Blood analysis system, method, device and computer storage medium

By combining impedance detection with a deep learning neural network model, the problems of strong dependence on optical signal detection and high hardware cost in existing technologies are solved, and efficient and accurate detection of blood cell parameters is achieved, especially accurate analysis of reticulocytes, reticulocytes and red blood cell fragments parameters.

CN115855752BActive Publication Date: 2025-09-12SHENZHEN MINDRAY BIO MEDICAL ELECTRONICS CO LTD
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Patent Information

Application Number
CN202111124135.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2021-09-24
Publication Date
2025-09-12
Estimated Expiration
2041-09-24

AI Technical Summary

Technical Problem

Existing blood cell analysis methods rely on optical signal detection, which is highly environmentally dependent and has high hardware costs. It is difficult to accurately detect parameters such as reticulocytes, reticulocytes, and red blood cell fragments, especially when abnormal cells are present, and false deviations are prone to occur.

Method used

Impedance detection is combined with a deep learning neural network model to obtain a blood cell histogram through an impedance pulse signal, and a pre-trained deep learning neural network model is used to output blood cell parameters, such as reticulocyte parameters, reticulocyte parameters, and red blood cell fragment parameters, avoiding the environmental dependence and hardware cost of optical signal detection.

Benefits of technology

It achieves accurate detection of blood cell parameters, simplifies the operation process, reduces detection costs, and improves the accuracy of detection results.

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Abstract

Disclosed are a blood analysis system, method, device, and computer storage medium. The blood analysis system includes: a sample collection device for collecting a blood sample and transferring the blood sample to a sample preparation device; a sample preparation device for processing the blood sample using a reagent to prepare a sample solution to be tested; an impedance detection device for detecting the sample solution to be tested to obtain an impedance pulse signal of cells in the sample solution to be tested; and a data processing device for performing the following steps: obtaining a blood cell histogram of the sample solution to be tested based on the impedance pulse signal, inputting the blood cell histogram into a pre-trained deep learning neural network model to obtain blood cell parameters output by the deep learning neural network model, wherein the blood cell parameters are at least one of the following: a reticulocyte parameter, a reticulocyte platelet parameter, and a red blood cell fragment parameter; and the deep learning neural network model is obtained by training blood training samples labeled with blood cell parameters using a machine learning algorithm.
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