NEW TECHNOLOGIES FOR CERVICAL CANCER SCREENING
Abstract
Cervical cancer is still a health worry around the world, especially in poorer countries. Traditional methods for detecting this disease are no longer as effective as they need to be. Many people cannot get to them; they are too expensive. Participation in cervical cancer screening remains low in many populations. Recent advances offer promising strategies to improve the accessibility and effectiveness of screening programs. Emerging technologies may enhance early detection and increase patient engagement, ultimately reducing cervical cancer incidence and mortality. Please, take a look at what is happening in a few key areas: studying the tiny components that make up our bodies, diagnosing problems at a molecular level using light to detect diseases, teaching computers to assist doctors, and analyzing the free-floating DNA in our blood. This report looks at all these emerging tools and technologies.
Researchers use metabolomics to study physiological processes by identifying small-molecule biomarkers, such as TMAO, which is a potential indicator of disease. Advances in spectroscopy, combined with machine learning, now enable non-invasive diagnostics. Recent innovations include rapid HPV tests and self-sampling kits. Doctors can analyze DNA fragments in blood as a non-surgical alternative to biopsies. With AI computers can interpret medical images to aid diagnosis and predict outcomes.
New technology has the potential to improve cervical cancer screening worldwide significantly. The ultimate goal is to catch early disease, reduce mortality, and work toward the elimination of cervical cancer. This marks a significant advance, and with effective strategies, the global health community can make substantial progress.
References
de Sanjosé S, Quint WG, Alemany L, Geraets DT, Klaustermeier JE, Lloveras B, Tous S, Felix A, Bravo LE, Shin HR, Vallejos CS, de Ruiz PA, Lima MA, Guimerá N, Clavero O, Alejo M, Llombart-Bosch A, Cheng-Yang C, Tatti SA, Bosch FX. Human papillomavirus genotype attribution in invasive cervical cancer: a retrospective cross-sectional worldwide study. Lancet Oncol. 2010;11(11):1048–56.
Zhou X, Li Y, Kuang H, Luo C, Li F, Chen Z, Yu J, Yang Y, Chen J, Yang Y, Yang Y, Zhu C, Zeng J, Wang J. Metabolomics in cancer research: a review. Front Oncol. 2019;9:1133.
Cao L, Cheng H, Jiang Q, Li H, Wu Z. Detection of circulating tumor DNA in cervical cancer: a promising biomarker for diagnosis and monitoring. Clin Chim Acta. 2020;510:370–7.
Hu L, Bell D, Antani S, Xue Z, Yu K, Horning MP, Gachuhi N, Wilson B, Jaiswal MS, Befano B, Long LR, Schiffman M, Zuna RE. An observational study of deep learning and automated evaluation of cervical images for cancer screening. J Natl Cancer Inst. 2019;111(9):923–32.
Matsui H, Einama T, Shichi S, Kanazawa R, Shibuya K, Suzuki T, Matsuzawa F, Hashimoto T, Homma S, Yamamoto J. L-carnitine supplementation reduces the general fatigue of cancer patients during chemotherapy. Mol Clin Oncol. 2018;8(3):413–6.
Baker MJ, Byrne HJ, Chalmers J. Clinical applications of infrared and Raman spectroscopy: state of play and future challenges. Analyst. 2018;143(8):1735–57. doi:10.1039/c7an01871a.
Kanter EM, Majumder S, Vargis E. Multiclass discrimination of cervical precancers using Raman spectroscopy. J Raman Spectrosc. 2009;40(2):205–11. doi:10.1002/jrs.2108.
Barik AK, M SP, N M. A micro-Raman spectroscopy study of inflammatory condition of human cervix: probing of tissues and blood plasma samples. Photodiagn Photodyn Ther. 2022;39:10294872. doi:10.1016/j.pdpdt.2022.10294872.
Hsiang E, Little KM, Haguma P. Higher cost of implementing Xpert MTB/RIF in Ugandan peripheral settings: implications for cost-effectiveness. Int J Tuberc Lung Dis. 2016;20(9):1212–8.
Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74(1):12–49.
Recio F, Scalise CB, Loar P, Lumish M, Berman T, Peddada A. Postsurgical ctDNA-based molecular residual disease detection in patients with stage I uterine malignancies. Gynecol Oncol. 2024;182:63–9.
Xie H, Zhao X, Zheng X, Zhang J, Zhao R, Wang Y. Artificial intelligence enables precision diagnosis of cervical cytology grades and cervical cancer. Nat Commun. 2024;15:48705.
Egemen D, Perkins RB, Cheung LC. Artificial intelligence-based image analysis in clinical testing: lessons from cervical cancer screening. J Natl Cancer Inst. 2024;116(1):26–33. doi:10.1093/jnci/djad202.
Jusman Y, Ng SC, Abu Osman NA. Intelligent screening systems for cervical cancer. Sci World J. 2014;2014:810368. doi:10.1155/2014/810368.
Alyafeai Z, Ghouti L. A fully automated deep learning pipeline for cervical cancer classification. Expert Syst Appl. 2020;141:112951.
Mendez MJ, Xue P, Qiao Y. Cervical cancer elimination in the era of COVID-19: the potential role of Artificial Intelligence (AI)-guided digital colposcope cloud platform. Eur J Gynaecol Oncol. 2022;43(1):160. doi:10.31083/j.ejgo4301019.
Grant BD, Quang T, Possati-Resende JC. A mobile phone based high-resolution microendoscope to image cervical precancer. PLoS One. 2019;14(2):e0211045.
Alajaji SA, Sabzian R, Wang Y, Sultan AS, Wang R. A scoping review of infrared spectroscopy and machine learning methods for head and neck precancer and cancer diagnosis and prognosis. Cancers. 2025;17(5):796. doi:10.3390/cancers17050796.
Views:
34
Downloads:
7
Copyright (c) 2026 Katsiaryna Miraniuk, Valeryia Milasheuskaya, Dmytro Kowalczuk, Darya Lazitskaya, Natalia Surosz, Mykola Sobchynskyi, Kamil Turlej, Andrzej Myrny, Iga Kiełbaszewska, Wiktoria Kasianik, Dawid Wiczkowski
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles are published in open-access and licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Hence, authors retain copyright to the content of the articles.
CC BY 4.0 License allows content to be copied, adapted, displayed, distributed, re-published or otherwise re-used for any purpose including for adaptation and commercial use provided the content is attributed.
