Artificial intelligence medical image quality control method applied to clinical image

Abstract

The invention provides an artificial intelligence medical image quality control method applied to a clinical image, a technician collects a patient image and transmits the image to an artificial intelligence medical image quality control management system, and the artificial intelligence medical image quality control management system is used for performing semantic segmentation, classification processing and quality control scoring on the image. The score is displayed on an operation interface of a technician previewing image, and the technician judges whether to remark or recollect the image according to the quality control score and the condition of a patient, so that the generation of low-score image quality is reduced, the limitations of low efficiency, large workload, small quantity of quality control samples and strong subjectivity of traditional quality control are solved, accurate and effective quality control means are applied to the whole process from image data acquisition to diagnosis and are combined with primary technician and physician team management, and a standardized and intelligent image quality control system is established.

Description

technical field [0001] The invention relates to the technical field of medical image quality control, in particular to an artificial intelligence medical image quality control method applied to clinical images. Background technique [0002] DR chest film, that is, Radio Radiography digital X-ray image, has become the best clinical diagnosis for the initial screening of chest and lung diseases in recent years because of its extremely low radiation dose, low cost of equipment, high density resolution, and fast imaging. Way. With the increase of patients and clinical needs and the trend of digital and intelligent transformation, the intelligent quality control of DR chest radiographs is particularly important. [0003] Focusing on the discussion and research on the future of quality control in imaging departments across the country, most of them discuss and formulate a series of solutions for the unity of quality control standards, consistency of norms, and objectivity of manu...

AI Technical Summary

Problems solved by technology

[0005] The application number is CN109741317A, which discloses a medical image intelligent evaluation method, which realizes automatic intelligent judgment of medical image quality with the help of multiple convolutional neural network models, adopts the U-Net model, but after adopting U-Net segmentation, due to the Often the color is darker. During the neural network prediction process, sometimes the surrounding dark area is mistaken for the lung field area, so there will be white noise blocks outside the lung field area; similarly, there will occasionally be in the actual lung field area. Black noise, as if there are "holes" in the lung field; Regarding the classification of foreign bodies, the foreign body classification module distinguishes the types of foreign bodies on the images that meet the requirements, and detects all foreign bodies, which requires a relatively large workload. Points will not be deducted for the rate of pacemakers, cardiac stents, CVC venous catheters, etc. Such foreign bodies cannot be removed, and should not be used as foreign body indicators for judging the quality of chest radiographs. Therefore, this patent is not accurate enough in foreign body identification and is prone to errors. sentence

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