Abstract: Systems and methods include an optimization-based load planning module including a data-reduction scheme for analyzers of bio-fluid samples. The optimization-based load planning module is executable on a computer server and is configured to optimize assay type assignments across a large number of analyzers based on one or more objectives, such as: load balancing, efficient reagent usage, reduced turn-around-time, reduced quality assurance costs, and/ or improved system robustness. The optimization-based load planning module uses a data-reduction scheme to generate a load plan comprising computer-executable instructions configured to cause a system controller of a diagnostic laboratory system to assign each of the requested test types to be performed over the planning period to one or more selected analyzers in accordance with the one or more preferences or priorities. Other aspects are also described.

Abstract: Methods of scheduling maintenance on a plurality of automated testing apparatus and possibly also on ancillary test processing apparatus are provided. The methods include inputting identification data on the plurality of automated testing apparatus to be maintained, inputting maintenance requirement data for the maintenance of each of the apparatus, inputting demand constraint data, operating on the identification data, maintenance requirement data, and demand constraint data using an optimization program, such as mixed integer linear programming (MILP), subject to demand constraints and at least one objective, and outputting an optimized maintenance schedule for a planning period. Systems and apparatus configured to carry out the methods are also provided, as are other aspects.

Abstract: Systems and methods include an optimization-based load planning module for laboratory analyzers of bio-fluid samples. The optimization-based load planning module is executable on a computer server and is configured to optimize assay (lab test) assignments across a large number of laboratory analyzers based on one or more of the following user selected and weighted objectives: reduced turn-around-time, load balancing, efficient reagent usage, lower quality assurance costs, and/or improved system robustness. The optimization-based load planning module outputs a load plan comprising computer executable instructions configured to cause a system controller of a laboratory analyzer system to schedule and direct each requested test to be performed at one or more selected laboratory analyzers of the laboratory analyzer system in accordance with the user selected and weighted objectives. Other aspects are also described.

Abstract: A method and system for deep learning based cardiac electrophysiological model personalization is disclosed. Electrophysiological measurements of a patient, such as an ECG trace, are received. A computational cardiac electrophysiology model is personalized by calculating patient-specific values for a parameter of the computational cardiac electrophysiology model based at least on the electrophysiological measurements of the patient using a trained deep neural network (DNN). The parameter of the computational cardiac electrophysiology model corresponds to a spatially varying electrical cardiac tissue property.

Abstract: For non-invasive EP mapping, a sparse number of electrodes (e.g., 10 in a typical 12-lead ECG exam setting) are used to generate an EP map without requiring preoperative 3D image data (e.g. MR or CT). An imager (e.g., a depth camera) captures the surface of the patient and may be used to localize electrodes in any positioning on the patient. Two-dimensional (2D) x-rays, which are commonly available, and the surface of the patient are used to segment the heart of the patient. The EP map is then generated from the surface, heart segmentation, and measurements from the electrodes.

Abstract: An optimization method of a diagnostic laboratory system. The method includes receiving, at a system controller, computer-readable data comprising an inventory of a plurality of analyzers included within the diagnostic laboratory system, and types of tests and numbers of the tests to be performed on samples by the diagnostic laboratory system over a planning period; and determining, via a reagent pack optimization module executing on the system controller, a reagent pack loading plan over the planning period. Diagnostic laboratory systems are disclosed, as are other aspects.

Abstract: Systems and methods are provided for classifying an abnormality in a medical image. An input medical image depicting a lesion is received. The lesion is localized in the input medical image using a trained localization network to generate a localization map. The lesion is classified based on the input medical image and the localization map using a trained classification network. The classification of the lesion is output. The trained localization network and the trained classification network are jointly trained.

Abstract: Systems and methods include an optimization-based load planning module for laboratory analyzers of bio-fluid samples. The optimization-based load planning module is executable on a computer server and is configured to optimize assay (lab test) assignments across a large number of laboratory analyzers based on one or more of the following user selected and weighted objectives: reduced turn-around-time, load balancing, efficient reagent usage, lower quality assurance costs, and/or improved system robustness. The optimization-based load planning module outputs a load plan comprising computer executable instructions configured to cause a system controller of a laboratory analyzer system to schedule and direct each requested test to be performed at one or more selected laboratory analyzers of the laboratory analyzer system in accordance with the user selected and weighted objectives. Other aspects are also described.

Abstract: Methods of scheduling maintenance on a plurality of automated testing apparatus and possibly also on ancillary test processing apparatus. The methods include inputting identification data on the plurality of automated testing apparatus to be maintained, inputting maintenance requirement data for the maintenance of each of the apparatus, inputting demand constraint data, operating on the identification data, maintenance requirement data, and demand constraint data using an optimization program, such as mixed integer linear programming (MILP), subject to demand constraints and at least one objective, and outputting an optimized maintenance schedule for a planning period. Systems and apparatus configured to carry out the methods are provided, as are other aspects.

Abstract: Systems and methods for automatically determining an assessment of a patient are provided. A patient is automatically interacted with, by a first trained machine learning based model, to acquire initial patient data. One or more risk factors associated with the patient are automatically determined, by a second trained machine learning based model, based on the received initial patient data. The patient is automatically interacted with, by the first trained machine learning based model, to acquire additional patient data based on the one or more determined risk factors. An assessment of the patient is automatically determined, by the second trained machine learning based model, based on the initial patient data and the additional patient data. The assessment of the patient is output.

Abstract: A scheduling system includes: a plurality of input devices configured to output medical data, a workforce storage, configured to store working characteristics of a plurality of doctors, and a scheduler configured to receive as input data related to the medical data and the working characteristics, and configured to provide as output a plurality of schedules for the plurality of doctors for analysing the medical data.

Abstract: Systems and methods for determining a schedule assigning individuals to shifts are provided. A plurality of constraints for scheduling individuals to shifts is received. The plurality of constraints comprise one or more hard constraints and one or more soft constraints. A schedule assigning the individuals to the shifts is determined that 1) satisfies the one or more hard constraints and 2) distributes deviations from the one or more soft constraints across the determined schedule. The determined schedule is output. In one embodiment, the individuals are nurses.

Abstract: Systems and methods are provided for classifying an abnormality in a medical image. An input medical image depicting a lesion is received. The lesion is localized in the input medical image using a trained localization network to generate a localization map. The lesion is classified based on the input medical image and the localization map using a trained classification network. The classification of the lesion is output. The trained localization network and the trained classification network are jointly trained.

Abstract: A computer-implemented method for decoding brain imaging data of individual subjects by using additional imaging data from other subjects includes receiving a plurality of functional Magnetic Resonance Imaging (fMRI) datasets corresponding to a plurality of subjects. Each fMRI dataset corresponds to a distinct subject and comprises brain activation patterns resulting from presentation of a plurality of stimuli to the distinct subject. A group dimensionality reduction (GDR) technique is applied to the example fMRI datasets to yield a low-dimensional space of response variables shared by the plurality of subjects. A model is trained to predict a set of target variables based on the low-dimensional space of response variables shared by all subjects, wherein the set of target variables comprise one or more characteristics of the plurality of stimuli.

Abstract: Methods and apparatus for cross-domain medical image analysis and cross-domain medical image synthesis using deep image-to-image networks and adversarial networks are disclosed. In a method for cross-domain medical image analysis a medical image of a patient from a first domain is received. The medical image is input to a first encoder of a cross-domain deep image-to-image network (DI2IN) that includes the first encoder for the first domain, a second encoder for a second domain, and a decoder. The first encoder converts the medical image to a feature map and the decoder generates an output image that provides a result of a medical image analysis task from the feature map.

Abstract: For non-invasive EP mapping, a sparse number of electrodes (e.g., 10 in a typical 12-lead ECG exam setting) are used to generate an EP map without requiring preoperative 3D image data (e.g. MR or CT). An imager (e.g., a depth camera) captures the surface of the patient and may be used to localize electrodes in any positioning on the patient. Two-dimensional (2D) x-rays, which are commonly available, and the surface of the patient are used to segment the heart of the patient. The EP map is then generated from the surface, heart segmentation, and measurements from the electrodes.

Abstract: Methods and apparatus for cross-domain medical image analysis and cross-domain medical image synthesis using deep image-to-image networks and adversarial networks are disclosed. In a method for cross-domain medical image analysis a medical image of a patient from a first domain is received. The medical image is input to a first encoder of a cross-domain deep image-to-image network (DI2IN) that includes the first encoder for the first domain, a second encoder for a second domain, and a decoder. The first encoder converts the medical image to a feature map and the decoder generates an output image that provides a result of a medical image analysis task from the feature map.

Abstract: A computer-implemented method for decoding patient characteristics and brain state from multi-modality brain imaging data includes receiving a plurality of brain imaging datasets comprising brain imaging data corresponding to plurality of subjects. The brain imaging datasets are aligned to a common reference space and quantitative measures are extracted from each brain imaging dataset. Non-imaging characteristics corresponding to each subject are received and a forward model is trained to map the plurality of characteristics to the quantitative measures.

Abstract: A computer-implemented method for decoding brain imaging data of individual subjects by using additional imaging data from other subjects includes receiving a plurality of functional Magnetic Resonance Imaging (fMRI) datasets corresponding to a plurality of subjects. Each fMRI dataset corresponds to a distinct subject and comprises brain activation patterns resulting from presentation of a plurality of stimuli to the distinct subject. A group dimensionality reduction (GDR) technique is applied to the example fMRI datasets to yield a low-dimensional space of response variables shared by the plurality of subjects. A model is trained to predict a set of target variables based on the low-dimensional space of response variables shared by all subjects, wherein the set of target variables comprise one or more characteristics of the plurality of stimuli.

Abstract: Systems and methods for high-throughput processing of assay plates include a calibration nanoparticle to facilitate automated focusing of the imaging system. An assay plate includes a base layer, a transparent layer in contact with the base layer, and at least one calibration nanoparticle having a pre-defined size immobilized on the assay plate surface. The assay plate surface can be functionalized to selectively bind to biological targets. The assay plate can be used in an imaging system for high-throughput autofocus and biological target detection.