A systematic meta-analysis on the role of artificial intelligence and machine learning in detection of gynaecological disorders

Abstract

Globally, one of the major concerns in women’s health issues is gynecological disorders such as cancer, which needs to be observed at its early stage. With traditional approaches, it is quite difficult to detect such disorders at its early stages. Therefore, more advanced tools need to be integrated. This paper focuses the advancements of artificial intelligence (AI) and machine learning (ML), exploring their potential in the early detection and diagnosis of these disorders. This paper presents a systematic meta-analysis of AI/ML approaches employed in the diagnosis of gynecological disorders using medical imaging modalities such as magnetic resonance imaging (MRI), ultrasound, etc. The flow for systematic meta-analysis is based on designing the research objective, selection andsearching approach with inclusion and exclusion strategy; quality assessment is performed then; and finally, discussion of interpretations is also presented. This paper investigates how ML algorithms can extract characteristics from MRI images and how to use ML to extract and recognize the features from medical images such as MRI, ultrasound, computed tomography (CT) scans, etc. for early detection of gynecological tumors and provision of more personalized risk assessment. However, it is observed that there is a significant impact of advancement of AI/ML on medical technology in the future. Therefore, this paper presents a significant contribution for future medical applications and innovations.

Keywords

Artificial Intelligence / Gynecological Cancer / Machine Learning / MRI / Ultrasound

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