Dataset Information

Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies.

ABSTRACT:

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??0.05). 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.

SUBMITTER: Xue P

PROVIDER: S-EPMC7754595 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies.

Xue Peng P Tang Chao C Li Qing Q Li Yuexiang Y Shen Yu Y Zhao Yuqian Y Chen Jiawei J Wu Jianrong J Li Longyu L Wang Wei W Li Yucong Y Cui Xiaoli X Zhang Shaokai S Zhang Wenhua W Zhang Xun X Ma Kai K Zheng Yefeng Y Qian Tianyi T Ng Man Tat Alexander MTA Liu Zhihua Z Qiao Youlin Y Jiang Yu Y Zhao Fanghui F

BMC medicine 20201222 1

<h4>Background</h4>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.<h4>Methods</h4>Anonymized digital records of 19,435 patients were obtained from six hospitals across China. These re ...[more]

PMID: 33349257

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Dataset Information

Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies.

Background

Methods

Results

Conclusions

Publications

Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies.

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