Abstract: Disclosed is a method of capturing an image of diagnostic test results. The method may include acquiring an image of a diagnostic test positioning card using an image capture device, determining the presence of a plurality of fiducial images on the test positioning card, determining the presence of a diagnostic test result carrier placed on the test positioning card and verifying positioning of the test result carrier on the test positioning card. An image of the test result carrier may then be captured. The method may also include acquiring an image of a test result carrier using an image capture device, determining image features in the image of the test result carrier and verifying positioning of the test result carrier relative to the image capture device using the image features. An image including the test result carrier may then be captured.

Abstract: A device may be configured to function as an image-based analyzer for one or more test kits. The device guides a user in capturing an image of a test kit within an appropriate window of time. The device analyzes the image of the test kit, recognizes one or more features of the test kit depicted in the image, obtains one or more results indicated by the one or more recognized features, and provides one or more of the one or more results. The device may be configured to provide some or all of the results. The results may be displayed visually on a display screen of the device, presented audibly via speaker of the device, or both.

Abstract: Example methods and systems train an end-to-end neural network machine to analyze images of lateral flow assay test strips by learning non-linear interactions among lighting variations, test strip reflections, bi-directional reflectance distribution functions, angles of imaging, response curves of smartphone cameras, or any suitable combination thereof. Such example methods and systems improve the limit of detection, the limit of quantification, and the coefficient of variation in the precision of quantitative test results, under ambient light settings.

Abstract: One or more devices may be configured to provide or otherwise facilitate contactless healthcare screening. A mobile app may be installed on a device of a patient, and the mobile app may cause the device to guide the patient through a healthcare screening, for example, including administration of a healthcare screening questionnaire, without risk of making physical contact with the healthcare worker. The same mobile app or another mobile app may be installed on a device of a healthcare worker, and the mobile app may cause the device to guide the healthcare worker in administering the healthcare screening to the patient, also without risk of making physical contact with the patient.

Abstract: A machine accesses a first image captured prior to initiation of a procedure, where the first image depicts a set of instruments, as well as a second image captured after initiation of the procedure, where the second image depicts a proper subset of the set of instruments depicted in the first image. From the first and second images, the machine may determine that an instrument among the set of instruments depicted in the first image is not depicted among the proper subset of the set of instruments in the second image, and then cause presentation of a notification that indicates the instrument not depicted in the second image is missing. Alternatively, or additionally, the machine may determine whether an instrument among the set of instruments was used in the procedure, and then cause presentation of a notification that indicates whether the instrument was used in the procedure.

Abstract: Source data is accessed for a content portion of digital content. The source data is usable to identify the content portion. The reference data is defined for the content portion by clustering the accessed source data. The reference data is usable to identify the content portion.