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Advancing cervical cancer diagnosis and screening with spectroscopy and machine learning

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Advancing cervical cancer diagnosis and screening with spectroscopy and machine learning. / Meza Ramirez, Carlos A; Greenop, Michael; Almoshawah, Yasser A et al.
In: Expert Review of Molecular Diagnostics, Vol. 23, No. 5, 04.05.2023, p. 375-390.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Meza Ramirez, CA, Greenop, M, Almoshawah, YA, Martin Hirsch, PL & Rehman, IU 2023, 'Advancing cervical cancer diagnosis and screening with spectroscopy and machine learning', Expert Review of Molecular Diagnostics, vol. 23, no. 5, pp. 375-390. https://doi.org/10.1080/14737159.2023.2203816

APA

Meza Ramirez, C. A., Greenop, M., Almoshawah, Y. A., Martin Hirsch, P. L., & Rehman, I. U. (2023). Advancing cervical cancer diagnosis and screening with spectroscopy and machine learning. Expert Review of Molecular Diagnostics, 23(5), 375-390. https://doi.org/10.1080/14737159.2023.2203816

Vancouver

Meza Ramirez CA, Greenop M, Almoshawah YA, Martin Hirsch PL, Rehman IU. Advancing cervical cancer diagnosis and screening with spectroscopy and machine learning. Expert Review of Molecular Diagnostics. 2023 May 4;23(5):375-390. Epub 2023 Apr 19. doi: 10.1080/14737159.2023.2203816

Author

Meza Ramirez, Carlos A ; Greenop, Michael ; Almoshawah, Yasser A et al. / Advancing cervical cancer diagnosis and screening with spectroscopy and machine learning. In: Expert Review of Molecular Diagnostics. 2023 ; Vol. 23, No. 5. pp. 375-390.

Bibtex

@article{6d3c008861504854bcbacb7166139198,
title = "Advancing cervical cancer diagnosis and screening with spectroscopy and machine learning",
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.",
keywords = "vibrational spectroscopy, HPV (human papillomavirus), Machine learning, diagnosis, cervical cancer, screening",
author = "{Meza Ramirez}, {Carlos A} and Michael Greenop and Almoshawah, {Yasser A} and {Martin Hirsch}, {Pierre L} and Rehman, {Ihtesham U}",
year = "2023",
month = may,
day = "4",
doi = "10.1080/14737159.2023.2203816",
language = "English",
volume = "23",
pages = "375--390",
journal = "Expert Review of Molecular Diagnostics",
issn = "1473-7159",
publisher = "Expert Reviews Ltd.",
number = "5",

}

RIS

TY - JOUR

T1 - Advancing cervical cancer diagnosis and screening with spectroscopy and machine learning

AU - Meza Ramirez, Carlos A

AU - Greenop, Michael

AU - Almoshawah, Yasser A

AU - Martin Hirsch, Pierre L

AU - Rehman, Ihtesham U

PY - 2023/5/4

Y1 - 2023/5/4

N2 - 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.

AB - 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.

KW - vibrational spectroscopy

KW - HPV (human papillomavirus)

KW - Machine learning

KW - diagnosis

KW - cervical cancer

KW - screening

U2 - 10.1080/14737159.2023.2203816

DO - 10.1080/14737159.2023.2203816

M3 - Journal article

C2 - 37060617

VL - 23

SP - 375

EP - 390

JO - Expert Review of Molecular Diagnostics

JF - Expert Review of Molecular Diagnostics

SN - 1473-7159

IS - 5

ER -