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A deep learning-based method for labeling patient problem areas

A deep learning and problem-solving technology, applied in the field of clinical medicine, can solve problems such as surgical site labeling errors, achieve high labeling accuracy, solve the risk of labeling errors, and have a high degree of intelligence

Active Publication Date: 2022-03-11
HUNAN VATHIN MEDICAL INSTR CO LTD
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Problems solved by technology

[0004] The purpose of the present invention is to provide a deep learning-based labeling method for patient problem parts to solve the technical problem in the prior art that there is a risk of wrong surgical site identification

Method used

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  • A deep learning-based method for labeling patient problem areas
  • A deep learning-based method for labeling patient problem areas
  • A deep learning-based method for labeling patient problem areas

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Embodiment Construction

[0024] In order to make the object, technical solution and advantages of the present invention more clear and definite, the present invention will be further described in detail below with reference to the accompanying drawings and examples.

[0025] refer to figure 1 , the present invention provides a method for labeling patient problem parts based on deep learning, comprising the following steps:

[0026] S1: Collect at least 20,000 3D depth images of the problematic parts of the patient, analyze the 3D depth images, and establish a sample database; in this embodiment, it is more appropriate to collect 28,000 3D depth images of the problematic parts of the patient to ensure a sufficient sample size The training of the model is carried out without increasing the burden on the system due to the large amount of data.

[0027] S2: mark the 3D depth image in the database to make an image annotation dataset N, the image annotation dataset N includes a training set, a label set an...

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Abstract

The invention discloses a deep learning-based marking method for patient problem parts, comprising the following steps: collecting a three-dimensional depth image, analyzing the three-dimensional depth image, and establishing a sample database; marking the three-dimensional depth image in the database; constructing and describing the patient's problem Dimensional features of the site; training a binary classification model; using the problem site detection model to mark the problem site of the patient in real time, which solves the technical problem of the risk of incorrectly identifying the surgical site in the prior art.

Description

technical field [0001] The present invention relates to the field of clinical medicine, in particular to a deep learning-based labeling method for problem parts of patients. Background technique [0002] Doctors need to mark the patient's problematic parts before the operation. There have been many medical accidents in China where the operation parts were wrongly marked, exposing the hidden safety hazards in the current medical system: wrong lateral labeling of images; preoperative rounds, Discussion and surgical approval did not find and correct the marking error; the operation application that did not indicate the surgical side passed the appointment review; the handover of the condition was unclear; Surgical site marking was performed without proper monitoring; tissue supervision was not in place. [0003] In order to avoid the reoccurrence of medical accidents caused by incorrect identification of surgical sites, an auxiliary means of marking patient problem areas is ur...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/64G06V10/25G06V10/762G06V10/764G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/64G06V10/25G06V2201/03G06N3/045G06F18/231G06F18/2433
Inventor 不公告发明人
Owner HUNAN VATHIN MEDICAL INSTR CO LTD
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