Abstract: In some embodiments, techniques for using machine learning to enable visible light pupilometry are provided. In some embodiments, a smartphone may be used to create a visible light video recording of a pupillary light reflex (PLR). A machine learning model may be used to detect a size of a pupil in the video recording over time, and the size over time may be presented to a clinician. In some embodiments, a system that includes a smartphone and a box that holds the smartphone in a predetermined relationship to a subject's face is provided. In some embodiments, a sequential convolutional neural network architecture is used. In some embodiments, a fully convolutional neural network architecture is used.

Abstract: In some embodiments, techniques for using machine learning to enable visible light pupilometry are provided. In some embodiments, a smartphone may be used to create a visible light video recording of a pupillary light reflex (PLR). A machine learning model may be used to detect a size of a pupil in the video recording over time, and the size over time may be presented to a clinician. In some embodiments, a system that includes a smartphone and a box that holds the smartphone in a predetermined relationship to a subject's face is provided. In some embodiments, a sequential convolutional neural network architecture is used. In some embodiments, a fully convolutional neural network architecture is used.

Abstract: In some embodiments, techniques for using machine learning to enable visible light pupilometry are provided. In some embodiments, a smartphone may be used to create a visible light video recording of a pupillary light reflex (PLR). A machine learning model may be used to detect a size of a pupil in the video recording over time, and the size over time may be presented to a clinician. In some embodiments, a system that includes a smartphone and a box that holds the smartphone in a predetermined relationship to a subject's face is provided. In some embodiments, a sequential convolutional neural network architecture is used. In some embodiments, a fully convolutional neural network architecture is used.

Abstract: In some embodiments, techniques for using machine learning to enable visible light pupilometry are provided. In some embodiments, a smartphone may be used to create a visible light video recording of a pupillary light reflex (PLR). A machine learning model may be used to detect a size of a pupil in the video recording over time, and the size over time may be presented to a clinician. In some embodiments, a system that includes a smartphone and a box that holds the smartphone in a predetermined relationship to a subject's face is provided. In some embodiments, a sequential convolutional neural network architecture is used. In some embodiments, a fully convolutional neural network architecture is used.

Abstract: Examples of systems and methods described herein may estimate the bilirubin level of an adult subject based on image data associated with a portion of the eye of the subject (e.g., a color of the sclera). Accessories are described which may facilitate bilirubin estimation, including sensor shields and calibration frames.

Abstract: Intraocular pressure measurement devices and techniques for use therewith are generally described. In some examples the pressure measurement device may include a sleeve comprising a lower end including a first opening and an upper end. In various examples, a fastener may be used to secure the upper end of the sleeve to a device comprising an image sensor. In some examples the intraocular pressure measurement devices may comprise an applanator having an optically clear body, a first end effective to protrude from the first opening of the sleeve, and a tip at the first end. In some examples, the applanator may be slidably retained within the sleeve such that when the sleeve is positioned vertically above an eye of a subject, the applanator may slide downward until the tip of the applanator protrudes from the first opening of the sleeve and rests upon a surface of the eye.