Internal and external access scans

Abstract

Methods related to internal and external scanning, including receiving a first data set from an ingestible scanning device inside a body, identifying a first point of interest within the body based on the data, determining a location of the first point of interest within the body, and scanning the point of interest with an external scanning device.

Description

Background Technology

[0001] This disclosure relates to internal and external proximity scanning; more specifically, it relates to medical device scanning.

[0002] Medical scanning is a technique and process that collects data about the body's interior for clinical analysis and medical intervention, as well as for the visual representation (physiological) of the function of certain organs or tissues. Medical scans can reveal internal structures hidden by skin and bone, and aid in the diagnosis and treatment of diseases. Medical scans also establish a database of normal anatomy and physiology, making it possible to identify abnormalities or anomalies. In the case of medical ultrasound, the probe emits ultrasound pressure waves and echoes that penetrate into the tissue to reveal internal structures. In the case of projection radiography, the probe uses X-ray radiation, which is absorbed at different rates by different tissue types such as bone, muscle, and fat.

[0003] Medical scanning includes radiological procedures that utilize imaging techniques such as X-ray radiography, magnetic resonance imaging, ultrasound, endoscopy, elastography, tactile imaging, thermal imaging, medical photography, and nuclear medicine functional imaging techniques, such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT).

[0004] Medical scanning also includes measurement and recording techniques that are not primarily designed to generate images, such as electroencephalography (EEG), magnetoencephalography (MEG), and electrocardiography (ECG). Summary of the Invention

[0005] This disclosure provides a method, computer program product, and system for internal and external proximity scanning. In some embodiments, the method includes receiving a first dataset from an ingestible scanning device inside the body, identifying a first point of interest within the body based on the first dataset, determining the location of the first point of interest within the body, and scanning the point of interest with an external scanning device.

[0006] In some embodiments, the computer program product includes a computer-readable storage medium having program instructions embodied therein, the program instructions being executable by a computer to cause the computer to receive a first dataset from an ingestible scanning device in the patient's body; identify points of interest within the body based on the first dataset; determine the location of the points of interest within the body; and scan the points of interest with an external scanning device.

[0007] In some embodiments, the system includes a processor; and a memory in communication with the processor, the memory containing program instructions that, when executed by the processor, are configured to cause the processor to perform a method comprising receiving a first set of data from an ingestible scanning device within a patient; identifying points of interest within the body based on the first set of data; determining the location of the points of interest within the body; and scanning the points of interest using an external scanning device. Attached Figure Description

[0008] Figure 1 A flowchart illustrating example methods for internal and external approximate scanning according to various embodiments of the present disclosure is shown.

[0009] Figure 2 A flowchart is shown illustrating an example method for configuring an external scan based on internal scan and historical data, according to various embodiments of the present disclosure.

[0010] Figure 3 This is a block diagram illustrating an example networking environment according to various embodiments of the present disclosure.

[0011] Figure 4 Computer systems according to various embodiments of the present disclosure are described.

[0012] Figure 5 A cloud computing environment according to embodiments of the present disclosure is described.

[0013] Figure 6 An abstract model layer according to an embodiment of this disclosure is shown. Detailed Implementation

[0014] Medical scans such as X-rays, ultrasound, CT scans, and MRI scans provide valuable information, but may not provide a complete picture on its own. For example, X-rays in medical diagnosis are often used to obtain a visual image of the object being radiographed. This image is produced by differential attenuation of radiation, which depends in part on the thickness, density, and orientation of the irradiated organ, and in part on the proportions and properties of the different chemical elements present. The nature of biological materials often results in poor contrast differences between organs or parts of organs, and this remains one of the major limitations despite methods to increase contrast. When examining living subjects, additional limitations are imposed because movement of the subject is rarely eliminated and can distort the image. Furthermore, because imaging radiation can be harmful to living subjects, it is preferable to keep the radiation dose at a reasonably practically low level. The high cost of some equipment and the lack of qualified personnel to operate the equipment and interpret the findings also impose further constraints. It is important to provide radiologists with only the most relevant and informative images possible. Moreover, in some seriously ill patients, the number and type of examinations performed may have to be limited as they reach their tolerance limits. Therefore, virtually every procedure has its specific advantages and limitations. However, by utilizing internal and external proximity scanning, some limitations can be alleviated, and a more complete understanding of the scanned target can be achieved.

[0015] The medical community continues to advance medical scanning techniques. However, a lack of information can sometimes lead to overuse, inaccuracy, and incorrect diagnoses. For example, a general area X-ray or CT scan may be used instead of a precise local scan when the exact location of interest (e.g., a growth) is unknown. This can result in excessive exposure to harmful elements such as radiation, additional costs, and wasted time for doctors to examine images and results.

[0016] In some embodiments, a system is proposed for combining internal scanning techniques (e.g., an uptake scanning device) with external scanning techniques. By combining internal scanning techniques (e.g., an uptake ultrasound pellet) and external scanning (e.g., X-rays), a more focused and efficient procedure can be achieved. Combining scans can mitigate the drawbacks of one or both of the individual scans. For example, where a wide range of X-rays is typically available, an uptake scanning device can be used to precisely tell a medical technician where X-rays need to be taken. Using this information, the medical technician can use a narrow range of X-rays at that location, thus limiting the area of ​​exposure. Similarly, by using two different scanning methods and correlating the results, a more complete picture of the region of interest can be determined. In some embodiments, the first internal scanning device can provide information that allows a second external scan to be selected or configured. For example, some points of interest can be analyzed more accurately with MRI, and some can be analyzed more accurately with X-rays.

[0017] Figure 1 An example method 100 for internal and external proximity scanning is shown. This example method 100 depicts a model for performing proximity scanning of a living organism.

[0018] In block 110, scan data is received from an ingestible scanning device. In some embodiments, the ingestible scanning device may be a wireless device capable of scanning the internal body (e.g., ultrasound scans / images, videos, pictures, temperatures, etc.), transmitting signals, receiving signals, and / or recording. In some embodiments, the ingestible scanning device performs scans continuously. In some embodiments, the ingestible scanning device performs scans after a specific trigger. For example, the ingestible scanning device may be configured to perform scans only after passing through the stomach, at set time intervals, or after receiving an external signal.

[0019] In some embodiments, an ingestible scanning device is used to describe a scanning apparatus that can be swallowed. In some embodiments, the ingestible scanning device may be an ultrasound capsule. Ultrasound capsule endoscopy (USCE) overcomes the limitations of surface-only imaging and provides transmural scanning of the gastrointestinal tract (GI tract). Integrating high-frequency micro-ultrasound (μUS) into capsule endoscopy allows for high-resolution transmural images and provides a method for qualitative and quantitative evaluation of the intestinal wall.

[0020] In some embodiments, the ingestible scanning device can be any other capsule with scanning capabilities. For example, capsule endoscopy is a procedure that uses a miniature wireless camera to take pictures of the digestive tract. The capsule endoscopy camera is located inside a capsule the size of a swallowed vitamin. As the capsule passes through the digestive tract, the camera takes thousands of pictures, which are then sent to a recorder.

[0021] In box 120, data from the uptakeable scanning device is analyzed to identify points of interest (POIs). In some embodiments, a POI may be a previously identified specific target region. For example, a POI may be an ulcer complained of by a patient, a previously discovered tumor, or a growth detected by the uptakeable scanning device. In some embodiments, a POI may be any region exhibiting anomalous characteristics. In some embodiments, the location and characteristics of the POI (e.g., size, shape, density, etc.) may be used to determine what further scans / tests should be performed and how the scans / tests should be configured. In some embodiments, the data may be analyzed against information indicating POIs (e.g., anomalous features). For example, the system may have readout thresholds that can indicate one or more target features. Some examples of readout thresholds may be shape, density, signal transmission, opacity, and / or signal reflection. For example, kidney stones and tumors refract ultrasound images more strongly than soft tissue. For example, certain shapes of growths in the intestine may indicate cancerous growth. In some embodiments, the data may be analyzed using an artificial neural network.

[0022] Artificial neural networks (ANNs) can be computational systems modeled after biological neural networks found in the animal brain. Such systems learn to perform tasks by considering examples (i.e., progressively improving performance), typically without task-specific programming. For example, in image recognition, an ANN can learn to recognize images containing tumors by analyzing example images that have been manually labeled as "tumor" or "no tumor" and using the analysis results to identify tumors in other images.

[0023] In some embodiments of this disclosure, a neural network can be used to identify points of interest in a data scan. The neural network can be trained to identify patterns in input data through a repetitive process of propagating training data through the network, identifying output errors, and modifying the network to address those errors. The training data can be propagated through the neural network, which identifies patterns in the training data. These patterns can be compared with patterns identified by human annotators in the training data to evaluate the accuracy of the neural network. In some embodiments, a mismatch between patterns identified by the neural network and those identified by annotators can trigger a review of the neural network architecture to identify specific neurons in the network that contribute to the mismatch. Those specific neurons can then be updated (e.g., by updating the weights of the function applied to those neurons) to attempt to reduce the contribution of those specific neurons to the mismatch. In some embodiments, random changes are made to update the neurons. This process can be repeated until the number of neurons contributing to the pattern mismatch slowly decreases, and eventually, as a result, the output of the neural network changes. If the new output matches the expected output based on the human annotator's review, the neural network is considered to have been trained on that data.

[0024] In some embodiments, once the neural network has been sufficiently trained on a training dataset for a specific topic, it can be used to detect patterns in a similar set of real-world data (i.e., non-training data that has not been previously reviewed by human annotators but is related to the same topic as the training data). The pattern recognition capabilities of the neural network can then be used for a variety of applications. For example, a neural network trained on a specific topic can be configured to examine real-world data for that topic and predict the probability of potential future events associated with that topic.

[0025] In some embodiments, a multilayer perceptron (MLP) is a type of feedforward artificial neural network. An MLP consists of at least three layers: an input layer, hidden layers, and an output layer. Each node, except for the input node, is a neuron that uses a non-linear activation function. MLPs are trained using a supervised learning technique called backpropagation. Their multilayered and non-linear activation distinguishes MLPs from linear perceptrons. They can distinguish data that is not linearly separable. Furthermore, MLPs can be applied to perform regression operations.

[0026] Accurate identification of points of interest in data scans (such as ultrasound images) relies on processing live datasets containing large amounts of data. For example, live datasets can include various biological data sources (such as ultrasound images, pictures, X-ray images, temperature, acidity, etc.). Furthermore, accurate prediction of some topics is difficult to achieve due to the sheer volume of data that may be relevant to the prediction. For example, an ultrasound pill may undergo thousands of scans as it passes through the digestive system. In some embodiments, such as those described in this disclosure, a neural network can be configured to generate predictions of the probability of points of interest associated with a specific set of conditions of a line asset (i.e., seeking probabilities of events in a target prediction). For example, in some embodiments, a predictive neural network can be used to predict the numerical probability that a specific part of the intestine contains an anomaly (e.g., the predictive neural network identifies the anomaly as a point of interest), which ensures external scanning performed by an external scanning device.

[0027] In box 130, the location and / or orientation of the uptakeable scanning device can be determined. In some embodiments, scan data can be used to determine location. For example, a tissue scan performed by an ultrasound uptakeable scanning device can identify its surrounding environment as the stomach, small intestine, or large intestine. This identification can then be used to determine the location of the scanning device. For example, in some embodiments, the uptakeable device can use a tissue scan to determine its location. In some embodiments, the uptakeable scanning device can send images to an external computer system, and the external computer system can determine where the uptakeable device is based on the received images. In some embodiments, the location of a point of interest can also be determined.

[0028] Various methods can be performed to determine the location of the pill. In some embodiments, location determination can be performed by a technique selected from the group consisting of X-ray detection, ultrasound detection, echo localization, Doppler localization, triangulation, or some combination thereof.

[0029] In some embodiments, X-rays can be used to determine location. This can be a specialized X-ray specifically designed to locate the pill. For example, if the pill is at least partially made of metal, it can be clearly seen even under low-power X-rays.

[0030] Pulse Doppler radar is a radar system that uses pulse timing techniques to determine the range of a target and uses the Doppler effect of the returned signal to determine the velocity of the target object. It combines the characteristics of pulse radar and continuous wave radar, features that were previously separate due to the complexity of electronics.

[0031] An ultrasound scanner consists of a computer console, a video display screen, and an attached transducer. The transducer is a small, handheld device similar to a microphone. Some examinations may use different transducers (with different capabilities) during a single examination. The transducer emits inaudible high-frequency sound waves into the body and then listens for the returning echoes. The principle is similar to sonar used on ships and submarines.

[0032] Triangulation is a process that determines the location of a radio transmitter by measuring the radial distance or direction of signals received from two or three different points. Triangulation is sometimes used in cellular communications to pinpoint the geographic location of users.

[0033] In box 140, the configuration for external scanning can be determined based on information received from the ingestible scanning device and / or the location of the ingestible scanning device. In some embodiments, the external scanning device can be positioned to produce better images. For example, if the ingestible scanning device identifies low blood flow in a specific region of the intestine, a specific orientation for X-ray exposure can be determined to provide the most relevant data. Similarly, the configuration can include one or more settings for external scanning, such as orientation, intensity, exposure area, exposure duration, etc. For example, images received from an ultrasound pill can identify specific regions of the intestine that require external scanning. Likewise, the location and orientation of the pill can help determine the settings for external scanning. The exact location of the images captured by the ingestible scanning device and the orientation of the ingestible scanning device can be used to guide the external device to the exact location for scanning inside the body. For example, the human intestine is approximately 25 feet long, and identifying the exact location and orientation of the ingestible scanning device when it detects points of interest (e.g., abnormalities) can allow for a much narrower external scan.

[0034] In some embodiments, the configuration of external scanning may include the type of scan to be performed. In some embodiments, the location and characteristics of the point of interest (e.g., low blood flow, calcification, etc.) may be used to determine what type of external scanning can be used. For example, for a point of interest with high calcification, X-ray may be a better choice than MRI.

[0035] At box 150, an external scan can be performed. In some embodiments, the scans can be synchronous; the ingestible scanning device can scan for points of interest, and the external scanning device can scan for points of interest at approximately the same time. Synchronous scanning can occur when points of interest have already been identified previously. For example, when the points of interest are known, the external scanning device can be in standby mode until the ingestible scanning device is in place, and then both can capture images at approximately the same time. In some embodiments, the external device can be in standby mode, waiting for the ingestible scanning device to detect a point of interest. For example, as the ingestible scanning device travels through the digestive tract, the external device can follow it through the body. If the ingestible scanning device detects a point of interest, the external scanning device can scan the area of ​​interest almost immediately. In some embodiments, the ingestible scanning device detects a point of interest and can subsequently perform an external scan. For example, if the ingestible scanning device detects a point of interest at a location, that location can be identified, and a subsequent external scan of that area can be performed based on that location and data received from the ingestible scanning device.

[0036] In some embodiments, data from an ingestible scanning device and external scans can be compared. Scans can be compared and correlated to obtain more information about points of interest. For example, shape and blood flow can be obtained from the ingestible scanning device, and density can be obtained from the external scanning device.

[0037] Figure 2 A flowchart is shown of an example method 200 for configuring an external scan based on internal scan and historical data, according to an embodiment of the present disclosure.

[0038] In some embodiments, historical data includes previous scans and their results. In some embodiments, historical data can indicate what features identified at a point of interest might be important, and / or how the point of interest could be scanned by an external scan to improve results. In a first example, comparing the shape of a cancerous polyp in a historical (previous) colonoscopy scan in which a cancerous polyp was identified with a polyp in the current scan can help identify a cancerous polyp in the current scan. Here, a colonoscopy can be used to describe a scan looking for abnormalities in the colon and can be performed via an uptake ultrasound capsule, an uptake camera, or others. The system can also look for any abnormal readings (such as low blood flow) that have been identified in previous scans to indicate other problems that have not been specifically searched.

[0039] In the second example, if the patient has known diverticulitis, the system can compare the inflammation around previously identified sacs with the inflammation in the current scan data to identify sacs in the patient. Diverticulitis is an infection or inflammation of sacs that can form in your intestines. These sacs are called diverticula.

[0040] In some embodiments, method 200 may represent method 100 ( Figure 1 Box 140 (as shown in the diagram). Method 200 may begin at 205, wherein current scan data is received from the ingestible scanning device. As discussed herein, the current scan data may be an image (e.g., an ultrasound image), medical data, signal data, etc.

[0041] At 210, a point of interest category is identified based on current scan data or historical data. The point of interest category can be the primary subject of the current scan data. Following the first example above, for a patient entering a routine colonoscopy with an uptake scanning device and without symptoms, the system can look for any abnormalities, identify abnormalities (such as polyps) as points of interest, assign one or more categories to the points of interest (e.g., low blood flow or asymmetrical shape), and find historical data with one or more categories for comparison. According to the second embodiment above, for a patient with a prior diagnosis complaining of bowel pain and diverticulitis, inflammation (which may surround the cyst) can be the primary category based on the prior diagnosis. The system can preemptively find historical data containing the identified categories for comparison during the scan (e.g., comparing the characteristics of the inflamed cyst).

[0042] In some embodiments, multiple categories can be identified for a single point of interest. For example, a single point of interest (e.g., a decolorized region of the intestine) can be labeled with a low blood flow category and a calcification category.

[0043] Techniques used to identify point-of-interest (POI) categories may include user selection, but they may additionally include automated techniques such as image recognition analysis (e.g., to identify objects in an image / video), anomaly detection (e.g., to identify anomalies within an image / video), location determination (e.g., to identify the location where an image was generated, or to determine location-based tissue scans / images), etc. In some embodiments, data from a scan may have an acceptable range, and any data outside this range can be identified as a POI. For example, typical blood flow in the intestine may be 6-7% of total blood flow, and if blood flow is below this range, it can be identified as having a "low blood flow" category. In embodiments, when current scan data falls outside the acceptable range, neural networks (e.g., cognitive image analysis, etc.) may be employed to identify POI categories. In some embodiments, the category may be based on the identified characteristics. For example, any growth protruding from the side of the intestine (such as a polyp) can be categorized as abnormal growth. In some embodiments, if a POI is identified, the user may be notified. For example, if a growth exceeding a certain size is detected, an ultrasound image can be displayed, in which the growth is highlighted.

[0044] At 215, a relevance score is generated from the current scan data for the identified point-of-interest (POI) categories. Following the first example above from colonoscopy, some categories can be designated as low-relevance (e.g., microcalcifications), which are unlikely to cause problems unless other underlying symptoms are present (e.g., they do not match any known problems), while other categories (e.g., large polyps) can be highly relevant (the characteristics of the POI closely match the problem). Following the second example above concerning diverticulitis, the system can identify inflammation as highly relevant (e.g., possibly indicating a cyst) and polyps as low-relevance (e.g., unlikely to indicate a cyst). In embodiments, the relevance score can be based on a continuum (e.g., the relevance score can be somewhere between "similar" and "irrelevant"), or it can be multidimensional (e.g., points plotted within one or more 3D models, where axes define a given analyzed component of the relevance score). In some embodiments, the relevance score can be based on a threshold. For example, the normal range of intestinal blood flow can be 0.88 + / - 0.13 ml / min / g, where 0.75 ml / min / g would be a lower threshold. For the "low blood flow" category, intestinal regions with blood flow at or just below a threshold can be labeled with a low relevance score (e.g., close to normal). Intestinal regions with no blood flow can be labeled as highly relevance ulcers in the "low blood flow" category. In some embodiments, the relevance score can be a numerical score based on the similarity between a point of interest and the category.

[0045] At point 220, the historical scan dataset is identified based on the point of interest category. In this embodiment, the historical scan data repository may be predefined (e.g., a specific folder on a computing device), or it may be undefined (e.g., the historical scan dataset may be obtained from the Internet, by searching an entire disk drive within a computing device, by scanning multiple shared folders on multiple devices, by searching a related historical scan data database, etc.).

[0046] In some embodiments, the historical scan dataset may have metadata tags that describe specific data ranges that should be further investigated. In this case, metadata tags with appropriate actions can be identified and returned by a search. In some embodiments, the historical scan dataset may have metadata tags indicating what further steps should be taken. For example, when a region of low blood flow is found in the intestine (e.g., based on scan data received in box 205), a historical scan showing similar blood flow can be identified. Metadata tags on similar scans can indicate that X-rays of that region can determine the cause of the historically reduced blood flow.

[0047] At box 230, the identified historical dataset can be analyzed based on the features and categories of the points of interest. In some embodiments, historical data can be analyzed to determine features of the points of interest (e.g., blood flow velocity, tumor size, etc.) and related external information (e.g., what scan to perform, at what angle to scan, what radiation intensity, etc.). In some embodiments, the categories of points of interest can be used to identify specific features in the historical dataset that may be present in the current scan data.

[0048] In some embodiments, the analysis may include assigning similarity scores to one or more datasets. For example, if an interest point (e.g., an anomaly) is identified in the internal scan data received at box 205, the system may retrieve that internal scan data and compare it with historical scan data. The closer the data in the historical scan data matches the current scan data, the higher the similarity score. In some embodiments, the identified historical datasets and the current scan data may be displayed to the user. For example, if the images show growth with tumor characteristics, several images of confirmed tumors may be displayed for comparison.

[0049] At box 240, the configuration for external scanning can be determined based on historical data. For example, when an uptake ultrasound device detects interference in tissue, such as an ulcer, a first orientation of the external scan may provide a better image than a second orientation. Historical data might show that X-rays from a lateral ulcer may provide worse data than a scan of the ulcer on the face. Similarly, historical data can be analyzed to determine areas that need to be scanned. For example, a tumor showing signs of malignancy may require a wider range of external scans (e.g., to determine if the cancer has spread) compared to a tumor showing signs of benignity.

[0050] In some embodiments, the configuration may include angle, direction, intensity, area, focal point, and signal strength. For example, historical data may be analyzed to determine the angle of the X-rays that should be used to scan the ulcer. Some angles may provide more or more complete information than others.

[0051] At box 250, a second scan (i.e., an external scan) can be performed based on the first scan. Following the first example above, the internal scanning device can be used to identify polyps and information about them, including their orientation. An X-ray can be performed based on the internal scan, where the X-ray machine is oriented to provide a view showing the polyp's outline. A cross-sectional view of the polyp can give a radiologist a better chance to diagnose whether the polyp is cancerous. Following the second example above, the internal scanning device can be used to detect possible diverticulum sacs. Based on a comparison of the configurations of boxes 230 and 240, an external scan (e.g., a computed tomography (CT) scan) of the immediate region surrounding the sac can be performed, rather than a large-area scan of the entire abdomen. In some embodiments, the external scan is performed automatically. In some embodiments, the external scan can be performed based on characteristics from box 240 that meet or exceed threshold levels, such as blood flow below an acceptable normal range. In some embodiments, at box 250, the external scan may not be performed automatically. Instead, in some embodiments, box 250 may include prompting the user to initiate an external scan. For example, if a threshold is exceeded during scanning, an image can be shown to the user and the user can be prompted to initiate an external scan.

[0052] In some embodiments, the threshold level can be one or more identified features. For example, certain shapes or colors can indicate that the second scan is reliable. Some shapes, such as polyps, can indicate an underlying medical problem, while other shapes, such as small bumps, can be normal. Significant discoloration can indicate an underlying condition such as necrosis or gangrene, while slight discoloration can be a natural change in skin color.

[0053] In some embodiments, the threshold can be a specific value exhibited by the scanned tissue. For example, the threshold could be blood flow rate, density, a specific bending angle for growth, or the size of the growth.

[0054] In some embodiments, the threshold can be a combination of features. For example, any region of the intestine having blood flow below a certain rate and temperature above a certain rate can trigger an external scan, even if the blood flow and temperature may not be able to trigger an external scan by themselves alone. For example, growth of a specific shape can trigger an external scan if blood flow is above a certain rate.

[0055] Turn now Figure 3 An example networking environment 300 according to an embodiment of the present disclosure is illustrated. The networking environment 300 may include a client device 324, a historical scan database 330, a network 340, and a historical scan data selection tool 301 (e.g., a system) for selecting historical scan data based on point-of-interest categories and scan types. The historical scan data selection tool 301 may be implemented as an application running on a user's computing device, a service provided via the cloud, a web browser plugin, a smartphone application, or a code-dependent application attached to an auxiliary application (e.g., an "overlay" or companion application to a partner application such as a text messaging application).

[0056] Network 340 can be any type of network or combination of networks. For example, network 340 can include any combination of Personal Area Network (PAN), Local Area Network (LAN), Metropolitan Area Network (MAN), Wide Area Network (WAN), Wireless Local Area Network (WLAN), Storage Area Network (SAN), Enterprise Private Network (EPN), or Virtual Private Network (VPN). In some embodiments, network 340 can refer to an IP network, a traditional coaxial cable-based network, etc. For example, the server storing historical scan data database 330 can communicate with various client devices (e.g., tablet computers, laptops, smartphones, portable terminals, client device 324, etc.) via the Internet.

[0057] In some embodiments, network 340 may be implemented within a cloud computing environment or using one or more cloud computing services. According to various embodiments, the cloud computing environment may include a network-based distributed data processing system that provides one or more cloud computing services. Furthermore, the cloud computing environment may include a number of computers (e.g., hundreds or thousands of computers or more) located within one or more data centers and configured to share resources through network 340. About Figure 5 and Figure 6 Let's discuss cloud computing in more detail.

[0058] Client device 324 may be a desktop computer, laptop computer, smartphone, tablet computer, or any other suitable computing device for a user to interact with and perform the methods / techniques described herein. In embodiments, client device 324 may store one or more scan datasets, such as scan data 302. As described herein, scan data 302 may be written text, audio streams, video streams, etc.

[0059] As envisioned herein, the historical scan data database 330 can store a wide variety of historical scan data. For example, historical scan data may include scan data, still images, videos, audio recordings (e.g., heartbeats from spectrograms), or any other type of historical scan data that the author / user of scan data 302 may wish to add to or combine with scan data 302. In embodiments, the historical scan database 330 may reside on a single server, multiple servers within a cloud computing environment, and / or on client device 324, or on the same physical or virtualized system as the historical scan data selection tool 301.

[0060] The historical scan data selection tool 301 can be a standalone computing system, such as a desktop or laptop computer; a server; or a virtualized system running on one or more servers in a cloud computing environment. The historical scan data selection tool 301 may include a historical scan data processing system 306, a scan data processing system 314, and a scoring module 322.

[0061] In this embodiment, scan data 302 may be received by a historical scan data selection tool 301 via a network 340. The scan data processing system 314 may include, for example, an image processor 316, a search application 318, and a content analysis module 320.

[0062] In one embodiment, the image processor 316 may be configured to analyze historical scans to identify one or more interest point categories. In another embodiment, the image processor 316 may also be configured to receive anomalous identification features, from which interest point categories can then be identified.

[0063] When a point of interest (POI) category is identified, a relevance score can be generated to determine one or more features (signal strength, density, shape of anomalies, size of anomalies, blood flow, etc.) of historical scan data related to the identified POI category via content analysis module 320. In embodiments, content analysis module 320 may include a relational database linking one or more features to one or more other features, or storing information in such a relational database. For example, a POI may have a higher temperature associated with increased blood flow, where either feature alone may not be an anomaly. In other embodiments, content analysis module 320 may include a convolutional neural network to generate the relevance score. In still other embodiments, content analysis module 320 may include both, for example, a relational database and a convolutional neural network, and data from the relational database may be used as input to the convolutional neural network. The relevance score can be output to scoring module 322 for similarity scoring.

[0064] Search application 318 can be used to find historical scan datasets by searching historical scan data database 330 to find categories of interest identified by image processor 316. As described herein, historical scan data database 330 may include predefined folders or computers, or it may be interpreted as a collection of websites, computers, servers, etc. Search results can be returned to historical scan data processing system 306.

[0065] In some embodiments, the historical scan data processing system 306 may include, for example, a data analysis module 308, an image analysis module 310, and a category receiving module 312. The category receiving module 312 may be configured to receive from the scan data processing system 314 categories of points of interest identified by analyzing scan data 302 that must be associated with regions of the historical scan dataset retrieved by the search application 318.

[0066] For example, in one embodiment, shape recognition can be used as part of the scan data processing system 314 to identify specific growths or formations. The superclass of the growth or formation can be determined by parsing a relational database of shapes, and this superclass can be designated as a point of interest category. After identifying the point of interest category, the scan data processing system 314 can send data about the point of interest category to the category receiving module 312, since shape attributes can inform the recognition of visual / audio attributes via the image analysis module 310 or the data analysis module 308, respectively.

[0067] Based on digital file formats (e.g., image file formats (e.g., .jpg), text formats (e.g., .docx, .raf, .txt, etc.), audio formats (e.g., .mp3, etc.), and video file formats (e.g., .wmv)), the historical scan data processing system 306 can determine which processing module (e.g., data analysis module 308 or image analysis module 310) should be used to analyze the historical scan data received in response to the results of the search application 318. In embodiments where textual historical scan data is received, the analysis of the historical scan data can be performed, for example, at the scan data processor 316. In other embodiments, the historical scan data processing system 306 may include its own scan data processor (not shown).

[0068] In embodiments, the image analysis module 310 may be configured to receive video and image formats to identify objects, locations, points of interest, etc. (e.g., subjects) within the images, as described herein. In embodiments receiving video files, still frame images may be selected at random intervals, at regular intervals, or a “best image” (e.g., the image that most clearly shows the tumor) may be selected based on still image selection criteria.

[0069] In embodiments, the image analysis module 310 may be configured to identify (e.g., from a still image, video, or a single frame of a video feed) objects or features (e.g., growth shape, coloration, inflammation, etc.). The image analysis module 310 may also identify the context of an image given a combination of objects in the image. For example, an image having a combination of one or more objects with shapes, growth, or discoloration can provide a basis for identifying the background of the image as necrosis. The image analysis module 310 may perform the analysis techniques described herein to output the probability of a specific point of interest in the analyzed image based on the received point of interest category.

[0070] Once the objects, attributes, context, and relevance score of an image have been identified, the image can be "tagged" or otherwise labeled using a list or table reflecting this information (e.g., as metadata) and stored in the historical scan data database 330. The relevance score generated by the image analysis module 310 is sent to the scoring module 322.

[0071] In an embodiment, as discussed herein, the scoring module 322 may be used to generate a similarity score based on the received relevance scores of both content and historical scan data, as discussed herein.

[0072] In an embodiment, the scoring module 322 may employ a neural network to generate a similarity score, as described herein. In an embodiment, the neural network may be a multilayer perceptron, a sigmoid neuron system, a directed acyclic graph comprising multiple small nuclei, or any other structure / system capable of neural networking.

[0073] The scoring module 322 can select one or more images from historical scan data to display to the user based on similarity scores, as described herein. The parameters used for selection can include a single dataset with the highest similarity score, or it can be a subset of historical scan data (e.g., ten historical scans with the highest similarity scores). The selection parameters can be adjustable.

[0074] Figure 4 Representative main components of an exemplary computer system 401 that can be used according to embodiments of the present disclosure are depicted. The specific components described are presented for illustrative purposes only and are not necessarily the only such variations. Computer system 401 may include a processor 410, memory 420, input / output interfaces (also referred to herein as I / O or I / O interfaces) 430, and a main bus 440. Main bus 440 may provide communication paths for other components of computer system 401. In some embodiments, main bus 440 may be connected to other components such as a dedicated digital signal processor (not shown).

[0075] The processor 410 of the computer system 401 may include one or more CPUs 412. The processor 410 may additionally include one or more memory buffers or caches (not shown) that provide temporary storage for instructions and data for the CPU 412. The CPU 412 may execute instructions on input provided from a cache or memory 420 and output results to the cache or memory 420. The CPU 412 may include one or more circuits configured to perform one or more methods consistent with embodiments of this disclosure. In some embodiments, the computer system 401 may include a relatively large number of processors 410 typical of a system. However, in other embodiments, the computer system 401 may be a single processor with a single CPU 412.

[0076] The memory 420 of computer system 401 may include a memory controller 422 and one or more memory modules (not shown) for temporary or permanent storage of data. In some embodiments, memory 420 may include random access semiconductor memory, storage devices, or storage media (volatile or non-volatile) for storing data and programs. Memory controller 422 may communicate with processor 410 to facilitate the storage and retrieval of information in the memory modules. Memory controller 422 may communicate with I / O interface 430 to facilitate the storage and retrieval of inputs or outputs in the memory modules. In some embodiments, the memory module may be a dual in-line memory module.

[0077] I / O interface 430 may include I / O bus 450, terminal interface 452, memory interface 454, I / O device interface 456, and network interface 458. I / O interface 430 can connect main bus 440 to I / O bus 450. I / O interface 430 can route instructions and data from processor 410 and memory 420 to various interfaces of I / O bus 450. I / O interface 430 can also route instructions and data from various interfaces of I / O bus 450 to processor 410 and memory 420. The various interfaces may include terminal interface 452, memory interface 454, I / O device interface 456, and network interface 458. In some embodiments, the various interfaces may include a subset of the aforementioned interfaces (e.g., embedded computer systems in industrial applications may not include terminal interface 452 and memory interface 454).

[0078] The logical modules throughout computer system 401—including, but not limited to, memory 420, processor 410, and I / O interface 430—can relay faults and changes to one or more components to a hypervisor or operating system (not depicted). The hypervisor or operating system can allocate various resources available in computer system 401 and track the location of data in memory 420 and the location of processes allocated to various CPUs 412. In embodiments where elements are combined or rearranged, aspects of the capabilities of the logical modules can be combined or redistributed. These variations will be apparent to those skilled in the art.

[0079] This invention can be a system, method, and / or computer program product at any possible level of technical detail integration. The computer program product may include a computer-readable storage medium (or media) having computer-readable program instructions thereon for causing a processor to perform aspects of the invention.

[0080] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes the following: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable optical disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices such as punch cards or recessed structures with instructions recorded thereon, and any suitable combination of the foregoing. As used herein, computer-readable storage media should not be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0081] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to a suitable computing / processing device, or via a network, such as the Internet, a local area network (LAN), a wide area network (WAN), and / or a wireless network, to an external computer or external storage device. The network may include copper cables, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to a computer-readable storage medium within the respective computing / processing device.

[0082] Computer-readable program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages ​​(including object-oriented programming languages ​​such as Smalltalk, C++, etc.) and procedural programming languages ​​(such as the "C" programming language or similar programming languages). The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, to perform aspects of this invention, electronic circuits, including, for example, programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), may execute computer-readable program instructions to personalize the electronic circuits by utilizing the status information of the computer-readable program instructions.

[0083] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0084] These computer-readable program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / actions specified in one or more blocks of a flowchart and / or block diagram. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and / or other device to operate in a particular manner, such that the computer-readable storage medium in which the instructions are stored includes an article of writing comprising instructions for implementing aspects of the functions / actions specified in one or more blocks of a flowchart and / or block diagram.

[0085] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer-implemented process, such that the instructions, which execute on the computer, other programmable apparatus or other device, perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0086] It should be understood that although this disclosure includes a detailed description of cloud computing, the implementation of the teachings set forth herein is not limited to a cloud computing environment. Rather, embodiments of the invention can be implemented in conjunction with any other type of computing environment now known or developed hereafter.

[0087] Cloud computing is a service delivery model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with service providers. This cloud model may include at least five features, at least three service models, and at least four deployment models.

[0088] The features are as follows:

[0089] On-demand self-service: Cloud consumers can unilaterally and automatically provide computing power, such as server time and network storage, as needed, without requiring manual interaction with the service provider.

[0090] Wide Area Network (WAN) Access: Capabilities are available on the network and accessed through standard mechanisms that facilitate use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

[0091] Resource pooling: A provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, where different physical and virtual resources are dynamically allocated and reallocated based on demand. Location independence has significance because consumers typically do not control or know the exact location of the resources provided, but can specify the location at a higher level of abstraction (e.g., country, state, or data center).

[0092] Rapid Flexibility: In some cases, the ability to scale outwards and inwards quickly and flexibly can be provided. For consumers, the available capacity often appears unlimited and can be purchased in any quantity at any time.

[0093] Measurement services: Cloud systems automatically control and optimize resource usage by leveraging metering capabilities at a level of abstraction appropriate to the service type (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency to both the providers and consumers of the services being utilized.

[0094] The service model is as follows:

[0095] Software as a Service (SaaS): The capability offered to consumers is the ability to use the provider's applications running on cloud infrastructure. Applications can be accessed from various client devices through thin client interfaces such as web browsers (e.g., web-based email). Consumers do not manage or control the underlying cloud infrastructure, including the network, servers, operating system, storage, or even individual application capabilities, with possible exceptions such as limited user-specific application configuration settings.

[0096] Platform as a Service (PaaS): This provides consumers with the ability to deploy consumer-created or acquired applications onto cloud infrastructure using programming languages ​​and tools supported by the provider. Consumers do not manage or control the underlying cloud infrastructure, including networks, servers, operating systems, or storage, but they have control over the deployed applications and the configuration of any application hosting environments.

[0097] Infrastructure as a Service (IaaS): This provides consumers with the capability to deliver processing, storage, networking, and other basic computing resources that enable them to deploy and run arbitrary software, which may include operating systems and applications. Consumers do not manage or control the underlying cloud infrastructure, but they do have control over the operating system, storage, deployed applications, and possibly limited control over selected networking components (e.g., host firewalls).

[0098] The deployment model is as follows:

[0099] Private cloud: Cloud infrastructure operated solely by an organization. It can be managed by the organization or a third party and can exist inside or outside a building.

[0100] Community cloud: Cloud infrastructure shared by several organizations and supporting a specific community with shared concerns (e.g., tasks, security requirements, policies, and compliance considerations). It can be managed by an organization or a third party and can exist on-site or off-site.

[0101] Public cloud: Cloud infrastructure available to the general public or large industrial groups and owned by organizations that sell cloud services.

[0102] Hybrid cloud: A cloud infrastructure is a combination of two or more clouds (private, community, or public) that remain a single entity but are bound together by standardized or proprietary technologies that enable data and applications to be ported together (e.g., cloud bursting for load balancing between clouds).

[0103] Cloud computing environments are service-oriented, focusing on statelessness, loose coupling, modularity, and semantic interoperability. At the heart of cloud computing is the infrastructure of a network of interconnected nodes.

[0104] Now for reference Figure 5The illustration depicts a cloud computing environment 50. As shown, the cloud computing environment 50 includes one or more cloud computing nodes 10 that can communicate with local computing devices used by cloud consumers, such as personal digital assistants (PDAs) or cellular phones 54A, desktop computers 54B, laptop computers 54C, and / or automotive computer systems 54N. The nodes 10 can communicate with each other. They can be physically or virtually grouped (not shown) in one or more networks, such as private clouds, community clouds, public clouds, or hybrid clouds, or combinations thereof, as described above. This allows the cloud computing environment 50 to provide infrastructure, platform, and / or software as a service, without requiring cloud consumers to maintain resources on their local computing devices. It should be understood that... Figure 5 The types of computing devices 54A-N shown are for illustrative purposes only, and computing node 10 and cloud computing environment 50 can communicate with any type of computing device over any type of network and / or network-addressable connection (e.g., using a web browser).

[0105] Now for reference Figure 6 This demonstrates a cloud computing environment of 50 ( Figure 5 This provides a set of functional abstractions. It should be understood beforehand that... Figure 6 The components, layers, and functions shown are for illustrative purposes only, and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided:

[0106] The hardware and software layer 60 includes hardware and software components. Examples of hardware components include: a host 61; a server 62 based on a RISC (Reduced Instruction Set Computer) architecture; a server 63; a blade server 64; a storage device 65; and network and networking components 66. In some embodiments, software components include network application server software 67 and database software 68.

[0107] The virtualization layer 70 provides an abstraction layer from which the following examples of virtual entities can be provided: virtual server 71; virtual storage 72; virtual network 73, including virtual private network; virtual application and operating system 74; and virtual client 75.

[0108] In one example, management layer 80 can provide the following functionalities: Resource Provisioning 81 provides dynamic procurement of computing resources and other resources used to perform tasks within the cloud computing environment. Metering and Pricing 82 provides cost tracking when utilizing resources in the cloud computing environment, as well as billing or invoicing for consuming these resources. In one example, these resources may include application software licenses. Security provides authentication for cloud consumers and tasks, and protection for data and other resources. User Portal 83 provides access to the cloud computing environment for consumers and system administrators. Service Level Management 84 provides cloud resource allocation and management to ensure that required service levels are met. Service Level Agreement (SLA) Planning and Fulfillment 85 provides pre-scheduling and procurement of cloud resources, where future needs are anticipated according to the SLA.

[0109] Workload layer 90 provides examples of functionalities that can be leveraged in a cloud computing environment. Examples of workloads and functionalities that can be provided from this layer include: mapping and navigation 91; software development and lifecycle management 92; virtual classroom education delivery 93; data analytics and processing 94; transaction processing 95; and predictive neural networks 96.

[0110] This invention can be a system, method, and / or computer program product at any possible level of technical detail integration. The computer program product may include a computer-readable storage medium (or media) having computer-readable program instructions thereon for causing a processor to perform aspects of the invention.

[0111] Computer-readable storage media can be tangible devices capable of retaining and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes the following: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable optical disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices such as punch cards or recessed structures with instructions recorded thereon, and any suitable combination of the foregoing. As used herein, computer-readable storage media should not be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0112] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to a suitable computing / processing device, or via a network, such as the Internet, a local area network (LAN), a wide area network (WAN), and / or a wireless network, to an external computer or external storage device. The network may include copper cables, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to a computer-readable storage medium within the respective computing / processing device.

[0113] Computer-readable program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages ​​(including object-oriented programming languages ​​such as Smalltalk, C++, etc.) and procedural programming languages ​​(such as the "C" programming language or similar programming languages). The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, to perform aspects of this invention, electronic circuits, including, for example, programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), may execute computer-readable program instructions to personalize the electronic circuits by utilizing the status information of the computer-readable program instructions.

[0114] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0115] These computer-readable program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / actions specified in one or more blocks of a flowchart and / or block diagram. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and / or other devices to operate in a particular manner, such that the computer-readable storage medium in which the instructions are stored includes an article of writing comprising instructions for implementing aspects of the functions / actions specified in one or more blocks of a flowchart and / or block diagram.

[0116] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer-implemented process, such that the instructions, which execute on the computer, other programmable apparatus or other device, perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0117] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of instructions comprising one or more executable instructions for implementing a specified logical function. In some alternative embodiments, the functions indicated in the blocks may occur in a non-consecutive order as shown in the figures. For example, two blocks shown consecutively may actually be executed substantially simultaneously, or these blocks may sometimes be executed in reverse order, depending on the functions involved. It will also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action or executes a combination of dedicated hardware and computer instructions.

[0118] As used herein, a “set” of objects does not equal all available instances of that object. For example, if four files are available, the set of files may not contain all four files. Furthermore, as used herein, the phrase “each in a set” of objects refers only to instances of the object in that set. For example, if four files are available, the phrase “a set of two files from four files, each of which is read-only” would be appropriately interpreted as implying that two files (the two files in the set) are read-only. Two files out of the four available files that are not in the set may or may not be read-only.

[0119] Various embodiments of this disclosure have been described for illustrative purposes, but are not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to explain the principles of the embodiments, their practical application, or improvements to existing technologies in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

[0120] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of instructions comprising one or more executable instructions for implementing a specified logical function. In some alternative embodiments, the functions indicated in the blocks may occur in a non-consecutive order as shown in the figures. For example, two blocks shown consecutively may actually be executed substantially simultaneously, or these blocks may sometimes be executed in reverse order, depending on the functions involved. It will also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action or executes a combination of dedicated hardware and computer instructions.

[0121] Various embodiments of this disclosure have been described for illustrative purposes, but are not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to explain the principles of the embodiments, their practical application, or improvements to existing technologies in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method comprising: The first dataset is received from the patient's internal scanning device at the earliest possible moment; Identify points of interest within the body based on the first dataset; Determine the location of the point of interest within the body; The external scanning device is guided to the point of interest based on the location of the point of interest identified in the first dataset; as well as The point of interest is scanned using the external scanning device at the first time.

2. The method according to claim 1, wherein, The determination is performed by selecting a technique from a group consisting of echo localization, Doppler localization, X-ray scanning, and triangulation.

3. The method according to claim 1, wherein, The captureable scanning device is a captureable ultrasonic scanning device, and the external scanning device is an X-ray machine.

4. The method according to claim 1, further comprising: The configuration of the external scanning device is determined based on the first dataset.

5. The method according to claim 4, wherein, The configuration includes the angle of the external scanning device relative to the point of interest.

6. The method according to claim 4, wherein, The configuration includes an exposure area for the external scanning device.

7. The method according to claim 4, wherein, The determination also includes: Determine the category of the points of interest; Historical datasets are received based on the aforementioned categories; Compare the first dataset with the historical data; and The external scan is configured based on the comparison.

8. The method according to claim 7, wherein, The historical dataset includes the type of external scan and the configuration of the external scan.

9. A computer program product comprising a computer-readable storage medium having program instructions embodied therein, the program instructions being executable by a computer to cause the computer to: The first dataset is received from the patient's internal scanning device at the earliest possible moment; Identify points of interest within the body based on the first dataset; Determine the location of the point of interest within the body; The external scanning device is guided to the point of interest based on the location of the point of interest identified in the first dataset; as well as The point of interest is scanned using the external scanning device at the first time.

10. The computer program product according to claim 9, wherein, The determination is performed by selecting a technique from a group consisting of echo localization, Doppler localization, X-ray scanning, and triangulation.

11. The computer program product according to claim 9, wherein, The captureable scanning device is a captureable ultrasonic scanning device, and the external scanning device is an X-ray machine.

12. The computer program product according to claim 9, further comprising: The configuration of the external scanning device is determined based on the first dataset.

13. The computer program product according to claim 12, wherein, The configuration includes the angle of the external scanning device relative to the point of interest.

14. The computer program product according to claim 12, wherein, The configuration includes an exposure area for the external scanning device.

15. The computer program product according to claim 12, wherein, The determination also includes: Determine the category of the points of interest; Historical datasets are received based on the aforementioned categories; Compare the first dataset with the historical data; and The external scan is configured based on the comparison.

16. A system comprising: processor; as well as A memory communicating with the processor, the memory containing program instructions that, when executed by the processor, are configured to cause the processor to perform a method comprising: The first dataset is received from the patient's internal scanning device at the earliest possible moment; Identify points of interest within the body based on the first dataset; Determine the location of the point of interest within the body; Based on the location of the interest point identified in the first dataset, the external scanning device is guided to the interest point; and The point of interest is scanned using the external scanning device at the first time.

17. The system according to claim 16, wherein, The determination is performed by selecting a technique from a group consisting of echo localization, Doppler localization, X-ray scanning, and triangulation.

18. The system according to claim 16, wherein, The captureable scanning device is a captureable ultrasonic scanning device, and the external scanning device is an X-ray machine.

19. The system of claim 16, further comprising: The configuration of the external scanning device is determined based on the first dataset.

20. The system according to claim 19, wherein, The configuration includes the angle of the external scanning device relative to the point of interest.

Images