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دسته بندی:
یادگیری عمیق - deep learning
سال انتشار:
2019
عنوان انگلیسی مقاله:
A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis
ترجمه فارسی عنوان مقاله:
مقایسه عملکرد یادگیری عمیق در برابر متخصصان مراقبت های بهداشتی در تشخیص بیماری ها از تصویربرداری پزشکی: یک مرور منظم و متاآنالیز
منبع:
Sciencedirect - Elsevier - The Lancet Digital Health, 1 (2019) e271-e297: doi:10:1016/S2589-7500(19)30123-2
نویسنده:
Xiaoxuan Liu*, Livia Faes*, Aditya U Kale, Siegfried K Wagner, Dun Jack Fu, Alice Bruynseels, Thushika Mahendiran, Gabriella Moraes, Mohith Shamdas, Christoph Kern, Joseph R Ledsam, Martin K Schmid, Konstantinos Balaskas, Eric J Topol, Lucas M Bachmann, Pearse A Keane, Alastair K Denniston
چکیده انگلیسی:
Background Deep learning offers considerable promise for medical diagnostics. We aimed to evaluate the diagnostic
accuracy of deep learning algorithms versus health-care professionals in classifying diseases using medical
imaging.
Methods In this systematic review and meta-analysis, we searched Ovid-MEDLINE, Embase, Science Citation Index,
and Conference Proceedings Citation Index for studies published from Jan 1, 2012, to June 6, 2019. Studies comparing
the diagnostic performance of deep learning models and health-care professionals based on medical imaging, for any
disease, were included. We excluded studies that used medical waveform data graphics material or investigated the
accuracy of image segmentation rather than disease classification. We extracted binary diagnostic accuracy data and
constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. Studies undertaking an
out-of-sample external validation were included in a meta-analysis, using a unified hierarchical model. This study is
registered with PROSPERO, CRD42018091176.
Findings Our search identified 31 587 studies, of which 82 (describing 147 patient cohorts) were included. 69 studies
provided enough data to construct contingency tables, enabling calculation of test accuracy, with sensitivity ranging
from 9·7% to 100·0% (mean 79·1%, SD 0·2) and specificity ranging from 38·9% to 100·0% (mean 88·3%, SD 0·1).
An out-of-sample external validation was done in 25 studies, of which 14 made the comparison between deep learning
models and health-care professionals in the same sample. Comparison of the performance between health-care
professionals in these 14 studies, when restricting the analysis to the contingency table for each study reporting the
highest accuracy, found a pooled sensitivity of 87·0% (95% CI 83·0–90·2) for deep learning models and 86·4%
(79·9–91·0) for health-care professionals, and a pooled specificity of 92·5% (95% CI 85·1–96·4) for deep learning
models and 90·5% (80·6–95·7) for health-care professionals.
Interpretation Our review found the diagnostic performance of deep learning models to be equivalent to that of
health-care professionals. However, a major finding of the review is that few studies presented externally validated
results or compared the performance of deep learning models and health-care professionals using the same
sample. Additionally, poor reporting is prevalent in deep learning studies, which limits reliable interpretation of
the reported diagnostic accuracy. New reporting standards that address specific challenges of deep learning could
improve future studies, enabling greater confidence in the results of future evaluations of this promising
technology.
قیمت: رایگان
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