A predictive method for chronic obstructive pulmonary disease based on reconstructed airway tree images

Abstract

The invention provides a method for predicting chronic obstructive pulmonary disease based on reconstructed airway tree images, and relates to the technical field of medical image processing. The method first collects multi-slice CT image files of COPD patients and healthy people from the same hospital, generates reconstructed airway tree images of healthy people and COPD patients, and converts different perspectives to generate reconstructed airway tree images of healthy people and COPD patients. Take a screenshot of the airway tree image, then remove the redundant background information of the intercepted airway tree image, and perform training and classification based on the convolutional neural network to predict whether someone has COPD in the test set; finally, reconstruct the image set intercepted by the airway tree Cross-validation ensures the accuracy of the prediction results. The COPD prediction method based on the reconstructed airway tree image provided by the present invention will be used as a technical means for preliminary screening of the disease, which is accurate and efficient, and effectively avoids misdiagnosis and missed diagnosis; meanwhile, it also eliminates the need for lung function tests. The complex process greatly reduces the workload of the hospital.

Description

technical field [0001] The invention relates to the technical field of medical image processing, in particular to a method for predicting chronic obstructive pulmonary disease based on reconstructed airway tree images. Background technique [0002] Chronic Obstructive Pulmonary Disease (COPD) is a common respiratory disease that seriously endangers the physical and mental health of patients and has become an important problem that global public health must face. Therefore, effective diagnostic methods are also extremely important. [0003] In traditional clinical testing, pulmonary function tests (Pulmonary Function Tests, PFTs) are often used, and the ratio of forced expiratory volume in one second (FEV1) to forced vital capacity (FVC) after bronchodilation is less than 70% is used as the standard for diagnosis , PFTs measure the macroscopic functional parameters of the whole lung, and cannot provide structural information of the COPD lung airway tree. In addition, the ra...

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Problems solved by technology

[0003] In traditional clinical testing, pulmonary function tests (Pulmonary Function Tests, PFTs) are often used, and the ratio of forced expiratory volume in one second (FEV1) to forced vital capacity (FVC) after bronchodilation is less than 70% is used as the standard for diagnosis , PFTs measure the macroscopic functional parameters of the whole lung, and cannot provide structural information on the airway tree of the COPD lung

In addition, the ratio of FEV1 to FVC in healthy people decreases with age, so this method is also prone to misdiagnosis and missed diagnosis

[0004] At the same time, the pulmonary function examination process in the hospital is also very cumbersome, and the steps are lengthy, generally including ventilation function, respiratory regulation and pulmonary circulation function.

The testing standards are also often different. The clinical diagnosis of COPD with pulmonary function tests requires a comprehensive assessment based on the patient’s symptoms and health status. Some severe patients cannot tolerate it, and it is difficult to distinguish it from airflow limitation caused by other diseases.

At the same time, medical staff and workers have limited understanding of relevant knowledge, which greatly limits clinical application

[0005] In summary, the current diagnostic methods for COPD have many defects, which increase the workload of doctors and the suffering of patients, and the diagnostic results are not satisfactory

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