目的

利用西藏军区总医院收集的肺超声图像数据，基于深度学习技术，建立一种适用于高原肺水肿（ high altitude pulmonary edema，HAPE）的肺超声筛查自动技术，用于自动筛选HAPE 病症，提升诊断的准确性。

方法

收集2021 年1 月至2023 年12 月期间，我院收治的高原肺水肿确诊病例共174 例，按照分层的方式划分为训练集121 例、验证集18 例和测试集35 例。 采集患者肺超声图像，通过卷积神经网络（Convolutional Neural Network，CNN）模型进行图像识别和分析，系统进行多次训练和验证。 模型被整合并优化以满足实时性和用户友好性需求，比较自动筛查技术系统与传统人工筛查方法诊断准确性。

结果

模型性能评估中，AI 模型的敏感性为95.00%，特异性为96.00%和总体准确率为95.50%（包含训练集115 张，验证集17 张，测试集33 张），高于医师组的敏感性84.33%，特异性87.67%和总体准确率85.50%（包含训练集106 张，验证集16 张，测试集31 张）。 统计学分析表明，AI 系统与人工筛查方法在诊断敏感性、特异性及准确率上的差异具有统计学意义（P＜0.05）。

结论

与传统人工筛查方法相比，AI 模型在诊断敏感性、特异性和准确率表现优异，可提高临床诊断。

Objective

To establish an automatic lung ultrasound screening technology for high altitude pulmonary edema based on deep learning technology based on the ultrasonic lung image data collected by the General Hospital of Xizang Military Region，which can automatically screen the disease of HAPE and improve the diagnostic accuracy.

Methods

This study investigated the application effect of Convolutional Neural Network （CNN） based artificial intelligence （AI） model in the diagnosis of high altitude pulmonary edema.The study included 174 confirmed cases of high altitude pulmonary edema admitted to our hospital from January 2021 to December 2023.The cases were divided into 121 training sets，18 verification sets and 35 test sets according to random stratification.The research methods include the collection of patients＇lung ultrasound image data，the automatic recognition and analysis of the image using CNN model，and the training and verification of the model for several times to improve the diagnostic performance.In the model performance evaluation，the diagnostic accuracy，recall （sensitivity） and specificity of the AI system wTo establish an automatic lung ultrasound screening technology for high altitude pulmonary edema based on deep learning based on the data of lung ultrasound images collected by the General Hospital of Xizang Military Region，and to automatically screen HAPE and improve the diagnostic accuracy.Methods： This study investigated the application effect of Convolutional Neural Network （CNN） based artificial intelligence （AI） model in the diagnosis of high altitude pulmonary edema.The study included 174 confirmed cases of high altitude pulmonary edema admitted to our hospital from January 2020 to December 2023.The cases were divided into 121 training sets，18 verification sets and 35 test sets according to random stratification.The research methods include the collection of patients′lung ultrasound image data，the automatic recognition and analysis of the image using CNN model，and the training and verification of the model for several times to improve the diagnostic performance.

Result

In the model performance evaluation，the sensitivity of the AI model was 95.00%，the specificity was 96.00%，and the overall accuracy rate was 95.50% （including 115 training set images，17 validation set images，and 33 test set images），which was higher than the sensitivity of 84.33%，specificity of 87.67%，and overall accuracy rate of 85.50% of the physician group （including 106 training set images，16 validation set images，and 31 test set images）.Statistical analysis indicated that the differences in diagnostic sensitivity，specificity，and accuracy rate between the AI system and the manual screening method were statistically significant （P ＜0.05）.

Conclusion

This study demonstrated the superior performance of CNN-based AI screening technology in the diagnosis of high altitude pulmonary edema.Compared with traditional manual screening methods，the AI model performs well in terms of diagnostic sensitivity，specificity and accuracy，and can effectively compensate for the limitations caused by the inexperience of doctors.