Ding Xin, Huang Yu, Tian Xu, Zhao Yang, Feng Guodong, Gao Zhiqiang
Department of Otorhinolaryngology Head and Neck Surgery, The Peaking Union Medical College Hospital, No. 1, Shuaifuyuan, Dongcheng District, Beijing 100010, China.
Diagnostics (Basel). 2023 Jul 7;13(13):2309. doi: 10.3390/diagnostics13132309.
A common infectious disease, otitis media (OM) has a low rate of early diagnosis, which significantly increases the difficulty of treating the disease and the likelihood of serious complications developing including hearing loss, speech impairment, and even intracranial infection. Several areas of healthcare have shown great promise in the application of artificial intelligence (AI) systems, such as the accurate detection of diseases, the automated interpretation of images, and the prediction of patient outcomes. Several articles have reported some machine learning (ML) algorithms such as ResNet, InceptionV3 and Unet, were applied to the diagnosis of OM successfully. The use of these techniques in the OM is still in its infancy, but their potential is enormous. We present in this review important concepts related to ML and AI, describe how these technologies are currently being applied to diagnosing, treating, and managing OM, and discuss the challenges associated with developing AI-assisted OM technologies in the future.
中耳炎（OM）是一种常见的传染病，早期诊断率较低，这显著增加了疾病治疗的难度以及出现严重并发症的可能性，包括听力损失、言语障碍，甚至颅内感染。医疗保健的几个领域在人工智能（AI）系统的应用方面显示出了巨大的前景，例如疾病的准确检测、图像的自动解读以及患者预后的预测。几篇文章报道了一些机器学习（ML）算法，如ResNet、InceptionV3和Unet，已成功应用于中耳炎的诊断。这些技术在中耳炎领域的应用仍处于起步阶段，但其潜力巨大。在本综述中，我们介绍了与机器学习和人工智能相关的重要概念，描述了这些技术目前在中耳炎诊断、治疗和管理中的应用方式，并讨论了未来开发人工智能辅助中耳炎技术所面临的挑战。
