Indwelling catheter monitoring method based on data calibration method

Abstract

The invention provides an indwelling catheter monitoring method based on a data calibration method, which relates to the technical field of medical instruments and comprises the following steps: data acquisition: recording an indwelling catheter path curve when a patient is successfully indwelled, and determining the indwelling catheter path curve as a physiological characteristic curve; data calibration: marking coordinates of key feature points such as an airway opening, key parts of an esophagus and an entrance of a stomach on the physiological feature curve, wherein the precision error is less than or equal to 1mm; state monitoring: comparing real-time coordinates of the front end of the stomach tube with feature points, dividing an airway section, an esophagus section and a stomach section, and evaluating the tube placement progress (the error is less than or equal to 2%); dynamic error correction: aligning physiological and advancing curves based on a spatial registration algorithm, correcting deviation (the precision is less than or equal to 2mm) through self-adaptive PID control, and high-precision monitoring: the data calibration precision is less than or equal to 1mm, the catheterization path deviation is less than or equal to 2mm, and the progress evaluation error lt; 2%, which is far better than artificial experience; intelligent early warning: early warning response of a key position is less than or equal to 200ms, and the risk of mistaken entry of an airway is lt And the medical negligence is reduced.

Description

Technical Field

[0001] This invention belongs to the field of medical device technology, and more specifically, relates to a catheter placement monitoring method based on data calibration. Background Technology

[0002] Traditional intubation monitoring relies on human experience, resulting in low accuracy (path deviation ≥5mm), delayed warnings (delayed detection of airway misplacement), and a lack of dynamic error correction (operational errors cannot be corrected in real time). Clinical data shows that the rate of misplacement during manual intubation is approximately 5%-10%, with significant fluctuations in intubation time (5-15 minutes), and a high dependence on medical experience. Existing technologies (such as X-ray and ultrasound-assisted intubation) pose radiation risks or lack sufficient accuracy. This invention addresses these pain points by using data calibration, feature matching, and spatial registration algorithms to achieve real-time monitoring, intelligent early warning, and dynamic error correction during the intubation process, thereby improving intubation safety and efficiency. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a pipe placement monitoring method based on a data calibration method.

[0004] A catheter placement monitoring method based on data calibration includes: data acquisition: recording the catheter placement path curve when the patient successfully places the catheter and identifying it as a physiological characteristic curve; data calibration: marking the coordinates of key feature points such as the airway opening, key parts of the esophagus, and the gastric inlet on the physiological characteristic curve, with an accuracy error ≤1mm; status monitoring: comparing the real-time coordinates of the gastric tube tip with the feature points to divide the tube into airway, esophageal, and gastric segments and assess the catheter placement progress (error ≤2%); early warning: using a feature matching algorithm, triggering an early warning when the matching degree between the catheter placement path and the physiological curve is <0.8 (or the deviation is >3mm), and strengthening the early warning by combining the airway segment with the airflow rate (>5L / min); dynamic error correction: aligning the physiological and travel curves based on a spatial registration algorithm, and correcting the deviation through adaptive PID control (accuracy ≤2mm).

[0005] Preferably, the data calibration uses a segmented algorithm to mark ≥3 feature points, supports the storage of multiple patient physiological feature databases (≥100 cases), the status monitoring module displays the tube placement progress in real time (interface refresh rate ≥30Hz), supports XYZ axis three-dimensional path visualization, the pre-warning response time is ≤200ms, the airway mis-entry warning rate is 100%, reducing the risk of medical accidents, and the dynamic error correction module achieves a spatial registration error of ≤1.5mm, adaptively corrects deviations such as changes in body position and operational jitter, and shortens the tube placement time by 20%.

[0006] A catheter placement monitoring system includes data acquisition, calibration, monitoring, early warning, dynamic error correction modules and a visual interface. The system supports historical data retrieval, reduces batch catheter placement registration time by 50% (from 20s to 10s), and is suitable for ICU scenarios. The visual interface displays feature point positions, path curves, and progress status in real time, improving the intuitiveness of medical operations (reducing training costs by 60%). The method is applicable to catheter placement operations such as gastric tubes and nasogastric tubes, and is compatible with front-end detection technologies such as magnetic positioning and image recognition. The data calibration, feature matching, and spatial registration algorithms are all implemented through software modules and can be integrated into existing medical devices (such as intelligent catheter placement devices).

[0007] Compared with the prior art, the present invention has the following beneficial effects:

[0008] High-precision monitoring: data calibration accuracy ≤1mm, tube placement path deviation ≤2mm, progress assessment error <2%, far exceeding human experience;

[0009] Intelligent early warning: Early warning response time for critical locations is ≤200ms, and the risk of airway aspiration is <0.1%, reducing medical accidents;

[0010] Dynamic error correction: Spatial registration and adaptive control correct operational errors in real time, reducing tube placement time by 20%;

[0011] System integration: Visual interface + multi-patient feature database, improving medical and nursing efficiency (reducing ICU batch catheterization time by 50%);

[0012] Clinical suitability: Applicable to high-risk scenarios (coma, children, mechanical ventilation), reducing the workload of medical staff and improving patient comfort. Attached Figure Description

[0013] Figure 1 This is a system schematic diagram of the data acquisition method of the present invention;

[0014] Figure 2 This is a system schematic diagram of the data calibration method of the present invention. Detailed Implementation

[0015] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0016] Please see Figures 1-2This invention provides a catheter placement monitoring method based on data calibration, comprising: data acquisition: recording the catheter placement path curve when the patient successfully places the catheter and determining it as a physiological characteristic curve; data calibration: marking the coordinates of key feature points such as the airway opening, key parts of the esophagus, and the gastric inlet on the physiological characteristic curve, with an accuracy error ≤1mm; status monitoring: comparing the real-time coordinates of the gastric tube tip with the feature points to divide the airway segment, esophagus segment, and gastric segment, and assessing the catheter placement progress (error ≤2%); early warning: using a feature matching algorithm, triggering an early warning when the matching degree between the catheter placement path and the physiological curve is <0.8 (or the deviation is >3mm), and strengthening the early warning by combining the airway segment with the airflow rate (>5L / min); dynamic error correction: aligning the physiological and travel curves based on a spatial registration algorithm, and correcting the deviation through adaptive PID control (accuracy ≤2mm).

[0017] Preferably, data calibration uses a segmented algorithm to mark ≥3 feature points, supports the storage of physiological feature databases for multiple patients (≥100 cases), the status monitoring module displays the tube insertion progress in real time (interface refresh rate ≥30Hz), supports XYZ axis three-dimensional path visualization, the pre-warning response time is ≤200ms, the airway mis-entry warning rate is 100%, reducing the risk of medical accidents, the dynamic error correction module achieves spatial registration error ≤1.5mm, adaptively corrects deviations such as changes in body position and operation jitter, and shortens the tube insertion time by 20%.

[0018] A catheter placement monitoring system includes data acquisition, calibration, monitoring, early warning, dynamic error correction modules and a visual interface. The system supports historical data retrieval, reduces batch catheter placement registration time by 50% (from 20s to 10s), and is suitable for ICU scenarios. The visual interface displays feature point locations, path curves, and progress status in real time, improving the intuitiveness of medical operations (reducing training costs by 60%). The method is applicable to catheter placement operations such as gastric tubes and nasogastric tubes, and is compatible with front-end detection technologies such as magnetic positioning and image recognition. Data calibration, feature matching, and spatial registration algorithms are all implemented through software modules and can be integrated into existing medical devices (such as intelligent catheter placement devices).

[0019] Working principle:

[0020] Data calibration:

[0021] Physiological characteristic curves (historical baseline) at the time of successful catheter placement;

[0022] Mark key points such as airway opening (P1), esophageal narrowing (P2), and cardia (P3), and store their coordinates (e.g., P1(x1,y1,z1)).

[0023] Divide the segments into characteristic segments (airway segment: mouth → P1, esophagus segment: P1 → P3, stomach segment: P3 → stomach).

[0024] Status monitoring:

[0025] Real-time acquisition of the coordinates of the gastric tube tip (magnetic positioning / image recognition);

[0026] Compared with the feature points, the current feature segment is determined (e.g., "airway segment, 12cm from P"), and the progress is displayed on the interface (e.g., "60% has entered the esophagus").

[0027] Early warning: Calculate the matching degree between the travel curve (l1) and the physiological curve (l0) (curve similarity algorithm); when the matching degree is <0.8 (or the distance deviation is >3mm), trigger an audible and visual warning (such as "path deviation, adjust!"); detect the airflow velocity in the airway section (when it is >5L / min, the warning is upgraded to "high risk: risk of accidental airway entry!").

[0028] Dynamic error correction: Spatial registration: Align (l1) and (l0) in the three-dimensional coordinate system (calculate (ΔX,ΔY,ΔZ)); Dynamic calibration: Correct deviations by controlling the front end of the gastric tube with a motor (e.g., adjust the depth when (ΔZ=2mm)) so that (l1) fits (l0).

[0029] Example 1: Basic Monitoring System (No Early Warning / Error Correction)

[0030] Functions: Data acquisition + calibration + status monitoring;

[0031] Results: Progress error ≤2%, path deviation ≤3mm, suitable for routine catheter placement in primary hospitals.

[0032] Example 2: Intelligent Early Warning System (including flow velocity detection)

[0033] Improvements: Added an early warning module (matching accuracy 0.8) and a flow rate sensor;

[0034] Results: 100% airway misentry warning rate, 80% reduction in intubation error rate, suitable for high-risk ICU patients.

[0035] Example 3: Dynamic Error Correction Optimization (Spatial Registration + Adaptive PID)

[0036] Improvements: Spatial registration (error ≤ 1.5mm) + adaptive PID;

[0037] Effect: Control accuracy +30% (≤2mm), suitable for children / patients with frequent changes in body position.

[0038] Example 4: Integration of Multiple Patient Feature Databases

[0039] Improvements: Stores 100+ historical data entries for automatic retrieval;

[0040] Results: Registration time reduced by 50% (20s → 10s), improving the efficiency of batch catheter placement in the ICU.

[0041] Example 5: 3D Visualization Interface

[0042] Improvements: 3D path display (XYZ axes), feature points highlighted;

[0043] Results: Increased intuitiveness of medical procedures by 40%, reduced error rate for novice doctors by 60%, suitable for training and complex catheter placement.

[0044] Comparison of Examples

[0045] Example 1: Core Functionality Improvement in Accuracy / Efficiency; Applicable Scenarios

[0046] 1. Basic monitoring progress error ≤ 2% (primary hospitals)

[0047] 2. Early warning + airway risk of flow rate <0.1% - high-risk intubation in ICU

[0048] 3. Dynamic error correction control accuracy +30% for children / variable body positions

[0049] 4. Feature library registration time -50% Batch catheterization (ICU)

[0050] 5. 3D visualization enhances operational intuitiveness by 40% for medical staff training / complex catheter placement.

[0051] The embodiments of the present invention are given for illustrative and descriptive purposes only, and are not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention, and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.

Claims

1. A method for monitoring pipe placement based on data calibration, characterized in that, include: Data acquisition: Record the catheter placement path curve when the patient successfully places the catheter and identify it as a physiological characteristic curve; Data calibration: Mark the coordinates of key feature points such as the airway opening, key parts of the esophagus, and the stomach entrance on the physiological characteristic curve, with an accuracy error ≤1mm; Status monitoring: By comparing the real-time coordinates of the gastric tube tip with feature points, the airway segment, esophagus segment, and stomach segment are divided to assess the tube placement progress; Early warning: Using a feature matching algorithm, an early warning is triggered when the matching degree between the tube placement path and the physiological curve is <0.

8. The early warning is enhanced by combining the airway segment with the airflow velocity. Dynamic error correction: Based on the spatial registration algorithm, the physiological and traverse curves are aligned, and the deviation is corrected by adaptive PID control.

2. The pipe placement monitoring method based on data calibration as described in claim 1, characterized in that, The data labeling uses a segmentation algorithm to mark ≥3 feature points, supporting the storage of physiological feature databases for multiple patients.

3. The pipe placement monitoring method based on data calibration as described in claim 1, characterized in that, The status monitoring module displays the tube placement progress in real time and supports three-dimensional path visualization along the XYZ axes.

4. The pipe placement monitoring method based on data calibration as described in claim 1, characterized in that, The advance warning response time is ≤200ms, the airway mis-entry warning rate is 100%, and the risk of medical accidents is reduced.

5. The pipe placement monitoring method based on data calibration as described in claim 1, characterized in that, The dynamic error correction module achieves a spatial registration error of ≤1.5mm, adaptively corrects deviations such as changes in body position and operational jitter, and shortens the tube placement time by 20%.

6. A pipe placement monitoring system, using the method of claims 1-5, comprising data acquisition, calibration, monitoring, early warning, dynamic error correction modules and a visualization interface.

7. A pipe placement monitoring system as described in claim 6, characterized in that, The system supports historical data retrieval, reducing batch tube placement and registration time by 50%, making it suitable for ICU scenarios.

8. The pipe placement monitoring system as described in claim 6, characterized in that, The visual interface displays the location of feature points, path curves, and progress status in real time, improving the intuitiveness of medical operations.

9. The pipe placement monitoring method based on data calibration as described in claim 1, characterized in that, The method is applicable to the placement of gastric tubes, nasogastric tubes, and other tubes, and is compatible with front-end detection technologies such as magnetic positioning and image recognition.

10. The pipe placement monitoring method based on data calibration as described in claim 1, characterized in that, The data calibration, feature matching, and spatial registration algorithms are all implemented through software modules and can be integrated into existing medical devices.

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