Method and system for prompting data donation for artificial intelligence tool development

Abstract

The invention is entitled "Methods and systems for prompting data donation for artificial intelligence tool development." The invention provides systems and methods for prompting data donation for artificial intelligence tool development. The method includes presenting, at a display system, an ultrasound image and at least one automated analysis feature. The at least one automated analysis feature includes one or more unenabled automated analysis features. The method includes receiving a user selection of at least one automated analysis feature of the one or more unenabled automated analysis features. The method includes presenting, at the display system, a prompt providing a user option to share user analysis data. The method includes receiving a user selection selecting to share the user analysis data. The method includes providing access to the at least one automated analysis feature of the one or more unenabled automated analysis features when a condition is met.

Description

Technical Field

[0001] Certain embodiments relate to ultrasound imaging. More particularly, certain embodiments relate to methods and systems for providing artificial intelligence tool development by facilitating user data donations. Background Art

[0002] Ultrasound imaging is a medical imaging technique used to image organs and soft tissues in the human body. Ultrasound imaging uses real-time, non-invasive, high-frequency sound waves to produce a series of two-dimensional (2D) images and / or three-dimensional (3D) images.

[0003] Artificial intelligence processing of ultrasound images and / or videos is often applied to process images and / or videos to assist ultrasound operators or other medical personnel who review the processed image data in providing a diagnosis. For example, artificial intelligence tools can be applied to ultrasound images to automatically provide annotations, measurements, and / or diagnoses that can be presented with the ultrasound image. However, artificial intelligence algorithms are typically developed using thousands of images that have been manually analyzed and provided with annotations, measurements, and / or diagnoses. The accuracy of artificial intelligence depends in part on the amount of samples used to develop the algorithm, the quality of the samples, the quality of the analysis accompanying the samples, the demographic diversity of the samples, and the like.

[0004] Further limitations and disadvantages of conventional and traditional approaches will become apparent to those skilled in the art by comparing such systems with certain aspects of the present disclosure as set forth in the remainder of this application with reference to the accompanying figures. Summary of the Invention

[0005] There is provided a system and / or method for prompting data donation for use in artificial intelligence tool development, substantially as shown and / or described in connection with at least one of the accompanying drawings, as more fully set forth in the claims.

[0006] These and other advantages, aspects and novel features of the present disclosure, as well as details of illustrated embodiments thereof, will be more fully understood from the following description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 is a block diagram of an exemplary ultrasound system operable to prompt data donation for artificial intelligence tool development, according to various embodiments.

[0008] Figure 2 is a block diagram of an exemplary medical workstation operable to prompt data donation for artificial intelligence tool development, according to various embodiments.

[0009] Figure 3 is a block diagram of an exemplary system in which representative embodiments may be practiced.

[0010] Figure 4 is a display of exemplary ultrasound images and tools for analyzing ultrasound images according to various embodiments.

[0011] Figure 5 is a display of exemplary ultrasound images, tools for analyzing ultrasound images, and prompts to donate data, according to various embodiments.

[0012] Figure 6 is a flow chart illustrating exemplary steps that may be used to prompt data donations for artificial intelligence tool development, according to various embodiments. DETAILED DESCRIPTION

[0013] Certain embodiments may be found in a method and system for prompting data donation for use in artificial intelligence tool development. Various embodiments have the technical effect of providing access to unenabled automated analysis features in exchange for sharing user analysis data. Aspects of the present disclosure have the technical effect of facilitating the donation of user analysis data for use in developing artificial intelligence tools.

[0014] When read in conjunction with the accompanying drawings, the foregoing summary of the invention and the following detailed description of certain embodiments will be better understood. With respect to the scope of the figures showing the functional blocks of various embodiments in the accompanying drawings, these functional blocks do not necessarily represent the division between the hardware circuits. Therefore, for example, one or more functional blocks (e.g., processors or memories) can be implemented in a single piece of hardware (e.g., a general-purpose signal processor or random access memory block, a hard disk, etc.) or multiple pieces of hardware. Similarly, a program can be an independent program, can be included in an operating system as a subroutine, can be a function in an installed software package, etc. It should be understood that the various embodiments are not limited to the arrangements and tools shown in the accompanying drawings. It should also be understood that embodiments can be combined, or other embodiments can be utilized, and structural, logical and electrical changes can be made without departing from the scope of the various embodiments. Therefore, the following detailed description should not be considered as restrictive, and the scope of this disclosure is limited by the appended claims and their equivalents.

[0015] As used herein, an element or step listed in the singular and beginning with the word "a" or "an" should be understood as not excluding a plurality of said elements or steps, unless such exclusion is explicitly stated. Furthermore, references to "exemplary embodiments," "various embodiments," "certain embodiments," "representative embodiments," etc. are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features. Furthermore, unless explicitly stated to the contrary, embodiments that "comprise," "include," or "have" an element or elements having a particular property may include additional elements that do not have that property.

[0016] In addition, as used herein, the term "image" refers broadly to both a visual image and data representing a visual image. However, many embodiments generate (or are configured to generate) at least one visual image. In addition, as used herein, the phrase "image" is used to refer to ultrasound modes, such as B-mode (2D mode), M-mode, three-dimensional (3D) mode, CF mode, PW Doppler, CW Doppler, MGD, and / or sub-modes of B-mode and / or CF, such as shear wave elastography (SWEI), TVI, Angio, B-flow, BMI, BMI_Angio, and in some cases also MM, CM, TVD, where "image" and / or "plane" includes a single beam or multiple beams.

[0017] Furthermore, as used herein, the term processor or processing unit refers to any type of processing unit, whether single-core or multi-core: CPU, accelerated processing unit (APU), graphics board, DSP, FPGA, ASIC, or a combination thereof, that can perform the required computations required by various implementations.

[0018] It should be noted that the various embodiments described herein for generating or forming an image may include a process for forming the image that, in some embodiments, includes beamforming and in other embodiments does not include beamforming. For example, an image may be formed without beamforming, such as by multiplying a matrix of demodulated data by a coefficient matrix such that the product is an image, and wherein the process does not form any "beams." Additionally, image formation may be performed using a combination of channels that may originate from more than one transmit event (e.g., synthetic aperture techniques).

[0019] In various embodiments, ultrasound processing to form images, including ultrasound beamforming, such as receive beamforming, is performed, for example, in software, firmware, hardware, or a combination thereof. Figure 1 One specific implementation of an ultrasound system having a software beamformer architecture formed in accordance with various embodiments is shown.

[0020] Figure 1 is a block diagram of an exemplary ultrasound system 100 operable to prompt data donation for artificial intelligence tool development, according to various embodiments. Figure 1 , an ultrasound system 100 is shown. The ultrasound system 100 includes a transmitter 102, an ultrasound probe 104, a transmit beamformer 110, a receiver 118, a receive beamformer 120, an A / D converter 122, an RF processor 124, an RF / IQ buffer 126, a user input device 130, a signal processor 132, an image buffer 136, a display system 134, an archive 138, a training engine 170, and a communication interface 180.

[0021] The transmitter 102 may include suitable logic, circuitry, interfaces, and / or code operable to drive the ultrasound probe 104. The ultrasound probe 104 may include a two-dimensional (2D) array of piezoelectric elements. The ultrasound probe 104 may include a set of transmit transducer elements 106 and a set of receive transducer elements 108, which are generally constructed as identical elements. In certain embodiments, the ultrasound probe 104 may be operable to acquire ultrasound image data covering at least a majority of an anatomical structure, such as a heart, a blood vessel, or any suitable anatomical structure.

[0022] The transmit beamformer 110 may comprise suitable logic, circuitry, interfaces, and / or code operable to control the transmitter 102 to drive the set of transmit transducer elements 106 via the transmit subaperture beamformer 114 to transmit ultrasound transmit signals into an area of ​​interest (e.g., a person, an animal, an underground cavity, a physical structure, etc.). The transmitted ultrasound signals may be backscattered from structures in the object of interest (e.g., blood cells or tissue) to generate echoes, which are received by the receive transducer elements 108.

[0023] A set of receive transducer elements 108 in the ultrasound probe 104 may be operable to convert received echoes into analog signals, which are sub-aperture beamformed by a receive sub-aperture beamformer 116 and then transmitted to a receiver 118. The receiver 118 may comprise suitable logic, circuitry, interfaces, and / or code that may be operable to receive the signals from the receive sub-aperture beamformer 116. The analog signals may be transmitted to one or more of the plurality of A / D converters 122.

[0024] The plurality of A / D converters 122 may comprise suitable logic, circuitry, interfaces, and / or code operable to convert analog signals from the receiver 118 into corresponding digital signals. The plurality of A / D converters 122 are disposed between the receiver 118 and the RF processor 124. However, the present disclosure is not limited in this respect. Thus, in some embodiments, the plurality of A / D converters 122 may be integrated within the receiver 118.

[0025] The RF processor 124 may comprise suitable logic, circuitry, interfaces, and / or code that may be operable to demodulate the digital signals output by the plurality of A / D converters 122. According to one embodiment, the RF processor 124 may comprise a complex demodulator (not shown) that may be operable to demodulate the digital signals to form I / Q data pairs representing corresponding echo signals. The RF or I / Q signal data may then be transferred to the RF / IQ buffer 126. The RF / IQ buffer 126 may comprise suitable logic, circuitry, interfaces, and / or code that may be operable to provide temporary storage of the RF or I / Q signal data generated by the RF processor 124.

[0026] The receive beamformer 120 may comprise suitable logic, circuitry, interfaces, and / or code operable to perform digital beamforming processing to, for example, sum delayed channel signals received from the RF processor 124 via the RF / IQ buffer 126 and output a beam-summed signal. The resulting processed information may be the beam-summed signal output from the receive beamformer 120 and communicated to the signal processor 132. According to some embodiments, the receiver 118, the plurality of A / D converters 122, the RF processor 124, and the receive beamformer 120 may be integrated into a single beamformer, which may be digital. In various embodiments, the ultrasound system 100 includes a plurality of receive beamformers 120.

[0027] The user input device 130 may be used to enter patient data, scan parameters, settings, select protocols and / or templates, annotate displayed images, perform measurements on displayed images, select automated analysis features and / or tools, etc. In an exemplary embodiment, the user input device 130 is operable to configure, manage, and / or control the operation of one or more components and / or modules in the ultrasound system 100. In this regard, the user input device 130 is operable to configure, manage, and / or control the operation of the transmitter 102, the ultrasound probe 104, the transmit beamformer 110, the receiver 118, the receive beamformer 120, the RF processor 124, the RF / IQ buffer 126, the user input device 130, the signal processor 132, the image buffer 136, the display system 134, the archive 138, the training engine 170, and / or the communication interface 180. The user input device 130 may include one or more buttons, one or more rotary encoders, a touch screen, motion tracking, voice recognition, a mouse device, a keyboard, a camera, and / or any other device capable of receiving user commands. In certain embodiments, for example, one or more user input modules in the user input device 130 can be integrated into other components, such as the display system 134. For example, the user input device 130 can include a touch screen display.

[0028] In various embodiments, anatomical structures depicted in the image data may be marked and / or measured in response to instructions received via the user input device 130. In certain embodiments, automated analysis features and / or tools may be selected in response to feature selection instructions received via the user input device 130. In a representative embodiment, user analysis data may be shared in response to data donation instructions received via the user input device 130.

[0029] The signal processor 132 may include suitable logic, circuitry, interfaces, and / or code operable to process ultrasound scan data (i.e., the summed IQ signal) to generate an ultrasound image for presentation on the display system 134. The signal processor 132 may be operable to perform one or more processing operations based on a plurality of selectable ultrasound modalities on the acquired ultrasound scan data. In an exemplary embodiment, the signal processor 132 may be operable to perform display processing and / or control processing, among other things. The acquired ultrasound scan data may be processed in real time during a scanning session as echo signals are received. Additionally or alternatively, the ultrasound scan data may be temporarily stored in the RF / IQ buffer 126 during a scanning session and processed in a less-than-real-time manner in either online or offline operations. In various embodiments, the processed image data may be presented at the display system 134 and / or may be stored in an archive 138. The archive 138 may be a local archive, a picture archiving and communication system (PACS), or any other suitable device for storing images and related information.

[0030] The signal processor 132 may be one or more central processing units, microprocessors, microcontrollers, and the like. For example, the signal processor 132 may be an integrated component or may be distributed across various locations. In an exemplary embodiment, the signal processor 132 may include a tag processor 140, an automated analysis processor 150, and a data sharing processor 160. The signal processor 132 may be capable of receiving input information from the user input device 130 and / or the archive 138, generating output that may be displayed by the display system 134, and manipulating the output in response to the input information from the user input device 130. The signal processor 132 (including the tag processor 140, the automated analysis processor 150, and the data sharing processor 160) may be capable of executing any of the one or more methods and / or one or more instruction sets discussed herein, for example, according to various embodiments.

[0031] The ultrasound system 100 is operable to continuously acquire ultrasound scan data at a frame rate appropriate for the imaging situation under consideration. Typical frame rates are in the range of 20-120, but can be lower or higher. The acquired ultrasound scan data can be displayed on the display system 134 at a display rate that is the same as the frame rate, or slower or faster than the frame rate. An image buffer 136 is included to store processed frames of acquired ultrasound scan data that are not scheduled for immediate display. Preferably, the image buffer 136 has sufficient capacity to store at least several minutes of frames of ultrasound scan data. The frames of ultrasound scan data are stored in a manner that allows for easy retrieval based on their acquisition order or time. The image buffer 136 can be embodied as any known data storage medium.

[0032] The signal processor 132 may include a marking processor 140 comprising suitable logic, circuitry, interfaces, and / or code operable to mark, for example, biological and / or artificial structures in an ultrasound image presented at the display system 134 with annotations, measurements, diagnoses, etc., in response to user instructions provided via the user input device 130. These structures may include artificial structures such as needles, catheters, etc. These structures may include anatomical structures such as the heart, lungs, structures of an embryo, or any suitable in vivo structure. For example, with respect to the heart, a user may provide instructions to the marking processor 140 via the user input device 130 for marking the mitral valve, the aortic valve, a ventricular cavity, an atrial cavity, the septum, the papillary muscles, the inferior wall, and / or any suitable cardiac structure. For another example, a user may provide instructions to the tag processor 140 via the user input device 130 to perform cardiac measurements such as end-systolic left ventricular internal diameter (LVIDs), end-systolic interventricular septum (IVSs), end-systolic left ventricular posterior wall (LVPWs), or aortic valve diameter (AV Diam) measurements, among others. The user may also provide instructions to the tag processor 140 via the user input device 130 to associate a diagnosis with an ultrasound image. For example, the user may select a diagnosis from a drop-down menu, enter text to be overlaid on the ultrasound image, and / or instruct the tag processor 140 to retrieve a diagnosis from a report. The tag processor 140 may overlay the annotations, measurements, diagnoses, etc. provided via the user input device 130 on the ultrasound image presented at the display system 134 or otherwise associate the annotations, measurements, diagnoses, etc. with the ultrasound image. For example, each of the annotations, measurements, and / or diagnoses associated with the ultrasound image may be stored as metadata with or in association with the associated ultrasound image. In various embodiments, the metadata may include a set of coordinates corresponding to the location of the annotation, measurement, and / or diagnosis in the ultrasound image. The annotation, measurement, and / or diagnosis with the set of coordinates may be stored in the archive 138 and / or any suitable storage medium.

[0033] The signal processor 132 may include an automated analysis processor 150 that includes suitable logic, circuitry, interfaces, and / or code operable to apply automated analysis features and / or tools that automatically analyze ultrasound images to identify, segment, annotate, perform measurements, provide a diagnosis, and / or the like for structures depicted in the ultrasound images. Biological structures may include, for example, nerves, blood vessels, organs, tissues, or any suitable biological structures. Artificial structures may include, for example, needles, implantable devices, or any suitable artificial structures. The automated analysis processor 150 may include artificial intelligence image analysis algorithms, one or more deep neural networks (e.g., convolutional neural networks), and / or may utilize any suitable form of artificial intelligence image analysis technology or machine learning processing functionality configured to provide one or more automated analysis features and / or one or more tools.

[0034] The automated analysis processor 150 may include suitable logic, circuitry, interfaces, and / or code operable to annotate, perform measurements, and / or provide diagnoses for structures depicted in ultrasound images. In various embodiments, the automated analysis processor 150 may be implemented as a deep neural network, which may be comprised of, for example, an input layer, an output layer, and one or more hidden layers between the input and output layers. Each layer may be comprised of multiple processing nodes, which may be referred to as neurons. For example, the automated analysis processor 150 may include an input layer having a neuron for each pixel or group of pixels from a scan plane of an anatomical structure. The output layer may have neurons corresponding to multiple predefined biological and / or artificial structures. For example, if performing ultrasound-based regional anesthesia procedures, the output layer may include neurons for the brachial plexus, the axillary artery, the bevel area on an anesthesia needle, and the like. Other ultrasound procedures may utilize output layers that include neurons for nerves, blood vessels, bones, organs, needles, implantable devices, or any other suitable biological and / or artificial structures. Each neuron in each layer may perform a processing function and pass processed ultrasound image information to one of multiple neurons in a downstream layer for further processing. For example, neurons in a first layer can learn to identify edges of structures in ultrasound image data. Neurons in a second layer can learn to identify shapes based on detected edges from the first layer. Neurons in a third layer can learn the positions of the identified shapes relative to landmarks in the ultrasound image data. The processing performed by an artificial intelligence segmentation processor deep neural network (e.g., a convolutional neural network) can identify biological and / or artificial structures in the ultrasound image data with a high degree of probability.

[0035] For example, the automated analysis processor 150 may include an input layer having a neuron for each pixel or group of pixels from a scan plan of biological and / or artificial structures (such as organs, nerves, blood vessels, tissues, needles, implantable devices, etc.). The output layer may have a neuron corresponding to each structure of the biological and / or artificial structures. For example, if imaging the heart, the output layer may include neurons for the mitral valve, aortic valve, tricuspid valve, pulmonary valve, left atrium, right atrium, left ventricle, right ventricle, septum, papillary muscles, inferior wall, unknown objects, and / or others. Other ultrasound procedures may utilize output layers that include neurons for nerves, blood vessels, bones, organs, needles, implantable devices, or any suitable biological and / or artificial structures. Each neuron in each layer may perform a processing function and pass the processed ultrasound image information to one of multiple neurons in a downstream layer for further processing. For example, neurons in a first layer may learn to identify edges of structures in ultrasound image data. Neurons in a second layer may learn to recognize shapes based on detected edges from the first layer. Neurons in a third layer may learn the positions of the recognized shapes relative to landmarks in volume rendering. The processing performed by the automated analysis processor 150 deep neural network can identify biological and / or artificial structures and the locations of these structures in the ultrasound image with a high degree of probability.

[0036] The automated analysis processor 150 may include suitable logic, circuitry, interfaces, and / or code operable to automatically annotate, measure, and / or diagnose biological and / or artificial structures depicted in an ultrasound image. For example, the automated analysis processor 150 may annotate, measure, and / or diagnose identified and segmented structures identified by the output layer of the deep neural network. For example, the automated analysis processor 150 may be configured to perform measurements of detected anatomical structures. For example, the automated analysis processor 150 may be configured to perform cardiac measurements such as end-systolic left ventricular internal diameter (LVIDs) measurement, end-systolic interventricular septum (IVSs) measurement, end-systolic left ventricular posterior wall (LVPWs) measurement, or aortic valve diameter (AV Diam) measurement. The annotations, measurements, and / or diagnoses may be superimposed on the ultrasound image and presented at the display system 134 and / or otherwise associated with the ultrasound image. For example, each of the annotations, measurements, and / or diagnoses associated with the ultrasound image may be stored as metadata along with or in association with the associated ultrasound image. In various embodiments, the metadata may include a set of coordinates corresponding to the location of the annotation, measurement, and / or diagnosis in the ultrasound image. The annotation, measurement, and / or diagnosis with the set of coordinates may be stored in the archive 138 and / or any suitable storage medium.

[0037] The signal processor 132 may include a data sharing processor 160 comprising suitable logic, circuitry, interfaces, and / or code operable to share ultrasound images tagged by the tagging processor 140. The data sharing processor 160 may be configured to prompt the user and / or patient to authorize the sharing of tagged images. For example, the data sharing processor 160 may present a prompt at the display system 134 for receiving the user's and / or patient's consent to share anonymous data. The tagged images may be uploaded to an automated analysis feature provider via the communication interface 180 so that the tagged images can be used to train an artificial intelligence image analysis algorithm, one or more deep neural networks (e.g., convolutional neural networks), and / or any suitable form of artificial intelligence image analysis technology or machine learning processing functionality to provide one or more automated analysis features and / or one or more tools. In various embodiments, the data sharing processor 160 may be configured to capture and share information about authorized users at the site so that the automated analysis feature provider can analyze differences in scanning positions, scanning technologies, image quality, tagging quality, etc., between different authorized users and / or different site locations.

[0038] The data sharing processor 160 may comprise suitable logic, circuitry, interfaces, and / or code that may be operable to anonymize data before sharing the data via the communication interface 180. For example, patient identifying information such as name, address, etc. may be removed from the tagged image metadata before sharing.

[0039] The data sharing processor 160 may include suitable logic, circuitry, interfaces, and / or code operable to enable unenabled automated analysis features and / or tools. For example, the data sharing processor 160 may enable a particular tool or set of tools in response to sharing a specified amount of data. In various embodiments, tiered access levels may be provided to automated analysis features and / or tools, such that, for example, a user gains access to a set of features upon sharing a specified level of data. In an exemplary embodiment, the data sharing processor 160 may be configured to provide points for purchasing or otherwise acquiring unenabled automated analysis features and / or tools. For example, a user may receive points in response to donating data, which can be redeemed in an application store accessible via the communication interface 180. For example, the user interface provided at the display system 134 may include a link, tab, etc. for accessing the application store. The application store may offer purchasable and / or licensable automated analysis features and / or tools. In various embodiments, the application store may provide a leaderboard that lists users in order of the amount of data shared to encourage data donations. Additionally and / or alternatively, a data donation leaderboard may be presented within a user interface provided at display system 134 .

[0040] Still refer to Figure 1 The training engine 170 may comprise suitable logic, circuitry, interfaces, and / or code operable to train neurons of one or more deep neural networks of the automated analysis processor 150. For example, the training engine 170 may train the deep neural network of the automated analysis processor 150 using one or more databases of ultrasound images labeled by the labeling processor 140. In various embodiments, the deep neural network of the automated analysis processor 150 may be trained by the training engine 170 using multiple different viewpoints of ultrasound images with associated structural coordinates to train the automated analysis processor 150 with respect to characteristics of a particular structure, such as the appearance of edges of the structure, the appearance of edge-based shapes of the structure, the location of the shapes in the ultrasound image data, and the like. In certain embodiments, the organ may be a heart and the structural information may comprise information regarding edges, shapes, locations, and timing information (e.g., end-diastole, end-systole, etc.) of the mitral valve, aortic valve, pericardium, posterior wall, septal wall, ventricular septum, right ventricle, left ventricle, right atrium, left atrium, and the like. In certain embodiments, the training engine 170 and / or the training image database can be one or more external systems communicatively coupled to the ultrasound system 100 via the communication interface 180. For example, the training engine 170 and / or the training database can be provided by an automated analysis feature provider. For another example, the automated analysis feature provider can provide trained artificial intelligence image analysis algorithms, one or more deep neural networks (e.g., convolutional neural networks), and / or any suitable form of artificial intelligence image analysis technology or machine learning processing functionality to the automated analysis processor 150 to provide one or more automated analysis features and / or one or more tools.

[0041] Display system 134 may be any device capable of conveying visual information to a user. For example, display system 134 may include a liquid crystal display, a light emitting diode display, and / or any other suitable display or displays. Display system 134 may be operable to display information from signal processor 132 and / or archive 138, such as medical images, marking tools, automated analysis tools, or any other suitable information.

[0042] The archive 138 may be one or more computer-readable memories, such as a picture archiving and communication system (PACS), a server, a hard disk, a floppy disk, a CD, a CD-ROM, a DVD, a compact disk, a flash memory, a random access memory, a read-only memory, an electrically erasable and programmable read-only memory, and / or any other suitable memory, that is integrated with and / or communicatively coupled (e.g., via a network) to the ultrasound system 100. The archive 138 may include, for example, a database, a library, a collection of information, or other memory that is accessed by and / or incorporated into the signal processor 132. For example, the archive 138 may be capable of storing data temporarily or permanently. The archive 138 may be capable of storing medical image data, data generated by the signal processor 132, and / or instructions readable by the signal processor 132, among others. In various embodiments, the archive 138 stores ultrasound images, labeled ultrasound images, ultrasound images processed by the automated analysis processor 150, parameters and settings, and / or instructions for performing labeling, automated analysis, data sharing, and / or training machine learning algorithms, among others.

[0043] For example, the communication interface 180 may include suitable logic, circuitry, interfaces, and / or code that are operable to allow communication between the ultrasound system 100 and other external systems. For example, the communication interface 180 may provide wired and / or wireless connections. The wireless connection may include, for example, any combination of short-range, long-range, Wi-Fi, cellular, personal communication system (PCS), Bluetooth, near field communication (NFC), radio frequency identification (RFID), or any suitable wireless connection. The ultrasound system 100 may be connected to a network such as the Internet, for example, individually or in groups with other ultrasound systems and / or medical workstations on site via any suitable combination of wired or wireless data communication links. In various embodiments, the selected ultrasound images tagged by the user via the tag processor 140 may be shared with the automated feature analysis provider by the data sharing processor 160 via the communication interface 180.

[0044] The components of the ultrasound system 100 may be implemented in software, hardware, firmware, etc. The various components of the ultrasound system 100 may be communicatively connected. The components of the ultrasound system 100 may be implemented separately and / or integrated in various forms. For example, the display system 134 and the user input device 130 may be integrated into a touch screen display.

[0045] Figure 2 is a block diagram of an exemplary medical workstation 200 operable to prompt data donation for development of artificial intelligence tools according to various embodiments. In various embodiments, components of the medical workstation 200 may be integrated with an ultrasound system 100 (e.g., Figure 1 The components shown and described above share various characteristics. Figure 2The medical workstation 200 includes a display system 134, a signal processor 132, an archive 138, a user input device 130, a training engine 170, and a communication interface 180. The components of the medical workstation 200 may be implemented in software, hardware, firmware, or the like. The various components of the medical workstation 200 may be communicatively linked. The components of the medical workstation 200 may be implemented separately and / or integrated in various forms. For example, the display system 134 and the user input device 130 may be integrated into a touchscreen display.

[0046] The display system 134 can be any device capable of conveying visual information to a user. Figure 1 As discussed, display system 134 may be operable to display information from signal processor 132 and / or archive 138, such as medical images, marking tools, automated analysis tools, or any suitable information.

[0047] The signal processor 132 may be one or more central processing units, microprocessors, microcontrollers, etc. For example, the signal processor 132 may be an integrated component or may be distributed in various locations. The signal processor 132 includes a marking processor 140, an automated analysis processor 150, and a data sharing processor 160, as described above with reference to FIG. Figure 1 As described, the signal processor 132, the tagging processor 140, the automated analysis processor 150, and / or the data sharing processor 160 may be capable of receiving input information from the user input device 130 and / or the archive 138, generating output that can be displayed by the display system 134, and manipulating the output in response to the input information from the user input device 130, etc. For example, the signal processor 132, the tagging processor 140, the automated analysis processor 150, and / or the data sharing processor 160 may be capable of executing any one of the one or more methods and / or one or more instruction sets discussed herein according to various embodiments.

[0048] The archive 138 can be one or more computer-readable memories integrated with and / or communicatively coupled (e.g., via a network) to the medical workstation 200, such as a picture archiving and communication system (PACS), a server, a hard disk, a floppy disk, a CD, a CD-ROM, a DVD, a compact memory, a flash memory, a random access memory, a read-only memory, an electrically erasable and programmable read-only memory, and / or any suitable memory. As described above with respect to Figure 1 As described above, the archive 138 can be configured to store ultrasound images, labeled ultrasound images, ultrasound images processed by the automated analysis processor 150, parameters and settings, and / or instructions for performing labeling, automated analysis, data sharing, and / or training machine learning algorithms, etc.

[0049] For example, the user input device 130 may include any one or more devices capable of transmitting information from a user and / or at the user's direction to the signal processor 132 of the medical workstation 200. Figure 1 As discussed, user input device 130 may include a touch panel, one or more buttons, a mouse device, a keyboard, a rotary encoder, a trackball, a camera, voice recognition, and / or any other device capable of receiving user commands.

[0050] The training engine 170 may comprise suitable logic, circuitry, interfaces, and / or code operable to train neurons of one or more deep neural networks of the automated analysis processor 150. Additionally and / or alternatively, an automated analysis feature provider may provide trained artificial intelligence image analysis algorithms, one or more deep neural networks (e.g., convolutional neural networks), and / or any suitable form of artificial intelligence image analysis technology or machine learning processing functionality to the automated analysis processor 150 to provide one or more automated analysis features and / or one or more tools.

[0051] The communication interface 180 may comprise suitable logic, circuitry, interfaces, and / or code that may be operable to allow communication between the ultrasound system 100 and other external systems. Figure 1 As described above, the communication interface 180 may provide, for example, a wired connection and / or a wireless connection. In certain embodiments, authorized ultrasound images tagged by the user via the tagging processor 140 may be shared by the data sharing processor 160 via the communication interface 180 with an automated feature analysis provider.

[0052] Figure 3 is a block diagram of an exemplary system 300 in which representative embodiments may be practiced. Figure 3 As shown, system 300 includes one or more servers 310. The one or more servers 310 may include, for example, one or more web servers, one or more database servers, one or more application servers, etc. The one or more servers 310 may be interconnected and may be connected to a network 320, such as the Internet, individually or in groups, for example, via any suitable combination of wired or wireless data communication links. Figure 3 Also included are external systems 330. The external systems 330 may be interconnected and may be connected individually or in groups to a network 320, such as the Internet, via any suitable combination of wired or wireless data communication links. One or more servers 310 and / or external systems 330 may include a signal processor 132 and / or an archive 138, as described above. Figure 3 Including references above Figure 1 and Figure 2The one or more ultrasound systems 100 and / or medical workstations 200 may be connected to the network 320 via any suitable combination of wired or wireless data communication links.

[0053] In various embodiments, the one or more servers 310 can be operated to automatically annotate, measure, and / or diagnose biological and / or artificial structures depicted in ultrasound images and / or anonymize and share authorized data. For example, the functions of one or more of the automated analysis processor 150 and / or the data sharing processor 160 can be performed by the one or more servers 310 in the background or at the direction of a user via one or both of the ultrasound system 100 and / or the medical workstation 200. The ultrasound image data processed and stored by the one or more servers 310 can be accessed by the ultrasound system 100 and / or the medical workstation 200 via one or more networks 320.

[0054] In certain embodiments, the external system 330 may be an automated analysis feature provider operable to provide trained artificial intelligence image analysis algorithms, one or more deep neural networks (e.g., convolutional neural networks), and / or any suitable form of artificial intelligence image analysis technology or machine learning processing functionality to the automated analysis processor 150 to provide one or more automated analysis features and / or one or more tools. Figure 1 and Figure 2 The functionality of the training engine 170 described above may be performed by an external system 330 and provided to one or both of the ultrasound system 100 and / or the medical workstation 200. The one or more automated analysis features and / or one or more tools generated and stored by the external system 330 may be accessed by the ultrasound system 100 and / or the medical workstation 200 via the one or more networks 320.

[0055] Figure 4 FIG4 is a display 400 of an exemplary ultrasound image 402 and tools for analyzing ultrasound images according to various embodiments. Figure 4 , the display 400 includes an ultrasound image 402, an automated analysis feature category 410 having automated analysis features 412, 414, and a user interface tab 420. The ultrasound image 402 may be superimposed with and / or otherwise associated with annotations, measurements 404, diagnoses, etc. For example, Figure 4The ultrasound image 402 shown includes a measurement of the end-diastolic interventricular septum (IVSd). Annotations, measurements, diagnoses, and the like can be provided manually using a labeling tool and / or automatically using automated analysis features 412 and 414. In various embodiments, the automated analysis features 412 and 414 can be grouped and / or otherwise organized into automated analysis feature categories 410 and / or subcategories. For example, automated analysis features 412 and 414 can be provided for different measurement types and for different applications. For example, a cardiac application may include categories for general measurements, dimensional measurements, area measurements, volume measurements, and mass measurements. Each of these categories may include specific measurements for that category. For example, the dimensional measurements category may include measurements for end-diastolic left ventricular internal diameter (LVIDd), end-diastolic interventricular septum (IVSd), and end-diastolic left ventricular posterior wall (LVPWd). In a representative embodiment, one or more of the automated analysis features 412 may be disabled and / or one or more of the automated analysis features 414 may be enabled. The display 400 of the automated analysis features 412, 414 may include, for example, a label, shading 416, highlighting 418, and / or any suitable identifier for designating whether the automated analysis features 412, 414 are disabled or enabled. The user interface tabs 420 may allow a user to navigate the user interface to a desired function. In various embodiments, the user interface tabs 420 may include a tab for accessing image analysis functions, a tab for accessing an application store to purchase access to the automated analysis features 412, 414, and / or any suitable tab functionality.

[0056] Figure 5 4 is a display 400 of an exemplary ultrasound image 402 , tools for analyzing the ultrasound image, and a prompt to donate data, according to various embodiments. Figure 5 The features provided in display 400 may be used with Figure 4 The features provided in display 400 share various characteristics as described above. Figure 5 , the display 400 includes an ultrasound image 402, an automated analysis feature category 410 having automated analysis features 412, 414, a user interface tab 420, and a data donation prompt 430. In various embodiments, the data donation prompt 430 can be presented when the user selects the unenabled automated analysis feature 412. For example, referring to Figure 5, if the user selects the end-diastolic ventricular septum (IVSd) measurement (which is not enabled as indicated by the grayed-out "Auto" box), a prompt may be presented to allow the user to share anonymous labeled data that may be used to develop artificial intelligence tools, such as automated analysis features 412, 414. In various embodiments, the unenabled automated analysis features 412 may be enabled if one or more conditions are met. For example, the conditions for enabling one or more automated analysis features 412, 414 may include consent to donate data, donation of a predetermined amount of data, and the like. In various embodiments, the user may be awarded points based on the amount of data donated for use in enabling one or more automated analysis features 412, 414, such as via an app store.

[0057] Figure 6 is a flowchart 500 illustrating exemplary steps 502-518 that may be used to prompt data donations for use in artificial intelligence tool development, according to various embodiments. Figure 6 , a flowchart 500 including exemplary steps 502 through 518 is shown. Certain embodiments may omit one or more steps, and / or perform steps in an order different from the order listed, and / or combine certain steps discussed below. For example, some steps may not be performed in certain embodiments. For another example, certain steps may be performed in a different chronological order than listed below, including simultaneously.

[0058] At step 502, the ultrasound system 100 or the medical workstation 200 presents the ultrasound image 402. For example, the ultrasound system 100 may acquire the ultrasound image 402 using the ultrasound probe 104 positioned at a scanning position over the region of interest and may present the ultrasound image 402 on the display system 134. For another example, the ultrasound system 100 or the medical workstation 200 may retrieve the ultrasound image 402 from the archive 138 or any suitable data storage medium and present the ultrasound image 402 on the display system 134.

[0059] At step 504, the ultrasound system 100 or the medical workstation 200 presents one or more automated analysis features 412, 414. For example, the automated analysis processor 150 of the signal processor 132 can be configured to present the ultrasound image 402 presented at step 502 to the automated analysis features 412, 414. In various embodiments, the automated analysis features 412, 414 can include tools for automatically annotating, measuring, and / or providing a diagnosis on the ultrasound image 402. In certain embodiments, the automated analysis features 412, 414 can include enabled tools and / or disabled tools. For example, an identifier can be presented to the automated analysis features 412, 414 that specifies whether the tool is enabled or disabled.

[0060] At step 506, the signal processor 132 of the ultrasound system 100 or the medical workstation 200 may receive a selection to disable the automated analysis feature 412. For example, the automated analysis processor 150 and / or the data sharing processor 160 of the signal processor 132 may receive the user selection to disable the automated analysis feature 412 via the user input device 130.

[0061] At step 508, the signal processor 132 of the ultrasound system 100 or medical workstation 200 may present an option to share the user's analysis data. For example, the data sharing processor 160 of the signal processor 132 may be configured to present a prompt on the display 400 of the display system 134. The prompt may provide an option or a link to an option for authorizing data sharing. The prompt may request the user and / or patient's consent to share the analysis data. The analysis data may include manually labeled images and information about the live user. For example, the analysis data may include anonymized images with annotations, measurements, and / or diagnoses. As another example, the analysis data may include information about the medical staff performing the analysis.

[0062] At step 510, the signal processor 132 of the ultrasound system 100 or the medical workstation 200 receives a user instruction to share the analyzed data or a user instruction not to share the analyzed data. For example, the data sharing processor 160 of the signal processor 132 may receive an instruction not to share the analyzed data, and the process 500 then ends at step 512. For another example, the data sharing processor 160 of the signal processor 132 may receive an instruction to authorize donation of the analyzed data, and the method proceeds to step 514.

[0063] At step 514, the signal processor 132 of the ultrasound system 100 or medical workstation 200 uploads the user analysis data to the automated analysis feature provider. For example, the data sharing processor 160 of the signal processor 132 selects analysis data that the user and / or patient has authorized for sharing, anonymizes the analysis data to remove personal patient identification information, and transmits the anonymized analysis data to the automated analysis feature provider via the communication interface 180. The user analysis data may include ultrasound images with annotations, measurements, and / or diagnoses provided by the medical staff user. In various embodiments, the user analysis data may include information about the medical staff who performed the donated analysis. The automated analysis feature provider may use the shared analysis data to develop artificial intelligence tools.

[0064] At step 516, the signal processor 132 of the ultrasound system 100 or medical workstation 200 provides access to the disabled automated analysis features 412 when a condition is met. For example, the data sharing processor 160 of the signal processor 132 may enable the selected disabled automated analysis features 412 when a condition is met. The condition may include one or more of authorization to share user analysis data, a specified amount of user analysis data being shared, and / or any other suitable condition. In various embodiments, the data sharing processor 160 may provide tiered access levels, wherein a user may gain access to a series of features upon sharing a specified level of data. In certain embodiments, the data sharing processor 160 may provide credits corresponding to the amount of user analysis data shared. The credits may be used to purchase access to one or more disabled automated analysis features 412. For example, the credits may be applied at the user interface display 400 and / or via an app store. The app store may be provided as part of the user interface display 400 and / or linked to from the user interface display 400, etc. When the data sharing processor 160 enables the selected automated analysis features, the process 500 ends at step 518.

[0065] Aspects of the present disclosure provide a method 500 and system (100, 200, 300) for prompting data donation for artificial intelligence tool development. According to various embodiments, the method 500 may include presenting 502, 504, by the system (100, 200, 300), at a display system 134 of the system (100, 200, 300), an ultrasound image 402 and at least one automated analysis feature 412, 414. The at least one automated analysis feature 412, 414 includes one or more unenabled automated analysis features 412. The method 500 may include receiving 506, by at least one processor (132, 150, 160) of the system (100, 200, 300), a user selection of at least one of the one or more unenabled automated analysis features 412. The method 500 may include presenting 508, by the at least one processor (132, 150, 160), at the display system 134, a prompt providing a user option to share user analysis data. The method 500 may include receiving, by at least one processor (132, 150, 160), a user selection to share user analysis data. The method 500 may include providing, by at least one processor (132, 150, 160), access 516 to at least one of the one or more unenabled automated analysis features 412 when at least one condition is satisfied.

[0066] In a representative embodiment, the system (100, 200, 300) can be a medical workstation 200 or an ultrasound system 100. In an exemplary embodiment, the at least one condition can include one or both of a user selection to share the user analysis data and sharing a specified amount of the user analysis data. In various embodiments, the user analysis data can include an ultrasound image 402 tagged with at least one annotation, at least one measurement 404, and / or at least one diagnosis. In certain embodiments, the user analysis data can also include information about a user of the system. In a representative embodiment, the method 500 can include, in response to receiving a user selection to share the user analysis data, anonymizing the user analysis data by at least one processor (132, 140, 160) and sharing 514 the user analysis data by at least one processor (132, 140, 160). In an exemplary embodiment, the method 500 can include presenting 508 a patient prompt by at least one processor (132, 150, 160) requesting the patient's consent to share the user analysis data. In some embodiments, at least one of the one or more unenabled automated analysis features 412 may be a series of unenabled automated analysis features, and access to the series of unenabled automated analysis features 412 is provided by at least one processor (132, 150, 160) when sharing data at a specified level.

[0067] Various embodiments provide a system (100, 200, 300) for prompting data donation for artificial intelligence tool development. The system (100, 200, 300) may include a display system 134 and at least one processor (132, 140, 150, 160). The display system 134 may be configured to present an ultrasound image 402 and at least one automated analysis feature 412, 414. The at least one automated analysis feature 412, 414 may include one or more unenabled automated analysis features 412. The at least one processor (132, 150, 160) may be configured to receive a user selection of at least one of the one or more unenabled automated analysis features 412. The at least one processor (132, 150, 160) may be configured to present a prompt at the display system 134 that provides a user option to share user analysis data. The at least one processor (132, 150, 160) may be configured to receive a user selection to share the user analysis data. At least one processor (132, 150, 160) may be configured to provide access to at least one of the one or more unenabled automated analysis features 412 when at least one condition is satisfied.

[0068] In an exemplary embodiment, the system (100, 200, 300) can be a medical workstation 200 or an ultrasound system 100. In various embodiments, the at least one condition can include one or both of a user selection to share user analysis data and sharing a specified amount of user analysis data. In certain embodiments, the user analysis data can include an ultrasound image 402 labeled with at least one annotation, at least one measurement 404, and / or at least one diagnosis and information about a user of the system (100, 200, 300). In a representative embodiment, in response to receiving a user selection to share user analysis data, the at least one processor (132, 140, 160) can be configured to anonymize the user analysis data and share the user analysis data. In an exemplary embodiment, the at least one processor (132, 150, 160) can be configured to present a patient prompt requesting the patient's consent to share the user analysis data. In various embodiments, at least one of the one or more unenabled automated analysis features 412 is a series of unenabled automated analysis features, and at least one processor (132, 150, 160) is configured to provide access to the series of unenabled automated analysis features 412 when sharing data of a specified level.

[0069] Certain embodiments provide a non-transitory computer-readable medium having a computer program stored thereon, the computer program having at least one code segment. The at least one code segment is executable by a machine to cause the machine to perform step 500. Step 500 may include presenting 502, 504 an ultrasound image 402 and at least one automated analysis feature 412, 414 on a display system 134. The at least one automated analysis feature 412, 414 may include one or more disabled automated analysis features 412. Step 500 may include receiving 506 a user selection of at least one of the one or more disabled automated analysis features 412. Step 500 may include presenting 508 a prompt on the display system 134 providing the user with an option to share user analysis data. Step 500 may include receiving a user selection to share user analysis data. Step 500 may include providing 516 access to at least one of the one or more disabled automated analysis features 412 when at least one condition is met.

[0070] In various embodiments, the at least one condition may include one or both of a user selection to share the user analysis data and sharing a specified amount of the user analysis data. In certain embodiments, the user analysis data may include an ultrasound image 402 tagged with at least one annotation, at least one measurement 404, and / or at least one diagnosis and information about a user of the system (100, 200, 300). In a representative embodiment, step 500 may include, in response to receiving the user selection to share the user analysis data, anonymizing 514 the user analysis data and sharing 514 the user analysis data. In an exemplary embodiment, at least one of the one or more unenabled automated analysis features 412 may be a series of unenabled automated analysis features, and access to the series of unenabled automated analysis features 412 is provided when the specified level of data is shared.

[0071] As used herein, the term "circuit" refers to physical electronic components (i.e., hardware) and any software and / or firmware ("code") that can be configured, executed by hardware, and / or can be associated with hardware in other ways. For example, as used herein, when executing one or more first codes, a particular processor and memory may include a first "circuit," and when executing one or more second codes, a particular processor and memory may include a second "circuit." As used herein, "and / or" represents any one or more of the items in a list connected by "and / or." As an example, "x and / or y" represents any element in a three-element set {(x), (y), (x, y)}. As another example, "x, y, and / or z" represents any element in a seven-element set {(x), (y), (z), (x, y), (x, z), (y, z), (x, y, z)}. As used herein, the term "exemplary" represents a non-limiting example, instance, or illustration. As used herein, the terms "e.g." and "for example" introduce a list of one or more non-limiting examples, instances, or illustrations. As used herein, a circuit is "operable to" and / or "configured to" perform a function whenever the circuit includes the necessary hardware and code (if necessary) to perform the function, regardless of whether the performance of the function is disabled or not enabled by some user-configurable setting.

[0072] Other embodiments may provide a computer-readable device and / or non-transitory computer-readable medium, and / or a machine-readable device and / or non-transitory machine-readable medium, on which the computer-readable device and / or non-transitory computer-readable medium and / or the machine-readable device and / or non-transitory machine-readable medium have machine code and / or a computer program having at least one code segment executable by a machine and / or computer, thereby causing the machine and / or computer to perform the steps for prompting data donation for smart tool development as described herein.

[0073] Thus, the present disclosure may be implemented in hardware, software, or a combination of hardware and software. The present disclosure may be implemented in a centralized manner in at least one computer system, or in a distributed manner, with different elements distributed across several interconnected computer systems. Any type of computer system or other device suitable for executing the methods described herein is suitable.

[0074] The various embodiments may also be embedded in a computer program product, which comprises all the features enabling the implementation of the methods described herein and which, when loaded into a computer system, is capable of carrying out these methods. A computer program in this context is any expression of a set of instructions in any language, code or notation, which is intended to cause a system with information processing capabilities to perform certain functions either directly or after either or both of the following: a) conversion into another language, code or notation; or b) reproduction in a different material form.

[0075] Although the present disclosure has been described with reference to certain embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the scope of the present disclosure. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the specific embodiments disclosed, but rather the present disclosure is intended to include all embodiments falling within the scope of the appended claims.

Claims

1. A method for promoting the development of artificial intelligence, comprising: presenting, by a system, an ultrasound image and at least one automated analysis feature at a display system of the system, wherein the at least one automated analysis feature includes one or more disabled automated analysis features; receiving, by at least one processor of the system, a user selection of at least one of the one or more unenabled automated analysis features; presenting, by the at least one processor, a prompt at the display system, the prompt providing a user option to share user analytical data; receiving, by the at least one processor, a user selection to share the user analytics data; as well as providing, by the at least one processor, access to the at least one of the one or more unenabled automated analysis features when at least one condition is satisfied, Wherein satisfying at least one condition includes an amount of the shared user analysis data reaching a specified amount corresponding to accessing the at least one automated analysis feature of the one or more disabled automated analysis features.

2. The method of claim 1, wherein the system is a medical workstation or an ultrasound system. 3 . The method of claim 1 , wherein the user analysis data comprises ultrasound images tagged with at least one annotation, at least one measurement, and / or at least one diagnosis. The method of claim 3 , wherein the user analytics data further comprises information about users of the system.

5. The method according to claim 1, comprising: In response to receiving the user selection to share the user analytics data: anonymizing, by the at least one processor, the user analytics data; as well as The user analysis data is shared by the at least one processor. 6 . The method of claim 1 , comprising presenting, by the at least one processor, a patient prompt requesting patient consent to sharing the user analytics data.

7. The method according to claim 1, wherein: The at least one automated analysis feature of the one or more unenabled automated analysis features is a series of unenabled automated analysis features; as well as Access to the disenabled set of automated analysis features is provided by the at least one processor when a specified level of data is shared.

8. A system for promoting the development of artificial intelligence, comprising: a display system configured to present an ultrasound image and at least one automated analysis feature, wherein the at least one automated analysis feature includes one or more inactivated automated analysis features; at least one processor configured to: receiving a user selection of at least one of the one or more unenabled automated analysis features; presenting a prompt at the display system, the prompt providing a user option to share user analytical data; receiving a user selection to share the user analytical data; as well as providing access to at least one of the one or more unenabled automated analysis features when at least one condition is satisfied, Wherein satisfying at least one condition includes an amount of the shared user analysis data reaching a specified amount corresponding to accessing the at least one automated analysis feature of the one or more disabled automated analysis features.

9. The system of claim 8, wherein the system is a medical workstation or an ultrasound system.

10. The system of claim 8, wherein the user analysis data comprises: an ultrasound image labeled with at least one annotation, at least one measurement, and / or at least one diagnosis; as well as Information about users of the system.

11. The system of claim 8, wherein in response to receiving the user selection to share the user analytics data, the at least one processor is configured to: anonymizing the user analytics data; and Sharing the user analysis data.

12. The system of claim 8, wherein the at least one processor is configured to present a patient prompt requesting patient consent to sharing the user analytics data.

13. The system of claim 8, wherein: The at least one automated analysis feature of the one or more unenabled automated analysis features is a series of unenabled automated analysis features; as well as The at least one processor is configured to provide access to the set of non-enabled automated analysis features when sharing a specified level of data.

14. A non-transitory computer-readable medium having a computer program stored thereon, the computer program having at least one code segment, the at least one code segment being executable by a machine to cause the machine to perform steps comprising: presenting an ultrasound image and at least one automated analysis feature at a display system, wherein the at least one automated analysis feature includes one or more inactivated automated analysis features; receiving a user selection of at least one of the one or more unenabled automated analysis features; presenting a prompt at the display system, the prompt providing a user option to share user analytical data; receiving a user selection to share the user analytical data; as well as providing access to at least one of the one or more unenabled automated analysis features when at least one condition is satisfied, Wherein satisfying at least one condition includes an amount of the shared user analysis data reaching a specified amount corresponding to accessing the at least one automated analysis feature of the one or more disabled automated analysis features.

15. The non-transitory computer-readable medium of claim 14, wherein the user analytics data comprises: an ultrasound image labeled with at least one annotation, at least one measurement, and / or at least one diagnosis; as well as Information about users of the system.

16. The non-transitory computer-readable medium of claim 14, comprising: In response to receiving the user selection to share the user analytics data: Anonymize said user analysis data; as well as Sharing the user analysis data.

17. The non-transitory computer-readable medium of claim 14, wherein: The at least one automated analysis feature of the one or more unenabled automated analysis features is a series of unenabled automated analysis features; as well as When sharing data at a specified level, access is provided to the set of automated analysis features that are not enabled.

Images