High-flux bacterial drug sensitivity test platform based on computer vision

Abstract

The invention discloses a high-throughput bacterial drug sensitivity test platform based on computer vision. The platform integrates the functions of automatic separation and identification of bacteria, drug sensitivity test and result analysis. Separation and identification of bacteria are realized through a microfluidic technology, and growth conditions of the bacteria under the action of different antibiotics are monitored in real time in combination with a computer vision technology, so that the antibiotic sensitivity of the bacteria is rapidly judged. The platform has the characteristics of high throughput, automation and intellectualization, can complete detection of a large number of samples in a short time, automatically generates a drug sensitivity test report, and provides powerful support for clinical diagnosis and treatment. By implementing the method, the speed and the accuracy of the bacterial drug sensitivity test are remarkably improved, and the method has a wide application prospect.

Description

Technical Field

[0001] The present invention relates to the field of medical detection technology, and in particular to a rapid, high-throughput antibiotic sensitivity testing (AST) platform for pathogenic bacterial infectious diseases. The platform combines computer vision technology to achieve automated and intelligent bacterial drug sensitivity testing. Background Art

[0002] Traditional bacterial drug sensitivity testing methods usually take hours to days, and are complex and inefficient. With the development of computer vision technology, its application in the field of medical testing is becoming more and more extensive, providing a new solution for fast and accurate bacterial drug sensitivity testing. Summary of the invention

[0003] The purpose of the present invention is to provide a high-throughput bacterial drug sensitivity testing platform based on computer vision, which integrates the functions of bacterial separation, identification, drug sensitivity testing and result analysis, and realizes an automated and intelligent detection process through computer vision technology, greatly improving the detection speed and accuracy.

[0004] The technical solution of the present invention includes the following parts:

[0005] Bacteria isolation and identification module: Utilize microfluidics technology to achieve automated separation and identification of bacteria. Combined with computer vision technology, the morphology, size and other characteristics of bacteria can be identified to provide basic data for subsequent drug sensitivity testing.

[0006] Drug sensitivity test module: multiple test areas are set up on the microfluidic chip, different antibiotics and bacterial samples are added to each test area, and computer vision technology is used to monitor the growth of bacteria in real time. By comparing the inhibitory effects of different antibiotics on bacterial growth, the antibiotic sensitivity of bacteria is determined.

[0007] Result analysis module: Using computer vision technology, image analysis and data processing are performed on the results of drug sensitivity tests to automatically generate drug sensitivity test reports, including sensitive, intermediate, and resistant results. At the same time, image information and growth curves of bacteria are provided to provide doctors with intuitive test results.

[0008] High-throughput design: By optimizing the design of microfluidic chips and the algorithms of computer vision technology, high-throughput bacterial drug sensitivity testing can be achieved, which can complete the testing of a large number of samples in a short time. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 It is a processing flow chart of the high-throughput bacterial drug sensitivity testing platform based on computer vision of the present invention. DETAILED DESCRIPTION

[0010] The specific implementation is as follows:

[0011] Optional, see Figure 1 , the processing flow chart of the high-throughput bacterial drug sensitivity testing platform based on computer vision provided in this application, such as Figure 1 As shown in the figure, the processing flow of the high-throughput bacterial drug sensitivity testing platform based on computer vision includes the following steps:

[0012] Step 101: Add the bacterial sample to the separation area of ​​the chip, and use microfluidic technology to achieve automated separation and identification of bacteria.

[0013] It should be noted that this step is to separate and identify bacteria, which is mainly achieved by using a bacteria separation and identification module. The module also includes a bacteria culture unit, which is used to culture the bacteria sample before automated separation to increase the number of bacteria, thereby improving the efficiency of automated separation.

[0014] Specifically, a microfluidic chip is prepared, and microfluidic technology is used to capture, separate and preliminarily identify bacteria, and computer vision technology is combined to identify the morphology, size and other characteristics of bacteria.

[0015] Step 102: Add different antibiotics and bacterial samples to the test area of ​​the chip, and use computer vision technology to monitor the growth of bacteria in real time.

[0016] This step conducts a drug sensitivity test, which is mainly implemented using a drug sensitivity test module. The module is set on a microfluidic chip and also includes an antibiotic concentration gradient setting unit, which is used to set different concentrations of antibiotics in the test area to more accurately determine the antibiotic sensitivity of bacteria.

[0017] Specifically, the microfluidic chip contains multiple test areas. Different antibiotics and bacterial samples are added to each test area of ​​the chip. Computer vision technology is used to monitor in real time the growth of bacteria under the action of different antibiotics. By comparing the inhibitory effects of different antibiotics on bacterial growth, the antibiotic sensitivity of bacteria is determined.

[0018] Step 103: Perform image analysis and data processing on the results of the drug sensitivity test through computer vision technology to automatically generate a drug sensitivity test report.

[0019] This step is to perform result analysis, which is mainly implemented by a result analysis module. The module also includes an image preprocessing unit, which is used to preprocess the collected bacterial growth images to improve the accuracy of image analysis.

[0020] Specifically, the results of the drug sensitivity test are image analyzed and data processed to automatically generate a drug sensitivity test report, which includes sensitive, intermediate, and resistant results, while providing bacterial image information and growth curves.

[0021] Step 104: Based on the drug sensitivity test report, the doctor can determine the antibiotic sensitivity of the bacteria and formulate an appropriate treatment plan.

[0022] The high-throughput design in this step mainly achieves high-throughput bacterial drug sensitivity testing by optimizing the design of the microfluidic chip and the algorithm of computer vision technology. It can complete the testing of a large number of samples in a short period of time and obtain a drug sensitivity test report. Doctors can judge the antibiotic sensitivity of bacteria based on the drug sensitivity test report and thus specify appropriate treatment plans.

[0023] Step 105: The manually confirmed drug sensitivity test report is used as a new sample and added to the training data set for improving system performance.

[0024] It should also be noted that the computer vision-based high-throughput bacterial drug sensitivity testing platform provided in this application also includes a data storage and transmission module for storing the results of drug sensitivity testing and bacterial image information, and can transmit the results of drug sensitivity testing to a remote server or medical device to achieve data sharing and remote consultation.

[0025] It should be understood that the above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and do not constitute a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the appended claims, or the equivalent forms of such scope and boundaries.

Claims

1. A high-throughput bacterial drug sensitivity testing platform based on computer vision, characterized in that: include: a) Bacteria separation and identification module, which is used to automatically separate and identify bacteria samples. This module uses microfluidics technology to capture, separate and preliminarily identify bacteria, and combines computer vision technology to identify the morphology, size and other characteristics of bacteria; b) a drug sensitivity test module, which is arranged on a microfluidic chip and includes a plurality of test areas, each of which is added with different antibiotics and bacterial samples, and the growth of bacteria under the action of different antibiotics is monitored in real time by using the computer vision technology, and the antibiotic sensitivity of bacteria is determined by comparing the inhibitory effects of different antibiotics on bacterial growth; c) a result analysis module, which is used to perform image analysis and data processing on the results of drug sensitivity testing, and automatically generate a drug sensitivity test report, which includes sensitive, intermediate, and resistant results, and provides bacterial image information and growth curves; d) High-throughput design: by optimizing the design of the microfluidic chip and the algorithm of the computer vision technology, high-throughput bacterial drug sensitivity testing can be achieved, and a large number of samples can be tested in a short time.

2. The high-throughput bacterial drug sensitivity testing platform based on computer vision according to claim 1, characterized in that: The bacterial separation and identification module also includes a bacterial culture unit, which is used to culture the bacterial sample before the automated separation to increase the number of bacteria.

3. The high-throughput bacterial drug sensitivity testing platform based on computer vision according to claim 1, characterized in that: The drug sensitivity test module also includes an antibiotic concentration gradient setting unit, which is used to set antibiotics of different concentrations in the test area.

4. The high-throughput bacterial drug sensitivity testing platform based on computer vision according to claim 1, characterized in that: The result analysis module also includes an image preprocessing unit for preprocessing the collected bacteria growth image.

5. The high-throughput bacterial drug sensitivity testing platform based on computer vision according to claim 1, characterized in that: The platform also includes a data storage and transmission module for storing the results of drug sensitivity tests and bacterial image information, and is capable of transmitting the results of the drug sensitivity tests to a remote server or medical device.

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