Habib A-R, Wong E, Sacks R, Singh N
Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, Sydney, Australia.
Greenslopes Private Hospital, Ramsay Health Care, Brisbane, Australia.
J Laryngol Otol. 2020 Apr;134(4):311-315. doi: 10.1017/S0022215120000717. Epub 2020 Apr 2.
To explore the feasibility of constructing a proof-of-concept artificial intelligence algorithm to detect tympanic membrane perforations, for future application in under-resourced rural settings.
A retrospective review was conducted of otoscopic images analysed using transfer learning with Google's Inception-V3 convolutional neural network architecture. The 'gold standard' 'ground truth' was defined by otolaryngologists. Perforation size was categorised as less than one-third (small), one-third to two-thirds (medium), or more than two-thirds (large) of the total tympanic membrane diameter.
A total of 233 tympanic membrane images were used (183 for training, 50 for testing). The algorithm correctly identified intact and perforated tympanic membranes (overall accuracy = 76.0 per cent, 95 per cent confidence interval = 62.1-86.0 per cent); the area under the curve was 0.867 (95 per cent confidence interval = 0.771-0.963).
A proof-of-concept image-classification artificial intelligence algorithm can be used to detect tympanic membrane perforations and, with further development, may prove to be a valuable tool for ear disease screening. Future endeavours are warranted to develop a point-of-care tool for healthcare workers in areas distant from otolaryngology.
探索构建一种用于检测鼓膜穿孔的概念验证人工智能算法的可行性，以便未来在资源匮乏的农村地区应用。
对使用谷歌的Inception-V3卷积神经网络架构进行迁移学习分析的耳镜图像进行回顾性研究。“金标准”“真实情况”由耳鼻喉科医生定义。穿孔大小分为小于鼓膜总直径的三分之一（小）、三分之一至三分之二（中）或大于三分之二（大）。
共使用了233张鼓膜图像（183张用于训练，50张用于测试）。该算法正确识别了完整和穿孔的鼓膜（总体准确率 = 76.0%，95%置信区间 = 62.1 - 86.0%）；曲线下面积为0.867（95%置信区间 = 0.771 - 0.963）。
一种概念验证的图像分类人工智能算法可用于检测鼓膜穿孔，并且随着进一步发展，可能被证明是一种用于耳部疾病筛查的有价值工具。未来有必要为远离耳鼻喉科的地区的医护人员开发一种即时护理工具。
