Abstract：
A machine learning platform operated without coding knowledge (Teachable machine®) has been introduced. The aims of the present study were to assess the performance of the Teachable machine® for diagnosing tympanic membrane lesions. A total of 3024 tympanic membrane images were used to train and validate the diagnostic performance of the network. Tympanic membrane images were labeled as normal, otitis media with effusion (OME), chronic otitis media (COM), and cholesteatoma. According to the complexity of the categorization, Level I refers to normal versus abnormal tympanic membrane; Level II was defined as normal, OME, or COM + cholesteatoma; and Level III distinguishes between all four pathologies. In addition, eighty representative test images were used to assess the performance. Teachable machine® automatically creates a classification network and presents diagnostic performance when images are uploaded. The mean accuracy of the Teachable machine® for classifying tympanic membranes as normal or abnormal (Level I) was 90.1%. For Level II, the mean accuracy was 89.0% and for Level III it was 86.2%. The overall accuracy of the classification of the 80 representative tympanic membrane images was 78.75%, and the hit rates for normal, OME, COM, and cholesteatoma were 95.0%, 70.0%, 90.0%, and 60.0%, respectively. Teachable machine® could successfully generate the diagnostic network for classifying tympanic membrane.
摘要：
已经引入了在没有编码知识的情况下操作的机器学习平台(Teachablemachine®)。本研究的目的是评估Teachable机器®诊断鼓膜病变的性能。总共使用3024张鼓膜图像来训练和验证网络的诊断性能。鼓膜图像被标记为正常，渗出性中耳炎（OME），慢性中耳炎（COM），和胆脂瘤.根据分类的复杂性，I级指正常与异常鼓膜；II级定义为正常，OME,或COM+胆脂瘤;和III级区分所有四种病理。此外,使用80张代表性测试图像来评估性能。Teachablemachine®自动创建分类网络，并在上传图像时提供诊断性能。用于将鼓膜分类为正常或异常(I级)的Teachable机器®的平均准确度为90.1%。对于二级，平均准确率为89.0%,Ⅲ级为86.2%.80例代表性鼓膜图像分类的总体准确率为78.75%,和正常的命中率，OME,COM,胆脂瘤占95.0%,70.0%,90.0%,和60.0%，分别。Teachable机器®可以成功地生成用于对鼓膜进行分类的诊断网络。
