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

Xue, Peng and Wang, Jiaxu and Qin, Dongxu and Yan, Huijiao and Qu, Yimin and Seery, Samuel and Jiang, Yu and Qiao, Youlin (2022) Deep learning in image-based breast and cervical cancer detection:a systematic review and meta-analysis. npj Digital Medicine, 5 (1). ISSN 2398-6352

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Abstract

Accurate early detection of breast and cervical cancer is vital for treatment success. Here, we conduct a meta-analysis to assess the diagnostic performance of deep learning (DL) algorithms for early breast and cervical cancer identification. Four subgroups are also investigated: cancer type (breast or cervical), validation type (internal or external), imaging modalities (mammography, ultrasound, cytology, or colposcopy), and DL algorithms versus clinicians. Thirty-five studies are deemed eligible for systematic review, 20 of which are meta-analyzed, with a pooled sensitivity of 88% (95% CI 85–90%), specificity of 84% (79–87%), and AUC of 0.92 (0.90–0.94). Acceptable diagnostic performance with analogous DL algorithms was highlighted across all subgroups. Therefore, DL algorithms could be useful for detecting breast and cervical cancer using medical imaging, having equivalent performance to human clinicians. However, this tentative assertion is based on studies with relatively poor designs and reporting, which likely caused bias and overestimated algorithm performance. Evidence-based, standardized guidelines around study methods and reporting are required to improve the quality of DL research.

Item Type:
Journal Article
Journal or Publication Title:
npj Digital Medicine
Subjects:
ID Code:
166973
Deposited By:
Deposited On:
08 Mar 2022 16:10
Refereed?:
Yes
Published?:
Published
Last Modified:
04 May 2022 02:47