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CN-121971263-A - Intraoperative pressure injury prevention and control system and method based on multi-modal sensing and intelligent early warning

CN121971263ACN 121971263 ACN121971263 ACN 121971263ACN-121971263-A

Abstract

The application discloses an intraoperative pressure injury prevention and control system and method based on multi-mode sensing and intelligent early warning. The method comprises the steps of collecting pressure values, temperature values and humidity values of a plurality of sensing units in a body supporting area of a surgical patient in real time, determining a temperature weighting factor and a humidity weighting factor for adjusting the pressure values according to the temperature values and the humidity values for each sensing unit, determining an adjusted pressure value based on the pressure values, the temperature weighting factors and the humidity weighting factors, accumulating the adjusted pressure values in time to generate a multi-modal risk integral value representing the risk of pressure injury, and generating and outputting early warning information when the multi-modal risk integral value triggers preset early warning conditions. According to the application, the temperature and the humidity are used as key variables to be introduced into the risk assessment model, so that the risk of pressure injury in operation can be identified and early and accurately, and the timeliness and the effectiveness of control are improved.

Inventors

  • Ying Zhiyu

Assignees

  • 浙江大学

Dates

Publication Date
20260505
Application Date
20251127

Claims (10)

  1. 1. The method for preventing and treating the intraoperative pressure injury based on the multi-mode sensing and the intelligent early warning is characterized by comprising the following steps of: Collecting pressure values, temperature values and humidity values of a plurality of sensing units distributed in a body supporting area of a patient in real time; determining, for any one of the plurality of sensing units, a temperature weighting factor and a humidity weighting factor for dynamically adjusting the pressure value of the target sensing unit, respectively, according to the temperature value and the humidity value of the target sensing unit; Determining an adjusted pressure value for the target sensing unit based on the pressure value, the temperature weighting factor, and the humidity weighting factor for the target sensing unit; accumulating the adjusted pressure values of the target sensing unit over time to generate a multi-modal risk integration value indicative of the risk of pressure damage to the target sensing unit, and And when the multi-modal risk integral value meets a preset early warning condition, generating and outputting early warning information associated with the target sensing unit.
  2. 2. The method of claim 1, wherein determining a temperature weighting factor and a humidity weighting factor for dynamically adjusting the pressure value of the target sensing unit based on the temperature value and the humidity value of the target sensing unit, respectively, comprises: determining a temperature weighting factor greater than 1 by a first nonlinear function based on the difference between the temperature value and the temperature threshold when the temperature value exceeds a preset temperature threshold, and And when the humidity value exceeds a preset humidity threshold, determining the humidity weighting factor larger than 1 through a second nonlinear function based on the difference value between the humidity value and the humidity threshold.
  3. 3. The method of claim 2, wherein the first nonlinear function is a power function and the second nonlinear function is a power function.
  4. 4. The method of claim 1, wherein said determining an adjusted pressure value for said target sensing unit based on said pressure value, said temperature weighting factor, and said humidity weighting factor for said target sensing unit comprises: And carrying out combination operation on the pressure value, the temperature weighting factor and the humidity weighting factor of the target sensing unit to obtain the adjusted pressure value.
  5. 5. The method of claim 4, wherein said combining said pressure value, said temperature weighting factor, and said humidity weighting factor of said target sensing unit comprises: Multiplying the pressure value, the temperature weighting factor, and the humidity weighting factor of the target sensing unit.
  6. 6. The method of claim 1, wherein the pre-warning conditions include at least two pre-warning levels, the method further comprising: triggering a first-level early warning information when the multi-modal risk integral value exceeds a first risk threshold value, and And triggering early warning information of a second level when the multi-modal risk integrated value exceeds a second risk threshold higher than the first risk threshold.
  7. 7. The method of claim 6, wherein the pre-warning condition further comprises a rate of change of the multi-modal risk integration value within a predetermined time window, and wherein the second level of pre-warning information is triggered when the rate of change exceeds a predetermined rate of change threshold.
  8. 8. The method according to claim 1, wherein the method further comprises: After the early warning information is generated, a unified position adjustment suggestion or a pressure adjustment instruction is generated for the physical area where the target sensing unit is located.
  9. 9. The method of claim 8, wherein a segmented airbag array is disposed below the body support region, the method further comprising: And based on the pressure regulating instruction, one or more air bags corresponding to the target sensing unit in the partitioned air bag array are controlled to be inflated or deflated so as to realize closed-loop self-adaptive regulation of the pressure distribution of the physical area where the target sensing unit is located.
  10. 10. An intraoperative pressure injury prevention and control system based on multi-modal sensing and intelligent early warning is characterized by comprising: The acquisition module is used for acquiring pressure values, temperature values and humidity values of a plurality of sensing units distributed in the body supporting area of the surgical patient in real time; an evaluation module for determining, for any one of the plurality of sensing units, a temperature weighting factor and a humidity weighting factor for dynamically adjusting the pressure value of the target sensing unit, respectively, based on the temperature value and the humidity value of the target sensing unit, determining an adjusted pressure value of the target sensing unit based on the pressure value, the temperature weighting factor, and the humidity weighting factor, and accumulating the adjusted pressure value of the target sensing unit over time to generate a multi-modal risk integral value indicative of a risk of pressure damage to the target sensing unit, and And the early warning module is used for generating and outputting early warning information associated with the target sensing unit when the multi-modal risk integral value meets a preset early warning condition.

Description

Intraoperative pressure injury prevention and control system and method based on multi-modal sensing and intelligent early warning Technical Field The invention relates to the technical field of medical monitoring, in particular to a system and a method for preventing and treating intraoperative pressure injury based on multi-mode sensing and intelligent early warning. Background Intraoperative pressure injury, commonly referred to as pressure sores, is one of the common serious complications for surgical patients. During long-term surgery, the patient is under anesthesia and cannot move autonomously, and the bony prominences of the body (e.g., sacral tail, heel, shoulder blade, etc.) are constantly under pressure. This prolonged compression results in a disturbance of blood circulation in the local tissue, which causes ischemia, hypoxia, and eventually may develop into tissue necrosis. Patients with advanced age, low Body Mass Index (BMI), and surgery times exceeding 4 hours, are at significantly increased risk of developing stress injuries. Existing precautions rely primarily on the experience of nurses, such as timing the patient's turn over or adjust position, and the use of static reduced pressure dressings or gel pads. However, these methods have significant limitations. On the one hand, manual examination and intervention is lagging, dynamic processes of pressure build-up are difficult to capture in real time, and frequent interruption of surgical procedures to adjust body position is not feasible in many complex surgeries. On the other hand, static gel pads, while being able to disperse a portion of the pressure, are fixed in performance and cannot be dynamically adjusted according to real-time risk changes. More importantly, other critical pathogenic factors besides stress, particularly the temperature and humidity of the local microenvironment, are generally ignored by the prior art. Leakage of sterilizing fluid, blood or body fluids during surgery can lead to local skin wetness and prolonged coverage can also lead to elevated temperatures. The moist and warm environment significantly reduces the mechanical strength and pressure resistance of the skin, thereby greatly accelerating the progression of the pressure damage. Therefore, in the prior art, due to the lack of comprehensive consideration of multi-mode information such as temperature, humidity and the like, the risk assessment model has congenital defects, and early warning in the true sense cannot be realized. Disclosure of Invention The application aims to provide a system and a method for preventing and controlling pressure injury in operation based on multi-mode sensing and intelligent early warning, and aims to solve the technical problems that in the prior art, the risk assessment dimension of the pressure injury is single, early warning is delayed, and closed loop intervention cannot be realized. The application provides an intraoperative pressure injury prevention and treatment method based on multi-modal sensing and intelligent early warning, which comprises the steps of collecting pressure values, temperature values and humidity values of a plurality of sensing units distributed in a body supporting area of an operation patient in real time, determining a temperature weighting factor and a humidity weighting factor which are respectively used for dynamically adjusting the pressure value of the target sensing unit according to the temperature value and the humidity value of any one of the plurality of sensing units, determining an adjusted pressure value of the target sensing unit based on the pressure value of the target sensing unit, the temperature weighting factor and the humidity weighting factor, accumulating the adjusted pressure values of the target sensing unit along with time to generate a multi-modal risk integral value representing the pressure injury risk of the target sensing unit, and generating and outputting early warning information related to the target sensing unit when the multi-modal risk meets a preset early warning condition. In a possible implementation manner of the first aspect, the determining a temperature weighting factor and a humidity weighting factor for dynamically adjusting the pressure value of the target sensing unit according to the temperature value and the humidity value of the target sensing unit includes determining the temperature weighting factor greater than 1 through a first nonlinear function based on a difference between the temperature value and the temperature threshold when the temperature value exceeds a preset temperature threshold, and determining the humidity weighting factor greater than 1 through a second nonlinear function based on a difference between the humidity value and the humidity threshold when the humidity value exceeds a preset humidity threshold. In a possible implementation manner of the first aspect, the first nonlinear function is a power function, and the secon