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Unassisted Clinicians Versus Deep Learning–Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis

التفاصيل البيبلوغرافية
العنوان: Unassisted Clinicians Versus Deep Learning–Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis
المؤلفون: Peng Xue, Mingyu Si, Dongxu Qin, Bingrui Wei, Samuel Seery, Zichen Ye, Mingyang Chen, Sumeng Wang, Cheng Song, Bo Zhang, Ming Ding, Wenling Zhang, Anying Bai, Huijiao Yan, Le Dang, Yuqian Zhao, Remila Rezhake, Shaokai Zhang, Youlin Qiao, Yimin Qu, Yu Jiang
المصدر: Journal of Medical Internet Research, Vol 25, p e43832 (2023)
بيانات النشر: JMIR Publications, 2023.
سنة النشر: 2023
المجموعة: LCC:Computer applications to medicine. Medical informatics
LCC:Public aspects of medicine
مصطلحات موضوعية: Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
الوصف: BackgroundA number of publications have demonstrated that deep learning (DL) algorithms matched or outperformed clinicians in image-based cancer diagnostics, but these algorithms are frequently considered as opponents rather than partners. Despite the clinicians-in-the-loop DL approach having great potential, no study has systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. ObjectiveWe systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. MethodsPubMed, Embase, IEEEXplore, and the Cochrane Library were searched for studies published between January 1, 2012, and December 7, 2021. Any type of study design was permitted that focused on comparing unassisted clinicians and DL-assisted clinicians in cancer identification using medical imaging. Studies using medical waveform-data graphics material and those investigating image segmentation rather than classification were excluded. Studies providing binary diagnostic accuracy data and contingency tables were included for further meta-analysis. Two subgroups were defined and analyzed, including cancer type and imaging modality. ResultsIn total, 9796 studies were identified, of which 48 were deemed eligible for systematic review. Twenty-five of these studies made comparisons between unassisted clinicians and DL-assisted clinicians and provided sufficient data for statistical synthesis. We found a pooled sensitivity of 83% (95% CI 80%-86%) for unassisted clinicians and 88% (95% CI 86%-90%) for DL-assisted clinicians. Pooled specificity was 86% (95% CI 83%-88%) for unassisted clinicians and 88% (95% CI 85%-90%) for DL-assisted clinicians. The pooled sensitivity and specificity values for DL-assisted clinicians were higher than for unassisted clinicians, at ratios of 1.07 (95% CI 1.05-1.09) and 1.03 (95% CI 1.02-1.05), respectively. Similar diagnostic performance by DL-assisted clinicians was also observed across the predefined subgroups. ConclusionsThe diagnostic performance of DL-assisted clinicians appears better than unassisted clinicians in image-based cancer identification. However, caution should be exercised, because the evidence provided in the reviewed studies does not cover all the minutiae involved in real-world clinical practice. Combining qualitative insights from clinical practice with data-science approaches may improve DL-assisted practice, although further research is required. Trial RegistrationPROSPERO CRD42021281372; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=281372
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1438-8871
Relation: https://www.jmir.org/2023/1/e43832; https://doaj.org/toc/1438-8871
DOI: 10.2196/43832
URL الوصول: https://doaj.org/article/c76d3b8a773c4d98a69b23af94c13499
رقم الأكسشن: edsdoj.76d3b8a773c4d98a69b23af94c13499
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:14388871
DOI:10.2196/43832