Chongxiang Chen, Fei Tang, Felix J F Herth, Yingnan Zuo, Jiangtao Ren, Shuaiqi Zhang, Wenhua Jian, Chunli Tang, Shiyue Li
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Bronchoscopic images of 21 patients with TO at Anhui Chest Hospital from October 2019 to October 2022 were collected for external validation.</p><p><strong>Methods: </strong>Bronchoscopic images of patients with multinodular airway lesions (including TO, amyloidosis, tumors, and inflammation) and without airway lesions in the First Affiliated Hospital of Guangzhou Medical University were collected. The images were randomized (4:1) into training and validation groups based on different diseases and utilized for deep learning by convolutional neural networks (CNNs).</p><p><strong>Results: </strong>We enrolled 201 patients with multinodular airway disease (38, 15, 75, and 73 patients with TO, amyloidosis, tumors, and inflammation, respectively) and 213 without any airway lesions. To find multinodular lesion images for deep learning, we utilized 2183 bronchoscopic images of multinodular lesions (including TO, amyloidosis, tumor, and inflammation) and compared them with images without any airway lesions (1733). The accuracy of multinodular lesion identification was 98.9%. Further, the accuracy of TO detection based on the bronchoscopic images of multinodular lesions was 89.2%. Regarding external validation (using images from 21 patients with TO), all patients could be diagnosed with TO; the accuracy was 89.8%.</p><p><strong>Conclusion: </strong>We built an AI model that could differentiate TO from other multinodular airway diseases (mainly amyloidosis, tumors, and inflammation) by using bronchoscopic images. 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Bronchoscopic images of 21 patients with TO at Anhui Chest Hospital from October 2019 to October 2022 were collected for external validation.</p><p><strong>Methods: </strong>Bronchoscopic images of patients with multinodular airway lesions (including TO, amyloidosis, tumors, and inflammation) and without airway lesions in the First Affiliated Hospital of Guangzhou Medical University were collected. The images were randomized (4:1) into training and validation groups based on different diseases and utilized for deep learning by convolutional neural networks (CNNs).</p><p><strong>Results: </strong>We enrolled 201 patients with multinodular airway disease (38, 15, 75, and 73 patients with TO, amyloidosis, tumors, and inflammation, respectively) and 213 without any airway lesions. 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引用次数: 0
摘要
背景：鉴于气管软骨发育不全（TO）的罕见性，许多基层医院的年轻医生无法根据支气管镜检查结果识别气管软骨发育不全：目的：建立一个人工智能（AI）模型，通过支气管镜图像区分气管骨软骨发育不良（TO）和其他多结节性气道疾病：设计：我们设计了这项研究，利用效能网对 2010 年 1 月至 2022 年 10 月期间接受支气管镜检查的患者的影像数据进行比较。收集2019年10月至2022年10月安徽省胸科医院21例TO患者的支气管镜图像进行外部验证：收集广州医科大学附属第一医院多结节性气道病变（包括TO、淀粉样变、肿瘤和炎症）和无气道病变患者的支气管镜图像。根据不同疾病将图像随机（4:1）分为训练组和验证组，并利用卷积神经网络（CNN）进行深度学习：我们招募了 201 名患有多结节性气道疾病的患者（分别为 38、15、75 和 73 名 TO、淀粉样变性、肿瘤和炎症患者）和 213 名无任何气道病变的患者。为了找到用于深度学习的多结节病变图像，我们利用了2183张多结节病变（包括TO、淀粉样变、肿瘤和炎症）的支气管镜图像，并将它们与没有任何气道病变的图像（1733张）进行了比较。多结节病变识别的准确率为 98.9%。此外，根据多结节病变的支气管镜图像检测 TO 的准确率为 89.2%。在外部验证方面（使用 21 名 TO 患者的图像），所有患者均可被诊断为 TO；准确率为 89.8%：结论：我们建立了一个人工智能模型，可以通过支气管镜图像将 TO 与其他多结节性气道疾病（主要是淀粉样变性、肿瘤和炎症）区分开来。该模型可帮助年轻医生识别这种罕见的气道疾病。
Building and validating an artificial intelligence model to identify tracheobronchopathia osteochondroplastica by using bronchoscopic images.
Background: Given the rarity of tracheobronchopathia osteochondroplastica (TO), many young doctors in primary hospitals are unable to identify TO based on bronchoscopy findings.
Objectives: To build an artificial intelligence (AI) model for differentiating TO from other multinodular airway diseases by using bronchoscopic images.
Design: We designed the study by comparing the imaging data of patients undergoing bronchoscopy from January 2010 to October 2022 by using EfficientNet. Bronchoscopic images of 21 patients with TO at Anhui Chest Hospital from October 2019 to October 2022 were collected for external validation.
Methods: Bronchoscopic images of patients with multinodular airway lesions (including TO, amyloidosis, tumors, and inflammation) and without airway lesions in the First Affiliated Hospital of Guangzhou Medical University were collected. The images were randomized (4:1) into training and validation groups based on different diseases and utilized for deep learning by convolutional neural networks (CNNs).
Results: We enrolled 201 patients with multinodular airway disease (38, 15, 75, and 73 patients with TO, amyloidosis, tumors, and inflammation, respectively) and 213 without any airway lesions. To find multinodular lesion images for deep learning, we utilized 2183 bronchoscopic images of multinodular lesions (including TO, amyloidosis, tumor, and inflammation) and compared them with images without any airway lesions (1733). The accuracy of multinodular lesion identification was 98.9%. Further, the accuracy of TO detection based on the bronchoscopic images of multinodular lesions was 89.2%. Regarding external validation (using images from 21 patients with TO), all patients could be diagnosed with TO; the accuracy was 89.8%.
Conclusion: We built an AI model that could differentiate TO from other multinodular airway diseases (mainly amyloidosis, tumors, and inflammation) by using bronchoscopic images. The model could help young physicians identify this rare airway disease.
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