Medical information processing device, medical information processing method, and program

Abstract

To provide a medical information processing device capable of stabilizing a performance of a medical information analysis system.SOLUTION: A medical information acquisition unit 21 acquires related medical information related to medical information. A determination unit consisting of a conversion formula selection unit 22 and a conversion formula adjustment unit 23 determines a conversion formula for converting the medical information on the basis of the related medical information. An information conversion unit 24 generates converted medical information obtained by converting the medical information using the conversion formula and outputs the converted medical information.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a medical information processing device, a medical information processing method, and a program. [Background technology]

[0002] In recent years, medical information analysis systems that support doctors in determining medical conditions have been attracting attention. Medical information analysis systems are sometimes constructed by performing machine learning or the like using medical information on patients. However, medical information often varies in nature depending on the individual patient or the facility and equipment that acquired the medical information. For this reason, medical information analysis systems have a problem in that the system performance is unstable, such as a decrease in accuracy when a group of information with a different nature from the group of information used to construct the medical information analysis system is input. In particular, in regional collaboration for medical purposes and collaboration between multiple medical facilities such as medical tourism, it is considered that the instability of performance due to the variation in the nature of this information will be a major issue.

[0003] In response to this, Patent Document 1 discloses a technique for converting a medical image acquired by another medical image display device into the same image as that of the other medical image display device by LUT (Look Up Table conversion) conversion using a lookup table. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2013-009877 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, in the technology described in Patent Document 1, since the lookup table is determined in advance, it is difficult to eliminate instability in the performance of a medical information analysis system that uses medical information whose properties vary depending on the patient, etc.

[0006] An object of the present disclosure is to provide a medical information processing device, a medical information processing method, and a program that are capable of stabilizing the performance of a medical information analysis system. [Means for solving the problem]

[0007] A medical information processing device according to one aspect of the present disclosure is a medical information processing device that converts a patient's medical information, and has an acquisition unit that acquires related medical information related to the medical information, a determination unit that determines a conversion formula for converting the medical information based on the related medical information, and a conversion unit that generates and outputs converted medical information by converting the medical information using the conversion formula. Effect of the Invention

[0008] According to the present invention, it is possible to stabilize the performance of a medical information analysis system. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating a configuration of a medical information analysis system according to an embodiment of the present disclosure. [Diagram 2] 11 is a flowchart illustrating an example of medical information processing. [Diagram 3] FIG. 13 is a diagram illustrating an example of a conversion formula. [Figure 4] FIG. 13 is a diagram showing an example of a converted medical image. [Diagram 5] FIG. 11 is a diagram showing another example of a converted medical image. [Figure 6] 13 is a flowchart illustrating another example of medical information processing. [Figure 7] FIG. 11 is a diagram showing another example of a converted medical image. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. EXAMPLES

[0011] In this embodiment, a medical information processing device, a medical information processing method, and a program are described that are capable of stabilizing system performance even when the input information input to a medical information analysis system that provides decision support to assist doctors in making decisions about medical conditions, etc. is of a different nature from the construction information used to construct the system (specifically, the construction of the medical information analysis device 13 described below), that is, even when the nature of the information varies depending on the individual patient or the facility and equipment that acquired the information.

[0012] Fig. 1 is a diagram showing the configuration of a medical information analysis system according to the present embodiment. The medical information analysis system 1 shown in Fig. 1 includes an input device 10, a medical information processing device 11, a medical information storage device 12, and a medical information analysis device 13.

[0013] The input device 10 accepts input information from an operator or a higher-level system, and transmits the input information to the medical information processing device 11. The medical information processing device 11 executes medical information processing to generate and output converted medical information by converting the medical information of a patient. The medical information storage device 12 stores various information generated or used by the medical information processing device 11. The medical information analysis device 13 analyzes the converted medical information from the medical information processing device 11.

[0014] The medical information processing device 11 includes a medical information acquiring unit 21, a conversion formula selecting unit 22, a conversion formula adjusting unit 23, and an information converting unit 24.

[0015] The medical information acquiring unit 21 is an acquiring unit that acquires medical information to be processed and related medical information related to the medical information.

[0016] The conversion formula selection unit 22 and the conversion formula adjustment unit 23 constitute a determination unit that determines a conversion formula (conversion rule) for converting medical information acquired by the medical information acquisition unit 21 based on the related medical information acquired by the medical information acquisition unit 21. The conversion formula selection unit 22 is a selection unit that selects (determines) one of a plurality of candidate conversion formulas as a conversion formula based on the related medical information. The conversion formula adjustment unit 23 is an adjustment unit that determines a conversion formula by adjusting coefficients of a predetermined reference conversion formula based on the related medical information. It is sufficient that the determination unit includes at least one of the conversion formula selection unit 22 and the conversion formula adjustment unit 23.

[0017] The information conversion unit 24 is a conversion unit that generates and outputs converted medical information by converting the medical information acquired by the medical information acquisition unit 21 using the conversion formula determined by the determination unit.

[0018] Each device such as the medical information processing device 11 and the medical information analysis device 13 shown in Fig. 1 is configured, for example, by a computer system equipped with a processor (computer) and a memory (both not shown). In this case, each of the above-mentioned components and functions is realized, for example, by the processor reading a program and executing the read program. The program can be recorded on a computer-readable recording medium 30 such as a memory. The recording medium 30 non-temporarily stores a computer program or data. At least a part of the computer program and data stored in the memory or storage unit can be stored on the recording medium 30.

[0019] Fig. 2 is a flowchart for explaining an example of medical information processing by the medical information processing device 11. In the example of Fig. 2, the medical information is a medical image of a patient, such as a CT (Computed Tomography) image or an X-ray fluoroscopic image, and more specifically, a medical image of the patient's lungs.

[0020] First, the medical information acquisition unit 21 acquires an input medical image, which is a medical image to be processed, and related medical information related to the input medical image (step S101). The input medical image may be input from the input device 10, or may be acquired from the medical information processing device 11 according to an instruction from the input device 10. In the example of FIG. 2, the related medical information is patient information about a patient appearing in the input medical image. Note that, for example, an ID number or a name is assigned to each of the input medical image and the patient information as identification information for identifying the patient, and the medical information acquisition unit 21 acquires, for example, patient information to which the same identification information as that of the input medical image is assigned.

[0021] Next, the conversion formula selection unit 22 acquires predetermined target information from the patient information, which is the acquired related medical information (step S102). The target information is information determined according to the analysis content by the medical information analysis device 13, and in this embodiment, it is the patient's medical history of lung cancer, more specifically, the presence or absence of the patient's medical history of lung cancer.

[0022] The conversion formula selection unit 22 selects a conversion formula for converting the input medical image from among a plurality of candidate conversion formulas prepared in advance, based on the patient's medical history of pneumonia (step S103). When there are a plurality of types of conversion formulas, at least one of the plurality of types of conversion formulas may be selected. In the example of FIG. 2, the conversion formulas include a conversion formula for the lung field area for converting the medical image into an image for observing the lung field area (an image for observing the lung field area), and a conversion formula for the vertical area for converting the medical image into an image for observing the vertical area (an image for observing the vertical area). Here, the conversion formula selection unit 22 selects one of a plurality of candidate conversion formulas for the lung field area based on the medical history of pneumonia, for the conversion formula for the lung field area, and selects a predetermined reference conversion formula for the conversion formula for the vertical area, regardless of the medical history of pneumonia.

[0023] Then, the information conversion unit 24 generates an image for observing the lung field area and an image for observing the vertical area by converting the input medical image using the conversion formula for the lung field area and the conversion formula for the vertical area selected by the conversion formula selection unit 22, respectively, as a converted medical image that is converted medical information (step S104).The information conversion unit 24 outputs the converted medical image to the medical information analysis device 13 (step S105), and ends the process.

[0024] The medical information analysis device 13 performs medical information analysis using two types of images, an image for observing a lung field region and an image for observing a longitudinal region. Examples of this type of medical information analysis include an analysis in which two types of images are displayed and compared visually by a user, or an analysis in which a predetermined region or feature such as a lung field region or a lesion is detected based on the image for observing a lung field region and the image for observing a longitudinal region. The medical information analysis device 13 may be constructed by machine learning using medical images as learning data.

[0025] In addition, the patient information acquired in step S101 may be information in a free description format such as an electronic medical record or a referral letter between medical facilities, or may be information in a prescribed format with a prescribed format generated by a dedicated application or the like. When the patient information is information in a prescribed format, the medical history of pneumonia is, for example, binarized binary information indicating the presence or absence. In this case, in step S102, the conversion formula selection unit 22 only needs to extract the medical history of pneumonia from the patient information. When the patient information is information in a free description format, the conversion formula selection unit 22 determines the medical history of pneumonia from the patient information, for example, using a text analysis technique such as NLP (Neuro Linguistic Programming). When the patient information is information in a free description format and handwritten, the conversion formula selection unit 22 recognizes handwritten character information using a character recognition technique such as OCR (Optical Character Recognition / Reader), and then determines the medical history of pneumonia using the text analysis technique.

[0026] Fig. 3 is a diagram showing an example of the conversion formula selected in step S103. In the example of Fig. 3, the conversion formula is a luminance conversion formula for converting the luminance value (pixel value) of the image. The conversion formula for the lung field region differs depending on whether or not there is a history of pneumonia, and the conversion formula for the longitudinal region is fixed regardless of whether or not there is a history of pneumonia.

[0027] Fig. 4 is a diagram for comparing a converted medical image according to this embodiment with a converted medical image according to a reference example. The reference example is an example in which a converted medical image (an image for observing a lung field area and an image for observing a vertical area) is generated using a conversion formula for a lung field area and a conversion formula for a vertical area that are determined in advance. Fig. 4(a) shows a converted medical image according to the reference example, and Fig. 4(b) shows a converted medical image according to this embodiment. The input medical image is common to the reference example and this embodiment.

[0028] In the reference example shown in Figure 4(a), for example, if the brightness value of the lung field region in the input medical image is generally high, the image for the lung field region will be brighter than if this is not the case, making it difficult to distinguish between diseased areas, which often appear as high-brightness regions in the lung field, and normal lung field regions.

[0029] On the other hand, in the case of a patient with a history of pneumonia, the brightness of the lung field region of the input medical image is expected to be high due to the influence of inflammatory scars in the lung field, etc., and in this embodiment, as shown in FIG. 4(b), a conversion formula is selected according to the history of pneumonia. As a result, for a patient with a history of pneumonia, a conversion formula for the lung field region that has a higher resolution of the high brightness region than for a patient without a history of pneumonia can be selected. In addition, as a conversion formula for the vertical region, a conversion formula similar to that of the reference example can be selected regardless of the presence or absence of a history of pneumonia. Therefore, the input medical image can be converted by increasing the resolution of the high brightness region in the image for observing the lung field region and by not unnecessarily emphasizing the high brightness region in the image for observing the vertical region.

[0030] The patient information used to select the conversion formula is not limited to a medical history of pneumonia. For example, when a medical image taken with a different imaging modality from the construction information used to construct the system is used as the input medical image, the characteristics may differ depending on the patient's physique. For example, when the input medical image is an ultrasound image, it is expected that the brightness value of the entire image will change depending on the distance between the imaging site and the probe, or the body tissue between them. In this case, the determination unit, for example, obtains physique information regarding the patient's physique from the patient information, estimates the amount of fat or muscle between the probe and the imaging site from the physique information, and determines the conversion formula based on the estimated value.

[0031] In addition, the related medical information is not limited to patient information, and may be the imaging conditions under which the input medical image was captured. Figure 5 is a diagram for comparing the converted medical image of this embodiment with the converted medical image of a reference example when a conversion formula is determined using imaging conditions as related medical information. Figure 4(a) shows the converted medical image of the reference example, and Figure 4(b) shows the converted medical image of this embodiment. Also, the example of Figure 5(a) is similar to the example of Figure 4(a).

[0032] 5(a), in the reference example, for example, when the brightness value of the input medical image is generally low, the brightness value of the converted medical image is also lower than when the brightness value is not low, and it becomes difficult to observe or identify a lesion or the like that appears in the image, which is recognized mainly by the brightness value difference with the background. For example, when an image is taken with a lower-than-expected imaging dose, the brightness value of the input medical image becomes lower overall.

[0033] In contrast, in this embodiment, as shown in Fig. 5(b), the conversion formula selection unit 22 selects a conversion formula depending on whether the imaging dose included in the imaging conditions exceeds a threshold. Therefore, for both the lung field region conversion formula and the vertical region conversion formula, when the imaging dose is equal to or less than the threshold, the conversion formula selection unit 22 can select a conversion formula that increases the resolution of the low brightness value region compared to when the imaging dose exceeds the threshold. Therefore, it is possible to make both the lung field region observation image and the vertical region observation image easy to analyze.

[0034] When the input medical image is a DICOM (Digital Imaging and Communication in Medicine) image, the imaging conditions used as patient information can be acquired from the header. In this case, since the imaging conditions are recorded as numerical values, the conversion formula selection unit 22 can select a conversion formula by comparing the numerical values ​​with a threshold value. The imaging conditions may also be acquired from information recorded separately from the input medical image. In this case, when the imaging conditions are recorded as numerical values, the conversion formula selection unit 22 can select a conversion formula by comparing the numerical values ​​with a threshold value. In addition, when the imaging conditions are information in a free description format or handwritten, the conversion formula selection unit 22 digitizes the imaging conditions using text analysis technology and character recognition technology.

[0035] In the above-described example, the conversion formula is determined by the conversion formula selection unit 22, but hereinafter, an example will be described in which the conversion formula is determined by both the conversion formula selection unit 22 and the conversion formula adjustment unit 23. Fig. 6 is a flowchart for explaining an example of medical information processing in this case. Note that the input information is a medical image of the patient's lungs, similar to the example in Fig. 2. Also, explanations of processes similar to those explained in Fig. 2 will be omitted as appropriate.

[0036] First, the medical information acquisition unit 21 acquires an input medical image, which is a medical image to be processed, and related medical information related to the input medical image (step S201). In the example of Fig. 6, the related medical information is the imaging conditions under which the patient was imaged when the input medical image was acquired. The conversion formula selection unit 22 acquires the imaging dose as predetermined target information from the imaging conditions (step S202).

[0037] The conversion formula selection unit 22 selects a conversion formula (a conversion formula for the lung field region and a conversion formula for the longitudinal region) for converting the input medical image from among a plurality of candidate conversion formulas prepared in advance based on the imaging dose (step S203). As a method for selecting the conversion formula, for example, the method described in FIG. 2 can be adopted.

[0038] Then, the conversion equation adjustment unit 23 adjusts the coefficients of the conversion equation selected by the conversion equation selection unit 22 based on the imaging dose (step S204). For example, the conversion equation adjustment unit 23 prepares in advance a calculation equation for calculating coefficients from the imaging dose, and adjusts the coefficients of the conversion equation to coefficients obtained by substituting the imaging dose into the calculation equation. The calculation equation is, for example, an equation for calculating coefficients such that the resolution of low luminance value areas of the input medical image increases as the imaging dose increases.

[0039] Then, the information conversion unit 24 generates a converted medical image by converting the input medical image using the conversion equation whose coefficients have been adjusted by the conversion equation adjustment unit 23 (step S205), and the information conversion unit 24 outputs the converted medical image to the medical information analysis device 13 (step S206), and ends the processing.

[0040] Instead of selecting a conversion formula by the conversion formula selection unit 22, the conversion formula adjustment unit 23 may adjust the coefficients of a predetermined reference conversion formula to determine a conversion formula for converting an input medical image.

[0041] Next, we will explain an example in which a 3D medical image, more specifically a 3D CT image, is used as the input medical image. The relevant medical information is the shooting conditions and reconstruction conditions of the 3D CT image, and the conversion formula is a resampling formula for resampling the 3D CT image. The reconstruction conditions are the conditions for obtaining a 3D CT image, which is a cross-sectional image, by analyzing X-ray transmission images, which are the original data from all directions obtained by CT scanning.

[0042] FIG. 7 is a diagram for explaining in more detail an example in which a three-dimensional CT image is used as an input medical image.

[0043] Fig. 7(a) shows a 3D CT image generated under the prescribed conditions, which are the imaging and reconstruction conditions of the 3D CT image used in constructing the system. Here, the imaging condition is the imaging dose, and the reconstruction conditions are the slice thickness and slice interval.

[0044] Figure 7(b) shows an example where the slice thickness and imaging dose are equivalent to the specified conditions, and the slice interval is narrower than the specified conditions. In this example, the image quality of each slice of the 3D CT image is assumed to be the same as the specified image quality, but the resolution in the slice direction is higher than the specified resolution. For this reason, a resampling formula that thins out the slices is selected as the conversion formula.

[0045] FIG. 7(c) shows an example in which the slice interval is equivalent to the specified condition, the slice thickness is narrower than the specified condition, and the imaging dose is higher than the specified condition. In this example, it is assumed that the image quality of each slice of the 3D CT image is higher than the specified image quality. For this reason, a resampling formula, which is a conversion formula, is selected that integrates slices. In the slice integration, the average value of the luminance values ​​of each slice to be integrated is used as the luminance value of the integrated slice. In other words, the input medical image is blurred.

[0046] In addition, if the image quality of the input medical image is higher than the specified image quality, there is a risk that parts other than those to be observed or analyzed (for example, noise, etc.) will also be sharpened. For example, when the medical information analysis device 13 performs an analysis to automatically detect a lesion, if the image quality of the input medical image is higher than the specified image quality, there is a risk of erroneous detection, such as recognizing a non-lesioned part as a lesion. For this reason, if the image quality of the input medical image is higher than the specified image quality, the performance of the medical information analysis device 13 can be stabilized by blurring the input medical image.

[0047] In the above-described example, medical images are used as medical information, but medical information is not limited to medical images. For example, medical information may be the results of a blood test of a patient. In this case, the conversion formula selection unit 22 selects a conversion formula that extracts necessary information from the results of a blood test or deletes unnecessary information based on personal information (e.g., whether or not pregnant). This allows selection not to use the test results, for example, when it is medically known that the reference value of the test result (e.g., the reference range of the white blood cell count) changes in a specific physical condition such as a pregnant state compared to other conditions (prescribed states). In this case, the medical information can be brought closer to the prescribed state, and the performance of the medical information analysis device 13 can be stabilized.

[0048] The above-described embodiments of the present disclosure are illustrative examples of the present disclosure, and are not intended to limit the scope of the present disclosure to only these embodiments. A person skilled in the art may implement the present disclosure in various other forms without departing from the scope of the present disclosure.

[0049] For example, the above-mentioned embodiments have been described in detail to clearly explain the present invention, and are not necessarily limited to those including all of the configurations described. Also, it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. Also, it is possible to add, delete, or replace a part of the configuration of each embodiment with another configuration.

[0050] In addition, the above-mentioned configurations, functions, processing units, processing means, etc. may be realized in part or in whole by hardware, for example, by designing them as integrated circuits. In addition, the above-mentioned configurations, functions, etc. may be realized in software by a processor interpreting and executing a program that realizes each function. Information such as the program, table, file, etc. that realizes each function can be stored in a memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD.

[0051] In addition, the control lines and information lines shown are those that are considered necessary for the explanation, and not all control lines and information lines in the product are necessarily shown. In reality, it can be considered that almost all components are interconnected. [Explanation of symbols]

[0052] 1: Medical information analysis system 10: Input device 11: Medical information processing device 12: Medical information storage device 13: Medical information analysis device 21: Medical information acquisition unit 22: Conversion formula selection unit 23: Conversion formula adjustment unit 24: Information conversion unit

Claims

1. A medical information processing device that converts medical information of a patient, an acquisition unit that acquires related medical information related to the medical information; a determination unit that determines a conversion formula for converting the medical information based on the related medical information; a conversion unit that converts the medical information using the conversion formula to generate and output converted medical information.

2. The medical image processing apparatus according to claim 1 , wherein the determination unit includes a selection unit that determines, as the transformation formula, one of a plurality of candidate transformation formulas that are candidates for the transformation formula, based on the related medical information.

3. The medical image processing apparatus according to claim 1 , wherein the determination unit includes an adjustment unit that determines the conversion formula by adjusting coefficients of a predetermined reference conversion formula based on the related medical information.

4. The medical image processing apparatus according to claim 1 , wherein the related medical information includes patient information relating to the patient.

5. the medical information is a medical image of the patient's lungs; The medical information processing device according to claim 4 , wherein the patient information includes a medical history of pneumonia of the patient.

6. 6. The medical information processing device according to claim 5, wherein the conversion formula includes a conversion formula for a lung field that converts the medical image into a lung field observation image for observing a lung field area, and a conversion formula for a vertical region that converts the medical image into a vertical region observation image for observing a vertical region.

7. The medical image processing apparatus according to claim 1 , wherein the medical information is a medical image of the patient.

8. The medical image processing apparatus according to claim 7 , wherein the related medical information includes imaging conditions for capturing the medical image.

9. The medical image processing apparatus according to claim 7 , wherein the conversion formula is a luminance conversion formula for converting a luminance value of the medical image.

10. the medical image is a three-dimensional medical image; the related medical information includes a reconstruction condition for reconstructing the three-dimensional medical image; The medical image processing apparatus according to claim 7 , wherein the transformation formula is a resampling formula for performing resampling of the three-dimensional medical image.

11. A medical information processing method by a medical information processing device that converts medical information of a patient, comprising: obtaining related medical information related to the medical information; determining a transformation formula for transforming the medical information based on the related medical information; A medical information processing method that generates and outputs converted medical information by converting the medical information using the conversion formula.

12. A computer that converts patient medical information obtaining related medical information related to the medical information; determining a transformation formula for transforming the medical information based on the related medical information; A program for executing a procedure of generating and outputting converted medical information by converting the medical information using the conversion formula.

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