Method and system for distinguishing different scenes inside and outside body cavity

Abstract

The invention relates to a method and system for distinguishing different scenes inside and outside a body cavity, and relates to the technical field of artificial intelligence, and the method comprises the steps: S1, constructing a database for training a classification model for recognizing the internal and external visual fields of the body cavity, and constructing the classification model for recognizing the internal and external visual fields of the body cavity; s2, accessing and analyzing different operation scenes in the operation video picture through a body cavity inside and outside visual field identification model to obtain classification data of different types of operation scenes, and outputting a classification data result; s3, counting the state of the surgical lens inside and outside the body cavity, the number of times of placing or moving the lens out of the body cavity in the surgical process, the time points and the duration of classification change and the proportion of the occupied total surgical duration. According to the method, the state outside the body cavity and the duration of the state outside the body cavity are distinguished from the invalid duration, and non-technical factors influencing the operation efficiency can be distinguished in operation quality control; and the distinguishing accuracy of different scenes of the operation video is further improved, so that the operation video reaches the clinical available level.

Description

A method and system for distinguishing different scenes inside and outside a body cavity technical field The present invention relates to artificial intelligence technology field, particularly a kind of method and system of distinguishing different scenes inside and outside body cavity system. Background technique The research of artificial intelligence in endoscopic minimally invasive surgery is increasing day by day, from instrument identification to stage, anatomical area, surgical Assessment of motion, anatomical handling, and initial prediction of surgical duration are currently available; However, few people are involved in the scene detection of the The same scenes and video clips without anatomy, organs, and surgical actions fall within the scope of invalid duration; however, with no The action sequences are different, and the scenes outside the body cavity are not only obviously different from those inside the body cavity, but also in clinical practice. ...

AI Technical Summary

Problems solved by technology

[0002] The research on artificial intelligence in minimally invasive laparoscopic surgery is increasing, from instrument identification to assessment of stage, anatomical region, surgical action, anatomical treatment, and preliminary prediction of operation time. Few people are involved in scene detection. At the same time, in the traditional sense, different scenes inside and outside the body cavity in laparoscopic minimally invasive surgery and video clips without anatomy, organs and surgical actions are also in the range of invalid duration; however, with no The action sequences are different, and the scenes outside the body cavity are not only obviously different from those inside the body cavity, but also in clinical practice. Unreasonable and poor anti-fog effect of the surgical lens, not necessarily the lack of technical proficiency of the surgical team

[0003] For different scenes inside and outside the body cavity in minimally invasive laparoscopic surgery, since the state and frequency of scene switching between inside and outside the body cavity during the operation are one of the criteria for evaluating the smoothness of the operation, the proficiency of skills, the distribution and effectiveness of surgical segments, And to a certain extent, it is related to the difficulty of the operation itself. The detection of different states inside and outside the body cavity provides a guarantee for the automatic implementation of the above functions. At the same time, using traditional computer vision algorithms to compare the scenes inside and outside the body cavity Simultaneous learning in the invalid stage will not only consume a large amount of data sets, but also have limited accuracy improvement, which cannot meet the needs of clinical work

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