Ultrasonic time series data processing device and ultrasonic time series data processing program

Abstract

To reduce a computation amount for predicting a disease indicated by time-series data on the basis of the time-series data acquired by repeating transmission and reception of an ultrasonic wave a plurality of times for the same position in a subject.SOLUTION: In a Doppler mode, a Doppler processing unit 18 generates Doppler data as object time-series data on the basis of a reception beam data string from a reception unit 16. In an M mode, a beam data processing unit 20 generates a reception beam data string subjected to various types of signal processing as object time-series data. A disease prediction unit 36 inputs the object time-series data to a disease prediction learning unit 32 that has learned so as to predict and output a disease indicated by the time-series data on the basis of the inputted time-series data, and predicts the disease indicated by the object time-series data on the basis of output data of the disease prediction learning unit 12 for the object time-series data. A display control unit 24 notifies a user of a result of the prediction by the disease prediction unit 36.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] This specification discloses an ultrasound time-series data processing device and an ultrasound time-series data processing program. [Background technology]

[0002] Conventionally, ultrasonic waves are repeatedly transmitted and received from the same position (same direction as seen from the ultrasound probe) within the subject, and the time-series data obtained thereby, which is a time-series received beam data sequence, is imaged or analyzed.

[0003] Examples of images of time-series data include M-mode images, in which the horizontal axis represents time and the vertical axis represents depth, and the movement of tissue in the depth direction is shown by brightness lines extending in the time axis direction; and Doppler waveform images, in which the horizontal axis represents time and the vertical axis represents velocity, and in which the velocity of blood flowing through or within the test site is calculated based on the difference between the frequency of the transmitted ultrasound and the frequency of the received ultrasound.

[0004] Patent Document 1 discloses a method for analyzing time-series data that is a non-invasive method for determining vascular diseases, particularly arteriosclerosis, vascular stenosis, and aneurysms with high accuracy, in which ultrasound waves (with a frequency of f) are transmitted to the pulsating vascular wall of a subject, a reflected echo whose frequency has changed to f0 is received, the reflected echo is subjected to a wavelet transform to obtain a wavelet spectrum, the wavelet spectrum is subjected to mode decomposition to obtain spectra by mode, waveforms by mode on the time axis are obtained by an inverse wavelet transform, a norm value is calculated for each mode, and the calculated norm value is compared with a norm distribution obtained from a normal individual, thereby determining the presence or absence of a vascular disease or the incidence rate of a specific vascular disease. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] International Publication No. 2012 / 008173 Summary of the Invention [Problem to be solved by the invention]

[0006] As described above, conventionally, disease has been diagnosed by analyzing time-series data, but there has been a problem in that the amount of calculation required for the diagnosis is large. For example, in the above-mentioned Patent Document 1, various processes are required, such as obtaining a wavelet spectrum by wavelet transform, performing mode decomposition of the wavelet spectrum to obtain a spectrum for each mode, obtaining a waveform for each mode on the time axis by inverse wavelet transform, calculating a norm value for each mode, and comparing it with a norm distribution obtained from a normal individual.

[0007] The purpose of the ultrasound time-series data processing device disclosed in this specification is to reduce the amount of calculation required to predict a disease indicated by time-series data, based on the time-series data obtained by repeatedly transmitting and receiving ultrasound waves to the same position within a subject multiple times. [Means for solving the problem]

[0008] The ultrasound time series data processing device disclosed in this specification is characterized by comprising: a disease prediction unit that inputs target time series data, which is time series data generated by repeatedly transmitting and receiving ultrasound waves to and from the same position within a target test site multiple times, to a disease prediction learner that is trained to predict and output a disease indicated by the time series data based on the input time series data, using a combination of training time series data that is time series data indicating time changes of signals generated based on reflected waves from the test site or blood flowing within the test site by repeatedly transmitting and receiving ultrasound waves to and from the same position within a target test site multiple times, and predicts the disease indicated by the target time series data based on the output of the disease prediction learner in response to the input; and a notification unit that notifies a user of the prediction result of the disease prediction unit.

[0009] According to this configuration, by inputting the target time series data into the trained disease prediction learning device, the disease prediction unit can predict the disease indicated by the target time series data based on the output of the disease prediction learning device. This reduces the amount of calculation required to predict the disease indicated by the target time series data compared to conventional methods.

[0010] The disease prediction learner is trained to output the possibility that the time series data for each of a plurality of diseases is applicable based on the input time series data, the disease prediction unit inputs the target time series data to the disease prediction learner and predicts the possibility that the target time series data for each of a plurality of diseases is applicable, and the notification unit notifies the user of the possibility that the target time series data for each of a plurality of diseases is applicable.

[0011] According to this configuration, the amount of calculation required to predict the possibility that the target time series data for each of the multiple diseases is applicable is reduced compared to conventional methods, and the user can grasp the possibility that the target time series data for each of the multiple diseases is applicable.

[0012] The notification unit may highlight, on the display unit, the disease that is most likely to be associated with the target time-series data, among the plurality of diseases.

[0013] According to this configuration, the user can easily understand the disease to which the target time-series data is most likely to apply.

[0014] Prior to predicting the disease indicated by the target time series data, the disease prediction unit may identify which part of the target test area is the target, and further predict the disease indicated by the target time series data based on the identified part.

[0015] According to this configuration, the output accuracy of the disease prediction learning device is improved, thereby improving the prediction accuracy of the disease indicated by the target time series data.

[0016] The target test area and the test area may be pulsating areas, and the target time series data and the learning time series data may be time series data corresponding to the same period in the pulsation cycle of the target test area and the test area.

[0017] According to this configuration, the output accuracy of the disease prediction learning device is improved by making the periods of the learning time series data and the target time series data in the pulsation cycle the same, thereby improving the prediction accuracy of the disease indicated by the target time series data.

[0018] The method may further include a time series data generation unit that generates the target time series data, wherein the disease prediction unit predicts a disease indicated by the target time series data in real time in response to the generation of the target time series data by the time series data generation unit, and the notification unit notifies the user of a prediction result of the disease prediction unit in real time in response to the prediction of a disease indicated by the target time series data by the disease prediction unit.

[0019] In the ultrasound time-series data processing device disclosed in this specification, the amount of calculation required to predict a disease indicated by target time-series data is reduced, thereby reducing the calculation time. Therefore, when notifying a user of a disease prediction result in real time as in this configuration, it is possible to reduce the delay in notifying the prediction result relative to the time of acquisition of the target time-series data.

[0020] The ultrasound time series data processing program disclosed in this specification causes a computer to function as a disease prediction unit that inputs target time series data, which is time series data generated by repeatedly transmitting and receiving ultrasound waves to and from the same position within a target test site multiple times, and predicts the disease indicated by the target time series data based on the output of the disease prediction learner in response to the input, and a notification unit that notifies a user of the prediction result of the disease prediction unit. The disease prediction unit is configured to use a combination of training time series data, which is time series data indicating time changes of signals generated based on reflected waves from the test site or blood flowing within the test site, and information indicating the disease present in the test site, as training data, and to predict the disease indicated by the target time series data based on the output of the disease prediction learner in response to the input. [Effects of the Invention]

[0021] According to the ultrasound time-series data processing device disclosed in this specification, it is possible to reduce the amount of calculation required to predict a disease indicated by time-series data obtained by repeatedly transmitting and receiving ultrasound waves to the same position within a subject multiple times. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a block diagram of an ultrasonic diagnostic apparatus according to an embodiment of the present invention. [Figure 2] FIG. 2 is a conceptual diagram showing the relationship between received beam data and received frame data. [Figure 3] FIG. 10 is a conceptual diagram showing the learning process of a disease prediction learning device. [Figure 4] FIG. 10 is a conceptual diagram showing the prediction process using a disease prediction learning device. [Figure 5] FIG. 10 is a diagram showing a first example of a notification screen for notifying the prediction result of the disease prediction unit. [Figure 6] FIG. 10 is a diagram showing a second example of a notification screen for notifying the prediction result of the disease prediction unit. [Figure 7] 4 is a flowchart showing the flow of processing performed by the ultrasound diagnostic apparatus according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0023] 1 is a block diagram of an ultrasound diagnostic apparatus 10 serving as an ultrasound time-series data processing apparatus according to this embodiment. The ultrasound diagnostic apparatus 10 is a medical device installed in a medical institution such as a hospital and used during ultrasound examinations.

[0024] The ultrasound diagnostic device 10 can operate in multiple operating modes, including B-mode, Doppler mode, and M-mode. B-mode is a mode in which a tomographic image (B-mode image) is generated and displayed based on reception frame data consisting of multiple reception beam data obtained by scanning an ultrasound beam (transmission beam). The amplitude intensity of a reflected wave from a scanning plane is converted into brightness. Doppler mode is a mode in which a waveform (Doppler waveform) indicating the tissue motion velocity along an observation line set within a subject is generated and displayed based on the difference in frequency between the transmission wave and the reflection wave along the observation line. Doppler modes may include continuous wave mode, pulse Doppler mode, color Doppler mode, and tissue Doppler mode. M-mode is a mode in which an M-mode image indicating the tissue motion along an observation line set within a subject is generated and displayed based on reception beam data corresponding to the observation line. This embodiment particularly focuses on the case in which the ultrasound diagnostic device 10 operates in Doppler mode or M mode.

[0025] The probe 12, which is an ultrasonic probe, is a device that transmits ultrasonic waves and receives reflected waves. Specifically, the probe 12 is placed in contact with the body surface of a subject, transmits ultrasonic waves toward the subject, and receives the reflected waves reflected by tissue within the subject. A transducer element array consisting of a plurality of transducer elements is provided within the probe 12. A transmission signal, which is an electrical signal, is supplied to each transducer element included in the transducer element array from a transmitter 14, which will be described later, thereby generating an ultrasonic beam (transmission beam). Furthermore, each transducer element included in the transducer element array receives reflected waves from the subject, converts the reflected waves into a reception signal, which is an electrical signal, and transmits the received signal to a receiver 16, which will be described later.

[0026] When transmitting ultrasonic waves, the transmitter 14 supplies a plurality of transmission signals in parallel to the probe 12 (specifically, the transducer element array) under the control of a processor 34, which will be described later. As a result, ultrasonic waves are transmitted from the transducer element array.

[0027] In Doppler mode or M mode, the transmitter 14 supplies a transmission signal to the probe 12 so that the probe 12 repeatedly transmits a transmission beam toward the same position within the region of interest of the subject, which position is determined by a user such as a doctor or medical technician. In other words, the transmitter 14 supplies a transmission signal to the probe 12 so that the probe 12 repeatedly transmits a transmission beam toward the same position within the region of interest. In B mode, the transmitter 14 supplies a transmission signal to the probe 12 so that the transmission beam transmitted from the probe 12 is electronically scanned within the scanning plane. Alternatively, time-division scanning can be performed so that the transmission beam is repeatedly transmitted toward the same position determined by the user while the transmission beam is electronically scanned within the scanning plane.

[0028] When receiving reflected waves, the receiving unit 16 receives multiple received signals in parallel from the probe 12 (specifically, the transducer array). The receiving unit 16 performs phasing addition (delay addition) on the multiple received signals, thereby generating received beam data.

[0029] In the Doppler mode or M mode, the probe 12 repeatedly transmits a transmission beam to the same position within the test area, and the receiver 16 receives multiple reflected waves from the test area or the blood flowing within the test area, and generates a time-series received beam data string based on the multiple reflected waves.In addition, in the B mode, the receiver 16 composes received frame data from multiple received beam data aligned in the scanning direction.

[0030] FIG. 2 is a conceptual diagram showing the relationship between receive beam data BD and receive frame data F. In Doppler mode or M mode, ultrasound is transmitted and received toward a position (direction) specified by the user. This generates a plurality of receive beam data DBs (i.e., receive beam data strings) in time series. The receive beam data DB contains information indicating the intensity and frequency of the reflected waves from each depth. In B mode, the transmit beam is scanned in the scanning direction θ, and receive frame data F is generated from a plurality of receive beam data aligned in the scanning direction θ.

[0031] In the Doppler mode, the receive beam data sequence is sent to a Doppler processor 18, and in the M mode, the receive beam data sequence is sent to a beam data processor 20.

[0032] Returning to FIG. 1 , in Doppler mode, the Doppler processing unit 18 generates Doppler data as time-series data indicating the time change in the velocity of the test site or the blood flowing within the test site based on the received beam data sequence from the receiving unit 16. Specifically, the Doppler processing unit 18 generates Doppler data by multiplying each received beam data by a reference frequency (transmission frequency), performing quadrature detection to extract Doppler shift through a low-pass filter, sample gate processing (in the case of pulsed Doppler mode) to extract only signals at the position of the sample volume, A / D conversion of the signal, and frequency analysis using the fast Fourier transform (FFT) method. The generated Doppler data is sent to the image generating unit 22 and the processor 34. In Doppler mode, the receiving unit 16 and Doppler processing unit 18 correspond to a time-series data generating unit.

[0033] In M mode, the beam data processing unit 20 performs various signal processing such as gain correction, logarithmic amplification, and filtering on the received beam data sequence from the receiver 16. The processed received beam data sequence is sent to the image generator 22 and processor 34. In this embodiment, in M ​​mode, the received beam data sequence processed by the beam data processing unit 20 corresponds to time-series data indicating changes in the position of the subject region over time. In this case, the receiver 16 and the beam data processing unit 20 correspond to a time-series data generator. Note that in B mode as well, the beam data processing unit 20 performs the above-mentioned various signal processing on the received frame data from the receiver 16.

[0034] The image generating unit 22 is configured by a digital scan converter and has a coordinate conversion function, a pixel interpolation function, a frame rate conversion function, and the like.

[0035] In the Doppler mode, the image generator 22 generates a Doppler waveform image based on the Doppler data from the Doppler processor 18. The Doppler waveform is a waveform displayed on a two-dimensional plane of time and velocity, and shows the change over time in the velocity of the test area on the observation line corresponding to the received beam data sequence or the velocity of blood flowing within the test area.

[0036] In the M-mode, the image generating unit 22 generates an M-mode image based on the received beam data sequence from the beam data processing unit 20. The M-mode image is a waveform displayed on a two-dimensional plane of time and depth, and shows the change over time in the position of the subject area on the observation line corresponding to the received beam data sequence.

[0037] In the B mode, the image generator 22 generates a B mode image in which the amplitude (intensity) of the reflected wave is expressed as brightness, based on the received frame data from the beam data processor 20.

[0038] The display control unit 24 causes various images, such as Doppler waveform images, M-mode images, or B-mode images, generated by the image generation unit 22 to be displayed on a display 26 serving as a display unit configured, for example, with a liquid crystal panel. The display control unit 24 also causes the display 26 to display prediction results by a disease prediction unit 36, which will be described later.

[0039] Each of the transmitter 14, receiver 16, Doppler processor 18, beam data processor 20, image generator 22, and display controller 24 is configured with one or more processors, chips, electric circuits, etc. Each of the components may be realized by a combination of hardware and software.

[0040] The input interface 28 is configured by, for example, a button, a trackball, a touch panel, etc. The input interface 28 is used to input user instructions to the ultrasound diagnostic apparatus 10.

[0041] The memory 30 includes a hard disk drive (HDD), a solid state drive (SSD), an embedded multimedia card (eMMC), a read-only memory (ROM), or a random access memory (RAM). The memory 30 stores an ultrasound time-series data processing program for operating each unit of the ultrasound diagnostic device 10. The ultrasound time-series data processing program can also be stored in a computer-readable non-transitory storage medium such as a universal serial bus (USB) memory or a CD-ROM. The ultrasound diagnostic device 10 or another computer can read and execute the ultrasound time-series data processing program from such a storage medium. As shown in FIG. 1, the memory 30 also stores a disease prediction learner 32.

[0042] The disease prediction learner 32 is configured with a learning model such as an RNN (Recurrent neural network), an LSTM (Long Short Term Memory), which is a type of RNN, a CNN (Convolutional Neural Network), or a DQN (Deep Q-Network) using a deep reinforcement learning algorithm. The disease prediction learner 32 is trained to predict and output a disease indicated by input time series data based on the time series data, using a combination of learning time series data, which is time series data generated based on reflected waves from the test site or the blood flowing within the test site, by repeatedly transmitting and receiving ultrasound waves to and from the same position within the test site. The learning data is a combination of information (label) indicating the disease of the test site.

[0043] FIG. 3 is a conceptual diagram illustrating the learning process of the disease prediction learner 32. For example, training time-series data obtained by transmitting and receiving ultrasound to a test site having a disease called "stenosis" is input to the disease prediction learner 32. In this case, the disease prediction learner 32 predicts and outputs the disease indicated by the training time-series data. The disease prediction learner 32 has an activation function such as a softmax function in its final stage (output layer), and outputs the possibility (probability) indicated by the training time-series data for each of multiple diseases as output data. The computer performing the learning process calculates the error between the output data and the label (in this case, "stenosis") attached to the training time-series data using a predetermined loss function, and adjusts each parameter of the disease prediction learner 32 (e.g., the weight and bias of each neuron) to reduce the error. By repeating this learning process, the disease prediction learner 32 can accurately output the possibility that the time-series data corresponds to each of multiple diseases based on the input time-series data.

[0044] The subject's target region for the training time series data may be a pulsating region. In this case, the training time series data may be data corresponding to a predetermined period in the pulsation cycle of the subject's region. For example, the training time series data may be time series data based on a received beam data sequence acquired during the period between R waves in the electrocardiogram waveform obtained from an electrocardiograph attached to the subject.

[0045] In this embodiment, the learning time series data is data before imaging (corresponding to the above-mentioned Doppler data or received beam data sequence), but the learning time series data may also be a Doppler waveform image or an M-mode image visualized based on the Doppler data or received beam data sequence (more specifically, data that quantifies the characteristics of the Doppler waveform image or M-mode image).

[0046] 3 includes time-series data (normal conditions) that do not indicate a disease as learning data, but normal time-series data need not be included in the learning data. Furthermore, the disease prediction learning device 32 may be provided with a separate learning device for each disease. In this case, each learning device is trained to output the probability that input time-series data indicates the corresponding disease.

[0047] In this embodiment, the disease prediction learner 32 is trained by a computer other than the ultrasound diagnostic device 10, and the trained disease prediction learner 32 is stored in the memory 30. However, the time series data acquired by the ultrasound diagnostic device 10 may be used as learning time series data, and the learning process of the disease prediction learner 32 may be performed by the ultrasound diagnostic device 10. In this case, the processor 34 functions as a learning processing unit that performs the learning process of the disease prediction learner 32.

[0048] Returning to FIG. 1, the processor 34 includes at least one of a general-purpose processing device (e.g., a CPU (Central Processing Unit)) and a dedicated processing device (e.g., a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a programmable logic device). The processor 34 may not be a single processing device, but may be configured by the cooperation of multiple processing devices located in physically separate locations. As shown in FIG. 1, the processor 34 functions as a disease prediction unit 36 ​​in accordance with an ultrasound time-series data processing program stored in the memory 30.

[0049] FIG. 4 is a conceptual diagram illustrating the prediction process using the disease prediction learner 32. The disease prediction unit 36 ​​inputs time series data (referred to herein as "target time series data") obtained by repeatedly transmitting and receiving ultrasound waves to the same position within a test site (referred to herein as "target test site") that is the target of disease prediction into the trained disease prediction learner 32. As described above, the target time series data is generated by the receiving unit 16 and Doppler processing unit 18 (in the case of Doppler mode) or the receiving unit 16 and beam data processing unit 20 (in the case of M mode). The target time series data may also be a Doppler waveform image or an M-mode image (more specifically, data that quantifies the features of a Doppler waveform image or an M-mode image) visualized based on Doppler data or a received beam data sequence.

[0050] The disease prediction learner 32 predicts the disease indicated by the target time series data based on the input target time series data and outputs output data indicating the prediction result. The disease prediction unit 36 ​​predicts the disease indicated by the target time series data based on the output data of the disease prediction learner 32. As described above, in this embodiment, the disease prediction learner 32 can output the possibility indicated by the target time series data for each of multiple diseases as output data. For example, the disease prediction learner 32 outputs "0.12 (12%)" as the possibility that the target time series data indicates disease A, "0.83 (83%)" as the possibility that the target time series data indicates disease B, "0.06 (6%)" as the possibility that the target time series data indicates disease C, and "0.03 (3%)" as the possibility that the target time series data indicates disease N. Based on such output data, the disease prediction unit 36 ​​can predict the possibility that the target time series data corresponds to each of multiple diseases.

[0051] If multiple disease prediction learners 32 are prepared for each disease, the disease prediction unit 36 ​​sequentially passes the target time series data to the multiple disease prediction learners 32, and predicts the possibility that the target time series data for each of the multiple diseases applies based on the output data of each of the multiple disease prediction learners 32.

[0052] As described above, in this embodiment, if the disease prediction unit 36 ​​inputs the target time series data to the trained disease prediction learner 32, it can predict the disease indicated by the target time series data based on the output of the disease prediction learner 32. This reduces the amount of calculation required to predict the disease indicated by the target time series data compared to conventional methods.

[0053] When the target test site and the test site that is the subject of the learning time series data are pulsating sites, the target time series data and the learning time series data may be time series data corresponding to the same period in the pulsation cycle of the target test site and the test site. For example, when the learning time series data is time series data based on a receive beam data sequence acquired during the period between R waves in an electrocardiogram waveform, the disease prediction unit 36 ​​may also set the target time series data to be time series data based on a receive beam data sequence acquired during the period between R waves in an electrocardiogram waveform. By making the learning time series data and the target time series data have the same period in the pulsation cycle, the output accuracy of the disease prediction learner 32 can be improved. In other words, the disease prediction unit 36 ​​can predict the disease indicated by the target time series data with improved accuracy.

[0054] In addition, prior to predicting the disease indicated by the target time series data, the disease prediction unit 36 ​​may identify which part of the subject area is the target test area, and then predict the disease indicated by the target time series data based on the identified part.

[0055] The location of the target examination region can be identified, for example, by analyzing an image (Doppler waveform image or M-mode image) generated by the image generation unit 22 based on the target time-series data. When received frame data is acquired together with the time-series data by time-division scanning, the location of the target examination region may be identified by analyzing a B-mode image generated by the image generation unit 22 based on the received frame data. Alternatively, the disease prediction unit 36 ​​may identify the location of the target examination region based on a user input from the input interface 28. For example, the location of the target examination region may be identified based on settings for ultrasound diagnosis input by the user from the input interface 28 (e.g., presets selected by the user).

[0056] The disease prediction unit 36 ​​inputs parameters indicating which part of the subject is the target test site into the disease prediction learner 32 along with the target time series data. This enables the disease prediction learner 32 to predict the disease indicated by the target time series data while taking into account which part of the subject is the target test site. For example, the disease prediction learner 32 can predict the disease indicated by the target time series data after excluding diseases that cannot occur in the target test site. This improves the output accuracy of the disease prediction learner 32, i.e., improves the prediction accuracy of the disease indicated by the target time series data by the disease prediction unit 36.

[0057] The display control unit 24 notifies the user of the prediction result of the disease prediction unit 36. That is, the display control unit 24 functions as a notification unit. In this embodiment, the prediction result of the disease prediction unit 36 ​​is displayed on the display 26 by the display control unit 24 as described below, but in addition to or instead of this, the prediction result of the disease prediction unit 36 ​​may be notified to the user by audio output or the like.

[0058] 5 is a diagram showing a first example of a notification screen 50 displayed on the display 26 to notify the user of the prediction result of the disease prediction unit 36. The notification screen 50 displays an ultrasound image 52 generated based on the target time-series data and a prediction result 54 of the disease prediction unit 36. Note that the notification screen 50 is in Doppler mode, and a Doppler waveform image is displayed as the ultrasound image 52; however, in M ​​mode, an M-mode image is displayed as the ultrasound image 52.

[0059] As described above, since the disease prediction unit 36 ​​can predict the possibility that the target time series data for each of the plurality of diseases is applicable, the display control unit 24 may notify the user of the possibility that the target time series data for each of the plurality of diseases is applicable. In the prediction result 54 on the notification screen 50, the display control unit 24 displays the names of the plurality of diseases ("Disease A," "Disease B," "Disease C," ..., "Disease N") and also displays the possibility that the target time series data for each disease is applicable in the form of a graph.

[0060] Fig. 6 is a diagram showing a second example of the notification screen 50. As shown in the notification screen 50 of Fig. 6, the display control unit 24 may highlight, in the prediction result 54', the disease that the disease prediction unit 36 ​​predicted the possibility that the target time-series data would apply to, which disease is most likely to apply to the target time-series data. In the example of Fig. 6, of the multiple diseases, "Disease B" is most likely to apply to the target time-series data, so "Disease B" is highlighted in the prediction result 54'.

[0061] Various modes of highlighting are possible. For example, the disease to be highlighted may be displayed in a different color or font from other diseases. Alternatively, the disease to be highlighted may be highlighted or shaded. Alternatively, multiple diseases may be arranged in descending order of the likelihood that the target time-series data corresponds to the disease. Alternatively, only the disease to be highlighted (the disease that the target time-series data was most likely to correspond to) may be displayed.

[0062] By highlighting the disease that is most likely to be associated with the target time series data among multiple diseases, the user can easily understand the disease that is most likely to be associated with the target time series data.

[0063] As described above, in this embodiment, the amount of calculation required to predict a disease indicated by the target time series data is reduced compared to conventional methods. In other words, the disease indicated by the target time series data can be predicted more quickly. Therefore, the disease prediction unit 36 ​​predicts the disease indicated by the target time series data in real time in response to the generation of the target time series data, and the display control unit 24 notifies the user of the prediction result of the disease prediction unit 36 ​​in real time in response to the prediction of the disease indicated by the target time series data by the disease prediction unit 36. According to this embodiment, even with such real-time processing, the prediction result of the disease prediction unit 36 ​​can be smoothly notified to the user without any delay.

[0064] The flow of processing performed by the ultrasound diagnostic apparatus 10 according to this embodiment will be described below with reference to the flowchart shown in FIG.

[0065] In step S10, in response to a user instruction from the input interface 28, the ultrasound diagnostic apparatus 10 starts the Doppler mode or the M mode.

[0066] In step S12, in the Doppler mode, the Doppler processing unit 18 generates Doppler data as target time-series data based on the received beam data sequence from the receiving unit 16. In the M mode, the beam data processing unit 20 generates, as target time-series data, a received beam data sequence that has been subjected to various signal processes.

[0067] In step S14, the disease prediction unit 36 ​​inputs the target time series data (Doppler data or received beam data sequence) generated in step S12 to the trained disease prediction learner 32. Then, the disease prediction unit 36 ​​predicts the disease indicated by the target time series data based on the output data of the disease prediction learner 32 for the target time series data.

[0068] In step S16, the display controller 24 causes the display 26 to display a Doppler waveform image based on the Doppler data generated in step S12 or an M-mode image based on the receive beam data sequence generated in step S12, as well as the prediction result by the disease predictor 36 in step S14. This notifies the user of the prediction result by the disease predictor 36.

[0069] In step S18, processor 34 determines whether Doppler mode or M mode has ended in response to a user instruction. If Doppler mode or M mode is continuing, the process returns to step S12 and repeats steps S12 to S18. If Doppler mode or M mode has ended, the process ends.

[0070] Although the embodiment of the present invention has been described above, the present invention is not limited to the above embodiment, and various modifications are possible without departing from the spirit of the present invention.

[0071] For example, in this embodiment, the ultrasound time-series data processing device is the ultrasound diagnostic device 10, but the ultrasound time-series data processing device is not limited to the ultrasound diagnostic device 10 and may be another computer. In this case, the trained disease prediction learner 32 is stored in a memory accessible from the computer serving as the ultrasound time-series data processing device, and the processor of the computer fulfills the functions of the disease prediction unit 36 ​​and the display control unit 24. [Explanation of symbols]

[0072] 10 Ultrasound diagnostic device, 12 Probe, 14 Transmitter, 16 Receiver, 18 Doppler processor, 20 Beam data processor, 22 Image generator, 24 Display controller, 26 Display, 28 Input interface, 30 Memory, 32 Disease prediction learner, 34 Processor, 36 Disease prediction unit.

Claims

1. a disease prediction learning device that is trained to predict and output the disease indicated by the waveform data inputted based on the waveform data, using a combination of learning waveform data, which is waveform data indicating time changes of signals generated based on reflected waves from the test site or blood flowing within the test site by repeatedly transmitting and receiving ultrasonic waves to and from the same position within the test site having the disease, and information indicating the disease that the test site has and information indicating the location of the test site as learning data; a disease prediction unit that, prior to predicting the disease indicated by the target waveform data, identifies the target test site, inputs the target waveform data, which is waveform data generated by repeatedly transmitting and receiving ultrasonic waves to the same position within the target test site multiple times, and information indicating the target test site identified by analyzing the target waveform data, into the disease prediction learning device, and predicts the disease indicated by the target waveform data based on the output of the disease prediction learning device in response to the input; a notification unit that notifies a user of a prediction result of the disease prediction unit; An ultrasonic time-series data processing device comprising:

2. the disease prediction learning device is trained to output, based on the input waveform data, a possibility that the waveform data corresponds to each of a plurality of diseases; the disease prediction unit inputs the target waveform data into the disease prediction learning device and predicts the possibility that the target waveform data corresponds to each of a plurality of diseases; the notification unit notifies the user of a possibility that the target waveform data for each of a plurality of diseases applies to the user.

2. The ultrasonic time-series data processing device according to claim 1,

3. the notification unit highlights on a display unit the disease that is most likely to be associated with the target waveform data among the plurality of diseases.

3. The ultrasonic time-series data processing device according to claim 2.

4. the target test site and the test site are pulsating sites, The target waveform data and the learning waveform data are waveform data corresponding to the same period in the pulsation cycle of the target test site and the test site.

2. The ultrasonic time-series data processing device according to claim 1,

5. a time-series data generating unit that generates the target waveform data; Furthermore, the disease prediction unit predicts a disease indicated by the target waveform data in real time in response to generation of the target waveform data by the time-series data generation unit; the notification unit notifies the user of the prediction result of the disease prediction unit in real time in response to the prediction of the disease indicated by the target waveform data by the disease prediction unit.

2. The ultrasonic time-series data processing device according to claim 1,

6. a computer including a disease prediction learning device that is trained to predict and output a disease indicated by input waveform data based on the waveform data, using a combination of learning waveform data, which is waveform data indicating time changes of signals generated based on reflected waves from the test site or the blood flowing within the test site by repeatedly transmitting and receiving ultrasonic waves to and from the same position within the test site having a disease, information indicating the disease the test site has, and information indicating which part of the test site the test site is; a disease prediction unit that, prior to predicting the disease indicated by the target waveform data, identifies the target test site, inputs the target waveform data, which is waveform data generated by repeatedly transmitting and receiving ultrasonic waves to the same position within the target test site multiple times, and information indicating the target test site identified by analyzing the target waveform data, into the disease prediction learning device, and predicts the disease indicated by the target waveform data based on the output of the disease prediction learning device in response to the input; a notification unit that notifies a user of a prediction result of the disease prediction unit; The ultrasonic time series data processing program is characterized by causing the program to function as follows.

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