Medical image scheduling method and system implemented by computer and storage medium

Abstract

The present disclosure relates to a medical image scheduling method and system implemented by computer and storage medium. The method comprises the steps of: acquiring medical image related data; processing, by the processor, the medical image related data to obtain at least a priority parameter of the medical image related data; using the processor, at least according to the priority of the medical image related data Level parameters, sorting medical image related data; and using a processor to present a queue of medical image related data and its processing results according to the sorting.The method is based on artificial intelligence to make preliminary judgments on medical images and optimize scheduling according to this. The medical image scheduling method is accurate, reliable andrapid, and can also provide artificial intelligence judgment results and medical image attribute information (such as data collection time) when needed. And the clinician's diagnosis results are combined to accurately sequence the cases.

Description

technical field [0001] The invention relates to an artificial intelligence-based workflow optimization method, in particular to a computer-implemented medical image scheduling method. The present invention also relates to a medical image scheduling system and a computer-readable storage medium capable of executing the method. Background technique [0002] With the continuous development of technology, medical images are playing an indispensable role in the process of clinical diagnosis. Most of these images need to be reviewed and marked by radiologists (such as radiologists) after they are collected, and a pathology report should be completed for reference by clinicians. Since each radiologist needs to process a large number of images a day, most hospitals or Internet-based remote diagnosis platforms will use a queuing system to manage these images, and then the radiologist will select the medical images of the case according to the priority of the case in the queue for pr...

AI Technical Summary

Problems solved by technology

For such a system, it cannot be ruled out that clinicians may make misjudgments in the case of poorly characterized patients, leading to underestimation of the priority of cases, thereby slowing down the processing of cases and delaying the optimal treatment time window

Furthermore, clinicians often need to refer to the results of medical image processing by radiologists to make accurate judgments. Sometimes when sorting the medical images of a case, an accurate medical diagnosis has not been made for the patient in the case, resulting in Lack of accurate prioritization basis

In addition, clinicians are faced with a heavy workload of diagnosis and treatment, and it is risky, inefficient, and not intelligent enough to sort medical images of cases by relying too much on their artificial diagnosis results

The above is just an example, and there are numerous factors in the current system that allows cases to be sorted by urgency that can lead to inaccurate or unreliable prioritization of cases, slow down the processing of cases (including their medical images), and even delay the optimal time window for treatment

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