PDF(Pubmed)
Abstract：
UNASSIGNED: Given the rarity of tracheobronchopathia osteochondroplastica (TO), many young doctors in primary hospitals are unable to identify TO based on bronchoscopy findings.
UNASSIGNED: To build an artificial intelligence (AI) model for differentiating TO from other multinodular airway diseases by using bronchoscopic images.
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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%.
UNASSIGNED: 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.
UNASSIGNED: To build an artificial intelligence (AI) model for differentiating TO from other multinodular airway diseases by using bronchoscopic images.
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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%.
UNASSIGNED: 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.
摘要：
■鉴于气管骨软骨症（TO）的罕见性，基层医院的许多年轻医生无法根据支气管镜检查结果识别TO.
■建立人工智能（AI）模型，用于通过使用支气管镜图像将TO与其他多结节气道疾病区分开来。
■我们通过使用EfficientNet比较2010年1月至2022年10月接受支气管镜检查的患者的影像学数据来设计研究。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。
■多结节气道病变患者的支气管镜图像（包括TO，淀粉样变性,肿瘤，和炎症），并且在广州医科大学附属第一医院没有气道病变。这些图像被随机(4:1)分为基于不同疾病的训练和验证组，并通过卷积神经网络(CNN)用于深度学习。
■我们招募了201例多结节性气道疾病患者（38、15、75和73例TO患者，淀粉样变性,肿瘤，和炎症，分别）和213无任何气道病变。为了找到用于深度学习的多结节病变图像，我们使用了2183张支气管镜图像的多结节病变（包括TO，淀粉样变性,肿瘤，和炎症），并将它们与没有任何气道病变的图像进行比较（1733）。多结节病变识别的准确率为98.9%。Further,基于多结节病变支气管镜图像的TO检测准确率为89.2%.关于外部验证（使用来自21名TO患者的图像），所有患者均可诊断为TO;准确率为89.8%.
■我们建立了一个AI模型，可以将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）通过使用支气管镜图像。该模型可以帮助年轻医生识别这种罕见的气道疾病。
■建立人工智能（AI）模型，用于通过使用支气管镜图像将TO与其他多结节气道疾病区分开来。
■我们通过使用EfficientNet比较2010年1月至2022年10月接受支气管镜检查的患者的影像学数据来设计研究。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。
■多结节气道病变患者的支气管镜图像（包括TO，淀粉样变性,肿瘤，和炎症），并且在广州医科大学附属第一医院没有气道病变。这些图像被随机(4:1)分为基于不同疾病的训练和验证组，并通过卷积神经网络(CNN)用于深度学习。
■我们招募了201例多结节性气道疾病患者（38、15、75和73例TO患者，淀粉样变性,肿瘤，和炎症，分别）和213无任何气道病变。为了找到用于深度学习的多结节病变图像，我们使用了2183张支气管镜图像的多结节病变（包括TO，淀粉样变性,肿瘤，和炎症），并将它们与没有任何气道病变的图像进行比较（1733）。多结节病变识别的准确率为98.9%。Further,基于多结节病变支气管镜图像的TO检测准确率为89.2%.关于外部验证（使用来自21名TO患者的图像），所有患者均可诊断为TO;准确率为89.8%.
■我们建立了一个AI模型，可以将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）通过使用支气管镜图像。该模型可以帮助年轻医生识别这种罕见的气道疾病。
