Artificial intelligence-based techniques for analysis of body cavity fluids: a review

This paper tends to present a systematic review of the applications of Artificial Intelligence (AI) in the field of medical diagnosis vis-a-vis fluid cytology. The study is based on the research articles published in various reputed international and national journals and the conference proceedings from 1990 to till date. AI-based systems are becoming useful as alternative approaches to conventional techniques and are also becoming integral components of many diagnostic systems. Artificial intelligence is being increasingly used to solve complex practical problems in various areas and is becoming more and more popular nowadays. The use of AI for tackling various problems in medical science has diversified significantly during this period. It emerges as a dominant technique for addressing various difficult research problems. This paper follows the trend and evolution of application-based research during the last three decades. It also presents a technical comparison of novel methodologies developed by the researchers to deal with diagnostic problems in the analysis of body cavity fluids. Abnormalities in body cavity fluids lead to various serious disorders like meningitis, subarachnoid hemorrhage, CNS malignancy, demyelinating disease, heart failure, pulmonary embolism, cirrhosis, pneumonia, pleurisy, and pericarditis, arthritis, joint Infection and joint hemorrhage. Moreover, the paper includes recent research articles published during 2018 and 2019 to present the recent progress in this domain.

Automated summary

This paper presents a systematic review of the applications of Artificial Intelligence in the field of medical diagnosis, specifically fluid cytology. Based on research articles published in reputable journals and conference proceedings, the study tracks the trend and evolution of research in the last three decades. AI-based systems are emerging as alternative approaches to conventional techniques, becoming integral components of diagnostic systems.