Koninklijke Philips Corporation N.v.

Ravindra Balasaheb Patil

Krishnamoorthy Palanisamy

Vikram Basawaraj Patil Okaly

Meru Adagouda Patil

Sarif Kumar Naik

Nagaraju Bussa

Rithesh Sreenivasan

Vidya Ravi

Rajendra Singh Sisodia

Oladimeji Feyisetan Farri

Shriram Sethuraman

Larry Nielsen

Sudipta Chaudhury

Shreya Anand

Shraddha Barodia

Sampad Kumar Mohanty

Rose Ramasamy

Karthick Raja

A method () of determining a layout for one or more medical devices () in a medical facility includes: obtaining images () of a plurality of locations of the medical facility; mapping the obtained images to an architectural layout of the medical facility; generating a three-dimensional (3D) model of the medical facility based on the mapping; determining a recommended location for the one or more medical devices using the 3D model; determining a recommended placement of the one or more medical devices in the recommended location based on the recommended location; and outputting an image () showing the determined recommended placement of the medical devices in the recommended location.

Artificial intelligence (AI)-based optimized solution for device localization in medical facility set-up

A device for optimizing an image acquisition device () includes at least one electronic processor () operatively connected to read a machine log () of the image acquisition device. A non-transitory computer readable medium () stores instructions readable and executable by the at least one electronic processor to perform an image acquisition method (). The method includes: extracting logged parameters of the image acquisition device from the machine log of the image acquisition device; identifying one or more out-of-range parameters of the image acquisition device from the logged parameters extracted from the machine log of the image acquisition device; automatically tuning one or more electrical or mechanical settings of the image acquisition device on the basis of the one or more out-of-range parameters to transform the image acquisition device into a tuned image acquisition device; and controlling the tuned image acquisition device to acquire one or more images of a patient.

Medical imaging systems and methods with auto-correction of image quality-based on the log analysis of medical devices

The invention discloses an apparatus for stroke characterization. The apparatus comprises a processor. The processor is configured to receive image data representing a three-dimensional image of a head of a subject; identify a region within the image data corresponding to bone in the head of the subject; apply an adjustment to the image data to compensate for effects resulting from radiation scattered from the bone during acquisition of the image data; generate a two-dimensional image based on the adjusted image data; and provide the generated two-dimensional image as an input to a predictive model to identify a region of interest for stroke characterization in the two-dimensional image.

Image processing for stroke characterization

A medical imaging system () includes a processor and memory with instructions executable by the processor to receive () three-dimensional medical image data () comprising multiple slices, receive () an imaging modality () of the three-dimensional medical image data, receive () an anatomical view classification () of the three-dimensional medical image data, select () a chosen abnormality detection module () from a set of abnormality detection modules () using the imaging modality and the anatomical view classification, wherein at least a portion of the abnormality detection modules is a convolution neural network trained for identifying if the at least a portion of the multiple slices as either normal or abnormal, classify () the at least a portion of the multiple slices as normal or abnormal using the abnormality detection module, and choose () a set of selected slices () from the multiple slices according to a predetermined selection criteria () if a predetermined number of the multiple slices are classified as abnormal.

Automatic slice selection in medical imaging

In a monitoring method for generating maintenance alerts, component IDs are extracted which identify medical imaging device components in electronic medical imaging device manuals (). Operating parameters of the medical imaging device components and associated operating parameter ranges are also extracted from the manuals, based on numeric values, parameter terms identifying operating parameters, and linking terms or symbols indicative of equality or inequality that connect the numeric values and parameter terms. The operating parameter ranges are formulated into decision rules () which are applied to log data () generated by a monitored medical imaging device () to detect out-of-range log data generated by the monitored medical imaging device. Maintenance alerts () are displayed on a display () in response to the detected out-of-range log data. The maintenance alerts are generated from out-of-range log data and are associated with component IDs contained in the out-of-range log data.

Self-correcting method for annotation of data pool using feedback mechanism

The invention relates to a system, an ultrasound probe and a corresponding method for measuring arterial parameters using non-imaging ultrasound. The system comprises an acquisition unit for acquiring doppler ultrasound signal from a blood vessel and a processing unit for processing the acquired doppler ultrasound signal and to determine the changes in the blood vessel through the measurements of at least Peak Systolic Velocity (PSV) and Pulse Wave Velocity (PWV). The acquisition unit comprises an ultrasound probe having a plurality of transducer elements arranged in a grid configuration, and comprising a first probe () and a second probe () detachably connected to each other. In the split configuration the ultrasound probe is provided to measure the PWV globally between the carotid and femoral arteries, or the PSV and PWV locally and simultaneously. In the integrated configuration the PSV or PWV may be measured locally.

Ultrasound probe, system and method for measuring arterial parameters using non-imaging ultrasound

A non-transitory computer readable medium () stores instructions executable by at least one electronic processor () to perform a component co-replacement recommendation method (). The method includes: identifying components of a medical device by analyzing a technical document () related to the medical device; identifying component symbols () representing the components in drawings of the technical document; extracting relationships between the components of the medical device based on graphical connections () between the component symbols in the drawings of the technical document; generating a component connections graph () representing the relationships between the components of the medical device, the graph including nodes () corresponding to the components and connections () between the components; receiving an identification of a component to be replaced; and determining a co-replacement recommendation () for the component to be replaced based on the component connections graph.

System and method to predict parts dependencies for replacement based on the heterogenous subsystem analysis

A device, system, and method is provided for detecting pain in a cardiac-related region of the body and determining whether that pain is cardiac or non-cardiac. The device, system, and method may include calculating or determining a first feature based on a variation in activity level and a variation in the detected heartrate measurement and a second feature based on a variation in the detected ECG features and a first feature and then subjecting at least the first feature and the second feature to a cardiac pain classifier to determine a cardiac classification.

Device, system, and method for detecting and determining cardiac and non-cardiac pain

Embodiments of present disclosure disclose method and system for generating a medical image based on a textual data in a medical report. For generation, a textual data from each of one or more medical reports of the patient is retrieved. The textual data comprises one or more medical events and corresponding one or more attributes associated with each of the one or more medical reports. Further, a matching score for each of plurality of reference images is computed based on the textual data, using a first machine learning model. Upon computing the matching score, one or more images are selected from the plurality of reference images based on the matching score associated with each of the plurality of reference images. The medical image for the patient is generated based on the one or more images and the textual data using a second machine learning model.

Method and system for generating medical image based on textual data in medical report

A central signal segregation station () employs a signal acquisition controller () and a signal segregation controller (). In operation, the signal acquisition controller () receives a plurality of different types of physiological signals from a plurality of unknown physiological sensors (). For a monitoring of the physiological signals, the signal segregation controller () identifies a particular type of each physiological signal based on distinct signal features of each physiological signal corresponding to a different physiological signal model () among a plurality of physiological signal models () derived from known types of physiological sensors. For analyzing the physiological signals, the station () may further employ a signal analyzing controller () executing signal quality processing of the physiological signals, providing signal-specific feedback to any physiological sensor(s) communicating low quality physiological signal(s), annotating specific regions of each physiological signal having maximum diagnostic information and/or performing a confirmatory diagnosis of the physiological signals.

Central signal segregation system

The present invention relates to a method of automatic labeling of activity of a subject on ECG data. The method of the invention comprises acquiring at least one physiological input signal purporting to an ECG signal and processing thereof. The processing of the at least one physiological input signal comprises conditioning the ECG signal and processing thereof, wherein the processing comprises obtaining respiration data from the ECG signal, identifying the activity pertaining to the said ECG data based on at least a signal specific feature of the said ECG signal, wherein the respiration data are used for differentiating activities performed by the subject, and labeling the said ECG data with the said activity, automatically. The present invention also relates to a system for automatic labeling of activity on ECG data in accordance with the method of the invention.

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Bengaluru, India

Ravindra Balasaheb Patil