Abstract: Presented herein are methods, systems, devices, and computer-readable media for image annotation for medical procedures. The system operates in a parallel manner. In one flow, the system starts from clinical terms and image and applies image detection module in order to get visual candidates for related radiological finding and provide them with semantic descriptors. In the second (parallel) flow, the system produces a list of prioritized semantic descriptors (with probabilities). The second flow is done by application of a reverse inference algorithm that uses clinical terms and expert clinical knowledge. The results of both flows combined by matching module for detection the best candidate and with limited user input images can be annotated. The clinical terms are extracted from clinical documents by textual analysis.

Abstract: An apparatus for analysing a head CT scan, where the head CT scan includes a plurality of cross-sections, includes a processor configured to select a first cross-section from among the plurality of cross-sections in the head CT scan; and analyse the first cross-section to determine whether the whole head CT scan was taken with contrast. The analyzing includes determining whether the first cross-section shows contrast, based on a presence of bright patches in the first cross-section, determining whether an amount of sulci in the first cross-section is below a threshold amount. In response to determining that the first cross-section shows contrast, determine that the whole head CT scan was taken with contrast, and in response to the determining that the first cross-section does not show contrast and that the amount of sulci is below the threshold amount determining that the whole head CT scan was taken without contrast.

Abstract: A system for determining whether a dataset including a plurality of cross-sectional images includes a predetermined feature is provided, the system including a first AI model to: receive a dataset including a plurality of cross-sectional images as an input, analyse the dataset to identify a subset of cross-sectional images of the dataset capable of including the predetermined feature, and output the subset; a second AI model to: receive a first cross-sectional image from the subset, analyse the first cross-sectional image to determine whether the first cross-sectional image includes the predetermined feature, and output an indication of whether the first cross-sectional image includes the predetermined feature; and a processor configured to: provide the dataset as an input to the first AI model, obtain the output subset from the first AI model, provide the first cross-sectional image from the subset as an input to the second AI model, obtain the output from the second AI model, and based on the output from th

Abstract: A system for identifying a potential altered image includes an image analysis unit to analyse an image and output a result indicating whether the image shows a predetermined feature and a processor to obtain a first output from the image analysis unit for an image that has a plurality of pixels each having a pixel value, obtain an altered version of the image by adjusting pixel values of up to a predetermined threshold amount of pixels in the image, obtain a second output from the image analysis unit for the altered version of the image, determine whether the second output is different to the first output, and identify that the image is a potential altered image in response to determining that the second output is different to the first output.

Abstract: Presented herein are methods, systems, devices, and computer-readable media for image annotation for medical procedures. The system operates in a parallel manner. In one flow, the system starts from clinical terms and image and applies image detection module in order to get visual candidates for related radiological finding and provide them with semantic descriptors. In the second (parallel) flow, the system produces a list of prioritized semantic descriptors (with probabilities). The second flow is done by application of a reverse inference algorithm that uses clinical terms and expert clinical knowledge. The results of both flows combined by matching module for detection the best candidate and with limited user input images can be annotated. The clinical terms are extracted from clinical documents by textual analysis.

Abstract: An apparatus for analysing a head CT scan is provided, wherein the head CT scan includes a plurality of cross-sections. The apparatus includes a processor configured to: select a first cross-section from among the plurality of cross-sections in the head CT scan; and analyse the first cross-section to determine whether the whole head CT scan was taken with contrast, the analysing including: determining whether the first cross-section shows contrast, based on a presence of bright patches in the first cross-section, determining whether an amount of sulci in the first cross-section is below a threshold amount, in response to determining that the first cross-section shows contrast, determine that the whole head CT scan was taken with contrast, and in response to the determining that the first cross-section does not show contrast and that the amount of sulci is below the threshold amount determining that the whole head CT scan was taken without contrast.

Abstract: A system for identifying a potential altered image includes an image analysis unit to analyse an image and output a result indicating whether the image shows a predetermined feature and a processor to obtain a first output from the image analysis unit for an image that has a plurality of pixels each having a pixel value, obtain an altered version of the image by adjusting pixel values of up to a predetermined threshold amount of pixels in the image, obtain a second output from the image analysis unit for the altered version of the image, determine whether the second output is different to the first output, and identify that the image is a potential altered image in response to determining that the second output is different to the first output.

Abstract: This invention relates to a method and system for creating Big Data applications that can be executed on heterogeneous clusters. The applications can be executed on a particular platform, such as SPARK or UIMA-AS, but the method and system are able to translate the input to these targeted platforms without the developer needing to tailor the application specifically to the platform. The method and system are based on the use of an execution dependency graph, a cluster configuration, and a data size to create a stages table. The stages table is then optimized to increase the overall efficiency of the heterogeneous cluster. The stages table is then translated into a platform specific Big Data application.

Abstract: A computer implemented method, a computerized system and a computer program product for generating questions. The computer implemented method comprising obtaining a question, wherein the question comprises one or more elements that define an answer for the question. The method further comprising obtaining the answer. The method further comprises automatically generating, by a processor, a new question based on the question and the answer. The automatic generation comprises determining a variant of the one or more elements, wherein the variant defines the answer, wherein the new question comprises the variant.

Abstract: Processing a chief medical complaint, associated with a patient, together with current clinical data items derived from current clinical data associated with the patient to establish a baseline medical diagnosis of the patient, for each of different historical clinical data items derived from historical clinical data associated with the patient, processing the chief medical complaint together with the current clinical data items and the historical clinical data item to establish a comparison medical diagnosis of the patient, where the comparison medical diagnosis results from an diagnostic effect of the historical clinical data item on the baseline medical diagnosis, and determining the diagnostic effect of each of the historical clinical data items on the baseline medical diagnosis, and visually displaying on a visual display medium any of the historical clinical data items in accordance with a prioritization arrangement based on the diagnostic effects of the historical clinical data items.

Abstract: A computer implemented method, a computerized system and a computer program product for automatic image segmentation. The computer implemented method comprises obtaining an image of a tissue, wherein the image is produced using an imaging modality. The method further comprises automatically identifying, by a processor, a tissue segment within the image, wherein said identifying comprises identifying an artifact within the image, wherein the artifact is a misrepresentation of a tissue structure, wherein the misrepresentation is associated with the imaging modality; and searching for the tissue segment in a location adjacent to the artifact.

Abstract: A computer-implemented method for automating interaction with a computer system includes linking a control computer system to an input interface and to an output interface of a client computer system, which is operative for producing user interface images on a display device. The control computer system executes distinct software modules that include a virtual operator for simulating actions of a human operator. Execution of the software modules causes the control computer system to capture an image from the output interface, and to recognize information in the image. In response to the information, the virtual operator controls an input device to automatically execute predetermined operations on the client computer system via the input interface.

Abstract: Presented herein are methods, systems, devices, and computer-readable media for image annotation for medical procedures. The system operates in a parallel manner. In one flow, the system starts from clinical terms and image and applies image detection module in order to get visual candidates for related radiological finding and provide them with semantic descriptors. In the second (parallel) flow, the system produces a list of prioritized semantic descriptors (with probabilities). The second flow is done by application of a reverse inference algorithm that uses clinical terms and expert clinical knowledge. The results of both flows combined by matching module for detection the best candidate and with limited user input images can be annotated. The clinical terms are extracted from clinical documents by textual analysis.

Abstract: This invention relates to a method and system for creating Big Data applications that can be executed on heterogeneous clusters. The applications can be executed on a particular platform, such as SAPRK or UIMA-AS, but the method and system are able to translate the input to these targeted platforms without the developer needing to tailor the application specifically to the platform. The method and system are based on the use of an execution dependency graph, a cluster configuration, and a data size to create a stages table. The stages table is then optimized to increase the overall efficiency of the heterogeneous cluster. The stages table is then translated into a platform specific Big Data application.

Abstract: This invention relates to a method and system for creating applications that can be executed on homogeneous big data platforms. The applications can be executed on a particular platform, such as SAPRK or UIMA-AS, but the method and system are able to translate the input to these targeted platforms without the developer needing to tailor the application specifically to the platform. The method and system are based on the use of an execution dependency graph and a cluster configuration. The execution dependency graph can include interdependencies in the data. Furthermore, the system optimizes for different analytics modules that have varying execution times. The output of the method is an application suitable for the target big data platform.

Abstract: A method comprising using at least one hardware processor for applying a mapping function to a medical image, to generate a semantic description of a visual finding in the medical image. The mapping function is optionally an MRF (Markov random field)-based, SVM (Support Vector Machine) mapping function.

Abstract: A computer-implemented method, computerized apparatus and computer program product for detecting fraud based on assessment of phyisicians' activity. An automatic diagnostic tool is applied to a benchmark of cases of a physician to diagnose whether a predetermined procedure is required. A discrepancy relation is determined by comparing the percentage of cases in the benchmark the tool diagnosed as requiring the procedure with an expected percentage determined based on the percentage of cases diagnosed by the physician as requiring the procedure and the tool's accuracy. An alert is provided to a supervising entity responsive to a discrepancy indicated by the discrepancy relation.

Abstract: A computer-implemented method, computerized apparatus and computer program product for providing performance feedback to physicians. An automatic diagnostic tool is applied to a benchmark of cases of a physician to diagnose whether a predetermined medical procedure is required. A discrepancy relation is determined by comparing the percentage of cases in the benchmark the tool diagnosed as requiring the procedure with an expected percentage determined based on the percentage of cases the physician diagnosed as requiring the procedure and the tool's accuracy. A feedback is provided to the physician responsive to a discrepancy indicated by the discrepancy relation.

Abstract: Methods, computing systems and computer program products implement embodiments of the present invention that include defining a first number of categories, each of the categories having a respective second number of values, and assigning, to each given examination question in a set of examination questions, a respective third number of categories and at least one given value for each of the third number of categories. A a set of requirements is retrieved, each given requirement including a respective fourth number of category requirements and a respective value-requirement for each of the category requirements, and a test-suite minimization algorithm is executed in order to select a minimum subset of the examination questions having categories and respective values including the category requirements and the value-requirements.

Abstract: Detecting regions-of-interest in medical images by identifying one or more image features in one or more medical images of a subject patient, identifying one or more clinical descriptors within clinical records of the subject patient, and identifying, using a visual-textual relationship model, regions-of-interest within the medical images of the subject patient based on relationships within the visual-textual relationship model corresponding to relationships between the image features identified in the subject patient medical images and the clinical descriptors identified in the subject patient clinical records.