How Artificial Intelligence Can Preemptively Diagnose Neurological Disorders: Hear From Expert Dr. Sudip Paul

Dr. Sudip Paul, from North-Eastern Hill University, India, is an expert in applying artificial intelligence to the field of neurology. His recent publication, Early Detection of Neurological Disorders Using Machine Learning Systems, which he co-edited with Prof. Pallab Bhattacharya, from the National Institute of Pharmaceutical Education and Research (NIPER) Ahmedabad, India, and Prof. Arindam Bit, from the National Institute of Technology Raipur, India, discusses building smart systems that are able to detect neurological diseases much faster than traditional means. IGI Global conducted an interview with him to dive into neurology, artificial intelligence, and the intersection of medicine and machine learning.

What inspired you to pursue research activities in computer science applied to neurology?

Computer science applied to neurology is a highly trending area. My specialization is on various areas of neuroscience and I felt it would be more interesting if it is merged with computer science. The brain signal analysis done through machine learning gives more accurate and interesting outcomes through which various neurological disorders can be detected. Nowadays, deep learning is used for the early detection of various neurological disorders.

Why is your area of research important to the field at large?

In computer science, what was once the subfield of artificial intelligence has grown into the field of statistical machine learning. In the industry, statistical machine learning often goes by the name "big data" and are powered by core algorithms, which are a key part in the operation of any intelligent machine. Our research into machine learning and its applications can lead to more accurate feedback for doctors and more lives saved.

The theoretical neuroscience community is converging towards the basic view that the brain is a complex, decentralized, and statistical computer, and the race is on to figure out how it works. Neuroscience experiments have lots of data to analyze as well, so the algorithms that work for "big data" also work for analyzing data collected during neuroscience experiments. Cognitive, computational, and theoretical neurosciences all focus on how the brain implements computations that give rise to behavior.

In your opinion, what are some of the benefits of your research to its community of users?

Firstly, deep learning can be used for the early detection of brain disorders, and it can help clinicians monitor the progression of the disease and treat the patient in the early stages. Radiological machines with high-end algorithms can quickly and accurately detect damage within a brain, which in turn can give doctors a faster, more precise diagnosis. Additionally, our research can lead to the ability to check one’s various body parameters with mobile applications on a smart phone, and it has improved patient database entry in hospitals using machine learning techniques.

What are the future directions of neurology and computer science?

I am looking towards biologic intelligence, an AI paradigm that emulates an animal’s brain and nervous system with software and robotics that is based on computational connectomics. It is a new form of artificial general intelligence that runs over the top of deep learning and machine learning-based systems to understand and act upon changing and unlabeled data in real time. Additionally, with the advancement of various computer science algorithms, various neurological disorders can be categorized in a more accurate manner.

I am also interested in the advancement in brain-computer interface, which will allow a person to access his or her computer just by thinking in the brain, which will lead in turn to the movement of the cursor in the computer screen. That person can also access the internet and send e-mail once he thinks it in the brain.

What are some other evolving research trends you have observed in your field over the past several months and what would you say are some of the innovative research directions you foresee in the future? How do you feel your publication sets the pace for these innovations?

The role of computer algorithms in neurology can be expected to expand as machine capabilities are upgraded. Advanced imaging techniques can now detect the exact location of various tumors in the brain, brain-controlled limbs and wheelchairs can now enable movement and day-to-day work, and the wireless biosignal acquisition system has streamlined the diagnostic process. With these upgrades and the implementation of such projects as the National Digital Library System and the Worm Community Initiative, the formation of national or international collaboratories has become a more realistic goal for many fields of science. For neuroscience, high-resolution computer imaging and high-bandwidth networking are converging, and major obstacles to the integration of enabling technologies in the research enterprise are quickly diminishing. By beginning now, the integration of these technologies into neuroscience will be ensured. As an added benefit, integrating these technologies into a field as complex as neuroscience will help to generate new ways of conducting scientific exploration applicable to all areas of biomedical and biological science.

What has your experience been like publishing with IGI Global?

Publishing with IGI Global was an awesome experience for me. I really appreciate the quick time of publishing as well reviewers’ quick assessment procedures. Editorial board members were quite helpful during my process of publication and were easily accessible. I hope to work with them in the future.

IGI Global would like to thank Dr. Paul for sharing his insight into neurology and computer science. For more information on his title, please visit the book’s webpage.

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Disclaimer: The opinions expressed in this article are the author’s own and do not reflect the views of IGI Global.