Cargando…
Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies
BACKGROUND: Colposcopy diagnosis and directed biopsy are the key components in cervical cancer screening programs. However, their performance is limited by the requirement for experienced colposcopists. This study aimed to develop and validate a Colposcopic Artificial Intelligence Auxiliary Diagnost...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7754595/ https://www.ncbi.nlm.nih.gov/pubmed/33349257 http://dx.doi.org/10.1186/s12916-020-01860-y |
_version_ | 1783626227542654976 |
---|---|
author | Xue, Peng Tang, Chao Li, Qing Li, Yuexiang Shen, Yu Zhao, Yuqian Chen, Jiawei Wu, Jianrong Li, Longyu Wang, Wei Li, Yucong Cui, Xiaoli Zhang, Shaokai Zhang, Wenhua Zhang, Xun Ma, Kai Zheng, Yefeng Qian, Tianyi Ng, Man Tat Alexander Liu, Zhihua Qiao, Youlin Jiang, Yu Zhao, Fanghui |
author_facet | Xue, Peng Tang, Chao Li, Qing Li, Yuexiang Shen, Yu Zhao, Yuqian Chen, Jiawei Wu, Jianrong Li, Longyu Wang, Wei Li, Yucong Cui, Xiaoli Zhang, Shaokai Zhang, Wenhua Zhang, Xun Ma, Kai Zheng, Yefeng Qian, Tianyi Ng, Man Tat Alexander Liu, Zhihua Qiao, Youlin Jiang, Yu Zhao, Fanghui |
author_sort | Xue, Peng |
collection | PubMed |
description | BACKGROUND: Colposcopy diagnosis and directed biopsy are the key components in cervical cancer screening programs. However, their performance is limited by the requirement for experienced colposcopists. This study aimed to develop and validate a Colposcopic Artificial Intelligence Auxiliary Diagnostic System (CAIADS) for grading colposcopic impressions and guiding biopsies. METHODS: Anonymized digital records of 19,435 patients were obtained from six hospitals across China. These records included colposcopic images, clinical information, and pathological results (gold standard). The data were randomly assigned (7:1:2) to a training and a tuning set for developing CAIADS and to a validation set for evaluating performance. RESULTS: The agreement between CAIADS-graded colposcopic impressions and pathology findings was higher than that of colposcopies interpreted by colposcopists (82.2% versus 65.9%, kappa 0.750 versus 0.516, p < 0.001). For detecting pathological high-grade squamous intraepithelial lesion or worse (HSIL+), CAIADS showed higher sensitivity than the use of colposcopies interpreted by colposcopists at either biopsy threshold (low-grade or worse 90.5%, 95% CI 88.9–91.4% versus 83.5%, 81.5–85.3%; high-grade or worse 71.9%, 69.5–74.2% versus 60.4%, 57.9–62.9%; all p < 0.001), whereas the specificities were similar (low-grade or worse 51.8%, 49.8–53.8% versus 52.0%, 50.0–54.1%; high-grade or worse 93.9%, 92.9–94.9% versus 94.9%, 93.9–95.7%; all p > 0.05). The CAIADS also demonstrated a superior ability in predicting biopsy sites, with a median mean-intersection-over-union (mIoU) of 0.758. CONCLUSIONS: The CAIADS has potential in assisting beginners and for improving the diagnostic quality of colposcopy and biopsy in the detection of cervical precancer/cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-020-01860-y. |
format | Online Article Text |
id | pubmed-7754595 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-77545952020-12-22 Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies Xue, Peng Tang, Chao Li, Qing Li, Yuexiang Shen, Yu Zhao, Yuqian Chen, Jiawei Wu, Jianrong Li, Longyu Wang, Wei Li, Yucong Cui, Xiaoli Zhang, Shaokai Zhang, Wenhua Zhang, Xun Ma, Kai Zheng, Yefeng Qian, Tianyi Ng, Man Tat Alexander Liu, Zhihua Qiao, Youlin Jiang, Yu Zhao, Fanghui BMC Med Research Article BACKGROUND: Colposcopy diagnosis and directed biopsy are the key components in cervical cancer screening programs. However, their performance is limited by the requirement for experienced colposcopists. This study aimed to develop and validate a Colposcopic Artificial Intelligence Auxiliary Diagnostic System (CAIADS) for grading colposcopic impressions and guiding biopsies. METHODS: Anonymized digital records of 19,435 patients were obtained from six hospitals across China. These records included colposcopic images, clinical information, and pathological results (gold standard). The data were randomly assigned (7:1:2) to a training and a tuning set for developing CAIADS and to a validation set for evaluating performance. RESULTS: The agreement between CAIADS-graded colposcopic impressions and pathology findings was higher than that of colposcopies interpreted by colposcopists (82.2% versus 65.9%, kappa 0.750 versus 0.516, p < 0.001). For detecting pathological high-grade squamous intraepithelial lesion or worse (HSIL+), CAIADS showed higher sensitivity than the use of colposcopies interpreted by colposcopists at either biopsy threshold (low-grade or worse 90.5%, 95% CI 88.9–91.4% versus 83.5%, 81.5–85.3%; high-grade or worse 71.9%, 69.5–74.2% versus 60.4%, 57.9–62.9%; all p < 0.001), whereas the specificities were similar (low-grade or worse 51.8%, 49.8–53.8% versus 52.0%, 50.0–54.1%; high-grade or worse 93.9%, 92.9–94.9% versus 94.9%, 93.9–95.7%; all p > 0.05). The CAIADS also demonstrated a superior ability in predicting biopsy sites, with a median mean-intersection-over-union (mIoU) of 0.758. CONCLUSIONS: The CAIADS has potential in assisting beginners and for improving the diagnostic quality of colposcopy and biopsy in the detection of cervical precancer/cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-020-01860-y. BioMed Central 2020-12-22 /pmc/articles/PMC7754595/ /pubmed/33349257 http://dx.doi.org/10.1186/s12916-020-01860-y Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Xue, Peng Tang, Chao Li, Qing Li, Yuexiang Shen, Yu Zhao, Yuqian Chen, Jiawei Wu, Jianrong Li, Longyu Wang, Wei Li, Yucong Cui, Xiaoli Zhang, Shaokai Zhang, Wenhua Zhang, Xun Ma, Kai Zheng, Yefeng Qian, Tianyi Ng, Man Tat Alexander Liu, Zhihua Qiao, Youlin Jiang, Yu Zhao, Fanghui Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies |
title | Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies |
title_full | Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies |
title_fullStr | Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies |
title_full_unstemmed | Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies |
title_short | Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies |
title_sort | development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7754595/ https://www.ncbi.nlm.nih.gov/pubmed/33349257 http://dx.doi.org/10.1186/s12916-020-01860-y |
work_keys_str_mv | AT xuepeng developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT tangchao developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT liqing developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT liyuexiang developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT shenyu developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT zhaoyuqian developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT chenjiawei developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT wujianrong developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT lilongyu developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT wangwei developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT liyucong developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT cuixiaoli developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT zhangshaokai developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT zhangwenhua developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT zhangxun developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT makai developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT zhengyefeng developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT qiantianyi developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT ngmantatalexander developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT liuzhihua developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT qiaoyoulin developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT jiangyu developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies AT zhaofanghui developmentandvalidationofanartificialintelligencesystemforgradingcolposcopicimpressionsandguidingbiopsies |