Internet of things along with the machine learning techniques has connected the medical devices and made remote care possible. Smart connected glucose monitoring systems, connected inhalers, connected sensors, smart watches and health bands are few examples of devices that are making use of automated sensors and Bluetooth technology for collection of health data. These smart devices, connected to the Internet, provide invaluable health data giving insights on the symptoms and trends in patients. Smart health devices have enabled patients to monitor and provide data regarding their sugar levels, blood pressure, pulse, heartbeat and so on. By comparing the data of one patient with a bigger data set of patients, certain parameters can be discovered.
Having severe impact on world’s economy and being identified as the most hazardous disease by the US government, the irreversible neurodegenerative Alzheimer’s disease (AD) has been challenging the medical fraternity and the researchers, because of its irrecoverable nature. Despite having a hand full of AD classification systems, the literature still impede in presenting a perfect diagnostic system. A deeper understanding of the brain anatomy and the methods available for its segmentation may help the new researchers step into the proper direction toward devising a new accurate system which is the research need of the day. This chapter attempts to bring forth an intelligible review of the existing systems, with an insight into the brain anatomy; the shifting of the AD classification from traditional image processing techniques toward the intelligent deep learning techniques has also been clearly explored.
After successful flourishing growth in the fields of speech recognition and computer vision, artificial intelligence (AI) has marked its presence in the biological domain. From pathology and diagnosis to epidemiology covering the entire range of drug discovery and genetic sequence analysis, AI is becoming one of the astonishing frontiers in the biological world. Huge amount of data generated through Human Genome Project has created a rich domain of biological research using AI. AI came into existence in 1950s and is defined as simulation by machines of human intelligence process. Novel approaches of machine learning (ML) and deep learning (DL) have made it possible to predict the protein structure and to perform simulation of biological systems. Nowadays, medicines are designed in accordance with the health profile of patient giving rise to personalized medicines and thereafter it can be said that ML is governing the field of personalized medicines by prescribing effective treatment for patients. AI is no more limited only to medicine but has successfully emerged in fields of molecular biology, neuroscience and radiology. Today, in the modeling of the brain functions, electromyography is applied for the analysis of the neuropsychological data with the help of ML and DL techniques that are faster and cheaper when compared with the existing technique of X-ray crystallography. The ultimate motive is to train AI against multiple diseases in order to obtain potential diagnosis. This chapter describes how ML has changed the aspects of molecular biology along with the advent of AI in the pharmaceutical industry. It will also be focusing on diagnosis of diseases using ML and prediction of protein structures through DL. Thus, we can say that AI with its approaches is storming the world along with the revolution in the ways biological research is performed, hence, paving ways for innovations in biotechnology.
Over the past 10 years, there is a rapid evolution in modalities of biomedical imaging. They act as aid for doctors in disease identification, estimation of disease occurrence and methodological treatment, thus degree of patient care has greatly strengthened. Medical images are usually generated either by ionizing radiation such as X-rays and gamma rays or by nonionizing radiation such as ultrasound and magnetic resonance imaging (MRI) techniques. Processing of biomedical images is analogous to acquiring biomedical signal from various dimensions. It comprises improvement, analysis and display of medical images. Quick ascertainment of deadly diseases like cancer largely improves the chances for effective treatment to save patient’s life. This can be happening fruitfully only with the effective extraction of informatics from acquired medical images. Usually, after acquiring biosignals from various biomedical modalities, they have to be properly sampled and quantized for effective signal processing. To explore valuable informatics from the signal, it should have been processed through proper techniques. The selection of techniques for exploring information from medical images contributes significantly for early ascertainment of fatal diseases. Because of biomedical image’s multifaceted nature, it isʚ challenging to explore fruitful essential statistics that can be incorporated into expert systems for diagnosis. At present, though a large number of techniques are available for processing the medical images, a single technique may not be flexible to use for various fields such as quantum mechanics, geography, medicine, pattern recognition, video processing and robot vision. The invention of wavelet method has overcome major limitations in collecting informatics form image and has a lot of impact on image processing. The discrete wavelet transform (DWT) is akin to a microscope through which we can discern various components of the signal by just altering the focus. Transformation of discrete wavelet extracts and separates a signal through a collection of quadrature filters, and they have corresponding filter properties specific to corresponding mother wavelet. The content captured by the coefficients of wavelet is unique, and it is possible to rebuild the original signal with no redundancy. This tremendous nature of wavelet inspired the development of many techniques for medical signal compression based on wavelet compression theory. These techniques are vital for improving chances to explore novel diagnosis information and medical data transmission. The signals generated form medical image modality devices have high chances for corruptiondue to photon noise, device noise, digitization noise and many more noises. This type of intrinsic noises is caused by imaging modalities, which are obstacles to explore valuable data from medical images and are very difficult to remove by applying traditional filters. The DWT procedure extracts the signal with extremely minimal distortion to avoid noise. This marvelous DWT working principle can be used on various biomedical signals and are decomposed by setting a threshold to every corresponding level detailed coefficient. These extracted coefficients from digitized images which are formed by medical signals are critical for diagnosis of disease in its initial stage so that we can save the human lives.