Advancing cervical cancer diagnosis and screening with spectroscopy and machine learning

Meza Ramirez, Carlos A and Greenop, Michael and Almoshawah, Yasser A and Martin Hirsch, Pierre L and Rehman, Ihtesham U (2023) Advancing cervical cancer diagnosis and screening with spectroscopy and machine learning. Expert Review of Molecular Diagnostics, 23 (5). pp. 375-390. ISSN 1473-7159

Abstract

INTRODUCTION: In the UK alone, the incidence of cervical cancer is increasing, hence an urgent need for early and rapid detection of cancer before it develops. Spectroscopy in conjunction with machine learning offers a disruptive technology that promises to pick up cancer early as compared to the current diagnostic techniques used. AREAS COVERED: This review article explores the different spectroscopy techniques that have been used for the analysis of cervical cancer. Along with the extensive description of spectroscopic techniques, the various machine learning techniques are also described as well as the applications that have been explored in the diagnosis of cervical cancer. This review delimits the literature specifically associated with cervical cancer studies performed solely with the use of a spectroscopy technique, and machine learning. EXPERT OPINION: Although there are several methods and techniques to detect cervical cancer, the clinical sector requires to introduce new diagnostic technologies that help improve the quality of life of patients. These innovative technologies involve spectroscopy as a qualitative method and machine learning as a quantitative method. In this article, both the techniques and methodologies that allow and promise to be a new screening tool for the detection of cervical cancer are covered.