Disease prediction method and device, equipment, computer-readable storage medium

Abstract

The invention discloses a method and a device for predicting disease, an equipment and a computer-readable storage medium. The disease predicting method comprises acquiring a medical image in responseto a disease prediction instruction; detecting the medical image to obtain position information of at least one lesion; extracting a lesion image for each of the lesions from the medical image basedon the position information. A lesion image of the lesion is described; each of the lesion images is input to a disease prediction model to obtain a corresponding disease prediction result. By adopting the invention, the prediction of the disease can be realized, so that the diagnosis efficiency and the accuracy of the disease can be improved.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a disease prediction method, device, device, and computer-readable storage medium. Background technique [0002] According to the data released by the National Cancer Center, lung cancer ranks first in the incidence and death of malignant tumors in the country, and about 591,000 people die of lung cancer every year. For lung cancer, the existing 11 mainstream diagnostic methods are: [0003] 1) Chest X-ray examination: It is an important means of diagnosing lung cancer. Lung shadows can be found through fluoroscopy or frontal and lateral chest X-rays; [0004] 2) Chest CT: It can detect and clearly show the size, shape and cumulative range of lesions in the hilum, lung and mediastinum earlier, which is helpful for diagnosing whether lung cancer can be resected; [0005] 3) Magnetic resonance imaging (MRI): determine the extent of lung cancer invasion, staging and judg...

AI Technical Summary

Problems solved by technology

[0014] Early lung cancer is mostly manifested as pulmonary nodules, which are small in size, low in contrast, and highly heterogeneous in shape. In the existing technology, the diagnosis of lung cancer is usually performed manually by doctors on radiological images such as CT images of patients. , to determine whether it contains nodules, therefore, it is very difficult to diagnose early lung cancer, and the diagnostic efficiency is very low

In addition, because manual diagnosis is very dependent on the experience and judgment of doctors, different doctors may give different diagnostic results, and when the number of CT images is very large, the workload of doctors is very huge, and it is easy to make diagnosis errors due to fatigue. Therefore, the accuracy of diagnosis is not high

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