A system, comprising: memory; one or more processors; and computer-executable instructions stored in the memory and executable by the one or more processors to perform operations comprising: causing an eccentric radiation source (110, 206) to generate one or more beams (112a, 112b, 208a, 208b) of near infrared radiation that are directed onto a retina of a patient (102, 202); causing a radiation sensor (108, 212) to collect information indicative of reflected NIR radiation (112c, 208c) from the retina; identifying, based at least in part on the information, a pupil of the patient (102, 202); causing an image capture device to capture a plurality of images of the pupil; determining, based at least in part on the plurality of images, a plurality of pupil positions; determining, based at least on the plurality of images, one or more radiation intensity profiles associated with the reflected NIR radiation (112c, 208c) detected by the radiation sensor (108, 212), generating, based at least on the one or more radiation intensity profiles, a linear function fitted to the one or more radiation intensity profiles; determining, based at least on the linear function, a spherical error slope associated with the linear function; and determining a refractive error of the patient (102, 202) based at least on the plurality of pupil positions, the SE slope, and the one or more radiation intensity profiles, wherein the refractive error is determined by a neural network algorithm trained to identify correlations between one or more active radiation point sources of the eccentric radiation source and the one or more radiation intensity profiles to determine the refractive error for the patient (102, 202). memory; one or more processors; and computer-executable instructions stored in the memory and executable by the one or more processors to perform operations comprising: causing an eccentric radiation source (110, 206) to generate one or more beams (112a, 112b, 208a, 208b) of near infrared radiation that are directed onto a retina of a patient (102, 202); causing a radiation sensor (108, 212) to collect information indicative of reflected NIR radiation (112c, 208c) from the retina; identifying, based at least in part on the information, a pupil of the patient (102, 202); causing an image capture device to capture a plurality of images of the pupil; determining, based at least in part on the plurality of images, a plurality of pupil positions; determining, based at least on the plurality of images, one or more radiation intensity profiles associated with the reflected NIR radiation (112c, 208c) detected by the radiation sensor (108, 212), generating, based at least on the one or more radiation intensity profiles, a linear function fitted to the one or more radiation intensity profiles; determining, based at least on the linear function, a spherical error slope associated with the linear function; and determining a refractive error of the patient (102, 202) based at least on the plurality of pupil positions, the SE slope, and the one or more radiation intensity profiles, wherein the refractive error is determined by a neural network algorithm trained to identify correlations between one or more active radiation point sources of the eccentric radiation source and the one or more radiation intensity profiles to determine the refractive error for the patient (102, 202). causing an eccentric radiation source (110, 206) to generate one or more beams (112a, 112b, 208a, 208b) of near infrared radiation that are directed onto a retina of a patient (102, 202); causing a radiation sensor (108, 212) to collect information indicative of reflected NIR radiation (112c, 208c) from the retina; identifying, based at least in part on the information, a pupil of the patient (102, 202); causing an image capture device to capture a plurality of images of the pupil; determining, based at least in part on the plurality of images, a plurality of pupil positions; determining, based at least on the plurality of images, one or more radiation intensity profiles associated with the reflected NIR radiation (112c, 208c) detected by the radiation sensor (108, 212), generating, based at least on the one or more radiation intensity profiles, a linear function fitted to the one or more radiation intensity profiles; determining, based at least on the linear function, a spherical error slope associated with the linear function; and determining a refractive error of the patient (102, 202) based at least on the plurality of pupil positions, the SE slope, and the one or more radiation intensity profiles, wherein the refractive error is determined by a neural network algorithm trained to identify correlations between one or more active radiation point sources of the eccentric radiation source and the one or more radiation intensity profiles to determine the refractive error for the patient (102, 202).