DR. ERIC A ELSTER, MD
Radiology at Wisconsin Ave, Bethesda, MD

2011028, Nov 24, 2011

Apr 8, 2011

13/083090

Alexander Stojadinovic - Chevy Chase MD, US
Eric Elster - Kensington MD, US
Doug K. Tadaki - Frederick MD, US
Trevor Brown - Washington DC, US
Thomas A. Davis - Oak Hill VA, US
Jonathan Forsberg - Kensington MD, US
Jason Hawksworth - Silver Spring MD, US

G06N 5/00, G06F 17/00

706 45

An embodiment of the invention provides a method for determining a patient-specific probability of disease. The method collects clinical parameters from a plurality of patients to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of the healing rate of an acute traumatic wound is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative planning. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of the healing rate of an acute traumatic wound.

2010018, Jul 22, 2010

Dec 9, 2009

12/634119

Eric A. Elster - Kensington MD, US
Doug K. Tadaki - Frederick MD, US
Nicole J. Crane - Silver Spring MD, US
Scott W. Huffman - Cullowhee NC, US
Ira W. Levin - Rockville MD, US

A61B 6/00, H04N 7/18

348 77, 600476, 348E07085

A system and method for real-time or near real-time monitoring of tissue/organ oxygenation through visual assessment of contrast enhanced images of the target area of tissue or organ. Video of a target tissue/organ was acquired during surgery, selected image frames were extracted. Each extracted image is separated into red, green and blue CCD responses. A modified contrast image was created by subtracting blue CCD responses from red CCD responses, and plotting the resultant image using a modified colormap. Overlaying said modified contrast image onto the original extracted image frame under a selected transparency range, and display it for review.

2011029, Dec 1, 2011

Oct 15, 2009

13/123406

Alexander Stojadinovic - Chevy Chase MD, US
Eric A. Elster - Kensington MD, US
Doug K. Tadaki - Frederick MD, US
Trevor S. Brown - Washington DC, US
Thomas A. Davis - Oak Hill VA, US
Jonathan Forsberg - Kensington MD, US
Jason Hawksworth - Silver Spring MD, US
Roslyn Mannon - Birmingham AL, US
Aviram Nissan - Aviram-Yehuda, IL

G06F 15/18

706 12

An embodiment of the invention provides a method for determining a patient-specific probability of disease. The method collects clinical parameters from a plurality of patients to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of disease is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative planning. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of disease.

2011028, Nov 24, 2011

Apr 8, 2011

13/083184

Alexander Stojadinovic - Chevy Chase MD, US
Eric A. Elster - Kensington MD, US
Doug K. Tadaki - Frederick MD, US
Trevor Brown - Washington DC, US
Thomas A. Davis - Oak Hill VA, US
Jonathan Forsberg - Kensington MD, US
Jason Hawksworth - Silver Spring MD, US
Roslyn Mannon - Birmingham AL, US

G06N 5/00, G06F 17/00

706 45

An embodiment of the invention provides a method for determining a patient-specific probability of transplant glomerulopathy. The method collects clinical parameters from a plurality of patients to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of transplant glomerulopathy is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative planning. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of transplant glomerulopathy.

2012007, Mar 29, 2012

Feb 4, 2011

13/021451

ERIC ELSTER - KENSINGTON MD, US
DOUG TADAKI - FREDERICK MD, US
JASON HAWKSWORTH - SILVER SPRING MD, US
THOMAS DAVIS - OAK HILL VA, US

A61K 39/395, A61P 29/00, A61P 43/00, A61K 31/137

4241331, 4241841, 4241731

A method for preventing inflammation, comprising administering to a subject a lymphocyte sequestrating or depletion agent before the onset of inflammation. A method for treating inflammation caused by an injury or an infection, comprising depleting immune lymphocyte of a subject by administering to said subject a lymphocyte sequestrating or depletion agent during or after said event. A method for preventing or treating abdominal adhesion comprising administering to a subject a lymphocyte sequestering or a lymphocyte depletion agent.