Abstract:

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.

Key words: Deep learning, High altitude pulmonary edema, Pulmonary ultrasound, Automatic screening technology, Artificial intelligence