Advancing cervical cancer diagnosis and screening with spectroscopy and machine learning - Review

Aug 3, 2026

Upload by Logic

The review addresses the rising incidence of cervical cancer and the pressing need for earlier, faster detection before disease progression. Spectroscopy combined with machine learning is presented as a disruptive approach capable of identifying cancer earlier than conventional diagnostic workflows. The article surveys spectroscopy methods applied to cervical cancer analysis, details corresponding machine learning techniques and explored diagnostic applications, and focuses on literature using spectroscopy plus machine learning. It also discusses clinical requirements for new qualitative–quantitative screening tools.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages17

Reading Time43 min

AccessFree to download

Introduction

Abstract

Expert opinion

Keywords

Expert Review of Molecular Diagnostics ISSN: (Print) (Online) Journal homepage: [https://www.tandfonline.com/loi/iero20](https://www.tandfonline.com/loi/iero20) Advancing cervical cancer diagnosis and screening with spectroscopy and machine learning Carlos A. Meza Ramirez, Michael Greenop, Yasser A. Almoshawah, Pierre L. Martin Hirsch & Ihtesham U. Rehman To cite this article: Carlos A. Meza Ramirez, Michael Greenop, Yasser A. Almoshawah, Pierre L. Martin Hirsch & Ihtesham U. Rehman (2023) Advancing cervical cancer diagnosis and screening with spectroscopy and machine learning, Expert Review of Molecular Diagnostics, 23:5, 375-390, DOI: 10. 1080/14737159 .2023.2203816 To link to this article: [https://doi.org/10.1080/14737159.2023.2203816](https://doi.org/10.1080/14737159.2023.2203816) © 2023 The Author(s) . Published by Informa UK Limited, trading as Taylor & Francis Group. | View supplementary material | | --- | | Published online: 19 Apr 2023. | | Submit your article to this journal | | Article views: 178 | | View related articles | | View Crossmark data | Full Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=iero20](https://www.tandfonline.com/action/journalInformation?journalCode=iero20) EXPERT REVIEW OF MOLECULAR DIAGNOSTICS 2023, VOL. 23, NO. 5, 375–390 [https://doi.org/10.1080/14737159.2023.2203816](https://doi.org/10.1080/14737159.2023.2203816) | REVIEW

Advancing cervical cancer diagnosis and screening with spectroscopy and machine learning
Carlos A. Meza Ramireza, Michael Greenopa, Yasser A. Almoshawah a,b, Pierre L. Martin Hirschcand Ihtesham U. Rehman d
aSchool of Engineering, Faculty of Science and Technology, Lancaster University, Lancaster, UK; bMechanical Engineering Department, College of Engineering, Shaqra University, Dawadmi, Saudi Arabia; cGynaecological Oncology, Clinical Research Facility, Lancashire Teaching Hospitals, Preston, UK; dSchool of Medicine, University of Central Lancashire, Preston, UK | | | | --- | --- | --- | |
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. | | ARTICLE HISTORY
Received 17 January 2023 Accepted 13 April 2023
KEYWORDS
Machine learning; cervical cancer; HPV (human papillomavirus); vibrational spectroscopy; diagnosis; screening | | 1. Introduction | impractical for patients with several suspicious lesions [11] | | | In the UK only, the cervical cancer rate is approximately 10 in every | Developing new approaches could provide improvements in | | | 100,000 women [1]]. In 2020, 604,000 new cases and 342,000 | the overall accuracy of cervical cancer diagno

FAQs

Why is earlier detection of cervical cancer important?

The document highlights an increasing incidence of cervical cancer in the UK and the urgent need for early and rapid detection before cancer develops.

What is the role of spectroscopy and machine learning in this review?

Spectroscopy is described as a qualitative method to analyze molecular signals, while machine learning is used as a quantitative approach to support diagnosis and screening.

Which topics does the review cover regarding techniques and applications?

It explores spectroscopy techniques used for cervical cancer analysis and describes various machine learning methods and the applications investigated for diagnosis and screening.

Aug 3, 2026

Upload by Logic