Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Macroscopic imaging data, such as CT, MR, PET, or SPECT, is obtained. Microscopic imaging data of at least a portion of the same tissue is obtained. The microscopic imaging data is spatially aligned with the macroscopic imaging data. The spatial alignment allows calculation and/or imaging using both types of data as a multi-resolution data set. A given image may include information about the relative position of the microscopically imaged tissue to the macroscopically imaged body portion. This positional relationship may allow viewing of affects or changes at cellular levels as well as less detailed tissue structure or organism levels and may allow determination of any correlation between changes in both levels.

Abstract: 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.