Abstract: A computer-implemented method for interrogating a sample with a microscopy device is disclosed. The computer-implemented method comprises capturing, by a microscopy device, one or more images of a biological sample. The computer-implemented method also comprises inputting the one or more images into one or more machine learning models and identifying, in the one or more images of the biological sample, via the one or more machine learning models, a plurality of images of a cell type. The computer-implemented method further comprises selecting, by the one or more machine learning models, a subset of the plurality of images of the cell type for transmission.

Abstract: An example method for refining a diagnostic machine-learning model includes receiving, at a central computing device and from multiple point-of-care (POC) analyzers of a plurality of POC analyzers, respective signals indicative of user input approving use of the multiple POC analyzers as remote training nodes, sending an initial training model to the multiple POC analyzers, at least some of the multiple POC analyzers training the initial training model on respective remote data sets stored on the at least some of the multiple POC analyzers using respective processors of the at least some of the multiple POC analyzers to form revised training models, receiving the revised training models from the at least some of the multiple POC analyzers, averaging parameters of the revised training models based on configurable criteria to form an adjusted training model, and sending the adjusted training model to the plurality of POC analyzers.

Abstract: An apparatus includes: a sample chamber having at least one depth dimension configured to allow an object of interest to move, where the object of interest has a size of at least 100 micrometers; an imaging device configured to capture images of the sample chamber, where the imaging device includes a single objective lens and has a resolving power of approximately 25 micrometers or larger than 25 micrometers; at least one processor; and at least one memory storing instructions. The instructions, when executed, cause the apparatus to: access at least one image, captured by the imaging device, of at least a portion of the sample chamber; processing the at least one image to provide an indication of one of: an object of interest, no object of interest, or a location in the sample chamber of a potential object of interest; and providing an output based on the indication.

Abstract: An example method includes capturing, via an x-ray machine, a plurality of x-ray images of a patient covering a number of different anatomy of the patient in any order, using a machine learning algorithm to process the plurality of x-ray images for identification of an anatomy in respective x-ray images of the plurality of x-ray images, associating a label with each of the plurality of x-ray images based on the identification of the anatomy, positioning each of the plurality of x-ray images upright based on a preset coordinate scheme for the anatomy, arranging the plurality of x-ray images into a predetermined order based on the species of the patient, and generating and outputting a data file including the plurality of x-ray images in the predetermined order, positioned upright, and labeled.

Abstract: Methods and systems for performing diagnosis using patient sample data are described. The method comprises receiving patient sample data at a point of care, analyzing the received patient sample data at the point of care, and evaluating a first result of analyzing the received patient sample data at the point of care to determine whether a criterion for advanced analysis at a central reference laboratory is met. In a case where the criterion for advanced analysis is met, the method further comprises reflexing the received patient sample data to the central reference laboratory for the advanced analysis, receiving, at the point of care, a second result of the advanced analysis from the central reference laboratory, and displaying at least one of the first result or the second result on a display at the point of care.

Abstract: An example method includes capturing, via an x-ray machine, a plurality of x-ray images of a patient covering a number of different anatomy of the patient in any order, using a machine learning algorithm to process the plurality of x-ray images for identification of an anatomy in respective x-ray images of the plurality of x-ray images, associating a label with each of the plurality of x-ray images based on the identification of the anatomy, positioning each of the plurality of x-ray images upright based on a preset coordinate scheme for the anatomy, arranging the plurality of x-ray images into a predetermined order based on the species of the patient, and generating and outputting a data file including the plurality of x-ray images in the predetermined order, positioned upright, and labeled.

Abstract: An example method includes capturing, via an x-ray machine, a plurality of x-ray images of a patient covering a number of different anatomy of the patient in any order, using a machine learning algorithm to process the plurality of x-ray images for identification of an anatomy in respective x-ray images of the plurality of x-ray images, associating a label with each of the plurality of x-ray images based on the identification of the anatomy, positioning each of the plurality of x-ray images upright based on a preset coordinate scheme for the anatomy, arranging the plurality of x-ray images into a predetermined order based on the species of the patient, and generating and outputting a data file including the plurality of x-ray images in the predetermined order, positioned upright, and labeled.

Abstract: An example method includes capturing, via an x-ray machine, a plurality of x-ray images of a patient covering a number of different anatomy of the patient in any order, using a machine learning algorithm to process the plurality of x-ray images for identification of an anatomy in respective x-ray images of the plurality of x-ray images, associating a label with each of the plurality of x-ray images based on the identification of the anatomy, positioning each of the plurality of x-ray images upright based on a preset coordinate scheme for the anatomy, arranging the plurality of x-ray images into a predetermined order based on the species of the patient, and generating and outputting a data file including the plurality of x-ray images in the predetermined order, positioned upright, and labeled.

Abstract: An example method includes capturing, via an x-ray machine, a plurality of x-ray images of a patient covering a number of different anatomy of the patient in any order, using a machine learning algorithm to process the plurality of x-ray images for identification of an anatomy in respective x-ray images of the plurality of x-ray images, associating a label with each of the plurality of x-ray images based on the identification of the anatomy, positioning each of the plurality of x-ray images upright based on a preset coordinate scheme for the anatomy, arranging the plurality of x-ray images into a predetermined order based on the species of the patient, and generating and outputting a data file including the plurality of x-ray images in the predetermined order, positioned upright, and labeled.