Medicine and Health

Deep learning in image-based breast and cervical cancer detection: a systematic review and meta-analysis

P. Xue, J. Wang, et al.

This groundbreaking meta-analysis investigates the efficacy of deep learning algorithms in the early detection of breast and cervical cancers. The research, conducted by a team of experts including Peng Xue and Jiaxu Wang, reveals a pooled sensitivity of 88% and specificity of 84%. However, the study highlights the need for standardized guidelines to ensure the reliability of these algorithms.

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Abstract

This meta-analysis assesses the diagnostic performance of deep learning (DL) algorithms for early breast and cervical cancer detection. Thirty-five studies were reviewed, with 20 meta-analyzed, showing pooled sensitivity of 88%, specificity of 84%, and AUC of 0.92. DL algorithms showed comparable performance across subgroups (cancer type, validation type, imaging modality). However, limitations in study design and reporting suggest potential bias and overestimation of algorithm performance. Standardized guidelines for study methods and reporting are needed to improve DL research quality.

Publisher

npj Digital Medicine

Published On

Feb 15, 2022

Authors

Peng Xue, Jiaxu Wang, Dongxu Qin, Huijiao Yan, Yimin Qu, Samuel Seery, Yu Jiang, Youlin Qiao

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