General Electric Company

Ge Precision Healthcare LLC

Katelyn Rose Nye

Gireesha Chinthamani Rao

Pál Tegzes

Alec Joseph Baenen

Gopal Biligeri Avinash

John Michael Sabol

Ravi Soni

Teri Lynn Fischer

Khaled Salem Younis

Levente Torok

Dejun Wang

Nasir Ahmed Desai

Poonam Dalal

Yash N Shah

John Eric Tkaczyk

Christopher B Austin

Brian David Yanoff

Biju Jacob

Yun Zou

Hao Lai

Cheryl Ruth Jones

Tabb A Patz

Yaan Ge

Zoltan Kiss

Feng Chen

Lehel Ferenczi

Raghu Prasad

German Guillermo Vera Gonzalez

Dibyajyoti Pati

Zita Herczeg

Buer Qi

Huanzhong Li

Srikrishnan Viswanathan

Robert A Buchanan

Chen Liu

Beth Ann Heckel

Arun Kumar Chandrashekarappa

Kristine Gould

Prabhu Rajasekaran

Jingyi Yang

Hongxu Yang

Aizhen Zhou

Najib Akram

Balazs Peter Cziria

Srikrishnan V

Eric Hart

Adam Pluim

Pulak Goswami

Dennis Wei Zhou

Manu Ramesh

Keval Harishabhai Jagatiya

Scott Schubert

Aishwarya Seth

Ariel Seitz Kniss

Jessica Tsai Wen

Benjamin James Beeman

Display screen or portion thereof with a computer generated graphical user interface for placement of a medical tube or line

An artificial intelligence (AI) lead marker detection system is employed either as a component of the X-ray imaging system or separately from the X-ray imaging system to scan post-exposure X-ray images to detect and insert various lead markers, to digitize information provided by the type and location of the lead marker, and to employ the marker information in different X-ray system workflow automations. The marker information obtained by the AI lead marker detection system can also provide useful data for use in downstream clinical and quality applications apart from the X-ray system, such as either AI or non-AI analytical applications.

X-ray lead marker detection system for x-ray imaging system

An image processing system is provided. The image processing system includes a display, a processor, and a memory. The memory stores processor-executable code that when executed by the processor causes receiving images of a region of interest of a patient with an enteric tube or line disposed within the region of interest, detecting the medical tube or line within the image, generating a combined image by combining the received images, and superimposing graphical markers on the combined image that indicate placement or misplacement of the enteric tube or line, and displaying the combined image on a display. In further aspects, a classification of the enteric tube or line (e.g., correctly placed tube present, malpositioned tube present, and so forth) and a detected positional change in the placement of the enteric tube or line may be determined and communicated to one or more clinicians. Additionally, the outputs of the image processing system may also be provided to facilitate triage of patients, helping prioritize which tube placements require further attention and in what order.

System and method for visualizing placement of a medical tube or line

An image processing system is provided. The image processing system includes a display, a processor, and a memory. The memory stores processor-executable code that when executed by the processor causes receiving an image of a region of interest of a patient with a medical tube or line disposed within the region of interest, detecting the medical tube or line within the image, generating a combined image by superimposing a first graphical marker on the image that indicates an end of the medical tube or line, and displaying the combined image on the display.

The current disclosure provides methods and systems for rapidly and consistently determining medical image quality metrics following acquisition of a diagnostic medical image. In one embodiment, the current disclosure teaches a method for determining an image quality metric by, acquiring a medical image of an anatomical region, mapping the medical image to a positional attribute of an anatomical feature using a trained deep neural network, determining an image quality metric based on the positional attribute of the anatomical feature, determining if the image quality metric satisfies an image quality criterion, and displaying the medical image, the image quality metric, and a status of the image quality criterion via a display device. In this way, a diagnostic scanning procedure may be expedited by providing technicians with real-time insight into quantitative image quality metrics.

System and methods for medical image quality assessment using deep neural networks

Methods and systems are provided for tuning a static model with multiple operating points to adjust model performance without retraining the model or triggering a new regulatory clearance. In one embodiment, a method comprises, responsive to a request to tune a model, obtaining a tuning dataset including a set of medical images, executing the model using the set of medical images as input to generate model tuning output, and determining, for each operating point of a set of operating points, a set of tuning metric values based on the tuning dataset and the model tuning output relative to each operating point. An operating point from the set of operating points may be selected based on each set of tuning metric values and, upon a request to analyze a subsequent medical image, a representation of a finding output from the static model executed at the selected operating point.

Methods and systems for tuning a static model

An x-ray image laterality detection system is provided. The x-ray image laterality detection system includes a detection computing device. The processor of the computing device is programmed to execute a neural network model for analyzing x-ray images, wherein the neural network model is trained with training x-ray images as inputs and observed laterality classes associated with the training x-ray images as outputs. The process is also programmed to receive an unclassified x-ray image, analyze the unclassified x-ray image using the neural network model, and assign a laterality class to the unclassified x-ray image. If the assigned laterality class is not target laterality, the processor is programmed to adjust the unclassified x-ray image to derive a corrected x-ray image having the target laterality and output the corrected x-ray image. If the assigned laterality class is the target laterality, the processor is programmed to output the unclassified x-ray image.

Systems and methods for detecting laterality of a medical image

Apparatus, systems, and methods to deliver point of care alerts for radiological findings are disclosed. An example imaging apparatus includes an image data store, an image quality checker, and a training learning network. The example image data store is to store image data acquired using the imaging apparatus. The example image quality checker is to evaluate image data from the image data store in comparison to an image quality measure. The example trained learning network is to process the image data to identify a clinical finding in the image data, the identification of a clinical finding to trigger a notification at the imaging apparatus to notify a healthcare practitioner regarding the clinical finding and prompt a responsive action with respect to a patient associated with the image data.

Systems and methods to deliver point of care alerts for radiological findings

An x-ray image orientation detection and correction system including a detection and correction computing device is provided. The processor of the computing device is programmed to execute a neural network model that is trained with training x-ray images as inputs and observed x-ray images as outputs. The observed x-ray images are the training x-ray images adjusted to have a reference orientation. The processor is further programmed to receive an unclassified x-ray image, analyze the unclassified x-ray image using the neural network model, and assign an orientation class to the unclassified x-ray image. If the assigned orientation class is not the reference orientation, the processor is programmed to adjust an orientation of the unclassified x-ray image using the neural network model, and output a corrected x-ray image. If the assigned orientation class is the reference orientation, the processor is programmed to output the unclassified x-ray image.

Systems and methods for detecting and correcting orientation of a medical image

Apparatus, systems, and methods to capture and combine patient vitals and image data are disclosed. An example apparatus includes an imaging device to capture imaging data of a patient; a patient monitor to capture non-imaging data of the patient; and a communication interface between the imaging device and the patient monitor to route the non-imaging data to the imaging device. The example imaging device is to combine the non-imaging data with the imaging data to form a combined data set to be processed to determine a clinical outcome.

Methods and apparatus to capture patient vitals in real time during an imaging procedure

Various methods and systems are provided for x-ray imaging. In one embodiment, a method for an image pasting examination comprises acquiring, via an optical camera and/or depth camera, image data of a subject, controlling an x-ray source and an x-ray detector according to the image data to acquire a plurality of x-ray images of the subject, and stitching the plurality of x-ray images into a single x-ray image. In this way, optimal exposure techniques may be used for individual acquisitions in an image pasting examination such that the optimal dose is utilized, stitching quality is improved, and registration failures are avoided.

Method and systems for anatomy/view classification in x-ray imaging

Apparatus, systems, and methods to improve imaging quality control, image processing, identification of findings, and generation of notification at or near a point of care are disclosed and described. An example imaging apparatus includes a processor to at least: process the first image data using a trained learning network to generate a first analysis of the first image data; identify a clinical finding in the first image data based on the first analysis; compare the first analysis to a second analysis, the second analysis generated from second image data obtained in a second image acquisition; and, when comparing identifies a change between the first analysis and the second analysis, generate a notification at the imaging apparatus regarding the clinical finding to trigger a responsive action.

Apparatus, systems, and methods to improve imaging quality control, image processing, identification of findings in image data, and generation of notification at or near a point of care for a patient are disclosed and described. An example imaging apparatus includes a memory including chest image data and instructions and a processor. The example processor is to execute the instructions to at least: process the chest image data using a trained learning network in real time after acquisition of the chest image data to identify a pneumothorax in the chest image data; receive feedback regarding the identification of the pneumothorax; and, when the feedback confirms the identification of the pneumothorax, trigger a notification at the imaging apparatus to notify a healthcare practitioner regarding the pneumothorax and prompt a responsive action with respect to a patient associated with the chest image data.

Growing community of inventors

Waukesha, WI, United States of America

Katelyn Rose Nye