D.r. Systems, Inc.

International Business Machines Corporation

Merge Healthcare Solutions Inc.

Health Beacons, Inc.

Merative US L.p.

Merge Healthcare, Inc.

Uplift Sporting Goods, LLC

Merge Healthcare Soltuions Inc.

Dominator Radiology, Inc.

Stasis, LLC

Murray A Reicher

Evan K Fram

Steven M Greim

John J Schumacher

Marwan Sati

Robert John Petcavich

Michael W Ferro, Jr

Jon T DeVries

Carol G Sloyer

Cole A Genovese

James G Thompson

Howard T Lam

Thomas J Edwards

Aviad Zlotnick

Mark D Bronkalla

Christopher Brander

Michael Trambert

Kevin Joseph Derichs

Sun Young Park

Dustin Michael Sargent

Paul Dufort

Flora Gilboa-Solomon

Guy Amit

Dante Togliatti

Stewart Nickolas

David Boloker

Florent Saint-Clair

Christopher S Franklin

James H Thompson

Alon Hazan

Ayelet Akselrod-Ballin

Sivan Ravid

Dale Seegmiller

Wesley W Hilton

Jordan Levy

Saikiran Komatineni

Shivani Bhakta

Ravi Patel

Albert Hernandez

Puja Gupta

Tanish Jain

Silvia Mihaela Dragomir

Tarek El Rashidy

Christopher W Lawson

An apparatus for locating an embedded passive integrated transponder (PIT) tag is provided. An embodiment of the locating apparatus includes a resonator capable of electromagnetically coupling to the PIT tag, and a feedback circuit connected to the resonator and configured to monitor a load conductance of the resonator. A distance between the resonator and the PIT tag is indicated by a change in the monitored load conductance when the resonator and the PIT tag are electromagnetically coupled. Another embodiment includes a resonator capable of stimulating a response signal from a PIT tag, and a processing circuit capable of calculating the distance between the resonator and the PIT tag based on the amplitude of the response signal.

Method and apparatus for locating passive integrated transponder tags

A probe including a first sensor having a first magnetometer and a first accelerometer and a second sensor having a second magnetometer and a second accelerometer is configured for determining the distance and direction to a marker. The marker may be magnetic and may be surgically inserted into a patient's body to mark a specific location. The probe may be used to locate the marker, thus identifying the location. The probe may include a microprocessor that receives an output from the first sensor and an output from the second sensor and determines the distance and direction to the marker.

Probe for determining magnetic marker locations

A conversation management system provides a conversational computing interface that manages verbal exchanges between a computer (e.g., artificial intelligence) and a human operator. In a medical embodiment, conversational input from the computing system is generated based on medical knowledge, uses deep learning algorithms, and/or intelligently tracks the state of a conversation so as to the most relevant data to the user.

Database systems and interactive user interfaces for dynamic conversational interactions

A wearable, flexible, bio-sensing housing with physical feedback, with software running on a user's wirelessly connected smart device. The housing has embedded at least one of pressure, movement, temperature, velocity, and acceleration sensors, and at least one of a haptic, vibrational, audio, visual, kinesthetic, tactile, olfactory, thermal, vestibular, and somatosensory feedback mechanisms. The feedback being triggered by measurements from the sensors and predetermined parameters, as compared by the smart device's software and/or a connected server. With real-time measurements and real-time feedback directly to the user's body, the user can perform real-time adjustment of his activity to obtain optimal performance and/or health.

Physical balance training system using foot sensors, real-time feedback, artificial intelligence, and optionally other body sensors

A user input interface on a mobile device may employ a set of routines to control computing functions on a computer. Computing functions may be associated with elements of the user input interface, such that the elements may be used to control the associated computing functions. The associations of computing functions and user input interface elements may be stored in a database.

Adaptable user input interface

Systems and techniques are disclosed for improvement of machine learning systems based on enhanced training data. An example method includes providing a visual concurrent display of a set of images of features, the features requiring classification by a reviewing user. The user interface is provided to enable the reviewing user to assign classifications to the images, the user interface being configured to create, read, update, and/or delete classifications. The user interface is responsive to the user, with the user response indicating at least two images with a single classification. The user interface is updated to represent the single classification.

Systems and user interfaces for enhancement of data utilized in machine-learning based medical image review

Inference of appropriate anatomical region from inconsistent descriptions in order to provide fast and accurate prefetching is provided. In various embodiments, an anatomical region of a first medical imaging study is determined. A first plurality of keywords is determined corresponding to the anatomical region of the first medical imaging study. A plurality of studies is accessed having a patient in common with the first medical imaging study. A second plurality of keywords is extracted from the plurality of studies. Those of the plurality of studies having extracted keywords in common with the first plurality of keywords are selected. The selected studies are pre-fetched for display to a user.

Exam prefetching based on subject anatomy

Systems and techniques are disclosed for improvement of machine learning systems based on enhanced training data. An example method includes accessing a database storing associations between objects included in medical images and classifications of the objects. A risk assessment model adapted to determine a risk condition for an object is accessed, the assessment based on features of the object. Risk conditions associated with respective objects are determined based on the risk assessment model. A group of objects associated with a first risk condition is identified. An interactive user interface is generated for display, the user interface concurrently displaying images of the group of objects. The interactive user interface enables a user to select subsets of images to be concurrently assigned a user-selected classification. User selected classifications are provided to a machine learning system adapted to update the risk assessment model based on the classifications to increase accuracy of the model.

A method, computer system, and a computer program product for dynamically altering at least one image is provided. The present invention may include receiving a plurality of data, wherein the received plurality of data includes at least one existing medical image. The present invention may also include determining that one or more user instructions for the received existing image were received. The present invention may then include implementing the one or more user instructions on the received existing medical image. The present invention may also include altering the received existing medical image based on the one or more implemented user instructions and a medical knowledge base.

Accelerating human understanding of medical images by dynamic image alteration

Methods and systems for performing image analytics using graphical reporting associated with clinical images. One system includes at least one data source and a server. The server includes an electronic processor and an interface for communicating with the data source. The electronic processor is configured to receive training information from the at least one data source over the interface. The training information includes a plurality of images and graphical reporting associated with each of the plurality of images. Each graphical reporting includes a graphical marker designating a portion of one of the plurality of images and diagnostic information associated with the portion of the one of the plurality of images. The electronic processor is also configured to perform machine learning to develop a model using the training information. The model is used to automatically analyze an image.

Methods and systems for performing image analytics using graphical reporting associated with clinical images

A medical and/or clinical report includes one or more links to various external (and/or internal) data sources and/or systems that include information relevant to the medical report. In an embodiment, a medical report may be in a PDF format and include links to images associated with the exam, information regarding the patient, a scheduling application useful to schedule additional procedures for the patient, and/or any other information associated with the patient or exam. The medical report, including various links, may be generated based on information received from external medical data systems. For example, a medical report from an external system may be updated to include various links to systems and sources of data related to the medical report, as described herein.

Report links

Systems and methods for generating electronic reports for medical images. One system includes an electronic processor configured to access a plurality of prior reports associated with a user and automatically generate a mapping using machine learning based on the plurality of prior reports. The mapping associates language included in the plurality of prior reports with at least one section of a report. The system stores the mapping to a memory, receives input from the user for an electronic report associated with a medical image, accesses the mapping from the memory, automatically determines a section in the electronic report associated with the input based on the mapping, and automatically inserts text into the section in the electronic report based on the input.

Automatically associating user input with sections of an electronic report using machine learning

Annotations of medical images may be generated using one or more lexicons so that terminology is consistent across multiple exams, users, facilities, etc. Measurements of lesions may be provided using a bilinear measurement tool that allows easier bilinear measurements. Disease assessment models may be selected and applied as measurements are acquired in order to provide immediate determination of disease staging according to one or more selected assessment models.

Annotation and assessment of images

Inference of appropriate anatomical region from inconsistent descriptions in order to provide fast and accurate prefetching is provided. In various embodiments, a first plurality of user-configurable rules is read from a data store. Each rule maps to a user-configurable anatomical region. A plurality of studies is accessed from the image archive. Each of the plurality of studies has associated metadata. The plurality of rules is applied to the metadata associated with the plurality of studies to determine an anatomical region of each of the plurality of studies. Based on the anatomical regions of the plurality of studies and one or more additional rule, a subset of the plurality of studies is selected for display to a user on a display.

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Rancho Santa Fe, CA, United States of America

Murray A Reicher