Koninklijke Philips Corporation N.v.

Sarif Kumar Naik

Ravindra Balasaheb Patil

Meru Adagouda Patil

Vidya Ravi

Subhendu Seth

Sunil Kumar

Pallavi Vajinepalli

Shankar Mosur Venkatesan

Vipin Gupta

Lu Wang

Payal Keswarpu

Sanjay Jayavanth

Sampad Kumar Mohanty

Keswarpu Payal

Rose Ramasamy

Karthick Raja

Shraddha Barodia

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

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

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

The invention provides a system () for acquiring cervical images which comprises an image acquisition subsystem () for acquiring cervical images () of a cervical region of a patient during a colposcopy procedure, and a display subsystem () for displaying the cervical images on a display () by providing image data () of the cervical images to the display. According to the invention, the image acquisition subsystem () is arranged for, when operating in an interval mode, acquiring the cervical images () at predetermined time intervals to obtain a time-series of cervical images showing changes in the cervical region over time, and reporting a progress of said acquiring to the display subsystem. Moreover, the display subsystem () is arranged for establishing a progress indicator (-) on the display by generating indicator data () and providing the indicator data to the display (), the progress indicator providing visual feedback on the progress of said acquiring to a user. Accordingly, the user is provided with visual feedback on a status of the acquiring of the cervical images. In particular, the progress indicator avoids that the user experiences a loss of control or confusion.

Acquiring cervical images

A method or system detects abnormalities in a biological sample cells. The method or system performs at least the following steps, or is capable to carry out at least the following steps, respectively: a) acquiring an image of the sample by digital image acquisition, b) optionally, carrying out digital image processing, c) selecting a field of view, d) determining, by digital image processing, whether or not, in the field of view, cell aggregates exist, and e1) selecting a new field of view and carrying on with step c) if, in step d), it turns out that, in the field of view, the determination of cell aggregates is negative, or e2) carrying out further process steps if in step d), it turns out that, in the field of view, the determination of cell aggregates is affirmative.

System and method for the detection of abnormalities in a biological sample

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Bengaluru, India

Sarif Kumar Naik