In an e-commerce risk screening system (900), transaction information is applied to multiple fraud risk models (915, 916, 918, 920) that produce raw scores, which are transformed with sigmoidal transform functions (910) to produce optimized likelihood of fraud risk estimates. Such estimates are combined using fusion proportions, producing a single point risk estimate, which is transformed with a sigmoidal function (910) to produce an optimized single point risk estimate.

Method and apparatus for evaluating fraud risk in an electronic commerce transaction

Systems and methods are described for obtaining and acting upon information indicative of circulatory health and related phenomena in human beings or other subjects.

Circulatory monitoring systems and methods

A method and apparatus for three-dimensional imaging and treatment of a patient's body. The method and apparatus utilize a system (10) for developing a therapy plan for treatment of an organ of the patient, a device for generating ultrasound image data from a treatment region and a device for providing a translucent volume image of a portion of a patient's body and a separate translucent image of the patient organ and a three-dimensional viewing device to superimpose a translucent article image to enable viewing of the article image simultaneously with the patient organ, and a portion of the patient's body.

Real time brachytherapy spatial registration and visualization system

A method and apparatus for three-dimensional imaging and treatment of a patient's body. The method and apparatus utilize a system for developing a therapy plan for treatment of an organ of the patient, a device for generating ultrasound image data from a treatment region and a device for providing a translucent volume image of a portion of a patient's body and a separate translucent image of the patient organ and a three dimensional viewing device to superimpose a translucent article image to enable viewing of the article image simultaneously with the patient organ and a portion of the patient's body.

Virtual reality 3D visualization for surgical procedures

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.

Systems and methods for treating, diagnosing and predicting the occurrence of a medical condition

A method for screening individuals at risk for prostate cancer progression is disclosed. The method is useful for evaluating cells from patients at risk for recurrence of prostate cancer following surgery for prostate cancer. Specifically, the method uses specific Markovian nuclear texture factors, alone or in combination with other biomarkers, to determine whether the cancer will progress or lose organ confinement. In addition, methods of predicting the development of fatal metastatic disease by statistical analysis of selected biomarkers is also disclosed. The invention also contemplates a method that uses a neural network to analyze and interpret cell morphology data. Utilizing Markovian factors and other biomarkers as parameters, the network is first trained with a sets of cell data from known progressors and known non-progressors. The trained network is then used to predict prostate cancer progression in patient samples.

Prediction of prostate cancer progression by analysis of selected predictive parameters

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Prediction of prostate cancer progression by analysis of selected predictive parameters