Abstract：Objective To develop a pulmonary tuberculosis lesion detection model based on deep convolutional neural network,
and evaluate its application value in mass population screening and clinical detection of pulmonary tuberculosis. Methods In this
study, the image data of 1217 patients in the Image Center of the First People’s Hospital of Kashi from March 2019 to July 2020
were retrospectively collected and randomly divided into 3 datasets for training, validation and testing on the improved tuberculosis
lesion detection model of RetinaNet at a ratio of 7∶2∶1. Two public data sets of tuberculosis with a total of 800 cases were
collected for external validation of the model. By constructing a loss function sensitive to small nidus, and introducing techniques
such as attention mechanism and multi-scale feature extraction in this detection model, the detection rate of small or hidden nidi was
optimized. Results The area under curve (AUC) of the improved RetinaNet model was slightly lower than the original RetinaNet
model only in the test set, and the AUC and accuracy of other data sets were higher than the original RetinaNet model. At the same
time, the improved RetinaNet model performed better diagnostic performance than the test and validation sets (AUC was 0.879,
accuracy was 0.847) in the model evaluation of the public data set in the external center. The sensitivity, specificity and accuracy of
the diagnosis of tuberculosis by radiologists with the aid of artificial intelligence (AI) system are significantly improved compared
with that without the aid of AI system. With the aid of AI system, the time for radiologists to read the image data of the cases was
significantly shorter than that without the aid of AI system (P<0.001). Conclusion Deep learning can be used to rapidly detect and
locate tuberculosis lesions in chest radiographs, and provide corresponding confidence index and lesion location information, which
can screen high-risk groups of tuberculosis in large quantities, and greatly improve the work efficiency of radiologists in areas with
scarce medical resources and the accuracy of tuberculosis diagnosis.
马依迪丽·尼加提，田序伟，米日古丽·达毛拉，阿布都克尤木·阿布力孜，. 基于深度卷积神经网络的肺结核病灶
检测模型开发[J]. 中国医疗设备, 2023, 38(10): 7-13.
MAYIDILI.Nijiati, TIAN Xuwei, MIRIGULI.Damaola, ABUDUKEYOUMU.Abulizi,
ALIMUJIANG.Abudukaiyoumu, DAI Guochao, DONG Jiake. Development of Deep Convolutional Neural Network-Based Detection Model of
Pulmonary Tuberculosis. China Medical Devices, 2023, 38(10): 7-13.
