深度学习辅助耳内镜诊断中耳疾病在不同临床经验水平医师中的应用：一项回顾性阅片研究

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中文摘要

背景： 由于耳镜判读的专业性以及全球耳鼻喉科医师短缺，中耳疾病的准确诊断仍然具有挑战性。
目的： 我们旨在评估基于深度学习的AI支持系统对不同专业水平临床医师诊断性能的影响，特别关注其缩小受训者与专家之间差距的能力。
方法： 在这项回顾性阅片研究中，使用包含7335张耳内镜图像的大规模数据集开发了一个深度学习模型（EfficientNet-B4）。该模型设计用于多分类，同时识别主要类别（慢性中耳炎、分泌性中耳炎或无[表示前两者均不存在]）和次要特征（上鼓室内陷、鼓膜炎、耳真菌病和通气管）。为评估临床影响，使用包含100张图像的独立临床评估集，比较了3名耳科学教授、5名高年资住院医师和5名低年资住院医师的诊断性能。参与者在有和无AI生成预测的情况下进行诊断，每张图像限时1分钟，以模拟真实临床压力。
结果： 在5折交叉验证中，深度学习模型的主要分类准确率达到94.8%。独立AI在临床评估集中的诊断准确率为94.0%至99.0%。AI支持显著提高了所有组别的主要分类诊断准确率，耳科学教授提高11.0%，高年资住院医师提高28.8%，低年资住院医师提高32.2%（P<.001）。准确率从耳科学教授的78.7%提高到89.7%，从高年资住院医师的65.0%提高到93.8%，从低年资住院医师的54.4%提高到86.6%。经验最丰富组（耳科学教授）与经验最少组（低年资住院医师）之间的准确率差距从24.3%缩小至3.1%。对于次要类别，住院医师在诊断上鼓室内陷和鼓膜炎方面观察到显著改善。高年资住院医师在AI支持下诊断上鼓室内陷的准确率高于独立AI，尽管95%CI大幅重叠，因此差异无统计学意义。
结论： 我们的研究结果表明，基于深度学习的AI支持可大幅提高中耳疾病的诊断准确率，尤其是对经验较少的临床医师。通过缩小受训者与专家之间的性能差距，该系统显示出作为耳科学临床决策和医学教育宝贵辅助工具的高度潜力。

英文摘要

BACKGROUND: Accurate diagnosis of middle ear diseases remains challenging due to the specialized nature of otoscopic interpretation and the global shortage of otolaryngologists.
OBJECTIVE: We aimed to evaluate the impact of a deep learning-based AI support system on the diagnostic performance of clinicians with varying levels of expertise, specifically focusing on its ability to bridge the gap between trainees and experts.
METHODS: In this retrospective reader study, a deep learning model (EfficientNet-B4) was developed using a large-scale dataset of 7335 endoscopic images. The model was designed for multi-class classification, simultaneously identifying primary categories (chronic otitis media, otitis media with effusion, or none [indicating the absence of the first two]) and secondary features (attic retraction, myringitis, otomycosis, and ventilation tube). To assess clinical impact, diagnostic performance was compared among 3 otology professors, 5 senior residents, and 5 junior residents using an independent clinical assessment set of 100 images. Participants performed diagnoses both with and without AI-generated predictions, with a time constraint of 1 minute per image to simulate real-world clinical pressure.
RESULTS: In 5-fold cross-validation, the deep learning model achieved 94.8% accuracy for the primary classification. The stand-alone AI achieved diagnostic accuracies ranging from 94.0% to 99.0% in the clinical assessment set. AI support significantly enhanced diagnostic accuracy across all groups for primary classification, with improvements of 11.0% for otology professors, 28.8% for senior residents, and 32.2% for junior residents (P<.001). Accuracy increased from 78.7% to 89.7% for otology professors, from 65.0% to 93.8% for senior residents, and from 54.4% to 86.6% for junior residents. The accuracy gap between the most experienced (otology professors) and least experienced (junior residents) groups was reduced from 24.3% to 3.1%. For secondary categories, significant improvements were observed in diagnosing attic retraction and myringitis among residents. AI-supported accuracy for attic retraction was higher in senior residents than that of the stand-alone AI, although the 95% CIs overlapped substantially, so the difference was not statistically significant.
CONCLUSIONS: Our findings demonstrate that deep learning-based AI support substantially improves the diagnostic accuracy of middle ear diseases, particularly for less experienced clinicians. By narrowing the performance gap between trainees and experts, this system shows high potential as a valuable adjunct for clinical decision-making and medical education in otology.