Olivier H Saidi

US Patent:

7461048, Dec 2, 2008

Filed:

Oct 13, 2006

Appl. No.:

11/581052

Inventors:

Mikhail Teverovskiy - Harrison NY, US
David A. Verbel - New York NY, US
Olivier Saidi - Greenwich CT, US

Assignee:

Aureon Laboratories, Inc. - Yonkers NY

International Classification:

G06F 15/00
G06F 15/18

US Classification:

706 62, 706 21, 435 723

Abstract:

Methods and systems are provided that use clinical information, molecular information and computer-generated morphometric information in a predictive model for predicting the occurrence (e. g. , recurrence) of a medical condition, for example, cancer. In an embodiment, a model that predicts prostate cancer recurrence is provided, where the model is based on features including seminal vesicle involvement, surgical margin involvement, lymph node status, androgen receptor (AR) staining index of tumor, a morphometric measurement of epithelial nuclei, and at least one morphometric measurement of stroma. In another embodiment, a model that predicts clinical failure post prostatectomy is provided, wherein the model is based on features including biopsy Gleason score, lymph node involvement, prostatectomy Gleason score, a morphometric measurement of epithelial cytoplasm, a morphometric measurement of epithelial nuclei, a morphometric measurement of stroma, and intensity of androgen receptor (AR) in racemase (AMACR)-positive epithelial cells.

US Patent:

7467119, Dec 16, 2008

Filed:

Mar 14, 2005

Appl. No.:

11/080360

Inventors:

Olivier Saidi - Greenwich CT, US
David A. Verbel - New York NY, US
Mikhail Teverovskiy - Harrison NY, US

Assignee:

Aureon Laboratories, Inc. - Yonkers NY

International Classification:

G06E 1/00
G06E 3/00
G06F 15/18
G06G 7/00

US Classification:

706 21, 600407

Abstract:

Methods and systems are provided that use clinical information, molecular information and computer-generated morphometric information in a predictive model for predicting the occurrence (e. g. , recurrence) of a medical condition, for example, cancer.

US Patent:

7483554, Jan 27, 2009

Filed:

Nov 17, 2004

Appl. No.:

10/991897

Inventors:

Angeliki Kotsianti - New York NY, US
Olivier Saidi - Greenwich CT, US
Mikhail Teverovskiy - Harrison NY, US

Assignee:

Aureon Laboratories, Inc. - Yonkers NY

International Classification:

G06K 9/00
C12N 15/07

US Classification:

382128, 382224, 435451

Abstract:

Embodiments of the present invention are directed to quantitative analysis of tissues enabling the measurement of objects and parameters of objects found in images of tissues including perimeter, area, and other metrics of such objects. Measurement results may be input into a relational database where they can be statistically analyzed and compared across studies. The measurement results may be used to create a pathological tissue map of a tissue image, to allow a pathologist to determine a pathological condition of the imaged tissue more quickly.

US Patent:

7505948, Mar 17, 2009

Filed:

Nov 17, 2004

Appl. No.:

10/991240

Inventors:

Olivier Saidi - Greenwich CT, US
David A. Verbel - New York NY, US

Assignee:

Aureon Laboratories, Inc. - Yonkers NY

International Classification:

G06E 1/00
G06E 3/00
G06G 7/00

US Classification:

706 14

Abstract:

A method of producing a model for use in predicting time to an event includes obtaining multi-dimensional, non-linear vectors of information indicative of status of multiple test subjects, at least one of the vectors being right-censored, lacking an indication of a time of occurrence of the event with respect to the corresponding test subject, and performing regression using the vectors of information to produce a kernel-based model to provide an output value related to a prediction of time to the event based upon at least some of the information contained in the vectors of information, where for each vector comprising right-censored data, a censored-data penalty function is used to affect the regression, the censored-data penalty function being different than a non-censored-data penalty function used for each vector comprising non-censored data.

US Patent:

7599893, Oct 6, 2009

Filed:

May 22, 2006

Appl. No.:

11/438789

Inventors:

Marina Sapir - Mamaroneck NY, US
Faisal M. Khan - New Rochelle NY, US
David A. Verbel - New York NY, US
Olivier Saidi - Greenwich CT, US

Assignee:

Aureon Laboratories, Inc. - Yonkers NY

International Classification:

G06F 15/18

US Classification:

706 12, 706 14, 706 20, 706 47, 382128, 382129, 382133, 382134, 600300, 600301

Abstract:

Methods and systems are provided for feature selection in machine learning, in which the features selected for inclusion in a prediction rule are selected based on statistical metric(s) of feature contribution and/or model fitness.

US Patent:

7702598, Apr 20, 2010

Filed:

Feb 21, 2008

Appl. No.:

12/009543

Inventors:

Olivier Saidi - Greenwich CT, US
David Verbel - New York NY, US
Lian Yan - Chester Springs PA, US

Assignee:

Aureon Laboratories, Inc. - Yonkers NY

International Classification:

G06N 3/08

US Classification:

706 21, 706 62

Abstract:

Embodiments of the present invention are directed to methods and systems for training a neural network having weighted connections for classification of data, as well as embodiments corresponding to the use of such a neural network for the classification of data, including, for example, prediction of an event (e. g. , disease). The method may include inputting input training data into the neural network, processing, by the neural network, the input training data to produce an output, determining an error between the output and a desired output corresponding to the input training data, rating the performance neural network using an objective function, wherein the objective function comprises a function C substantially in accordance with an approximation of the concordance index and adapting the weighted connections of the neural network based upon results of the objective function.

US Patent:

7761240, Jul 20, 2010

Filed:

Aug 9, 2005

Appl. No.:

11/200758

Inventors:

Olivier Saidi - Greenwich CT, US
Ali Tabesh - Tucson AZ, US
Mikhail Teverovskiy - Harrison NY, US

Assignee:

Aureon Laboratories, Inc. - Yonkers NY

International Classification:

G06F 19/00
G06K 9/00
G06K 9/20
G06K 9/36

US Classification:

702 19, 382128, 382282, 382286

Abstract:

Systems and methods are provided for automated diagnosis and grading of tissue images based on morphometric data extracted from the images by a computer. The morphometric data may include image-level morphometric data such as fractal dimension data, fractal code data, wavelet data, and/or color channel histogram data. The morphometric data may also include object-level morphometric data such as color, structural, and/or textural properties of segmented image objects (e. g. , stroma, nuclei, red blood cells, etc. ).

US Patent:

7933848, Apr 26, 2011

Filed:

Jan 30, 2009

Appl. No.:

12/322329

Inventors:

Olivier Saidi - Greenwich CT, US
David A. Verbel - New York NY, US

Assignee:

Aureon Laboratories, Inc. - Yonkers NY

International Classification:

G06E 1/00
G06E 3/00
G06G 7/00

US Classification:

706 14

Abstract:

A method of producing a model for use in predicting time to an event includes obtaining multi-dimensional, non-linear vectors of information indicative of status of multiple test subjects, at least one of the vectors being right-censored, lacking an indication of a time of occurrence of the event with respect to the corresponding test subject, and performing regression using the vectors of information to produce a kernel-based model to provide an output value related to a prediction of time to the event based upon at least some of the information contained in the vectors of information, where for each vector comprising right-censored data, a censored-data penalty function is used to affect the regression, the censored-data penalty function being different than a non-censored-data penalty function used for each vector comprising non-censored data.