Fiberoptic intubation aided decision-making system based on deep learning

Abstract

The invention discloses an auxiliary decision-making method for fiberoptic bronchoscopic intubation based on deep learning, which includes: collecting intubation video and splitting the video into image frames; formulating a decision-making instruction set; using the operation instruction as a category label for image decision-making, for Annotate the image frame frame by frame; process the original image and make a data set; extract the training set and verification set; perform feature extraction on the training sample, and gradually obtain high-level feature maps; send the results of the feature map transformation to Naive Bayesian The classifier and the softmax activation function are calculated; input training samples, set the loss function loss, and separately train the operation instruction decision model and the naive Bayesian classifier model; repeat the training steps to cross-validate the network model. The invention adopts an end-to-end method, directly obtains the decision result from the input image, greatly reduces the algorithm cost, has high decision-making speed and high real-time performance.

Description

technical field [0001] The invention belongs to the field of deep learning and image processing, and in particular relates to a deep learning-based assistant decision-making system for fiberoptic bronchoscope intubation. Background technique [0002] Before some operations that require general anesthesia, in order to ensure the safety of the patient and prevent the patient from suffocating due to loss of consciousness or tracheal obstruction, it is necessary to intubate the patient's trachea to provide oxygen supply to the patient's lungs. At present, the most widely used method is to intubate the patient's trachea by using a fiberoptic bronchoscope to coat the trachea. A fiberoptic bronchoscope is a tube-shaped medical device with a lens on its head, which can be bent up and down perpendicular to the plane of the field of view where the front lens is located through a push rod on the rear handle; The fiberoptic lens can also rotate parallel to the field of view, and the fi...

AI Technical Summary

Problems solved by technology

At present, there is a shortage of anesthesiologists in my country, a serious imbalance in the ratio of anesthesiologists to patients, and the status quo of overworked anesthesiologists.

On the other hand, due to the long working hours and the need for rich work experience in intubation work, it is inevitable that the anesthesiologist will make misoperations during the tracheal intubation process, or the intubation is too long, resulting in hypoventilation of the patient, and an assistant is urgently needed. Method helps anesthesiologists perform intubation

[0004] At present, all intubation operations are performed manually by anesthesiologists, and such a complicated operation requires doctors to have rich medical knowledge and clinical experience

Due to the differences in individual conditions of patients, the tracheal environment in the body is also different, and certain physiological or pathological conditions will make it difficult to perform artificial intubation

To sum up, there are two difficulties in the existing manual intubation, one is the recognition of the picture taken by the bronchoscopic lens and the judgment of the moving direction of the lens; the other is the cumbersome lens control operation based on the picture

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