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首页> 外文期刊>Journal of the European Academy of Dermatology and Venereology: JEADV >Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas
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Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas

机译：深度学习卷积神经网络在纳维西和黑素瘤结合中的深度学习卷积神经网络的诊断性能

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Abstract Background Deep learning convolutional neural networks (CNN) may assist physicians in the diagnosis of melanoma. The capacity of a CNN to differentiate melanomas from combined naevi, the latter representing well‐known melanoma simulators, has not been investigated. Objective To assess the diagnostic performance of a CNN when used to differentiate melanomas from combined naevi in comparison with dermatologists. Methods In this study, a CNN with regulatory approval for the European market (Moleanalyzer‐Pro, FotoFinder Systems GmbH, Bad Birnbach, Germany) was used. We attained a dichotomous classification (benign, malignant) in dermoscopic images of 36 combined naevi and 36 melanomas with a mean Breslow thickness of 1.3?mm. Primary outcome measures were the CNN's sensitivity, specificity and the diagnostic odds ratio (DOR) in comparison with 11 dermatologists with different levels of experience. Results The CNN revealed a sensitivity, specificity and DOR of 97.1% (95% CI [82.7–99.6]), 78.8% (95% CI [62.8–89.1.3]) and 34 (95% CI [4.8–239]), respectively. Dermatologists showed a lower mean sensitivity, specificity and DOR of 90.6% (95% CI [84.1–94.7]; P ?=?0.092), 71.0% (95% CI [62.6–78.1]; P ?=?0.256) and 24 (95% CI [11.6–48.4]; P ?=?0.1114). Under the assumption that dermatologists use the CNN to verify their (initial) melanoma diagnosis, dermatologists achieve an increased specificity of 90.3% (95% CI [79.8–95.6]) at an almost unchanged sensitivity. The largest benefit was observed in ‘beginners’, who performed worst without CNN verification (DOR?=?12) but best with CNN verification (DOR?=?98). Conclusion The tested CNN more accurately classified combined naevi and melanomas in comparison with trained dermatologists. Their diagnostic performance could be improved if the CNN was used to confirm/overrule an initial melanoma diagnosis. Application of a CNN may therefore be of benefit to clinicians.
机译：抽象背景深度学习卷积神经网络（CNN）可以帮助医生在诊断黑色素瘤中。尚未调查CNN从组合Naevi分化黑色素的能力，后者代表着名的黑色素瘤模拟器。目的探讨用于将黑色素组合与皮肤科医生相比分化的CNN诊断性能。本研究的方法，使用了欧洲市场监管批准的CNN（Moleanalyzer-Pro，Fotofinder Systems GmbH，Bad Birnbach，Germany）。我们在36组合的Naevi和36个黑色素瘤中的皮肤镜像中的二分体分类（良性恶性），平均厚度为1.3Ωmm。与11名具有不同经验水平的皮肤科医生相比，主要结果措施是CNN的敏感性，特异性和诊断量比（DOR）。结果CNN显示敏感性，特异性和DOR为97.1％（95％CI [82.7-99.6]），78.8％（95％CI [62.8-89.1.3]）和34（95％CI [4.8-239]） ， 分别。皮肤科医生表现出较低的平均敏感性，特异性和DOR为90.6％（95％CI [84.1-94.7]; p？= 0.092），71.0％（95％CI [62.6-78.1]; p？= 0.256）和24 （95％CI [11.6-48.4]; p？= 0.1114）。在假设皮肤科医生使用CNN验证其（初始）黑色素瘤诊断时，皮肤病学家的特异性达到90.3％（95％CI [79.8-95.6]），几乎不变的敏感性。在“初学者”中观察到最大的好处，他们在没有CNN验证的情况下表现最差（DOR？=？12），但最佳CNN验证（DOR？=？98）。结论与培训的皮肤科医生相比，测试CNN更准确地分类Naevi和黑素瘤。如果使用CNN用于确认/抑制初始黑素瘤诊断，则可以改善它们的诊断性能。因此，CNN的应用可能对临床医生有益。

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