WO2021062741A1

WO2021062741A1 - Information processing method, sample testing system, and computer storage medium

Info

Publication number: WO2021062741A1
Application number: PCT/CN2019/109658
Authority: WO
Inventors: 郭芳; 曾凡顺
Original assignee: 深圳迈瑞生物医疗电子股份有限公司
Priority date: 2019-09-30
Filing date: 2019-09-30
Publication date: 2021-04-08
Also published as: CN114424065A

Abstract

An information processing method, a sample testing system (700), and a computer storage medium. The method comprises: acquiring a microscopy result of a sample to be tested (S101); acquiring a preset abnormality rule (S102); if there is result information, matching the preset abnormality rule, in the microscopy result, generating corresponding abnormality result information (S103); and displaying, in a microscopy result interface, the abnormality result information (S104).

Description

Information processing method, sample detection system and computer storage medium

Technical field

This application relates to the field of sample analysis, and relates to, but is not limited to, an information processing method, a sample detection system, and a computer storage medium.

Background technique

At present, when performing sample testing, if certain classifications reach or exceed a certain ratio in the microscopic examination results of the reading software, the results need to be recorded and presented in a report for clinicians to assist in diagnosis. At present, after reading the film in the reading software, the readers need to manually enter the abnormal result information on the Laboratory Information System (LIS) side; and manually edit the abnormal information on the reading software side, and then Send a report. This is not only time-consuming and labor-intensive, but it is also prone to hand errors during entry, causing inaccurate detection results.

Summary of the invention

In view of this, the embodiments of the present application provide an information processing method, a sample detection system, and a computer storage medium in order to solve the problems existing in the prior art.

The embodiment of the present application provides an information processing method, and the method includes:

Obtain the result of microscopic examination of the sample to be tested;

Obtain the preset exception rules;

If there is result information matching the preset abnormal rule in the microscopic examination result, generate corresponding abnormal result information;

The abnormal result information is displayed on the microscopic examination result interface.

In the above scheme, before obtaining the microscopic examination result of the sample to be tested, it also includes:

Performing blood routine testing on the sample to be tested;

If the result of the routine blood test meets the preset re-inspection rule, push and read the image of the sample to be tested to obtain the result of the microscopic examination of the sample to be tested.

In the above solution, the way of reading the pictures includes automatic reading of the pictures by the reading machine.

In the above solution, the method further includes:

Generate a test result report of the sample to be tested based on the abnormal result information and the result of the blood routine test; or

The test result report of the sample to be tested is generated based on the abnormal result information, the blood routine test result, and the test result information of other test items of the sample to be tested.

In the above solution, said generating corresponding abnormal result information includes:

Receive an input command to generate abnormal result information with one key; and

The corresponding abnormal result information is generated according to the instruction of one-key generation of abnormal result information.

In the above solution, the method further includes:

Determine each result item corresponding to each abnormal rule in the microscopic examination result;

Match each of the result items with the corresponding abnormal rules in sequence;

If there is a target result item matching the abnormal rule in the microscopic examination result, the target result item is determined as result information matching the abnormal rule.

In the above solution, the method further includes:

Receiving a selection operation for a target result item in the microscopic examination result, wherein the selection operation is performed based on the abnormal rule;

Determine the selected target result item as the result information that matches the exception rule.

In the above solution, the method further includes:

Obtain the pre-classified cell types for which abnormal rules need to be set in each microscopic examination type;

Determine the abnormal reference range of the pre-classified cell type based on the normal reference range corresponding to the pre-classified cell type;

Based on the abnormal reference range of the pre-classified cell type, an abnormal rule corresponding to the pre-classified cell type is generated.

The embodiment of the present application provides a sample detection system, the sample detection system at least includes:

The blood analyzer is used to perform routine blood test on the sample to be tested, and send the test data of the blood routine test to the control device;

The control device is used to process the blood routine test data of the sample to obtain the blood routine test result, and is also used to determine whether the blood routine test result of the sample meets the re-inspection rules, and if it meets the re-inspection rules, the control will The sample is transported to the pusher;

Slide pusher, used to push slides on the sample to make glass slides to be inspected;

Scanning machine, used to take image of the glass slide to be inspected, obtain the cell image, and send the cell image to the control device;

The control device is further configured to obtain the microscopic examination result of the sample to be tested based on the cell image, and obtain a preset abnormality rule; if the microscopic examination result contains result information that matches the preset abnormality rule , Generate corresponding abnormal result information; display the abnormal result information on the microscopic examination result interface.

In the above solution, the control device is further configured to generate a test result report of the sample to be tested based on the abnormal result information and the result of the blood routine test; or

In the above solution, the control device further includes a human-computer interaction module, the human-computer interaction module is configured to receive an instruction input by the user to generate abnormal result information with one key; the control device is also configured to generate an abnormal result according to one key The instruction of the result information generates the corresponding abnormal result information.

In the above solution, the control device is further used for:

In the above solution, the human-computer interaction module is further configured to receive a selection operation of a target result item in the microscopic examination result, wherein the selection operation is performed based on the abnormal rule;

The control device is also used to determine the selected target result item as result information matching the abnormal rule.

In the above solution, the control device is further used for:

The embodiment of the present application further provides a sample detection system, the sample detection system at least includes:

The control device is used to obtain the microscopic examination result of the glass slide to be examined based on the cell image, and obtain a preset abnormality rule; if the microscopic examination result contains result information that matches the preset abnormality rule, generate Corresponding abnormal result information; the abnormal result information is displayed on the microscopic examination result interface.

In the above solution, the control device is further used for:

An embodiment of the present application provides a computer storage medium in which an executable program is stored, and when the executable program is executed, the steps of the information processing method described above are implemented.

In the embodiment of the present application, after obtaining the microscopic examination result of the sample to be tested, the preset abnormality rule is further obtained; the abnormal result information in the microscopic examination result is determined through the abnormality rule, and the abnormal result information is displayed on the microscopic examination result interface The abnormal result information; in this way, after the microscopic examination result is obtained, the abnormal result information can be determined through the preset abnormal rules without manual input by medical staff, which not only reduces manual operation, but also avoids manual input Error-prone problems improve the accuracy of the microscopy report.

Description of the drawings

FIG. 1 is a schematic diagram of an implementation flow of an information processing method according to an embodiment of this application;

2 is a schematic diagram of another implementation process of the information processing method according to the embodiment of the application;

FIG. 3A is a schematic diagram of generating an abnormal rule according to an embodiment of the application;

3B is a schematic diagram of an interface for setting an exception rule according to an embodiment of the application;

FIG. 3C is a schematic diagram of another interface for setting exception rules in an embodiment of this application;

4A is a schematic diagram of the interface of the microscopic examination result of the embodiment of the application;

4B is a schematic diagram of an interface for generating abnormal result information with one click according to an embodiment of the application;

FIG. 5 is a schematic diagram of an implementation process of generating abnormal result information after a user's one-key operation according to an embodiment of the application;

6A is a schematic diagram of an interface of an abnormal result information generation rule according to an embodiment of the application;

[Corrected according to Rule 91 on 17.12.2019]
6B is a schematic diagram of an abnormal result information display interface of an embodiment of the application;
6C is a schematic diagram of an abnormal result information display interface of an embodiment of the application;

FIG. 7 is a schematic diagram of the composition structure of a sample detection system according to an embodiment of the application.

Detailed ways

In order to make the purpose, technical solutions, and advantages of the application more clear, the application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the application, and those of ordinary skill in the art have not made any All other embodiments obtained under the premise of creative labor belong to the scope of protection of this application.

In the following description, “some embodiments” are referred to, which describe a subset of all possible embodiments, but it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments, and Can be combined with each other without conflict.

If there is a similar description of "first/second" in the application documents, add the following description. In the following description, the term "first\second\third" is only used to distinguish similar objects. Represents a specific ordering of objects. Understandably, "first\second\third" can be interchanged in a specific order or sequence when permitted, so that the embodiments of the application described here can be used except here It is carried out in an order other than that shown or described.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of this application. The terminology used herein is only for the purpose of describing the embodiments of the application, and is not intended to limit the application.

The embodiments of the present application provide an information processing method, which is applied to an information processing device. The information processing device may be a terminal device with computing and communication capabilities such as a desktop computer, a desktop computer, etc., which is inconvenient to move, or it may be a terminal device such as a tablet computer or a mobile computer. Mobile terminals such as phones and laptops. FIG. 1 is a schematic diagram of an implementation flow of an information processing method according to an embodiment of the application. As shown in FIG. 1, the method includes:

Step S101: Obtain the microscopic examination result of the sample to be detected.

Here, after the sample to be tested is tested using the sample testing equipment and the sample test result is obtained, it can be determined based on the sample test result whether a retest is required. When the sample test result indicates that a retest is required, then the sample to be tested is generally mirrored. Under test, in order to get more accurate microscopic examination results.

The inspection under the microscope can be manual inspection, but the detection efficiency is low and the labor cost is high. Therefore, in the current inspection under the microscope, the image reader automatically takes the image of the slide to be inspected to obtain multiple cell images. Image reading is performed based on multiple cell images, that is, the image reader analyzes and processes the image under the microscope to obtain the result of the microscope inspection.

Wherein, the microscopic examination result includes multiple result items, and each result item corresponds to a pre-classified cell type and numerical information (such as the number of cells) of the pre-classified cell type.

Step S102: Obtain a preset exception rule.

Here, the preset exception rule can be one or multiple. Since different types of microscopy have different detection targets, that is to say, different types of microscopy need to detect different cell types. Therefore, in general, multiple exception rules are preset (the exception rule set may also be hereinafter). The abnormal rule or set of abnormal rules corresponding to the blood microscopy type is for the cell types that need to be detected by the blood microscopy type. For example, the abnormal rule can be: the proportion of primitive cells is greater than 1%, or it can be: neutral rod-shaped nucleosome The proportion of cells is greater than or equal to 5%, and the proportion of neutral lobulated nuclear granulocytes is greater than 50%.

For the type of urine microscopy, the cell types that need to be detected can include: white blood cells, then the abnormal rules corresponding to the type of urine microscopy are for the cell types that need to be detected for the type of urine microscopy.

Step S103, if there is result information matching the preset abnormality rule in the microscopic examination result, corresponding abnormal result information is generated.

In some embodiments, before step S103, it is necessary to determine whether there is result information matching a preset abnormality rule in the microscopic examination result. Among them, the control device in the sample detection system may be based on each result item of the microscopic examination result. Automatically determine whether there is result information matching the abnormal rule with the preset abnormality rule, or it may be that the medical staff manually selects the result information matching the preset abnormality rule after obtaining the microscopic examination result according to the abnormal rule.

In the embodiment of the present application, the abnormal result information is generated based on the result information matching the abnormal rule. Further, the result information matching the abnormal rule can be directly determined as the abnormal result information, and the result matching the abnormal rule can also be passed Information and in accordance with certain generation rules, generate abnormal result information.

Step S104, displaying the abnormal result information on the microscopic examination result interface.

Here, the microscopic examination result interface can display various result items of the microscopic examination result. When there is abnormal result information, the abnormal result information can be displayed in the abnormal result prompt box preset in the microscopic examination result interface, so that the medical staff can be intuitive and intuitive. Conveniently understand the abnormal result information in the microscopic examination result.

In the information processing method provided by the embodiment of the present application, after obtaining the microscopic examination result of the sample to be tested, the preset abnormality rule is further obtained; the abnormal result information in the microscopic examination result is determined by the abnormality rule, and The abnormal result information is displayed on the microscopic examination result interface; in this way, after the microscopic examination result is obtained, the abnormal result information can be determined through the preset abnormal rules without manual input by medical staff, which not only reduces manual operations, but also It can also avoid problems that are prone to errors due to manual entry, and improve the accuracy of the microscopy report.

In some embodiments, before obtaining the microscopic examination result of the sample to be tested in step S101, as shown in FIG. 2A, the following steps may be further included, which are described below in conjunction with each step and FIG. 2A.

Step S001: Perform blood routine testing on the sample to be tested.

Here, in implementation, the blood analyzer in the sample detection system may perform routine blood testing on the sample to be tested, and send the obtained detection data to the control device of the sample detection system.

In step S002, it is determined whether the result of routine blood test meets the preset retest rule.

Here, step S002 may be implemented by the control device in the sample detection system. After receiving the test data sent by the blood analyzer, the control device obtains the test result based on the test data, and further determines whether the result of the blood routine test meets the re-inspection rules. Among them, when the result of the blood routine test meets the re-inspection rules, enter Step S003: Push and read the pictures of the samples that need to be re-examined; when the results of routine blood tests do not meet the re-examination rules, step S004 is entered to end the detection process of the samples to be tested.

In the embodiment of the present application, the re-inspection rule may be that there is a test result item in the test result that is very different from the normal value reference range. For example, the re-inspection rule may be that the number of white blood cells is less than one.

Similar to the preset abnormal rules, the recheck rule can include one or more rule items. When the recheck rule includes multiple rule items, when the blood test result meets at least one of the multiple rule items , That is, it is considered that the result of routine blood test meets the re-inspection rule, and step S003 needs to be entered at this time to perform re-test (such as microscopic inspection) on the sample whose test result triggers the re-inspection rule. When the blood routine test result of a certain sample does not meet any one of the multiple rule items, the blood routine test result of the sample does not meet the re-inspection rule, and the test process of the sample is ended at this time.

Step S003: If the result of the routine blood test meets the preset re-inspection rule, push and read the image of the sample to be tested to obtain the result of the microscopic examination of the sample to be tested.

Here, the re-examination rules include at least the microscopic examination rules. When the results of routine blood tests meet the re-examination rules, at least it indicates that the blood sample needs to be microscopically examined. Before performing microscopic inspection, the sample to be tested needs to be pushed. In the embodiment of the present application, the sample to be tested may be pushed by a slide pusher to obtain the glass slide to be inspected, and then the glass slide to be inspected is read to obtain The result of the microscopic examination of the sample to be tested. In the embodiment of the present application, the method of reading the image is the automatic image reading by the image reading machine.

Step S004: End the detection process of the sample to be detected.

In some embodiments, after step S104, the test result report of the sample to be tested can also be generated in the following two ways:

The first method: generating a test result report of the sample to be tested based on the abnormal result information and the result of the blood routine test;

The second method is to generate a test result report of the sample to be tested based on the abnormal result information, the blood routine test result, and the test result information of other test items of the sample to be tested.

When generating the test result report of the sample to be tested, it may be triggered based on the operation of generating the test report made by the medical staff, or it may be automatically executed after step S104.

In the above two methods, after the abnormal result information is determined, the abnormal result information can be combined with the result of the routine blood sample test, or the abnormal result information, the blood routine test result, and the test results of other test items of the sample to be tested can be combined. The information is output together to provide doctors and patients with more comprehensive and complete information, so that doctors can take more accurate treatments for patients based on the test results of the samples to be tested and the abnormal results in the microscopy, so that the patients can treat their own body Have a more comprehensive understanding of the status.

In some embodiments, the corresponding abnormal result information can be generated through step S1031 to step S1032 as shown in FIG. 2B, and the relevant description will be given below in combination with each step.

Step S1031: Receive a one-key command to generate abnormal result information input by the user.

Here, the instruction may be a button control displayed on the display device of the control device by the user to click or touch to press the button control for one-key generation of abnormal result information. For example, the button control may be an edit button control 411 as shown in FIG. 4B.

In step S1032, corresponding abnormal result information is generated according to the one-key generating abnormal result information instruction.

As shown in Figure 4B, after the user clicks (by mouse click or touch click) the edit button 411, if there is result data that meets the preset abnormal rules on the current image reading result interface, it will be automatically generated in the remark box on the left Abnormal result information, such as "Visible under the microscope: the proportion of neutral rod-shaped nucleus granulocytes is 6.52%".

Here, when receiving an instruction to generate abnormal result information with one key, the control device first obtains the result information of the microscopic examination result that matches the preset abnormality rule, and based on the result of the microscopic examination result that matches the preset abnormality rule The information generates the corresponding abnormal result information.

In the implementation, the result information matching the abnormal rule can be directly determined as the abnormal result information, and the abnormal result information can also be generated through the result information matching the abnormal rule and in accordance with a certain generation rule.

In some embodiments, after step S102 and before step S103, the following steps may be used to determine whether there is result information that matches a preset abnormal rule in the microscopic examination result:

Step S21: Determine each result item corresponding to each abnormal rule in the microscopic examination result.

Here, there are multiple exception rules in the embodiment of this application. When step S21 is implemented, it is necessary to obtain corresponding result items from the microscopic inspection results according to the abnormal rules. For example, the abnormal rule can be: the proportion of neutral rod-shaped nucleus granulocytes is greater than or equal to 5%, and the proportion of neutral lobular nucleus granulocytes is greater than 50%, then the ratio of neutral rod-shaped nucleus granulocytes needs to be determined from the results of the microscopy. The result item and the result item of the proportion of neutrophil nuclei and granulocytes.

Step S22: Match each of the result items with the corresponding abnormal rules in sequence.

Here, FIG. 2C is a schematic diagram of the interface of the microscopic examination results of the embodiment of the application. In the microscopic examination results shown in FIG. 2C, the proportion of neutral rod-shaped nucleus granulocytes is 4%, and the proportion of neutral lobulated nucleus granulocytes is 74%. , The ratio of neutral rod-shaped nucleus granulocytes of 4% and the ratio of abnormally ruled neutral rod-shaped nucleus granulocytes greater than or equal to 5% are matched, and the ratio of neutral lobulated nucleus granulocytes of 74% is matched with that of abnormally ruled neutral. The proportion of leaf nucleus granulocytes is greater than 50% for matching, and the matching result is obtained.

Step S23: Determine whether there is a target result item matching the abnormal rule in the microscopic examination result.

Here, following the above example, since 4% is less than 5%, then the result item of the proportion of neutral rod-shaped nucleus granulocytes does not satisfy that the proportion of neutral rod-shaped nucleus granulocytes is greater than or equal to 5%, in other words, the neutral rod-shaped nucleus granule cell ratio is greater than or equal to 5%. The result item of the ratio of rod-shaped nucleus granulocytes does not match the ratio of abnormally ruled neutral rod-shaped granulocytes greater than or equal to 5%. Since 74% is greater than 50%, then the result item of the proportion of neutral lobulated nuclei and granulocytes meets the abnormal rules. The proportion of neutral lobulated nuclei and granulocytes is greater than 50%, in other words, the result of the proportion of neutral lobulated nuclei and granulocytes The item does not match the ratio of neutral lobulated nuclei and granulocytes greater than 50% with abnormal rules.

In the embodiment of this application, when there are multiple abnormal rules, as long as there is a result item of the microscopy result that matches the abnormal rule, it means that there is a target result item that matches the abnormal rule in the microscopy result. When all the result items in the inspection result do not match the exception rule, it can be explained that there is no target result item that matches the exception rule in the microscopic inspection result.

Step S24: If there is a target result item matching the abnormal rule in the microscopic examination result, it is determined that there is result information matching the abnormal rule in the microscopic examination result, and the target result item is determined as a result matching the abnormal rule information.

Step S25: If there is no target result item matching the abnormal rule in the microscopic examination result, it is determined that there is no result information matching the preset abnormal rule in the microscopic examination result.

Here, if there is no target result item that matches the abnormal rule in the microscopy result, it means that there is no abnormal result item in the microscopy result, which means that the microscopy result is normal. At this time, only the mirror can be displayed in the microscopy result interface. Each result item of the inspection result is sufficient.

In some embodiments, the medical staff can also manually select the result information that matches the preset abnormal rule in the following steps:

Step 31: Receive a selection operation on the target result item in the microscopic examination result.

Here, the selection operation is performed at least based on the abnormal rule, that is, the selection operation triggered by the medical staff is for the result item that meets the abnormal rule. For example, the abnormal rule is that the proportion of neutral rod-shaped nuclear granulocytes is greater than or equal to 5%, and the proportion of neutral lobulated nuclear granulocytes is greater than 50%. Then, when the medical staff masters the abnormal rules, they can follow the results of the microscopic examination. Result item, select the result item that meets the exception rule.

Step 32: Determine the selected target result item as result information that matches the abnormal rule.

In some embodiments, if the medical staff finds that in addition to the abnormal rules, there are other obviously abnormal result items in the microscopic examination result, they can also use other obviously abnormal result items as the selected target result items, and determine them as the same The result information of abnormal rule matching, and then generate abnormal result information from these selected target result items, and output it in the subsequent steps, which can make up for the defect of incomplete coverage of the preset abnormal rules, so as to provide complete and accurate information for doctors and patients. The test results.

In some embodiments, the exception rule can be generated through the following steps:

Step 41: Obtain the pre-classified cell types for which abnormal rules need to be set in each microscopic examination category.

Here, the pre-classified cell types for which abnormal rules need to be set may be set by default by the system, or may be set by medical personnel according to the detection needs. In some embodiments, it may also be determined according to the detection items of the sample to be detected.

Step 42: Determine an abnormal reference range of the pre-classified cell type based on the normal reference range corresponding to the pre-classified cell type.

Here, the abnormal reference range of the pre-classified cell type is the range outside the normal reference range. For example, the pre-classified cell type is the proportion of neutral rod-shaped nucleus granulocytes, and the normal reference range is less than 5%, then the neutral rod The abnormal reference range of the pre-classified cell type, the proportion of nucleus granulocytes, is greater than or equal to 5%.

Step 43: Generate an abnormal rule corresponding to the pre-classified cell type based on the abnormal reference range of the pre-classified cell type.

Here, when step 43 is implemented, it may be directly determining the abnormal reference range of the pre-classified cell type as the corresponding abnormal rule, or it may be based on the abnormal reference range of the pre-classified cell type and then performing certain arithmetic processing to determine the abnormal rule. For example, the lower limit of the abnormal rule can be increased to a certain multiple of the lower limit of the abnormal reference range. For example, the lower limit of the abnormal rule can be increased to 1.2 times the lower limit of the abnormal reference range. The reference range is greater than or equal to 5%, that is, the lower limit of the abnormal reference range is 5%, then the lower limit of the abnormal rule is 1.2 times the lower limit of the abnormal reference range, which is 6%, then the abnormal rule can be Is greater than or equal to 6%.

Based on the foregoing embodiment, the embodiment of the present application further provides an information processing method for quickly entering abnormal result information. The information processing method includes the following three stages:

In the first stage, you can customize the rules that characterize abnormal results. The setting items can be set for all pre-classified cell types, and they can be stored persistently after setting.

In some embodiments, the rules may be extracted and defined as default abnormal rules based on the experience of the examiner, and at the same time support the user to customize these rules. When the result of the image reading comes out, through these rules, the remark information (that is, abnormal result information) that meets the expectations can be automatically generated. FIG. 3A is a schematic diagram of generating an exception rule according to an embodiment of the application. As shown in FIG. 3A, the system 301 has a built-in default exception rule, and at the same time supports the user 302 to modify and edit the exception rule.

Fig. 3B is a schematic diagram of the interface for setting abnormal rules in the embodiment of the application. As shown in Fig. 3B, the cell type can be selected in the parameter column 311, and the relationship can be selected in the relationship column 312 to be greater than, less than, greater than or equal to, less than or equal to, etc. You can set a value in the value column 313. In this way, an abnormal rule can be determined through the parameter column, relationship column, and value column in the same row. As shown in Figure 3B, the abnormal rule in the first row is neutral rod-shaped nucleus granulocytes. If it is greater than or equal to 5%, the abnormal rule in the second row is that the neutral lobulated nuclear granulocytes are greater than 50%.

Fig. 3C is another schematic diagram of an interface for setting exception rules according to the embodiment of the application. As shown in Fig. 3C, in addition to selecting the values of parameter columns, relation columns, and value columns, you can also edit them in the edit box shown in 321 The prefix, for example, can be "visible under the mirror:".

In the second stage, abnormal result information is generated after the user's one-key operation;

Here, FIG. 4A is a schematic diagram of the interface of the microscopy results of the embodiment of the application. As shown in FIG. 4A, in the microscopy results, the percentage of neutral rod-shaped nucleus granulocytes is 6.52%, and the percentage of neutral lobular nucleus granulocytes is 6.52%. It is 48.91%, because the set abnormal rule is that the neutral rod-shaped nucleus granulocyte is greater than or equal to 5%, and the neutral lobular nucleus granule cell is greater than 50%, which means that the percentage of the neutral rod-shaped nucleus granule cell is greater than the abnormal The ratio set by the rule, the percentage of neutral lobulated nucleus granulocytes is not greater than the ratio set in the abnormal rule, and the information shown in Figure 4B is generated at this time. Figure 4B is a schematic diagram of the interface for generating abnormal result information with one key in the embodiment of the application. As shown in FIG. 4B, the abnormal result information in the abnormal result information prompt box 412 can be generated by clicking the edit button control 411: "Visible under the microscope: the proportion of neutral rod-shaped nucleus granulocytes: 6.52%".

In the third stage, if a test report needs to be sent to the LIS system, the remarks will be sent to the LIS system as part of the test results.

Fig. 5 is a schematic diagram of the implementation process of generating abnormal result information after a user’s one-key operation according to an embodiment of the application. As shown in Fig. 5, it includes the following steps:

Step S501: Obtain all current classification items and parameter values.

Here, all the classification items and parameter values of the microscopic examination result obtained by the current microscopic examination are acquired.

Step S502: Obtain rule setting items.

Here, the rule setting item is a preset exception rule, and there are generally multiple rule setting items.

In step S503, the classification items are matched with the rule setting items one by one.

Step S504: It is judged whether the classification item matches one of the rule setting items.

Here, if the classification item matches one of the rule setting items, it indicates that the classification item is an abnormal result item, and step S505 is entered at this time.

Step S505: Record the classification item and parameter value.

Here, the classification item and parameter value are recorded so as to determine the abnormal result information based on the classification item and parameter value. After step S505, it proceeds to step S507.

Step S506, skip the classification item if it does not match.

Step S507: It is determined whether all the classification items have been processed.

Here, if the classification items are not all processed, go to step S503 to process the next classification item; if the classification items are all processed, go to step S508.

In step S508, all recorded parameter items and their parameter values are displayed on the interface.

Here, when step S508 is implemented, it may be executed automatically, or it may be executed based on the user's click or touch operation on the edit button control. Displaying all recorded parameter items and their parameter values on the interface is also abnormal result information.

In step S508, the recorded parameter items and their parameter values are directly displayed on the interface as abnormal result information. In some embodiments, it is also possible to follow preset rules for generating abnormal result information, according to the recorded parameter items and their values. The parameter value generates abnormal result information.

Fig. 6A is a schematic diagram of the pre-classification interface of red blood cells according to the embodiment of the application. As shown in Fig. 6A, the pre-classification of red blood cells includes uneven red blood cells, large red blood cells, small red blood cells, hypochromic red blood cells, pleochroic red blood cells, and red blood cells. The morphology is irregular. The 0, 1+, 2+, and 3+ shown in Fig. 6A indicate the degree of these pre-classifications. When all are 0, it means that there is no abnormality in the detection result.

Fig. 6B is a schematic diagram of the interface of the abnormal result information generation rule according to the embodiment of the application. As shown in Fig. 6B, the degree 611 and the description 612 can be set. When the red blood cell morphology degree is 1+, it is described as a small amount; the red blood cell morphology degree is 2+ When the red blood cell morphology is 3+, it is described as a medium amount; when the red blood cell morphology degree is 3+, it is described as a large amount. Since the red blood cell size unevenness shown in FIG. 6A is 1+, when the user clicks the button control 601 of the one-key note in FIG. 6A, the abnormal result information generation rule can generate the abnormality shown in FIG. 6C. Result information: "A small amount of red blood cell size is uneven under the microscope."

Through the information processing method provided above, it is possible to automatically record abnormal result information, thereby reducing the workload of the readers and separating them from pure text editing work, and also avoids clerical errors, and improves the ease of use of the product And accuracy.

The embodiment of the application provides a sample detection system. FIG. 7 is a schematic diagram of the composition structure of the sample detection system according to the embodiment of the application. As shown in FIG. 7, the sample detection system at least includes:

The blood analyzer 701 is used to perform routine blood test on the sample to be tested, and send the blood routine test test data to the control device;

The control device 702 is used to process the blood routine test data of the sample to obtain the blood routine test result, and is also used to determine whether the blood routine test result of the sample meets the re-inspection rules, and if it meets the re-inspection rules, control Transport the sample to the pusher;

Slide pusher 703, used to push slides of the sample to make glass slides to be inspected;

The slide reader 704 is used to take image shots of the glass slide to be inspected, obtain cell images, and send the cell images to the control device;

The control device 702 is further configured to obtain the microscopic examination result of the sample to be detected based on the cell image, and obtain a preset abnormality rule; if there is a result that matches the preset abnormality rule in the microscopic examination result Information, generate corresponding abnormal result information; display the abnormal result information on the microscopic examination result interface.

In the above solution, the control device 702 is further configured to generate a test result report of the sample to be tested based on the abnormal result information and the result of the blood routine test; or

In the above solution, the control device 702 further includes a human-computer interaction module, and the human-computer interaction module 7021 is configured to receive an instruction input by the user to generate abnormal result information with one key; the control device is also configured to: The instruction that generates abnormal result information generates corresponding abnormal result information.

In the above solution, the control device 702 is further configured to:

The control device 702 is further configured to determine the selected target result item as the result information matching the abnormal rule.

In the above solution, the control device 702 is further configured to:

In the embodiments of the present application, if the above method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product in essence or a part that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for A computer device (which may be a personal computer, a server, or a network device, etc.) executes all or part of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, Read Only Memory (ROM, Read Only Memory), magnetic disk or optical disk and other media that can store program codes. In this way, the embodiments of the present application are not limited to any specific combination of hardware and software.

In some embodiments, the control device further includes a man-machine interaction module, the man-machine interaction module is configured to receive an instruction input by the user to generate abnormal result information with one key; the control device is also configured to generate information according to one key The instruction of abnormal result information generates corresponding abnormal result information.

In some embodiments, the control device is also used for:

In some embodiments, the human-computer interaction module is further configured to receive a selection operation of a target result item in the microscopic examination result, wherein the selection operation is performed based on the abnormal rule;

In some embodiments, the control device is also used for:

Correspondingly, an embodiment of the present application further provides a computer storage medium with computer executable instructions stored on the computer storage medium, and when the computer executable instructions are executed, the steps of the information processing method provided in the above embodiments are implemented.

The above description of the sample detection system and the computer storage medium embodiment is similar to the description of the above method embodiment, and has similar beneficial effects as the method embodiment. For technical details not disclosed in the embodiments of the sample detection system and the computer storage medium of the present application, please refer to the description of the method embodiments of the present application for understanding.

It should be understood that “one embodiment” or “an embodiment” mentioned throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of the present application. Therefore, the appearances of "in one embodiment" or "in an embodiment" in various places throughout the specification do not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in the various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not correspond to the embodiments of the present application. The implementation process constitutes any limitation. The serial numbers of the foregoing embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

It should be noted that in this article, the terms "include", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, It also includes other elements not explicitly listed, or elements inherent to the process, method, article, or device. If there are no more restrictions, the element defined by the sentence "including a..." does not exclude the existence of other identical elements in the process, method, article, or device that includes the element.

In the several embodiments provided in this application, it should be understood that the disclosed device and method may be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other divisions in actual implementation, such as: multiple units or components can be combined, or It can be integrated into another system, or some features can be ignored or not implemented. In addition, the coupling, or direct coupling, or communication connection between the components shown or discussed may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms. of.

The units described above as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, the functional units in the embodiments of the present application can be all integrated into one processing unit, or each unit can be individually used as a unit, or two or more units can be integrated into one unit; The unit can be implemented in the form of hardware, or in the form of hardware plus software functional units.

Those of ordinary skill in the art can understand that all or part of the steps in the above method embodiments can be implemented by a program instructing relevant hardware. The foregoing program can be stored in a computer readable storage medium. When the program is executed, the execution includes The steps of the foregoing method embodiment; and the foregoing storage medium includes various media that can store program codes, such as a mobile storage device, a read only memory (Read Only Memory, ROM), a magnetic disk, or an optical disk.

Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it may also be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product in essence or a part that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for A computer device (which may be a personal computer, a server, or a network device, etc.) executes all or part of the methods described in the various embodiments of the present application. The aforementioned storage media include: removable storage devices, ROMs, magnetic disks or optical discs and other media that can store program codes.

The above are only specific implementations of this application, but the protection scope of this application is not limited to this. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application. Should be covered within the scope of protection of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Industrial applicability

The information processing method in the embodiment of the present application includes: obtaining a microscopic examination result of a sample to be detected; obtaining a preset abnormality rule; if the microscopic examination result contains result information that matches the preset abnormality rule, generating Corresponding abnormal result information; the abnormal result information is displayed on the microscopy result interface; this can not only reduce manual operations, but also avoid problems that are prone to errors due to manual entry, thereby improving the accuracy of the test report.

Claims

An information processing method, the method comprising:

Obtain the result of microscopic examination of the sample to be tested;

Obtain the preset exception rules;

If there is result information matching the preset abnormal rule in the microscopic examination result, generate corresponding abnormal result information;

The abnormal result information is displayed on the microscopic examination result interface.
The method according to claim 1, before obtaining the result of the microscopic examination of the sample to be tested, the method further comprises:

Performing blood routine testing on the sample to be tested;

If the result of the routine blood test meets the preset re-inspection rule, push and read the image of the sample to be tested to obtain the result of the microscopic examination of the sample to be tested.
According to the method of claim 2, the method of microscopic examination includes the scanning machine automatically taking a cell image of the sample to be tested and displaying the scanning result.
The method according to claim 2, further comprising:

Generate a test result report of the sample to be tested based on the abnormal result information and the result of the blood routine test; or

The test result report of the sample to be tested is generated based on the abnormal result information, the blood routine test result, and the test result information of other test items of the sample to be tested.
The method according to any one of claims 1 to 4, wherein said generating corresponding abnormal result information comprises:

Receive an input command to generate abnormal result information with one key; and

The corresponding abnormal result information is generated according to the instruction of one-key generation of abnormal result information.
According to the method of claim 1, the method further comprises:

Determine each result item corresponding to each abnormal rule in the microscopic examination result;

Match each of the result items with the corresponding abnormal rules in sequence;

If there is a target result item matching the abnormal rule in the microscopic examination result, the target result item is determined as result information matching the abnormal rule.
According to the method of claim 1, the method further comprises:

Receiving a selection operation for a target result item in the microscopic examination result, wherein the selection operation is performed based on the abnormal rule;

Determine the selected target result item as the result information that matches the exception rule.
According to the method of claim 1, the method further comprises:

Obtain the pre-classified cell types for which abnormal rules need to be set in each microscopic examination type;

Determine the abnormal reference range of the pre-classified cell type based on the normal reference range corresponding to the pre-classified cell type;

Based on the abnormal reference range of the pre-classified cell type, an abnormal rule corresponding to the pre-classified cell type is generated.
A sample detection system, the sample detection system at least includes:

The blood analyzer is used to perform routine blood test on the sample to be tested, and send the blood routine test test data to the control device;

The control device is used to process the blood routine test data of the sample to obtain the blood routine test result, and is also used to determine whether the blood routine test result of the sample meets the re-inspection rules, and if it meets the re-inspection rules, the control will The sample is transported to the pusher;

Slide pusher, used to push slides on the sample to make glass slides to be inspected;

Scanning machine, used to take image of the glass slide to be inspected, obtain the cell image, and send the cell image to the control device;

The control device is further configured to obtain the microscopic examination result of the sample to be tested based on the cell image, and obtain a preset abnormality rule; if the microscopic examination result contains result information that matches the preset abnormality rule , Generate corresponding abnormal result information; display the abnormal result information on the microscopic examination result interface.
According to the sample detection system according to claim 9, the microscopic examination method includes the scanning machine automatically photographing the cell image of the sample to be tested and outputting the scanning result.
According to the sample detection system described in claim 9,

The control device is further configured to generate a test result report of the sample to be tested based on the abnormal result information and the result of the blood routine test; or

The test result report of the sample to be tested is generated based on the abnormal result information, the blood routine test result, and the test result information of other test items of the sample to be tested.
According to the sample detection system of claims 9 to 11, the control device further comprises a human-computer interaction module, the human-computer interaction module is configured to receive a one-key command to generate abnormal result information input by the user; the control device It is also used to generate corresponding abnormal result information according to the one-click command to generate abnormal result information.
According to the sample detection system of claim 9, the control device is further used for:

Determine each result item corresponding to each abnormal rule in the microscopic examination result;

Match each of the result items with the corresponding abnormal rules in sequence;

If there is a target result item matching the abnormal rule in the microscopic examination result, the target result item is determined as result information matching the abnormal rule.
According to the sample detection system of claim 12, the human-computer interaction module is further configured to receive a selection operation of a target result item in the microscopic examination result, wherein the selection operation is performed based on the abnormality rule ；

The control device is also used to determine the selected target result item as result information matching the abnormal rule.
According to the sample detection system of claim 9, the control device is further used for:

Obtain the pre-classified cell types for which abnormal rules need to be set in each microscopic examination type;

Determine the abnormal reference range of the pre-classified cell type based on the normal reference range corresponding to the pre-classified cell type;

Based on the abnormal reference range of the pre-classified cell type, an abnormal rule corresponding to the pre-classified cell type is generated.
A sample detection system, the sample detection system at least includes:

Scanning machine, used to take image of the glass slide to be inspected, obtain the cell image, and send the cell image to the control device;

The control device is used to obtain the microscopic examination result of the glass slide to be examined based on the cell image, and obtain a preset abnormality rule; if the microscopic examination result contains result information that matches the preset abnormality rule, generate Corresponding abnormal result information; the abnormal result information is displayed on the microscopic examination result interface.
According to the sample detection system according to claim 16, the control device further comprises a human-computer interaction module, the human-computer interaction module is configured to receive an instruction input by the user to generate abnormal result information with one key; the control device also uses Therefore, the corresponding abnormal result information is generated according to the one-click command to generate abnormal result information.
According to the sample detection system of claim 16, the control device is further used for:

Determine each result item corresponding to each abnormal rule in the microscopic examination result;

Match each of the result items with the corresponding abnormal rules in sequence;

If there is a target result item matching the abnormal rule in the microscopic examination result, the target result item is determined as result information matching the abnormal rule.
According to the sample detection system of claim 17, the human-computer interaction module is further configured to receive a selection operation of the target result item in the microscopic examination result, wherein the selection operation is performed based on the abnormality rule ；

The control device is also used to determine the selected target result item as result information matching the abnormal rule.
According to the sample detection system of claim 16, the control device is further used for:

Obtain the pre-classified cell types for which abnormal rules need to be set in each microscopic examination type;

Determine the abnormal reference range of the pre-classified cell type based on the normal reference range corresponding to the pre-classified cell type;

Based on the abnormal reference range of the pre-classified cell type, an abnormal rule corresponding to the pre-classified cell type is generated.
A computer storage medium in which an executable program is stored, and when the executable program is executed, the steps of the information processing method according to any one of claims 1 to 8 are realized.