利用人工智能在纤维内镜检查中识别口咽癌

文献信息

中文摘要

目的： 具有黏膜下扩散倾向的口咽肿瘤在内镜检查中难以被发现和界定。为便于其检测，我们开发了一种人工智能（AI）算法，用于分割口咽部内的肿瘤及其他解剖学重要结构。
研究设计： 模型开发与验证研究。
地点： 三级学术医疗中心。
方法： 我们收集了106例接受咽喉镜检查患者的视频，提取942张图像，并将每个像素标注为12个类别之一，例如肿瘤、悬雍垂和健康黏膜。我们训练了一种AI算法，并在多类别分割和基于肿瘤的分割中评估其性能。
结果： 该AI算法在肿瘤检测中达到0.93的召回率统计量和0.79的Dice评分。咽黏膜（0.90）、会厌（0.88）和声带（0.82）观察到高Dice评分。
结论： 本研究表明，AI能够对内镜检查中可视化的所有结构进行语义分割，为肿瘤识别的计算机辅助系统以及内镜检查或插管期间的计算机辅助导航铺平了道路。

英文摘要

OBJECTIVE: Oropharyngeal tumors with a propensity for submucosal spread can be difficult to detect and delineate during endoscopy. To facilitate their detection, we developed an Artificial Intelligence (AI) algorithm to segment tumors and other anatomically significant structures within the oropharynx.
STUDY DESIGN: Model development and validation study.
SETTING: Tertiary academic medical center.
METHODS: We collected videos from 106 patients undergoing pharyngolaryngoscopy, extracted 942 images, and annotated each pixel as one of 12 classes, such as tumor, uvula, and healthy mucosa. We trained an AI algorithm and evaluated its performance in multiclass and tumor-based segmentation.
RESULTS: The AI algorithm achieved a recall statistic of 0.93 and a Dice score of 0.79 for tumor detection. High Dice scores were observed for pharyngeal mucosa (0.90), epiglottis (0.88), and vocal cords (0.82).
CONCLUSIONS: This study demonstrates that AI can semantically segment all the structures visualized during endoscopy, paving the way for computer-assisted systems for tumor identification and computer-assisted navigation during endoscopy or intubation.