Blood sampling device and method

Abstract

To provide a blood sampling device and method capable of stably sampling blood without manual operation.SOLUTION: The blood sampling device comprises: a light source for illuminating a biological portion of a person to be sampled; a camera for capturing images of a blood vessel running under the skin of the biological portion from the same side or the opposite side of the light source; a movement mechanism which moves the relative position of a puncture needle with respect to the biological portion; and an arithmetic unit which respectively calculates, as quantitative numeric values, puncture suitability degrees being evaluation values about suitability as a puncture object, with respect to respective points within a movable range of the puncture needle on the captured image of the blood vessel captured by the camera. The arithmetic unit selects a position most suitable to the puncture with the puncture needle at the biological portion based on the puncture suitability degrees respectively calculated with respect to the respective points within the movable range of the puncture needle on the captured image, and controls the movement mechanism so as to punctuate the selected position with the puncture needle.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] The present invention relates to a blood sampling device and method, and is suitable for use in, for example, an automatic blood sampling device that automatically performs blood sampling. [Background technology]

[0002] Blood tests have become indispensable in the medical field as they provide basic information for diagnosing various diseases. In recent years, blood tests are also expected to be used for early diagnosis of disease symptoms in order to curb ever-increasing medical costs, and the demand for blood tests is expected to continue to increase in the future.

[0003] With this increase in demand, there are concerns about the increased burden on medical professionals who collect blood samples. Against this background, automatic blood collection devices that automatically collect blood samples without human intervention have been attracting attention in recent years, and there is a particular demand for small, space-saving automatic blood collection devices that can be placed without taking up space in the limited space available at medical facilities.

[0004] A known example of this type of automatic blood sampling device is the blood sampling device disclosed in Patent Document 1. When a person to be sampled places his / her fingertip at a specified position on the device, a needle automatically moves to puncture the fingertip, creating a small wound, and blood is sampled by collecting the blood in a collection container. This blood sampling device has been made smaller by using the fingertip as the part to be sampled. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent No. 6994910 specification [Non-patent literature]

[0006] [Non-Patent Document 1] Li, X., Lin, J., Pang, Y., Huang, L., Zhong, L., Li, Z., “FingertipBlood Collection Point Localization Research Based on Infrared Finger VeinImage Segmentation”, IEEE Transactions on Instrumentation and Measurement,Vol.71, pp.1-12 (2021) Summary of the Invention [Problem to be solved by the invention]

[0007] Generally, there are some parts of a human finger that are suitable for blood sampling and some that are not. However, Patent Document 1 does not take into consideration the suitability of the blood sampling position, and only discloses that the blood sampling device mechanically punctures the puncture needle at a fixed position. This causes a problem that a sufficient amount of blood may not be sampled due to individual differences in the subject's finger and changes in the presented posture and position.

[0008] On the other hand, one example of research aimed at stable automatic blood sampling is the research example described in Non-Patent Document 1. In fact, Non-Patent Document 1 discloses a method of photographing the vein pattern of a finger using a near-infrared camera, analyzing the photographed image, and extracting the branching points of the blood vessels as the puncture positions.

[0009] Even when medical professionals manually draw blood, they generally aim the blood collection needle at the vein in the arm, and since the branching point of blood vessels is where multiple blood vessels converge, it is expected that the blood flow rate is relatively high. Therefore, we aim to realize stable blood collection by enabling the blood collection needle to be controlled by a robot arm so that it can puncture any position on the finger, and by programming the blood collection needle to puncture this vascular branching point.

[0010] However, there are often multiple branching points of blood vessels in one finger, and Non-Patent Document 1 does not disclose or suggest a method for prioritizing which of them should be selected as the puncture target. This poses the problem that manual judgment is ultimately required.

[0011] The present invention has been made in consideration of the above points, and aims to provide a blood sampling device and method that can perform stable blood sampling without manual intervention. [Means for solving the problem]

[0012] In order to solve such problems, in the present invention, a blood sampling device that collects blood by inserting a puncture needle into a biological site presented by a subject to be sampled is provided with a light source that irradiates light onto the biological site, a camera that photographs the blood vessels running under the skin of the biological site from the same side or opposite side as the light source, a movement mechanism that moves the relative position of the puncture needle to the biological site, and a calculation unit that calculates a puncture suitability, which is an index of whether or not a point is suitable as a puncture target, as a quantitative numerical value for each point within a range in which the puncture needle can move on an image of the blood vessels captured by the camera, and the calculation unit selects the most suitable position for puncturing the biological site with the puncture needle based on the puncture suitability calculated for each point within the range in which the puncture needle can move on the captured image, and controls the movement mechanism to puncture the puncture needle at the selected position.

[0013] In addition, the present invention provides a blood sampling method performed by a blood sampling device that collects blood by inserting a puncture needle into a biological part presented by a subject, the blood sampling device being provided with a light source that irradiates light onto the biological part, a camera that photographs the blood vessels running under the skin of the biological part from the same side or opposite side as the light source, a movement mechanism that moves the relative position of the puncture needle with respect to the biological part, and a calculation unit that calculates a puncture suitability, which is an index of whether or not the point is suitable as a puncture target, as a quantitative numerical value for each point within a range in which the puncture needle can move on an image of the blood vessels captured by the camera, and the calculation unit is provided with a first step of selecting a position most suitable for puncturing the biological part with the puncture needle based on the puncture suitability calculated for each point within the range in which the puncture needle can move on the captured image, and a second step of controlling the movement mechanism so that the calculation unit punctures the puncture needle at the selected position.

[0014] According to the blood sampling device and method of the present invention, the most suitable position for puncturing with the puncture needle can be selected based on the puncture suitability. Effect of the Invention

[0015] According to the present invention, stable blood collection can be performed regardless of individual differences such as the posture in which the subject presents the body part, and the condition and shape of the body part. [Brief description of the drawings]

[0016] [Figure 1] FIG. 1 is a diagram showing the overall configuration of an automatic blood sampling device according to first to fourth embodiments. [Diagram 2] FIG. 4 is a top view showing a partial configuration example of a moving mechanism. [Diagram 3] FIG. 2 is a diagram showing an example of a captured image. [Figure 4] 13 is a flowchart showing a processing procedure of an automatic blood sampling process. [Diagram 5] 13 is a flowchart showing a processing procedure for optimal puncture point extraction processing according to the first embodiment. [Figure 6]1 is a diagram showing the processing contents of blood vessel emphasis processing. [Figure 7] 13A to 13C are diagrams showing changes in a captured image due to optimal puncture point extraction processing. [Figure 8] FIG. 11 is a characteristic curve diagram illustrating a method for calculating a blood vessel evaluation value. [Figure 9] 13A to 13C are diagrams for explaining a neighboring blood vessel evaluation value sum calculation process. [Figure 10] 13 is a flowchart showing a processing procedure for optimal puncture point extraction processing according to the second embodiment. [Figure 11] FIG. 11 is a diagram for explaining a second embodiment. [Figure 12] 13 is a flowchart showing a processing procedure for optimal puncture point extraction processing according to the second embodiment. [Figure 13] FIG. 13 is a diagram for explaining a third embodiment. [Figure 14] FIG. 13 is a perspective view illustrating the overall configuration of an automatic blood sampling system according to a fourth embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] An embodiment of the present invention will now be described in detail with reference to the drawings.

[0018] (1) First embodiment (1-1) Puncture compatibility First, we will explain the puncture suitability. Since the location of the branching point of the blood vessel in the finger is completely different for each individual, it is not possible to guarantee that the branching point is present within the range where the needle can move and puncture. In particular, in a small blood sampling device such as that disclosed in Patent Document 1, the mechanism for moving the needle must also be made small, and the degree of freedom of movement must be restricted.

[0019] Therefore, it is desirable to obtain a quantitative numerical index according to the expected blood collection amount for any point within the target range, rather than relying on information that cannot be evaluated unless the feature, such as a branching point of a blood vessel, is present within the target range, and to select a suitable point for puncturing based on the relative level of the index. This allows the selection of the next best position within the range even if the optimal position for puncturing is not within the target range, and a significant decrease in the amount of collected blood can be avoided, thereby improving the stability of blood collection even with a small blood collection device. Hereinafter, this numerical index will be referred to as the puncturing suitability.

[0020] When calculating the puncture suitability, it is necessary to numerically reflect how much blood can be collected. Since the amount of blood collected is considered to be roughly proportional to the amount of blood at the puncture position, the basic condition for the puncture position is that it should be a point on a blood vessel where blood is densely collected in the target body part. Furthermore, the puncture suitability needs to be an index that gives a high evaluation to the part of the blood vessel in the target body part that has accumulated the most blood.

[0021] Near-infrared measurement, which is also used in the above-mentioned Non-Patent Document 1, can be used as a method for measuring the distribution of blood volume in a target biological part. When near-infrared light is transmitted through a biological body and photographed with a camera compatible with the near-infrared wavelength range, blood vessels are displayed as dark lines. This is because hemoglobin contained in blood has the property of absorbing near-infrared light, and when near-infrared light is transmitted through blood vessels containing a large amount of hemoglobin, the light is weakened when passing through the blood vessels, making the blood vessels appear darker than the surrounding area. In the blood vessel image obtained in this way, the darker the line, the greater the blood volume, and basically, the puncture suitability of each point on the blood vessel can be calculated by analyzing this photographed image.

[0022] However, images of blood vessels captured using infrared light are generally unclear, and unevenness in the ease of light transmission is likely to occur due to differences in not only blood volume but also tissue composition in the living body. When determining the puncture suitability at an arbitrary point, it is difficult to accurately distinguish whether there is actually a lot of blood or whether such a feature just occurs by chance due to unevenness, based only on local features such as low brightness at that point. If the judgment is inaccurate, it will be impossible to ensure a stable amount of blood to be collected, so a means for robustly detecting areas with a large amount of blood is essential. Therefore, when selecting a point to be punctured (hereinafter referred to as the puncture point), it is necessary to set and use a puncture suitability that suppresses the influence of local variations by reflecting not only the local features of a single candidate point, but also the distribution state of the blood volume in a wider range including the vicinity of the point.

[0023] Below, we will explain the automatic blood sampling device of this embodiment, which selects the optimal puncture point (hereinafter referred to as the optimal puncture point) from the body part presented by the subject, taking into account such puncture suitability, and then inserts the puncture needle into the selected puncture point to collect blood.

[0024] (1-2) Configuration of the automatic blood sampling device according to this embodiment In Fig. 1, the automatic blood sampling device according to this embodiment is generally designated by 1. This automatic blood sampling device 1 is configured with a calculation unit 2, a light source control unit 3, an image input unit 4, a mechanism control unit 5, a communication unit 6, a camera 7, a near-infrared light source 8, a movement mechanism 9, and a blood sampling tube 10.

[0025] The calculation unit 2 is configured to include a CPU (Central Processing Unit) 21, a memory 22, and an auxiliary storage device 23, which are interconnected via an internal bus 20, and interfaces 24 provided corresponding to the light source control unit 3, the image input unit 4, the mechanism control unit 5, and the communication unit 6. Note that the CPU 21, the memory 22, the auxiliary storage device 23, and each interface 24 may be interconnected by a dedicated bus for the purpose of increasing speed, etc.

[0026] The CPU 21 is a processor that controls the overall operation of the automatic blood sampling device 1. The memory 22 is composed of a RAM (Random Access Memory) and a ROM (Read Only Memory), and is used to permanently store programs required for processing and temporarily store data required for processing. The CPU 21 executes the programs stored in the memory 22, thereby executing various processes of the automatic blood sampling device 1 as a whole, as described below.

[0027] The auxiliary storage device 23 is composed of, for example, a flash memory, a hard disk drive, etc., and is used to store data that needs to be saved permanently. Data is input / output to / from the auxiliary storage device 23 via the memory 22 under the control of the CPU 21.

[0028] The interface 24 is a device having the function of connecting the calculation unit 2 and the corresponding functional blocks (light source control unit 3, image input unit 4, mechanism control unit 5 or communication unit 6) so that necessary data can be exchanged between the calculation unit 2 and the corresponding functional blocks.

[0029] With the presence of such an interface, the calculation unit 2 can control the light emission state of the near-infrared light source 8 via the light source control unit 3, input the output image of the camera 7 via the image input unit 4, and drive and control the moving mechanism 9 via the mechanism control unit 5. The calculation unit 2 can also communicate with an external device (not shown) connected to the network 11 via the communication unit 6. Such communication can, for example, share part or all of the processing associated with blood collection with an external device (for example, an external calculation device serving as a host), or link with various services on the cloud.

[0030] The camera 7 is composed of, for example, a CCD (Charge Coupled Device) image sensor or a CMOS (Complementary metal-Oxide Semiconductor) image sensor, and captures the subcutaneous vascular pattern of the subject's biological part (hereinafter referred to as the finger) 12 from which blood will be collected, and outputs image data of the captured image to the image input unit 4.

[0031] Further, the near-infrared light source 8 is composed of, for example, a near-infrared LED (Light Emitting Diode) and emits near-infrared light of an intensity according to the amount of driving power based on the driving power given from the light source control unit 3. The near-infrared light source is fixedly disposed such that its optical axis coincides with the optical axis of the camera 7 at a distance that allows the blood sample recipient's finger 12 to be inserted, as shown in FIG. 1. The reason why the blood sample recipient's finger 12 is sandwiched between the near-infrared light source 8 and the camera 7 in this manner is that a human finger is thick enough for near-infrared light to pass through sufficiently, and a blood vessel pattern with higher contrast can be obtained by passing near-infrared light through the side opposite the finger surface facing the camera 7 to capture the image.

[0032] There is also a method in which a light source is disposed on the same side as the camera 7, light is irradiated onto the surface of the finger 12 to be photographed, and the vascular pattern is photographed with the light that is reflected back. In this case, the light is strongly reflected on the surface of the skin of the finger 12, and the light reflected by the blood vessels behind it is relatively weak, resulting in a vascular pattern with low contrast. This type of reflected light method has the advantage that the camera 7 and the light source can be compactly arranged on the same side, thereby reducing the size of the imaging system, and is a method suitable for cases where miniaturization of the device is prioritized. Even when blood is to be collected from a body part with a thickness such as an arm, the reflected light method is more suitable because the light is attenuated in the body and a sufficient amount of light cannot pass through, making it impossible to obtain contrast in the vascular pattern, in a method that transmits light.

[0033] The moving mechanism 9 is a mechanism for moving the relative position of the puncture needle 13 with respect to the finger 12, including the direction in which the puncture needle 13 is inserted into the finger 12 presented in a predetermined position and in a predetermined state by the subject of blood collection, and the direction in which the puncture needle 13 is removed from the finger 12 after having been inserted into the finger 12, and is configured with a pushing mechanism 9A having the puncture needle 13 replaceably attached to its tip. The pushing mechanism 9A has an actuator such as a motor (not shown), and the actuator can move the puncture needle 13 in the direction in which it is inserted into the finger 12 of the subject of blood collection, and in the direction in which the puncture needle is removed after having been inserted.

[0034] The light source control unit 3 is a functional block having a function of controlling the brightness of the near-infrared light emitted by the near-infrared light source 8 so that the blood vessel pattern becomes the clearest in the image captured by the camera 7, based on a target value specified by the program executed by the CPU 21. Specifically, the light source control unit 3 performs PWM (Pulse Width Modulation) control of the amount of driving power applied to the near-infrared light source 8 so that it becomes the target value specified by the program executed by the CPU 21.

[0035] The image input unit 4 is a functional block having a function of converting the above-mentioned image data provided from the camera 7 into a format that is easy for the calculation unit 2 to handle, and storing the converted image data in the memory 22. The mechanism control unit 5 controls the moving mechanism 9 according to instructions of the program executed by the CPU 21.

[0036] In the automatic blood sampling device 1 having the above configuration, the CPU 21 analyzes the captured image of the vascular pattern obtained as described above based on the program stored in the memory 22, and obtains the coordinate value of the point on the captured image that is determined to be most suitable for puncturing (optimum puncturing point). Then, the CPU 21 notifies the mechanism control unit 5 of the obtained coordinate value of the optimum puncturing point.

[0037] When the coordinate value of the optimal puncture point is notified from CPU 21, mechanism control unit 5 controls movement mechanism 9 to cause puncture needle 13 to puncture that coordinate value on finger 12 presented by the subject. Prior to this, mechanism control unit 5 performs calibration in advance so that any coordinate of finger 12 displayed on the image captured by camera 7 coincides with the actual point on the finger to be punctured by puncture needle 13, and stores the correspondence.

[0038] Furthermore, when the puncture needle 13 punctures the subject's finger 12, the mechanism control unit 5 operates the moving mechanism 9 to move the puncture needle 13 in a direction to pull it out of the subject's finger 12, and then controls the moving mechanism 9 to move the blood collection tube 10 directly below the site on the subject's finger 12 that has been punctured by the puncture needle 13. As a result, the blood bleeding from the subject's finger 12 is received by the blood collection tube, and when a specified volume of blood has been collected, the blood collection is completed.

[0039] It is also possible to use a thin tubular needle similar to an injection needle as the puncture needle 13, and connect the puncture needle 13 to the blood collection tube with a tube or the like, and wait for a specified volume of blood to accumulate in the blood collection tube 10 while keeping the puncture needle 13 inserted into the finger 12 of the person to be blood-collected.

[0040] Fig. 2 shows an example of a portion of the configuration of the moving mechanism 9. The moving mechanism 9 includes a turntable 9B that is rotated by a motor or the like, and a pushing mechanism 9A (Fig. 1) that pushes out the puncture needle 13 from the upper surface of the turntable 9B toward the finger 12 of the blood sample recipient that is presented at a predetermined position.

[0041] The turntable 9B has a plurality of holes 9BX (four in FIG. 2) on the same circumferential axis, and the puncture needle 13 is positioned so as to be pushed out from at least one of these holes 9BX toward the finger of the person to be blood-collected, and the blood collection tube 10 is set so as to be fitted into another hole 9BX adjacent to this hole 9BX.

[0042] In the moving mechanism 9, under the control of the mechanism control unit 5 (FIG. 1), during blood collection, the turntable 9B is rotated so that the puncture needle 13 is positioned directly under the finger 12 of the person to be collected, which is presented at a predetermined position and in a predetermined state, and then the puncture needle 13 is pushed out by the pushing mechanism 9A from the hole 9BX of the turntable 9B and punctures the finger 12 of the person to be collected at the optimal puncture point. After this, the puncture needle 13 is returned by the pushing mechanism 9A into the hole 9BX of the turntable 9B, and then the turntable 9B is rotated so that the blood collection tube 10 is positioned directly under the finger 12. As a result, blood bleeding from the finger is collected by the blood collection tube 10.

[0043] According to the moving mechanism 9 of the present embodiment, blood can be collected simply by providing two degrees of freedom, namely, rotation of the turntable 9B and movement of the puncture needle 13 in the puncture direction and in the opposite direction, and there is an advantage that consumables such as the puncture needle 13 and the blood collection tube 10, which are used only once per blood collection, can be easily managed (replaced, etc.) all together from the upper surface side of the turntable 9B. In addition, according to the moving mechanism 9 of the present embodiment, the structure is simple and therefore it can be made compact, which is an advantage that it can contribute to making the entire automatic blood collection device 1 compact.

[0044] 3 shows an example of a captured image 30 of a blood sample recipient's finger 12 captured by the camera 7 of the automatic blood sampling device 1 of this embodiment. In this captured image 30, an image 31 of the blood sample recipient's finger 12 is shown in the center of the image, and a blood vessel pattern 32 in the finger 12 appears inside the image 31. Points on this blood vessel pattern 32 indicate locations where the amount of blood is relatively large.

[0045] Moreover, the arcuate trajectory 33 in the figure represents the movable range of the puncture needle 13. The optimal puncture point needs to be extracted from each point on the arcuate trajectory 33, and among them, an intersection point 34 with a blood vessel estimated to have a large amount of blood is selected. Generally, since the blood vessel pattern 32 is formed in a mesh-like shape, the arcuate trajectory 33 and the blood vessel pattern 32 often intersect at multiple points. Therefore, from among these intersection points 34, one point estimated to have the largest amount of blood needs to be extracted and determined as the point at which the puncture needle 13 is actually inserted.

[0046] In this embodiment, the movable range of the puncture needle 13 is an arc orbit, but the moving mechanism 9 that moves the puncture needle 13 is not limited to the configuration shown in FIG. 2, and the movable range of the puncture needle 13 changes depending on the configuration of the moving mechanism 9 applied. If the degree of freedom of movement of the puncture needle 13 is increased, the movable range of the puncture needle 13 will also be wider, and it is possible to make it a band-like range with width rather than a line as shown in FIG. 3. In addition, the movable range of the puncture needle 13 does not need to be arc-shaped, and may be linear, rectangular, or fan-shaped. However, in the following, for ease of explanation, it is assumed that the movable range of the puncture needle 13 in the captured image 30 is a horizontal straight line.

[0047] 4 shows the flow of a series of processes (hereinafter referred to as automatic blood sampling process) executed by the CPU 21 (FIG. 1) of the calculation unit 2 of the automatic blood sampling device 1 when blood is sampled. This automatic blood sampling process is started when, for example, a subject or an attendant performs a predetermined operation on the automatic blood sampling device 1.

[0048] Then, CPU 21 first initializes various processes and hardware to be executed (S1). CPU 21 also starts camera 7 (FIG. 1) to start shooting, and causes image input unit 4 (FIG. 1) to import image data of the captured image obtained by the shooting (S2). The image data imported at this time is stored by image input unit 4 in a predetermined area in memory 22 (FIG. 1) of calculation unit 2.

[0049] Next, the CPU 21 compares the captured image based on the image data previously stored in the memory 22 with the captured image based on the image data stored in the memory 22 this time in step S2 (S3), and determines whether or not a significant image change (an image change equal to or greater than a predetermined threshold) that occurs due to the body part from which blood is to be collected (here, a finger) being presented at a predetermined position and in a predetermined state has been detected (S4).

[0050] Obtaining a negative result in this determination means that the subject has not yet presented finger 12 (FIG. 1) at the predetermined position in the predetermined state. Thus, at this time, CPU 21 returns to step S2, and thereafter repeats the processes of steps S2 to S4 until obtaining a positive result in step S4.

[0051] Then, when the CPU 21 obtains a positive result in step S4 by the subject's finger 12 being presented at a predetermined position in a predetermined state, the CPU 21 notifies the subject or the attendant that blood collection will begin as necessary (S5). For example, the CPU 21 notifies the subject or the attendant that blood collection will begin by displaying text or an icon on a display or the like indicating that the subject's finger 12 has been detected, or by issuing a voice announcement or an alarm sound, or by other method that is optimal for the application environment. The CPU 21 may detect whether the subject's finger 12 is presented at a predetermined position in a predetermined state using a dedicated sensor. The processing of step S5 may also be omitted.

[0052] Next, the CPU 21 instructs the light source control unit 3 (FIG. 1) to start turning on the near-infrared light source 8 (FIG. 1), and adjusts the amount of light emitted by the near-infrared light source 8 via the light source control unit 3 so that the subject's finger 12 is photographed at an appropriate brightness (S6). Whether the subject's finger 12 is at an appropriate brightness can be determined by analyzing the photographed image based on the image data stored in the memory 22 and determining whether the average brightness of the image 31 (FIG. 3) of the finger 12 is within a predetermined range.

[0053] Furthermore, CPU 21 determines whether or not the amount of light emitted from near-infrared light source 8 has been adjusted so that finger 12 of the blood sample recipient has an appropriate brightness (S7), and if a negative result is obtained, the process returns to step S2, and thereafter repeats the processes of steps S2 to S7 until a positive result is obtained in step S7.

[0054] When CPU 21 eventually obtains a positive result in step S7 by completing the adjustment of the amount of light emitted by near-infrared light source 8, it executes an optimal puncture point extraction process to extract an optimal puncture point from within the area of ​​image 31 of finger 12 displayed in the captured image (S8). Furthermore, when CPU 21 extracts the optimal puncture point by the optimal puncture point extraction process, it controls moving mechanism 9 (FIG. 1) via mechanism control unit 5 (FIG. 1) to move puncture needle 13 to the extracted optimal puncture point and insert puncture needle 13 into the optimal puncture point (S9).

[0055] As a result, the puncture needle is inserted into the optimal puncture point to start blood collection, and then blood collection is completed when the required amount of blood has been collected. Whether or not the required amount of blood has been collected may be determined by monitoring the weight of the blood collection tube 10 (FIG. 1) and judging that the required amount of blood has been collected when the weight of the blood collection tube 10 reaches a certain level or when the height of the blood in the blood collection tube 10 is monitored by a sensor or the like and judging that the required amount of blood has been collected when the height reaches a certain level. Then, when the required amount of blood has been collected, the CPU 21 ends this automatic blood collection process.

[0056] 5 shows specific processing contents of the optimum puncture point extraction processing executed by CPU 21 in step S8 of the automatic blood sampling processing described above. When CPU 21 proceeds to step S8 of the automatic blood sampling processing, it starts the optimum puncture point extraction processing shown in FIG.

[0057] Then, CPU 21 first executes a blood vessel enhancement process (S10) for enhancing blood vessels on the captured image acquired in the immediately preceding step S2, which is then stored in memory 22. This is because the puncture point is basically a point on a blood vessel, and therefore, by performing an amplification process prior to the analysis process so that the characteristics of the blood vessel become clearer, the analysis accuracy can be improved.

[0058] Therefore, the CPU 21 performs blood vessel enhancement processing based on a general method for contour extraction called Gaussian subtraction. Gaussian subtraction is a method for amplifying and emphasizing local changes in brightness, so not only contours but also lines such as blood vessels are emphasized. In addition, by using Gaussian subtraction to take the difference between two images (hereinafter referred to as image G1 and image G2) that are generated from the same image but have different smoothing parameters (the radius of the area referenced during smoothing), the amplification of fine noise can be suppressed and relatively thick blood vessels can be stably emphasized.

[0059] The surface of a living body such as a finger has unevenness as typified by fingerprints, and the effect of keratinization due to rough skin also tends to cause fine noise in the captured image of blood vessels. By appropriately setting the degree of smoothing in the Gaussian difference, such noise is removed during smoothing, and only the remaining stable blood vessels are emphasized. By multiplying the pixel value of each pixel in the difference image (G1-G2) of such images G1 and G2 by N and then adding a constant C to the pixel value of each pixel, a blood vessel-emphasized image in which the amplification rate increases as N is increased can be obtained. The constant C in this case is a correction term to prevent pixel values ​​from taking negative values ​​due to the difference. For example, if the captured image is an image with 256 grayscales, the median value of 128 is set as the constant C.

[0060] 6 shows an example of a specific diagram of such a blood vessel enhancement process. In this example, first, a smoothing process (Gaussian filter process) is performed on the captured image I whose image data is stored in the memory 22, and then a smoothing process (Gaussian filter process) is further performed on the first smoothed image G1 thus obtained to generate a second smoothed image G2. The basic idea of ​​using image differences with different smoothing parameters is the same as the above-mentioned flow, but the first smoothing process first generates an image from which noise other than blood vessels has been removed, and then difference processing is performed only on the image without noise, thereby further suppressing the influence of noise.

[0061] On the other hand, when calculating the above-mentioned puncture suitability, it is desirable to measure the thickness and darkness of blood vessels based on the same standard regardless of the position in the finger area. For example, even if blood vessels are originally of the same darkness, if the brightness varies depending on the location, the obtained puncture suitability is likely to be an unstable index that depends on the position. In particular, near-infrared images of a living body are prone to gradual unevenness in light intensity due to the influence of the relationship between the arrangement of the light source and the position of the living body presented, the variation in the composition of the living body tissue, and the like.

[0062] Therefore, in order to correct such unevenness, a normalization process is added to the blood vessel enhancement process. Specifically, the pixel value of each pixel in the difference image (G1-G2) between the first smoothed image G1 and the second smoothed image G2 is multiplied by N, and the pixel value of each pixel is normalized by dividing it by the pixel value of the corresponding pixel in the second smoothed image G2, which has a high degree of smoothness. A high degree of smoothness means that local features are lost and only global features remain, and since most unevenness in light is a global feature, normalization can be performed by such a division process. Although this normalization method is simple, a certain degree of effect can be expected, but if it is required to more stably remove unevenness in light due to the device configuration, other normalization methods may be adopted.

[0063] Furthermore, in the blood vessel enhancement process, the above-mentioned constant C is added to the pixel value of each pixel of the normalized image. This makes it possible to obtain an image E in which blood vessels are enhanced (hereinafter, this will be referred to as a blood vessel enhanced image).

[0064] Next, the CPU 21 executes a vascular pattern extraction process (S11) to extract a vascular pattern 32 (FIG. 3) from the vascular enhancement image obtained by the above-mentioned vascular enhancement process. Various existing methods used in vein authentication, which is one type of biometric authentication, can be used to extract the vascular pattern 32, but it is preferable to adopt a method that can uniformly extract vascular patterns regardless of the darkness or thickness of the blood vessels, such as a method based on the maximum curvature. In the vascular evaluation described below, the darkness and thickness of the blood vessels are used as features, but by processing the extraction of the vascular pattern and the evaluation of the blood vessels in separate stages, the accuracy of each process can be improved, and the accuracy of the final extraction of the optimal puncture point can be improved.

[0065] After that, the CPU 21 executes a thinning process to generate a pattern image of only center lines that does not depend on the thickness of blood vessels from the blood vessel emphasis image that has been subjected to the blood vessel pattern extraction process (S12). The reason for thinning the blood vessel pattern in this way is to prevent the amount of processing from increasing too much because if the thickness of the blood vessels is maintained, more pixels than necessary will be involved in one blood vessel in the blood vessel evaluation that will be performed later.

[0066] Fig. 7 shows a schematic diagram of how the captured image changes as a result of the above-mentioned processing in steps S10 to S12. The top part of Fig. 7 shows a captured image 40 captured by camera 7, which is converted into a vessel-enhanced image 41 in which the contrast of blood vessels is generally enhanced by the vessel enhancement processing in step S10. This vessel enhancement processing makes thin and fine blood vessels that were previously difficult to see clearer, and also suppresses noise caused by the surface condition of the living body.

[0067] By performing the vascular pattern extraction process in step S11 on this vascular pattern emphasized image 41, a vascular pattern extracted image 42 is obtained in which the vascular pattern is uniformly extracted regardless of its darkness or thickness. By uniformly extracting the vascular pattern, binarization that separates blood vessels from other parts becomes possible.

[0068] Furthermore, by performing a vascular pattern thinning process in step S12 on this vascular pattern extraction image 42, a thinned image 43 in which the vascular pattern is expressed with the minimum number of pixels is obtained. This thinned image 43 is used as auxiliary information for determining whether each point is on a blood vessel, as a prerequisite for determining the optimal puncture point. For this reason, in the vascular pattern extraction process in step S11, extraction conditions are relaxed to extract points that may be blood vessels, so as to avoid omissions as much as possible. If a method can sufficiently reduce extraction omissions by relaxing the conditions, excessive detection is relatively tolerable, so a lighter and faster method may be adopted.

[0069] 5, the CPU 21 then executes a point-by-point blood vessel evaluation value calculation process (S13) to calculate an evaluation value (hereinafter, referred to as a blood vessel evaluation value) for each point (pixel) in the thinned image 43 extracted as a blood vessel pattern in the processes of steps S10 to S12. The blood vessel evaluation value is used as an index for estimating the amount of blood, and therefore focuses on the thickness of blood vessels, which can be quantitatively evaluated from a captured image of the blood vessels.

[0070] An example of a method for calculating a blood vessel evaluation value will be described with reference to Fig. 8. In Fig. 8, curve K1 shows an example of a brightness cross-sectional profile around a blood vessel in a blood vessel-enhanced image, and curve K2 shows the corresponding inflection rate. The inflection rate represents the brightness change rate at each point of the brightness cross-sectional profile, and takes a maximum positive value at the peak where the brightness is lowest, and conversely takes a minimum negative value at the peak where the brightness is highest.

[0071] Here, the curvature C(x) at the x-coordinate point P(x) on the brightness cross-sectional profile is expressed by the following formula when the width is δ.

number

[0072] In this case, in the brightness cross-sectional profile, the area below the line connecting the brightness at both ends of the section of the blood vessel width W becomes a comprehensive index that simultaneously reflects the thickness and darkness of the blood vessel, and this is used as the blood vessel evaluation value. This blood vessel evaluation value shows a larger value as the blood vessel width W becomes larger and the blood vessel becomes darker, i.e., the larger the drop in brightness from the blood vessel boundary, so it matches the initial purpose.

[0073] Such a blood vessel evaluation value is calculated for each point on the thinned blood vessel pattern in the thinned image 43 (Fig. 7), and a blood vessel evaluation value image 44 (Fig. 7) in which the pixel value of each point on the blood vessel pattern is replaced with the blood vessel evaluation value is generated by the following procedure. First, all pixels of the thinned image 43 are scanned, and it is determined for each pixel whether it is a point on the thinned blood vessel pattern or not. For pixels determined to be points on the blood vessel pattern, a luminance cross-sectional profile centered on that pixel is obtained from the blood vessel emphasis image 41, and the blood vessel evaluation value at that pixel (point) is calculated by the above-mentioned method.

[0074] However, blood vessel evaluation values ​​can only be obtained from a brightness cross-sectional profile that cuts across the blood vessel in the width direction. For example, a cross-sectional profile cut in the horizontal direction of an image can only obtain evaluation values ​​for blood vessels running vertically on the image. For blood vessels running horizontally, only a profile obtained by scanning the inside of the blood vessel can be obtained, and since there is no information on the width direction, the thickness or darkness of the blood vessels cannot be measured.

[0075] Therefore, blood vessel evaluation values ​​are calculated using brightness cross-sectional files in both the vertical and horizontal directions, and the blood vessel evaluation value calculated using the profile in the direction closest to the width direction of the blood vessel is used as the final evaluation value for that pixel (point). Since the brightness change in the width direction of a blood vessel is more rapid than the brightness change in the direction of travel, the brightness inflection rate for each scanning direction at that pixel (point) is compared, and the direction showing the larger value is considered to be closer. If necessary, a brightness cross-sectional profile in an oblique direction may be added to calculate the blood vessel evaluation value.

[0076] Next, as shown in Fig. 9, the CPU 21 executes a neighboring blood vessel evaluation value sum calculation process (S14) for calculating the sum of the blood vessel evaluation values ​​of each point on the blood vessel existing in a neighborhood area 45 of a predetermined size centered on each point (pixel) on the movable range of the puncture needle 13 in the blood vessel evaluation value image 44 as the puncture suitability. The reason for calculating the puncture suitability in this manner is to prevent local abnormalities in the blood vessel evaluation value caused by unevenness, etc., from being directly reflected in the puncture suitability when evaluating only one point within the movable range of the puncture needle 13. As described above, by setting the sum of the blood vessel evaluation values ​​calculated for each corresponding pixel (each point on the blood vessel, the same applies below) in the neighborhood area 45 for each target pixel (point) as the puncture suitability of that pixel (point), such sudden abnormalities can be statistically eliminated and a stable judgment can be performed.

[0077] However, simply expanding the area can easily result in a loss of sharpness in the evaluation for each pixel (point), so by weighting the vascular evaluation value of each corresponding pixel (point) in the neighboring area 45 according to the distance from the center of the neighboring area 45 to that pixel, it is possible to obtain a higher puncture suitability for pixels that have high vascular evaluation values ​​concentrated near the center.

[0078] Next, CPU 21 executes a maximum evaluation value calculation process to compare the puncture suitability obtained for all corresponding pixels (points) within the movable range of puncture needle 13 and selects the most suitable pixel (point) as the optimal puncture point (S15).

[0079] The optimum puncture point may be selected simply based on the puncture suitability, or may further include other conditions related to ease of puncture. For example, when the biological part to be punctured is a finger, since the finger has a cylindrical shape, the skin surface tends to be more easily faced to the puncture needle at a position closer to the center of the finger. Conversely, the closer to the edge of the finger, the more likely it is that an angle is generated with the skin surface, making it more difficult to puncture with the puncture needle. Therefore, in order to make it easier to select the center of the finger, the puncture suitability can be adjusted so that the suitability is higher at a position closer to the center of the finger. Since the position of the finger area in the captured image varies depending on the presentation posture of the finger, the outline of the finger may be extracted by image processing, and the suitability may be higher at the center of the finger area in the captured image.

[0080] (1-3) Advantages of this embodiment With the automatic blood sampling device 1 of this embodiment having the above configuration, it is possible to stably obtain the puncture suitability based on the blood vessel evaluation value reflecting the thickness and density of the blood vessel for any point on the blood vessel within the movable range of the puncture needle 13, and automatically select the point having the greatest suitability among them as the optimum puncture point to be punctured by the puncture needle 13. Therefore, with this automatic blood sampling device 1, stable blood sampling can be performed regardless of individual differences such as the posture of the subject presenting the body part and the condition and shape of the body part.

[0081] (2) Second embodiment FIG. 10 shows the flow of optimal puncturing point extraction processing of the second embodiment executed by CPU 21 of automatic blood sampling device 1 described above with reference to FIGS. 1 and 2 in place of the optimal puncturing point extraction processing described above with reference to FIG.

[0082] In this optimal puncture point extraction process, the processing of step S24 differs from the processing content of step S14 of the optimal puncture point extraction process of the first embodiment, and the processing contents of the other steps S20 to S23 and step S25 are similar to the processing contents of steps S10 to S13 and step S15, respectively, of the optimal puncture point extraction process of the first embodiment.

[0083] In other words, the optimal puncture point extraction process of this embodiment differs from the optimal puncture point extraction process of the first embodiment in that in step S24, instead of calculating the sum of nearby vascular evaluation values ​​for the corresponding pixel (point), an accumulation of nearby result evaluation values ​​by vascular tracking is performed.

[0084] Here, in the method of step S14 of the optimal puncture point extraction process in the first embodiment, the continuity of blood vessels in the vicinity region 45 (FIG. 9) is not taken into consideration when calculating the puncture suitability. For example, even in a case where a large amount of dark spot noise that looks like blood vessels occurs near the point for which the puncture suitability is calculated, the puncture suitability will show high suitability. Such noise can be easily excluded by adding the condition that the blood vessels have continuity.

[0085] Therefore, in this embodiment, in step S24 of the optimal puncture point extraction process, as shown in FIG. 11, for each point (pixel) on the blood vessel within the movable range 46 of the puncture needle 13, the blood vessel is continuously tracked for a predetermined distance starting from that point (arrows a1, a2 in FIG. 11), and the puncture suitability of that point is determined by accumulating the blood vessel evaluation values ​​of each point passed through during this process.

[0086] By doing so, instead of mechanically expanding the neighborhood area 45 (FIG. 9) of the point for which the puncture suitability is calculated as in step S14 in the optimal puncture point extraction process of the first embodiment, the investigation range is limited to the blood vessels that pass through the point and expanded to the neighborhood according to their continuity, so that the puncture suitability can be calculated more stably without being influenced by the blood vessel evaluation values ​​of points that are not connected to the starting point by blood vessels. When accumulating the blood vessel evaluation values, the blood vessel evaluation values ​​of each point on the blood vessel may be weighted according to the tracking distance from the starting point so that the closer the distance, the heavier the weight.

[0087] According to the optimal puncturing point extraction process of the present embodiment as described above, blood can be collected more stably than the optimal puncturing point extraction process of the first embodiment.

[0088] (3) Third embodiment In the first and second embodiments, the calculation of the puncture suitability of a pixel (point) on a blood vessel in a captured image 40 (FIG. 7) of the blood vessel has been described as being based on a logical basis established in advance for blood vessel characteristics including the surrounding area. However, recently, a recognition method based on machine learning that prepares a large amount of learning data and learns patterns from the learning data to make an estimation has also come into practical use. Therefore, in this embodiment as well, machine learning is applied to the calculation of the puncture suitability.

[0089] Specifically, in the case of this embodiment, as shown in Fig. 12, from the vessel-enhanced image 41 (Fig. 7) generated in step S10 of the optimum puncture point extraction process of the first embodiment described above with reference to Fig. 10 and step S20 of the optimum puncture point extraction process of the first embodiment described above with reference to Fig. 11, the CPU 21 cuts out partial images similar to the neighboring region 45 set in the neighboring vessel evaluation value sum calculation process of step S14 or step S24 for each point (pixel) on the vessel pattern, labels the puncture suitability at the center points of these partial images, and generates a learning model by machine learning in advance using a learning dataset consisting of a pair of these partial images and the puncture suitability at the center points of the partial images. At this time, the puncture suitability to be labeled to the partial image is the puncture suitability calculated for the center point of the partial image.

[0090] During blood sampling, CPU 21 calculates the puncture suitability of each point (pixel) on the blood vessel according to the processing procedure shown in Fig. 12. Fig. 12 is a flowchart showing the flow of optimal puncture point extraction processing of the third embodiment executed by CPU 21 of automatic blood sampling device 1 described above with reference to Figs. 1 and 2 in place of the optimal puncture point extraction processing described above with reference to Fig. 5.

[0091] In practice, in the case of this embodiment, when CPU 21 proceeds to step S8 of the automatic blood sampling process described above with reference to Fig. 4, it starts the optimum puncture point extraction process shown in Fig. 12, and processes steps S30 to S32 in the same manner as steps S10 to S12 of Fig. 5. Then, CPU 21 then estimates the puncture suitability of each point (pixel) on the blood vessel within the movable range of puncture needle 13 in thinned image 43 (Fig. 7) obtained in step S32, using the above-mentioned learning model (S33). CPU 21 then compares the puncture suitability obtained for each of the points on the blood vessel within the movable range of puncture needle 13, and selects the most suitable point as the optimum puncture point (S34).

[0092] In learning the puncture suitability, a large amount of data is required to realize highly accurate estimation, but many variations of partial images 41A can be extracted from one vessel-enhanced image 41 by overlapping as shown in Fig. 13. In addition, when labeling the puncture suitability, if an error occurs using the results of the theoretical calculation method described in the first and second embodiments, the data can be manually corrected as necessary to create a data set of learning data. This can speed up the construction of the data set of learning data.

[0093] According to the optimal puncturing point extraction process of the present embodiment as described above, blood can be collected more quickly and stably than the optimal puncturing point extraction process of the first embodiment.

[0094] (4) Fourth embodiment When selecting the optimal puncture point, a point with a significantly high puncture suitability may exist outside the movable range of the puncture needle. If cooperation is obtained from the subject, the subject's body part presented to the automatic blood sampling device 1 can be moved within the movable range of the puncture needle 13, so that the puncture needle 13 can be inserted into a position with a higher puncture suitability within the body part, thereby enabling more stable blood sampling.

[0095] 14 shows the configuration of an automatic blood sampling system 50 according to this embodiment, including an automatic blood sampling device 51 equipped with a function for prompting the blood sampling subject to move a body part (here, a finger) presented by the blood sampling subject as necessary. This automatic blood sampling system 50 is configured with a display device 52 and the automatic blood sampling device 51. Note that, in FIG. 14, the display device 52 is shown as a device separate from the automatic blood sampling device 51, but the automatic blood sampling device 51 may include the display device 52.

[0096] The display device 52 is composed of a general-purpose display device such as a liquid crystal display device or an organic EL (Electro-Luminescence) display device. The display device is connected to the automatic blood sampling device 51 via a cord (not shown) and displays an image based on image data provided from the automatic blood sampling device 51.

[0097] The automatic blood sampling device 51 basically has the same configuration as the automatic blood sampling device 1 according to the first to third embodiments. However, the CPU 53 (FIG. 1) of the automatic blood sampling device 51 of this embodiment determines the puncturing suitability for each point on the vascular pattern other than the movable range of the puncture needle 13 in steps S10 to S14 in FIG. 5, steps S20 to S24 in FIG. 10, or steps S30 to S33 in FIG. 12.

[0098] In addition, in step S15 of FIG. 5, step S25 of FIG. 10, or step S34 of FIG. 12, the CPU 53 selects one most suitable point (pixel), and if the selected point is not within the movable range of the puncture needle 13, displays a message or the like on the display device 52 urging the subject to move the finger 12 so that the point is located within the movable range of the puncture needle 13.

[0099] At this time, the display device 52 displays, while updating in real time, a photographed image 40 (FIG. 7) of the finger 12 photographed by the camera 7, a line 54 indicating the corresponding movable range of the puncture needle 13 or a frame such as a rectangle indicating the movable range of the puncture needle, and a point indicating the most suitable point selected as described above (hereinafter referred to as the out-of-range puncture candidate point) or a line (hereinafter referred to as the reference line) 55 or frame indicating the movable range of the puncture needle 13 passing through the out-of-range puncture candidate point. As a result, the distance between the out-of-range puncture candidate point and the movable range of the puncture needle 13 is visually displayed in real time.

[0100] According to such a display, when the blood sample subject moves his / her finger 12, the line 54 and frame body showing the movable range of the puncture needle 13 displayed on the display device 52 remain fixed, and only the image of the finger 12 moves in the photographed image 40 in accordance with the movement of the finger 12 by the blood sample subject, and accordingly, the reference line 55 passing through the out-of-range puncturing candidate point and the like also move in the photographed image 40 together with the image of the finger 12. This allows the blood sample subject to easily recognize in which direction and how much to move his / her finger 12, based on the line 54 and frame body showing the movable range of the puncture needle 13 displayed on the display device 52 and the line (reference line) 54 and frame body passing through the out-of-range puncturing candidate point.

[0101] The CPU 53 monitors the coordinates of the movable range of the puncture needle 13 and the coordinates of the out-of-range optimal puncture point in real time, and when the movable range of the puncture needle 13 is located in the fingertip direction from the out-of-range puncture candidate point, for example, the CPU 53 displays an instruction message or icon, etc., on the display device 52 to move the finger 12 in the fingertip direction. When the blood sample subject moves the finger 12 in response to the message or icon, etc., and as a result the out-of-range puncture candidate point overlaps with the puncture needle movable range (the out-of-range puncture candidate point enters the puncture needle movable range), the CPU 53 informs the user that the finger 12 has reached the optimal position by a message or an alarm sound, etc., and prompts the blood sample subject to stop the finger at that position. This enables the blood sample subject to intuitively place the optimal puncture point within the puncture needle movable range.

[0102] In addition, since the vein pattern of the finger 12 is personal information and is also used for biometric authentication, it may be inappropriate to display the captured image as is. Therefore, although it is necessary to maintain the outline of the finger so that it can be seen, image processing may be performed, such as painting the inside of the finger with black or the like, so that the vein pattern becomes unrecognizable.

[0103] As described above, according to the automatic blood sampling system of the present embodiment, it is possible to more stably sample a required amount of blood compared to the automatic blood sampling device 1 of the first embodiment.

[0104] (5) Other embodiments In the above-mentioned first to fourth embodiments, the automatic blood sampling device 1, 51 is described as being configured as shown in Figures 1 and 2, but the present invention is not limited to this and various other configurations can be widely applied.

[0105] In addition, in the above-mentioned third embodiment, a case has been described in which a learning model is generated by machine learning using a learning dataset consisting of a pair of a partial image in the vicinity of a point on a blood vessel in a captured image centered on that point and the puncture suitability at that point. However, the present invention is not limited to this, and a learning model may be generated by machine learning using a learning dataset consisting of a pair of a partial image in the vicinity of a point on a blood vessel in a captured image centered on that point and some index of suitability in accordance with the puncture suitability at that point. [Industrial Applicability]

[0106] The present invention can be applied to a blood sampling device that samples blood by inserting a puncture needle into a body site presented by a subject. [Explanation of symbols]

[0107] 1,51...automatic blood sampling device, 2...calculation unit, 3...light source control unit, 4...image input unit, 5...mechanism control unit, 7...camera, 8...near-infrared light source, 9...movement mechanism, 10...blood sampling tube, 12...finger, 13...puncture needle, 21,53...CPU, 40...captured image, 41...vascular enhancement image, 42...vascular pattern extraction image, 43...thinned image, 44...vascular evaluation value image, 45...nearby area, 50...automatic blood sampling system, 52...display device.

Claims

1. A blood collection device that collects blood by inserting a puncture needle into a body part presented by a subject, a light source that irradiates the living body part with light; a camera that captures an image of blood vessels running under the skin of the living body part from the same side or the opposite side as the light source; a movement mechanism that moves the relative position of the puncture needle with respect to the living body site; a calculation unit that calculates or estimates, as a quantitative numerical value, a puncture suitability, which is an index of whether a point is suitable for puncturing, for each point within a range in which the puncture needle can move on the photographed image of the blood vessel obtained by photographing with the camera; Equipped with The calculation unit selecting a position in the biological region that is most suitable for puncturing with the puncture needle based on the puncture suitability calculated or estimated for each point within a range in which the puncture needle can move on the captured image, and controlling the movement mechanism to puncture the puncture needle at the selected position; The calculation unit The puncture suitability is calculated or estimated by any one of the following methods (A), (B), or (C): (A) When the target point on the photographed image is on the blood vessel, a value calculated based on the blood vessel evaluation value indicating a value corresponding to the thickness and darkness of the blood vessel, of each point on the blood vessel within a region surrounding the target point on the photographed image as a center, is calculated as the puncture suitability. (B) Starting from the point on the blood vessel on the photographed image, tracing is performed continuously for a certain distance along the blood vessel, and a value calculated based on the blood vessel evaluation value of each point on the photographed image along the blood vessel is calculated as the puncture suitability of the point on the blood vessel. (C) generating a learning model by machine learning using a learning dataset consisting of a set of a partial image in the vicinity of a point on the blood vessel in the photographed image as a center and the puncture suitability at that point or an index of suitability according to the puncture suitability, and using the generated learning model to estimate the puncture suitability for each point on the blood vessel in the photographed image; A blood collection device characterized by:

2. The calculation unit When calculating the puncture suitability by the method (A), the vascular evaluation value of each point in the neighboring region is weighted according to the distance from the center of the neighboring region to calculate the puncture suitability.

2. The blood collection device according to claim 1.

3. The calculation unit When calculating the puncture suitability by the method (B), the vascular evaluation value of each of the points on the captured image that are passed through is weighted according to the tracing distance from the starting point to calculate the puncture suitability.

2. The blood collection device according to claim 1.

4. The calculation unit In the captured image, the puncture suitability is also calculated for each point on the blood vessel outside the movable range of the puncture needle, and if there is a point among the points outside the movable range of the puncture needle that has a higher puncture suitability than the maximum puncture suitability within the movable range of the puncture needle, an instruction is given to the subject to move the living body part so as to move the point into the movable range of the puncture needle.

2. The blood collection device according to claim 1.

5. The calculation unit A direction in which the subject should move the body part is visually displayed.

5. The blood collection device according to claim 4.

6. The calculation unit When the point outside the movable range of the puncture needle, which has a puncture suitability higher than the maximum puncture suitability within the movable range of the puncture needle, enters the movable range of the puncture needle, the blood sample recipient is notified that the point has entered the movable range of the puncture needle.

5. The blood collection device according to claim 4.

7. A blood collection method performed by a blood collection device that collects blood by puncturing a body part presented by a blood collection subject with a puncture needle, comprising: The blood collection device is a light source that irradiates the living body part with light; a camera that captures an image of blood vessels running under the skin of the living body part from the same side or the opposite side as the light source; a movement mechanism that moves the relative position of the puncture needle with respect to the living body site; a calculation unit that calculates or estimates, as a quantitative numerical value, a puncture suitability, which is an index of whether a point is suitable for puncturing, for each point within a range in which the puncture needle can move on the photographed image of the blood vessel obtained by photographing with the camera; and a first step in which the calculation unit selects a position in the biological region that is most suitable for puncturing with the puncture needle based on the puncture suitability calculated or estimated for each point within a range in which the puncture needle can move on the captured image; a second step in which the calculation unit controls the moving mechanism to insert the puncture needle into the selected position; Equipped with In the first step, the calculation unit calculates or estimates the puncturing suitability by any one of the following methods (A), (B), or (C): (A) When the target point on the photographed image is on the blood vessel, a value calculated based on the blood vessel evaluation value indicating a value corresponding to the thickness and darkness of the blood vessel, of each point on the blood vessel within a region surrounding the target point on the photographed image as a center, is calculated as the puncture suitability. (B) Starting from the point on the blood vessel on the photographed image, tracing is performed continuously for a certain distance along the blood vessel, and a value calculated based on the blood vessel evaluation value of each point on the photographed image along the blood vessel is calculated as the puncture suitability of the point on the blood vessel. (C) generating a learning model by machine learning using a learning dataset consisting of a set of a partial image in the vicinity of a point on the blood vessel in the photographed image as a center and the puncture suitability at that point or an index of suitability according to the puncture suitability, and using the generated learning model to estimate the puncture suitability for each point on the blood vessel in the photographed image; A blood collection method characterized by:

Images