Automated Detection of Benign and Malignant Skin Lesions from Reflectance Confocal Microscopy Images Using Deep Learning

Jesutofunmi A. Omiye , Babar K. Rao , Shazli Razi , Nadiya Chuchvara , Fred M. Baik , Roxana Daneshjou , Lisa C. Zaba
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Abstract

Reflectance confocal microscopy offers a noninvasive approach for diagnosing skin lesions at the point of care, but it remains underutilized owing to the specialized skill required for interpretation. Artificial intelligence provides an opportunity to automate this process. We developed deep learning models to automate the analysis of reflectance confocal microscopy block images. Reflectance confocal microscopy images acquired from 3rd and 4th generation VivaScope 1500 devices were preprocessed and split for training and testing. Two models were developed: a modified convolutional neural network ResNet-18, for skin layer detection, and a ResNet-34 integrated with a gated recurrent unit for lesion classification. The models were pretrained on 3rd generation images and fine tuned on 4th generation data, utilizing 5-fold cross-validation. Our cohort included 845 patients, 1147 lesions, and 4391 VivaBlock images. The layer detection model identified the dermis, epidermis, and dermoepidermal junction, achieving an area under the curve of 0.70, 0.71, and 0.57, respectively. The lesion classification model distinguished malignant from benign lesions with an area under the curve of 0.80 and specificity of 0.91. Our convolutional neural network gated recurrent unit approach effectively distinguished benign from malignant lesions, showing impressive diagnostic accuracy mimicking expert dermatological assessments. This highlights artificial intelligence's potential in improving reflectance confocal microscopy image interpretation, reducing unnecessary biopsies, and paves the way for future research.
利用深度学习从反射共聚焦显微镜图像中自动检测良性和恶性皮肤病变
反射共聚焦显微镜为诊断皮肤病变提供了一种非侵入性的方法,但由于解释所需的专业技能,它仍然未得到充分利用。人工智能为自动化这一过程提供了机会。我们开发了深度学习模型来自动分析反射共聚焦显微镜块图像。从第三代和第四代VivaScope 1500设备获得的反射共聚焦显微镜图像进行预处理和分割,用于训练和测试。开发了两种模型:用于皮肤层检测的改进卷积神经网络ResNet-18,以及与门控复发单元集成的用于病变分类的ResNet-34。模型在第三代图像上进行预训练,并在第四代数据上进行微调,利用5倍交叉验证。我们的队列包括845名患者、1147个病变和4391张VivaBlock图像。层检测模型对真皮层、表皮、皮表皮连接处进行了识别,曲线下面积分别为0.70、0.71、0.57。病变分类模型区分良恶性病变的曲线下面积为0.80,特异性为0.91。我们的卷积神经网络门控复发单元方法有效地区分了良性和恶性病变,显示出令人印象深刻的诊断准确性,模拟了皮肤科专家的评估。这凸显了人工智能在改善反射共聚焦显微镜图像解释、减少不必要的活检方面的潜力,并为未来的研究铺平了道路。
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