Diagnostic Performance of Deep Learning Algorithms Applied to Three Common Diagnoses in Dermatopathology.
Diagnostic Performance of Deep Learning Algorithms Applied to Three Common Diagnoses in Dermatopathology.
复制标题
DOI：
10.4103/jpi.jpi_31_18
复制
发表时间：
2018-01-01
影响因子：
--
通讯作者：
Soans, Rajath E
中科院分区：
文献类型：
--
作者：
Olsen, Thomas George;Jackson, B Hunter;Soans, Rajath E
背景技术背景：人工智能正在加速进入临床应用，通过计算机辅助诊断提供了提高效率，提高准确性和节省成本的机会。Dermatopathology强调模式识别，为测试深度学习算法提供了一个独特的机会。AIMS：本研究旨在确定深度学习算法诊断三种常见皮肤病理学诊断的准确性。METHODS：对先前诊断的结节性基底细胞癌（BCC）、皮肤痣和脂溢性角化病的全切片图像（WSI）进行注释，以确定不同形态的区域。每个训练集中包括未注释的WSI，包括常见肿瘤和炎症诊断的五个干扰诊断。开发了一种专有的全卷积神经网络来训练算法，以将测试图像分类为相对于地面真值诊断的阳性或阴性。（99.45%）基底细胞癌（结节性），113/114皮肤痣123例（99.4%），脂溢性角化病123例（100%）。使用深度学习算法的人工智能是诊断的潜在辅助手段，可能会提高皮肤病理学家和实验室的工作流程效率。
BACKGROUND: Artificial intelligence is advancing at an accelerated pace into clinical applications, providing opportunities for increased efficiency, improved accuracy, and cost savings through computer-aided diagnostics. Dermatopathology, with emphasis on pattern recognition, offers a unique opportunity for testing deep learning algorithms.AIMS: This study aims to determine the accuracy of deep learning algorithms to diagnose three common dermatopathology diagnoses.METHODS: Whole slide images (WSI) of previously diagnosed nodular basal cell carcinomas (BCCs), dermal nevi, and seborrheic keratoses were annotated for areas of distinct morphology. Unannotated WSIs, consisting of five distractor diagnoses of common neoplastic and inflammatory diagnoses, were included in each training set. A proprietary fully convolutional neural network was developed to train algorithms to classify test images as positive or negative relative to ground truth diagnosis.RESULTS: Artificial intelligence system accurately classified 123/124 (99.45%) BCCs (nodular), 113/114 (99.4%) dermal nevi, and 123/123 (100%) seborrheic keratoses.CONCLUSIONS: Artificial intelligence using deep learning algorithms is a potential adjunct to diagnosis and may result in improved workflow efficiencies for dermatopathologists and laboratories.