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Multicontrast Pocket Colposcopy Cervical Cancer Diagnostic Algorithm for Referral Populations
Objective and Impact Statement. We use deep learning models to classify cervix images—collected with a low-cost, portable Pocket colposcope—with biopsy-confirmed high-grade precancer and cancer. We boost classification performance on a screened-positive population by using a class-balanced loss and...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AAAS
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521679/ https://www.ncbi.nlm.nih.gov/pubmed/37850189 http://dx.doi.org/10.34133/2022/9823184 |
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author | Skerrett, Erica Miao, Zichen Asiedu, Mercy N. Richards, Megan Crouch, Brian Sapiro, Guillermo Qiu, Qiang Ramanujam, Nirmala |
author_facet | Skerrett, Erica Miao, Zichen Asiedu, Mercy N. Richards, Megan Crouch, Brian Sapiro, Guillermo Qiu, Qiang Ramanujam, Nirmala |
author_sort | Skerrett, Erica |
collection | PubMed |
description | Objective and Impact Statement. We use deep learning models to classify cervix images—collected with a low-cost, portable Pocket colposcope—with biopsy-confirmed high-grade precancer and cancer. We boost classification performance on a screened-positive population by using a class-balanced loss and incorporating green-light colposcopy image pairs, which come at no additional cost to the provider. Introduction. Because the majority of the 300,000 annual deaths due to cervical cancer occur in countries with low- or middle-Human Development Indices, an automated classification algorithm could overcome limitations caused by the low prevalence of trained professionals and diagnostic variability in provider visual interpretations. Methods. Our dataset consists of cervical images ([Formula: see text]) from 880 patient visits. After optimizing the network architecture and incorporating a weighted loss function, we explore two methods of incorporating green light image pairs into the network to boost the classification performance and sensitivity of our model on a test set. Results. We achieve an area under the receiver-operator characteristic curve, sensitivity, and specificity of 0.87, 75%, and 88%, respectively. The addition of the class-balanced loss and green light cervical contrast to a Resnet-18 backbone results in a 2.5 times improvement in sensitivity. Conclusion. Our methodology, which has already been tested on a prescreened population, can boost classification performance and, in the future, be coupled with Pap smear or HPV triaging, thereby broadening access to early detection of precursor lesions before they advance to cancer. |
format | Online Article Text |
id | pubmed-10521679 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | AAAS |
record_format | MEDLINE/PubMed |
spelling | pubmed-105216792023-10-17 Multicontrast Pocket Colposcopy Cervical Cancer Diagnostic Algorithm for Referral Populations Skerrett, Erica Miao, Zichen Asiedu, Mercy N. Richards, Megan Crouch, Brian Sapiro, Guillermo Qiu, Qiang Ramanujam, Nirmala BME Front Research Article Objective and Impact Statement. We use deep learning models to classify cervix images—collected with a low-cost, portable Pocket colposcope—with biopsy-confirmed high-grade precancer and cancer. We boost classification performance on a screened-positive population by using a class-balanced loss and incorporating green-light colposcopy image pairs, which come at no additional cost to the provider. Introduction. Because the majority of the 300,000 annual deaths due to cervical cancer occur in countries with low- or middle-Human Development Indices, an automated classification algorithm could overcome limitations caused by the low prevalence of trained professionals and diagnostic variability in provider visual interpretations. Methods. Our dataset consists of cervical images ([Formula: see text]) from 880 patient visits. After optimizing the network architecture and incorporating a weighted loss function, we explore two methods of incorporating green light image pairs into the network to boost the classification performance and sensitivity of our model on a test set. Results. We achieve an area under the receiver-operator characteristic curve, sensitivity, and specificity of 0.87, 75%, and 88%, respectively. The addition of the class-balanced loss and green light cervical contrast to a Resnet-18 backbone results in a 2.5 times improvement in sensitivity. Conclusion. Our methodology, which has already been tested on a prescreened population, can boost classification performance and, in the future, be coupled with Pap smear or HPV triaging, thereby broadening access to early detection of precursor lesions before they advance to cancer. AAAS 2022-08-25 /pmc/articles/PMC10521679/ /pubmed/37850189 http://dx.doi.org/10.34133/2022/9823184 Text en Copyright © 2022 Erica Skerrett et al. https://creativecommons.org/licenses/by/4.0/Exclusive Licensee Suzhou Institute of Biomedical Engineering and Technology, CAS. Distributed under a Creative Commons Attribution License (CC BY 4.0). (https://creativecommons.org/licenses/by/4.0/) |
spellingShingle | Research Article Skerrett, Erica Miao, Zichen Asiedu, Mercy N. Richards, Megan Crouch, Brian Sapiro, Guillermo Qiu, Qiang Ramanujam, Nirmala Multicontrast Pocket Colposcopy Cervical Cancer Diagnostic Algorithm for Referral Populations |
title | Multicontrast Pocket Colposcopy Cervical Cancer Diagnostic Algorithm for Referral Populations |
title_full | Multicontrast Pocket Colposcopy Cervical Cancer Diagnostic Algorithm for Referral Populations |
title_fullStr | Multicontrast Pocket Colposcopy Cervical Cancer Diagnostic Algorithm for Referral Populations |
title_full_unstemmed | Multicontrast Pocket Colposcopy Cervical Cancer Diagnostic Algorithm for Referral Populations |
title_short | Multicontrast Pocket Colposcopy Cervical Cancer Diagnostic Algorithm for Referral Populations |
title_sort | multicontrast pocket colposcopy cervical cancer diagnostic algorithm for referral populations |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521679/ https://www.ncbi.nlm.nih.gov/pubmed/37850189 http://dx.doi.org/10.34133/2022/9823184 |
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