Abstract: There is provided a computer-implemented method for analysing echocardiograms, the method comprising: obtaining (302) a plurality of pairs of consecutive echocardiograms for a plurality of subjects from a database (200), each echocardiogram having an associated indication of the content of the echocardiogram; analysing (304) each pair of consecutive echocardiograms to determine an associated class, the class indicating whether there is a change or no change between the consecutive echocardiograms in the pair; for each pair of consecutive echocardiograms, determining (306) an abstract representation of each echocardiogram by performing one or more convolutions and/or reductions on the echocardiograms in the pair, the abstract representation comprising one or more features indicative of the class of the pair; and training (308) a predictive model to determine a class for a new pair of echocardiograms based on the abstract representations for the plurality of pairs of consecutive echocardiograms.

Abstract: A system and method for selecting image sequences of echocardiogram studies for visual comparison by a user. The method includes receiving image sequences from a current study and a prior study to be compared. The method includes determining a relevant view of the image sequences of the current and prior studies and selecting an image sequence of the relevant view of one of the current and prior studies for comparison. The method includes displaying the image sequence from one of the current and prior studies to a user via an interactive comparison panel. The method includes determining which image sequence of the relevant view of an other of the current and prior studies most closely matches the selected image sequence and displaying the image sequence from the other of the current and prior studies that most closely matches the selected image sequence via the interactive comparison panel.

Abstract: A lesion tracking system (10) includes a data interface (12), a computation engine (18), and a visualization engine (26). The data interface (12) is configured to receive an identification (44) and measurements (56) of at least one target lesion (42), which includes at least one undetermined target lesion (52) according to a plurality of determined categories (54), and each undetermined target lesion (52) is quantified differently according to each of the plurality of determined categories (54). The computation engine (18) is configured to compute a range of quantified measurements according to each determined category for each undetermined target lesion (52), and to compute a quantified total range (70) for the at least one target lesion based on a quantified measurement (62) for each determined target lesion and the computed range for each of the at least one undetermined target lesion.

Abstract: A system and method are provided for enabling a user to annotate a medical image. A collection of key-value pairs is provided, in which a key represents an image-observable quantity and a value represents the value of the image-observable quantity. A collection of structured finding objects is provided, wherein each structured finding object represents a set of key-value pairs, each set of key-value pairs representing a different annotation of the medical image. The user is enabled to select one or more of the collection of key-value pairs, thereby obtaining a user-selected structured finding object which represents a preliminary annotation of the medical image by the user. At least one recommended structured finding object is selected by using the user-selected structured finding object as input to a probabilistic recommendation algorithm. Feedback is provided to the user on the basis of the recommended structured finding object. The annotation is well suited for, e.g.

Abstract: A system (100) includes a feature extraction engine (130), a finding code comparison engine (140), and a mapping interface (160). The feature extraction engine (130) extracts features of a statement of a finding code in a source vocabulary (110) and features of a second statement of a second finding code in a target vocabulary (112). The finding code comparison engine (140) determines a mapping between the statement of the source vocabulary and the second statement of the target vocabulary by comparing the extracted features based on at least one identified concept that comprises the extracted features. The mapping interface (160) presents the determined mapping on a display device (162).

Abstract: Echocardiogram reporting uses a list of standard echocardiogram findings (20) and a standard echocardiogram finding-relevant echocardiogram view lookup table (22). An echocardiogram reporting workstation (10) comprises a display component (12) and at least one user input component (14, 16, 18), and performs an echocardiogram examination reporting method including: interfacing with a user to enable the user to enter a current report (30) on a current echocardiogram examination of a cardiac subject; detecting a current finding value for a standard echocardiogram finding in the current report; and detecting a prior finding value for the echocardiogram finding in a prior report (36) on a prior echocardiogram examination of the cardiac subject.

Abstract: In a tool for assisting in summarizing salient medical report findings, finding tokens (66) representing findings are extracted from a current medical report, and a salience value is computed for each such finding token based on statistics for the finding token in a reference database (32) including a count of reference medical reports of the containing the finding token in a report summary section and a count of reference medical reports containing the finding token anywhere in the report. A ranking or sub-set of findings extracted from the current medical report is displayed. The displayed findings may be ranked, or the sub-set chosen, by salience value. To generate the reference database, for each reference medical report (42) finding tokens (46) are extracted and it is determined whether each such finding token is salient based on whether the finding token is extracted from a report summary section of the reference report.

Abstract: Systems for determining an erroneous code in a medical report comprising a plurality of codes, each code representing a comment in the medical report the system, comprise a memory comprising instruction data representing a set of instructions and a processor configured to communicate with the memory and to execute the set of instructions. The set of instructions, when executed by the processor, cause the processor to determine a respective vector representation for each of the plurality of codes in the medical report, wherein relative values of any selected pair of vector representations are correlated with a co-occurrence of the corresponding codes in a set of reference medical reports. The set of instructions when exectured by the processor further cause the processor to determine an erroneous code in the medical report, based on the vector representations.

Abstract: The following relates to medical equipment technology, and in particular to radiology report technology. In one embodiment, an original radiological report and an addended radiological report are received. The original radiological report and the addended radiological report may be part of the same or separate documents. The original radiological report and the addended radiological report may be compared. Each difference may be classified and scored. A display device may be controlled to display at least one of the scores.

Abstract: A radiology workstation (14) used to interpret a radiology examination (48) includes a display (20, 22), a user input device (24, 26, 28), and an electronic processor (12, 16). A radiology image of the radiology examination is displayed on the display. A radiology report is entered. An imaged anatomical region (54) is determined from the stored radiology examination. An examination basis medical condition is identified from the reason for examination (52). At least one additional medical condition is determined based on information on the patient retrieved from one or more medical databases (10, 44, 46) and is classified as reviewable or not reviewable based on the imaging modality (50) and the imaged anatomical region. An attention list (40) is created with items directed to the examination basis medical condition and to each reviewable additional medical condition.

Abstract: A device, system, and method optimizes image acquisition workflows. The method performed at a workflow server includes receiving data associated with previous image acquisition procedures that have been performed, the image acquisition procedures having respective image orders, the image orders having respective contexts, each of the image acquisition procedures having used at least one sequence defining settings to capture an image. The method includes determining at least one metric associated with a use of a first one of the at least one sequence for a first one of the contexts. The method includes generating a rule indicative of whether the first sequence is to be included or excluded for a subsequent image acquisition procedure having the first context based on the at least one metric.

Abstract: A device, system, and method determines when to select limited echocardiography exams. The method is performed at a workflow server. The method includes receiving an image order for a patient, the image order including information indicative of a reason to perform an image acquisition procedure. The method includes determining whether the patient has had a prior image acquisition procedure performed within a time threshold relative to the image order. The method includes when the prior image acquisition procedure had been performed within the time threshold, determining whether the reason requires a number of views within a view threshold. The method includes when the number of views is within the view threshold, generating an indication that the image acquisition Communications procedure is to be performed with a limited capacity.

Abstract: A patient monitor (8) includes a display (10). Patient values (38) are obtained for one or more known variables of a risk prediction function (30). One or more unknown variables of the risk prediction function are determined, and at least one hyperplane (40) is defined as values assumable by the one or more unknown variables. Values of the risk prediction function are computed over the at least one hyperplane using the obtained patient values. A visualization template is selected from a database of visualization templates (50) using template selection indices including the risk prediction function and the one or more unknown variables. Using the visualization template, a visualization (52) of the computed values of the risk prediction function over the at least one hyperplane is displayed.

Abstract: A device and method integrates a lesion stager in a reporting tool. The method includes receiving a user input. The user input is image data including at least one variable value. The variable value is indicative of a variable parameter of the image data. The method includes associating each of the at least one variable value with a corresponding variable parameter to generate at least one variable-value pair. The method includes determining a computed output value as a function of select ones of the at least one variable-value pair.

Abstract: The present disclosure pertains to computer-assisted search of image slices. In some embodiments, an image slice having a detected finding (and representing a cross section of at least a portion of an individual) may be determined. A search space may be reduce to a subset of image slices respectively representing a cross section corresponding to the cross section represented by the determined image slice. The search space reduction may comprise filtering a set of image slices based on the cross section represented by the determined image slice. Image slice information related to the determined image slice and related one or more image slices (of the image slices subset) may be obtained based on the search space reduction. A determination of whether indications of the detected finding exist in the image slices (of the image slices subset) may be effectuated based on the image slice information.

Abstract: A system (100) includes an end point prediction engine (150) that predicts an end point (302) using a machine learning model (132) and one or more clinical report objects (152) for a patient, wherein the machine learning model inputs the one or more clinical report objects and outputs the predicted end point according to phrases or n-grams in the one or more clinical report objects. An end point visualization interface (160) visualizes the predicted end point (302) using a scorecard (162) or a timeline (164). An end point modeling engine (130) generates the machine learning model from training data that includes validated end points (122) and clinical report objects (116).

Abstract: Phenotype analysis systems and methods. The methods described herein may involve receiving a phenotype query related to at least one phenotype of a patient. A processor executing instructions stored on memory may then select at least one of a first phenotype engine provided by a first entity and a second phenotype engine provided by a second entity to execute the received query on medical data associated with the patient. An interface may then output a result in response to the received phenotype query from at least one of the first and second phenotype engines.

Abstract: A system (100) includes an echocardiogram measurement tool (150) that determines a subset (152) of measurements from a list of echocardiogram measurements according to a view of an ultrasound imaging sequence (110) and a mapping (154) between the view and the subset of measurements.

Abstract: A system (100) includes a parser engine (120), a context inferring engine (130), and an action selection engine (140). The parser engine (120) detects a recommendation in a received clinical report (112) for a patient. The context inferring engine (130) extracts and normalizes recommendation elements in the detected recommendation. The action selection engine (140) generates a patient actionable clinical report (142) based on the received clinical report and the extracted and normalized recommendation elements.

Abstract: An image interpretation workstation includes a display (12), user input devices (14, 16, 18), an electronic processor (20, 22), and a non-transitory storage medium storing instructions readable and executable by the electronic processor. An image interpretation environment (31) is implemented by instructions (30) which display medical images on the at least one display, manipulate the displayed medical images, generate finding objects, and construct of an image examination findings report (40). Finding object detection instructions (32) monitor the image interpretation environment to detect generation of a finding object or user selection of a finding object. Patient record retrieval instructions (34) identify and retrieve patient information relevant to a finding object (40) detected by the finding object detection instructions from at least one electronic patient record (24, 25, 26).