Siemens Healthcare Diagnostics Gmbh

Siemens Healthcare Gmbh

The Cleveland Clinic Foundation

Siemens Corporation

Lance Anthony Ladic

Ali Kamen

Bogdan Georgescu

Shanhui Sun

Noha Youssry El-Zehiry

Dorin Comaniciu

Leo John Grady

Gianluca Paladini

Maneesh Kumar Singh

Bin Lou

Mohamed Abazeed

John C Pearson

David Rapaport

Lawrence Tarbox

Sebastian Poelsterl

Nilesh Mistry

For decision support in a medical therapy, machine learning provides a machine-learned generator for generating a prediction of outcome for therapy personalized to a patient. The outcome prediction may be used to determine dose. To assist in decision support, a regression analysis of the cohort used for machine training relates the outcome from the machine-learned generator to the dose and an actual control time (e.g., time-to-event). The dose that minimizes side effects while minimizing risk of failure to a time for any given patient is determined from the outcome for that patient and a calibration from the regression analysis.

Decision support system for individualizing radiotherapy dose

A method of identifying an optimum treatment for a patient suffering from coronary artery disease, comprising: (i) providing patient information selected from: (a) status in the patient of one or more coronary disease associated biomarkers; (b) one or more items of medical history information selected from prior condition history, intervention history and medication history; (c) one or more items of diagnostic history, if the patient has a diagnostic history; and (d) one or more items of demographic data; (ii) aggregating the patient information in: (a) a Bayesian network; (b) a machine learning and neural network; (c) a rule-based system; and (d) a regression-based system; (iii) deriving a predicted probabilistic adverse event outcome for each intervention comprising percutaneous coronary intervention by placement of a bare metal stent, or a drug-coated stent; or by coronary artery bypass grafting; and (iv) determining the intervention having the lowest predicted probabilistic adverse outcome.

Method to identify optimum coronary artery disease treatment

A method for analyzing digital holographic microscopy (DHM) data for hematology applications includes receiving a DHM image acquired using a digital holographic microscopy system and identifying one or more erythrocytes in the DHM image. For each respective erythrocyte included in the one or more erythrocytes, a cell thickness value for the respective erythrocyte using a parametric model is estimated, and a cell volume value is calculated for the respective erythrocyte using the cell thickness value.

Analyzing digital holographic microscopy data for hematology applications

A method for analyzing digital holographic microscopy (DHM) data for hematology applications includes receiving a plurality of DHM images acquired using a digital holographic microscopy system. One or more connected components are identified in each of the plurality of DHM images and one or more training white blood cell images are generated from the one or more connected components. A classifier is trained to identify a plurality of white blood cell types using the one or more training white blood cell images. The classifier may be applied to a new white blood cell image to determine a plurality of probability values, each respective probability value corresponding to one of the plurality of white blood cell types. The new white blood cell image and the plurality of probability values may then be presented in a graphical user interface.

The present invention discloses a method and system for deploying software, e.g., software modules designed for acquisition and/or post-processing of images related to a particular imaging agent. The modules can be remotely accessed from a software server.

System and method for deployment of configuration and analysis software

An imaging system for detecting targets of interest (TOIs) in multispectral imaging data includes a memory device storing a plurality of instructions embodying the system for detecting TOIs, a processor for receiving the multispectral imaging data and executing the plurality of instructions to perform a method including determining a list of events collocated across images of the multispectral imaging data and labeling each event as one of a TOI or non-TOI.

Identification, classification and counting of targets of interest in multispectral image data

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Robbinsville, NJ, United States of America

Lance Anthony Ladic