卷积神经网络人工智能在皮肤病诊断中的应用进展

Advancements in the application of convolutional neural network artificial intelligence for skin diseases diagnosis

皮肤病种类繁多,临床诊断和鉴别诊断困难,辅助诊疗手段有限。而近年来兴起的基于深度学习的卷积神经网络人工智能在皮肤病的诊断与鉴别诊断中表现出了与皮肤科医生相似的水平,特别是在皮肤恶性肿瘤、色素障碍性皮肤病、炎症性及感染性皮肤病中均有重要的应用价值和前景。该文主要对卷积神经网络的基本原理及结构、常见模型、卷积神经网络在皮肤病诊断中的应用,以及卷积神经网络的优势和局限性进行综述。未来人工智能将有可能为更多的临床医生提供帮助,解决部分医疗水平欠发达地区看病难问题,为慢性皮肤病患者提供居家护理服务,以及自动跟踪和监测皮肤病等。

Skin diseases exhibit a wide range of variations,posing challenges in clinical diagnosis and differential diagnosis due to limited availability of auxiliary diagnostic tools.However,convolutional neural network(CNN),an artificial intelligence approach based on deep learning,have demonstrated comparable performance levels to dermatologists in the diagnosis and differentiation of skin diseases.This holds particularly true for conditions such as malignant skin tumors,pigmentary skin disorders,inflammatory skin diseases,and infectious skin diseases where CNN show significant potential and promise.This paper provides an overview of the fundamental principles and structure of CNN,commonly used models,their application in diagnosing skin diseases,as well as their advantages and limitations.In the future,artificial intelligence is expected to assist clinicians further by addressing healthcare access issues in regions with underdeveloped medical infrastructure while offering home care services for patients with chronic skin conditions and enabling automatic tracking and monitoring of such conditions.