Koninklijke Philips Corporation N.v.

Michael Chun-Chieh Lee

Eric Cohen-Solal

Julien Senegas

Lilla Boroczky

Stefanie Remmele

Robert Manzke

Lalitha Agnihotri

Sebastian Peter Michael Dries

Luyin Zhao

Alain C Borczuk

Steven Kawut

Charles Andrew Powell

Aleksandra Popovic

Raymond C Chan

Jens Von Berg

Yuechen Qian

Bharat Ramachandran

Axel Saalbach

Haytham Elhawary

Ying Wu

Yinhui Deng

Qi Zhong Lin

Matthew J Walker

A robotic control system employs a robot unit () and a control unit (). The robot unit () includes an endoscope () for generating an endoscope image (), and a robot () for moving the endoscope () within an anatomical region. The control unit () includes an endoscope controller () for a determination of an endoscope pose within the anatomical region for an intraoperative visualization of the anatomical feature within the endoscope image (), wherein the endoscope path is derived from a delineation of a volume coordinate position of the anatomical feature within a pre-operative image () of the anatomical region. The control unit () further includes a robot controller () for commanding the robot () to move the endoscope () to the endoscope pose within the anatomical region to visualize the anatomical feature within the endoscope image ().

Robotic control of an endoscope from anatomical features

A system, method and non-transitory computer-readable storage medium for monitoring motion during medical imaging. The monitoring of the motion includes initiating an acquisition of image data, measuring physiological signals of a patient, generating a prediction signal by integrating the physiological signals, determining whether patient motion is likely to occur based on the prediction signal and modifying the acquisition of image data, if it is predicted that patient motion is likely to occur.

Neurophysiological monitoring for prospective motion gating in radiological imaging

Methods are herein provided for decision support in diagnosis of a disease in a subject, and for extracting features from a multi-slice data set. Systems for computer-aided diagnosis are provided. The systems take as input a plurality of medical data and produces as output a diagnosis based upon this data. The inputs may consist of a combination of image data and clinical data. Diagnosis is performed through feature selection and the use of one or more classifier algorithms.

Advanced computer-aided diagnosis of lung nodules

A training or rating system includes a shape sensing enabled device () and a database () of possible shapes and sequences of shapes for the shape sensing enabled device. The possible shapes and sequences of shapes include a collection of poses derived by appropriately performing a procedure with the shape sensing enabled device. A comparison module () is configured to compare real-time poses of the shape sensing enabled device with the collection of poses in the database to output comparison feedback for a user of the shape sensing enabled device.

Clinical decision support and training system using device shape sensing

A method includes obtaining, in electronic format, an image () including a medical image display region () and an information display region (). The at least one of the medical image display region or the information display region includes graphical indicia representing at least one of an annotation () or alphanumeric information (). The method further includes evaluating pixels of the image to identify pixels including the graphical indicia representing an annotation or alphanumeric information of interest in the image. The method further includes extracting the annotation or alphanumeric information of interest from the identified graphical indicia from the image. The method further includes inserting the extracted annotation or alphanumeric information of interest in an electronically formatted clinical report for the image.

Extraction of information from an image and inclusion thereof in a clinical report

A method includes identifying medical concepts in identified patient cases that are missing from medical concepts in first electronically formatted medical information as missing medical concepts, and selecting an imaging protocol for an imaging procedure based on a combination of the medical concepts from the first electronically formatted medical information and the missing medical concepts, and generating a signal indicative of the selected imaging protocol. A method includes identifying at least one of one or more medical concepts as a relevant additional concept, and selecting an imaging protocol for the imaging procedure based on a combination of one or more clinical indications and the relevant additional concept, and generating a signal indicative of the selected imaging protocol.

Identification of medical concepts for imaging protocol selection

When evaluating patient cases to determine complexity thereof, a computer-aided stratification technique is applied to analyze historical patient case diagnoses and correctness thereof in order to calculate a stratification score () for each of a plurality of abnormality types and/or anatomical locations. When a new patient case is received, the computer-aided stratification technique is applied to evaluate the patient case in view of historical data and assign a stratification score thereto. A ranked list () of current patient cases can be generated according to stratification scores, and physician workload can be adjusted as a function thereof so that workload is balanced across physicians and/or according to physician experience level.

Method and system for computer-aided patient stratification based on case difficulty

A medical selection systemfor generating selection data includes a user inputfor enabling a user to establish a selection of one or more medical images amongst a plurality of medical imagesfor establishing the one or more medical images as baseline images for use in a follow-up examination of a patient. A processorgenerate selection databeing indicative of said selection, and includes selection metadata in the selection data for enabling associating the selection data with the plurality of medical images. An outputprovides the selection data to a medical processing systemfor enabling the medical processing system to select, based on the selection data, the one or more medical images amongst the plurality of medical images for use as the baseline images in the follow-up examination of the patient.

Medical selection system

A computing device () includes a recommender () that evaluates at least one of a user interaction with a displayed image of a scan of an imaging examination protocol or information about the scan in an electronically formatted radiology report, and generates a signal including a recommendation to remove the scan only in response to at least one of the user interaction or the radiology report information satisfying predetermined criteria and a output device () that visually presents the signal, thereby visually presenting the recommendation.

Imaging examination protocol update recommender

A system includes a modeler that generates a model which models a quality of findings in radiologist reports as a function of deposited dose of scans from which the radiologist reports are created and a dose optimizer that determines an optimal dose value for a planned scan based on the model and one or more optimization rules. A method includes generating a model which models a quality of findings in radiologist reports as a function of deposited dose of scans from which the radiologist reports are created and determining an optimal dose value tar a planned scan based on the model and one or more optimization rules.

Dose optimization based on outcome quality

A method includes obtaining image data for a patient. The image data corresponds to acquisition data from an imaging acquisition from a set of planned image acquisitions in an examination plan for the patient. The method further includes analyzing the image data with a processor based on an imaging practice guideline and producing electronically formatted data indicative of the analysis. The processor generates a signal indicative of a recommendation of a change to the examination plan based on the data indicative of the analysis.

Image acquisition and/or image related parameter recommender

A method includes obtaining electronically formatted information about previously performed imaging procedures, classifying the information into groups of protocols based on initially selected protocols for the previously performed imaging procedures and generating data indicative thereof, identifying deviations between the classified information and the corresponding initially selected protocols for the previously performed imaging procedures, and generating a signal indicative of the deviations. A method includes recommending at least one of a plurality of protocols for an imaging procedure based on at least one of a score, a probability, or a pre-determined rule, which is based on extracted medical concepts from patient information and extracted medical concepts from previously imaged patient information, and generating a signal indicative of the recommendation.

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Lexington, MA, United States of America

Michael Chun-Chieh Lee