人工智能在胆脂瘤诊断中的应用。

Use of artificial intelligence for the diagnosis of cholesteatoma.

作者信息

Tseng Christopher C, Lim Valerie, Jyung Robert W

机构信息

Department of Otolaryngology - Head and Neck Surgery Rutgers New Jersey Medical School Newark New Jersey USA.

出版信息

Laryngoscope Investig Otolaryngol. 2023 Jan 17;8(1):201-211. doi: 10.1002/lio2.1008. eCollection 2023 Feb.

DOI:10.1002/lio2.1008

PMID:36846416

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9948563/

Abstract

OBJECTIVES

Accurate diagnosis of cholesteatomas is crucial. However, cholesteatomas can easily be missed in routine otoscopic exams. Convolutional neural networks (CNNs) have performed well in medical image classification, so we evaluated their use for detecting cholesteatomas in otoscopic images.

STUDY DESIGN

Design and evaluation of artificial intelligence driven workflow for cholesteatoma diagnosis.

METHODS

Otoscopic images collected from the faculty practice of the senior author were deidentified and labeled by the senior author as cholesteatoma, abnormal non-cholesteatoma, or normal. An image classification workflow was developed to automatically differentiate cholesteatomas from other possible tympanic membrane appearances. Eight pretrained CNNs were trained on our otoscopic images, then tested on a withheld subset of images to evaluate their final performance. CNN intermediate activations were also extracted to visualize important image features.

RESULTS

A total of 834 otoscopic images were collected, further categorized into 197 cholesteatoma, 457 abnormal non-cholesteatoma, and 180 normal. Final trained CNNs demonstrated strong performance, achieving accuracies of 83.8%-98.5% for differentiating cholesteatoma from normal, 75.6%-90.1% for differentiating cholesteatoma from abnormal non-cholesteatoma, and 87.0%-90.4% for differentiating cholesteatoma from non-cholesteatoma (abnormal non-cholesteatoma + normal). DenseNet201 (100% sensitivity, 97.1% specificity), NASNetLarge (100% sensitivity, 88.2% specificity), and MobileNetV2 (94.1% sensitivity, 100% specificity) were among the best performing CNNs in distinguishing cholesteatoma versus normal. Visualization of intermediate activations showed robust detection of relevant image features by the CNNs.

CONCLUSION

While further refinement and more training images are needed to improve performance, artificial intelligence-driven analysis of otoscopic images shows great promise as a diagnostic tool for detecting cholesteatomas.

LEVEL OF EVIDENCE

摘要

目的

准确诊断胆脂瘤至关重要。然而，在常规耳镜检查中胆脂瘤很容易被漏诊。卷积神经网络（CNN）在医学图像分类方面表现出色，因此我们评估了其在耳镜图像中检测胆脂瘤的应用。

研究设计

设计并评估用于胆脂瘤诊断的人工智能驱动工作流程。

方法

从资深作者的临床实践中收集的耳镜图像进行去识别处理，并由资深作者标记为胆脂瘤、异常非胆脂瘤或正常。开发了一种图像分类工作流程，以自动区分胆脂瘤与其他可能的鼓膜表现。在我们的耳镜图像上对八个预训练的CNN进行训练，然后在保留的图像子集中进行测试，以评估它们的最终性能。还提取了CNN中间激活值以可视化重要的图像特征。

结果

共收集了834张耳镜图像，进一步分为197例胆脂瘤、457例异常非胆脂瘤和180例正常。最终训练的CNN表现出强大的性能，区分胆脂瘤与正常的准确率为83.8%-98.5%，区分胆脂瘤与异常非胆脂瘤的准确率为75.6%-90.1%，区分胆脂瘤与非胆脂瘤（异常非胆脂瘤+正常）的准确率为87.0%-90.4%。DenseNet201（灵敏度100%，特异性97.1%）、NASNetLarge（灵敏度100%，特异性88.2%）和MobileNetV2（灵敏度94.1%，特异性100%）是区分胆脂瘤与正常表现最佳的CNN之一。中间激活值的可视化显示CNN对相关图像特征有强大的检测能力。