Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Department of Physical Medicine and Rehabilitation, Johns Hopkins University, Baltimore, MD, USA.
Ann Otol Rhinol Laryngol. 2021 Mar;130(3):286-291. doi: 10.1177/0003489420950364. Epub 2020 Aug 14.
Computer-aided analysis of laryngoscopy images has potential to add objectivity to subjective evaluations. Automated classification of biomedical images is extremely challenging due to the precision required and the limited amount of annotated data available for training. Convolutional neural networks (CNNs) have the potential to improve image analysis and have demonstrated good performance in many settings. This study applied machine-learning technologies to laryngoscopy to determine the accuracy of computer recognition of known laryngeal lesions found in patients post-extubation.
This is a proof of concept study that used a convenience sample of transnasal, flexible, distal-chip laryngoscopy images from patients post-extubation in the intensive care unit. After manually annotating images at the pixel-level, we applied a CNN-based method for analysis of granulomas and ulcerations to test potential machine-learning approaches for laryngoscopy analysis.
A total of 127 images from 25 patients were manually annotated for presence and shape of these lesions-100 for training, 27 for evaluating the system. There were 193 ulcerations (148 in the training set; 45 in the evaluation set) and 272 granulomas (208 in the training set; 64 in the evaluation set) identified. Time to annotate each image was approximately 3 minutes. Machine-based analysis demonstrated per-pixel sensitivity of 82.0% and 62.8% for granulomas and ulcerations respectively; specificity was 99.0% and 99.6%.
This work demonstrates the feasibility of machine learning via CNN-based methods to add objectivity to laryngoscopy analysis, suggesting that CNN may aid in laryngoscopy analysis for other conditions in the future.
喉镜图像的计算机辅助分析有可能为主观评估增添客观性。由于需要的精度和可用于训练的有限标注数据量，对生物医学图像进行自动分类极具挑战性。卷积神经网络（CNN）具有提高图像分析能力的潜力，并在许多环境中表现出良好的性能。本研究将机器学习技术应用于喉镜，以确定计算机识别拔管后患者已知喉部病变的准确性。
这是一项概念验证研究，使用了重症监护病房中经鼻、软性、远端芯片喉镜拔管后患者的便利样本。在手动进行像素级别的图像标注后，我们应用了一种基于 CNN 的方法来分析肉芽肿和溃疡，以测试用于喉镜分析的潜在机器学习方法。
共对 25 名患者的 127 张图像进行了手动标注，以确定这些病变的存在和形状——100 张用于训练，27 张用于评估系统。共识别出 193 个溃疡（训练集中 148 个；评估集中 45 个）和 272 个肉芽肿（训练集中 208 个；评估集中 64 个）。每张图像的标注时间约为 3 分钟。基于机器的分析显示，肉芽肿和溃疡的逐像素敏感性分别为 82.0%和 62.8%；特异性分别为 99.0%和 99.6%。
这项工作通过基于 CNN 的方法证明了机器学习的可行性，为喉镜分析增添了客观性，表明 CNN 可能有助于未来其他情况下的喉镜分析。
