Abstract: A compensation circuit, a touch screen, and an electronic device are provided in the disclosure. The compensation circuit includes a logic circuit, a selection circuit, and a switch circuit. The logic circuit is configured to obtain a first selection signal, a second selection signal, and a third selection signal according to an enable signal and a timing signal. The selection circuit is configured to obtain a first control signal according to the first selection signal, obtain a second control signal according to the second selection signal, and obtain a third control signal according to the third selection signal.

Abstract: A fitness motion recognition method and system, as well as an electronic device are disclosed. The fitness motion recognition method includes: collecting motion data and heart rate data of a human body during motion using a nine-axis inertial sensor and a heart rate sensor, respectively; calculating a resultant acceleration, a resultant angular velocity, and a roll angle of the nine-axis inertial sensor, as well as a real-time heart rate value using a motion recognition algorithm based on the motion data and heart rate data; and recognizing the fitness motion based on characteristics of the resultant acceleration, the resultant angular velocity and the roll angle of the nine-axis inertial sensor, and the real-time heart rate value.

Abstract: A non-stereo fundus image is used to obtain a plurality of glaucoma indicators. Additionally, genome data for the subject is used to obtain genetic marker data relating to one or more genes and/or SNPs associated with glaucoma. The glaucoma indicators and genetic marker data are input into an adaptive model operative to generate an output indicative of a risk of glaucoma in the subject. In combination, the genetic indicators and genome data are more informative about the risk of glaucoma than either of the two in isolation. The adaptive model may be a two-stage model, having a first stage in which individual genetic indicators are combined with respective portions of the genome data by first adaptive model modules to form respective first outputs, and a second stage in which the first outputs are combined by a second adaptive mode.

Abstract: A non-stereo fundus image is used to obtain a plurality of glaucoma indicators. Additionally, genome data for the subject is used to obtain genetic marker data relating to one or more genes and/or SNPs associated with glaucoma. The glaucoma indicators and genetic marker data are input into an adaptive model operative to generate an output indicative of a risk of glaucoma in the subject. In combination, the genetic indicators and genome data are more informative about the risk of glaucoma than either of the two in isolation. The adaptive model may be a two-stage model, having a first stage in which individual genetic indicators are combined with respective portions of the genome data by first adaptive model modules to form respective first outputs, and a second stage in which the first outputs are combined by a second adaptive mode.

Abstract: A non-stereo fundus image is used to obtain a plurality of glaucoma indicators. Additionally, genome data for the subject is used to obtain genetic marker data relating to one or more genes and/or SNPs associated with glaucoma. The glaucoma indicators and genetic marker data are input into an adaptive model operative to generate an output indicative of a risk of glaucoma in the subject. In combination, the genetic indicators and genome data are more informative about the risk of glaucoma than either of the two in isolation. The adaptive model may be a two-stage model, having a first stage in which individual genetic indicators are combined with respective portions of the genome data by first adaptive model modules to form respective first outputs, and a second stage in which the first outputs are combined by a second adaptive mode.

Abstract: A two-dimensional retinal fundus image of the retinal fundus of an eye is processed by optic disc segmentation (2) followed by cup segmentation 4. Data derived from the optic disc segmentation (i.e. the output of the disc segmentation (2) and/or data derived from the output of the optic disc segmentation step, e.g. by a smoothing operation 3) and data derived from the out-put of the optic cup segmentation (i.e. the output of the cup segmentation (4) and/or data derived from the output of the optic disc segmentation, e.g. by a smoothing operation 5) are fed (6) to an adaptive model which has been trained to generate from such inputs a value indicative of cup-to-disc ratio (CDR) of the eye. The CDR is indicative of glaucoma. Thus, the method can be used to screen patients for glaucoma.

Abstract: A method for determining the position of an optic cup boundary in a 2D retinal image. The method includes detecting kinks in blood vessels at an estimated boundary of the optic cup and the optic disc, and determining the position of the optic cup boundary in the 2D retinal image based on the detected kinks. The determined optic cup boundary may be used for determining a CDR which may in turn be used for determining a risk of contracting glaucomatous.

Abstract: A non-stereo fundus image is used to obtain a plurality of glaucoma indicators. Additionally, genome data for the subject is used to obtain genetic marker data relating to one or more genes and/or SNPs associated with glaucoma. The glaucoma indicators indicators and genetic marker data are input into an adaptive model operative to generate an output indicative of a risk of glaucoma in the subject. In combination, the genetic indicators and genome data are more informative about the risk of glaucoma than either of the two in isolation. The adaptive model may be a two-stage model, having a first stage in which individual genetic indicators are combined with respective portions of the genome data by first adaptive model modules to form respective first outputs, and a second stage in which the first outputs are combined by a second adaptive mode.

Abstract: A method for determining a grade of nuclear cataract in a test image. The method includes: (1a) defining a contour of a lens structure in the test image, the defined contour of the lens structure comprising a segment around a boundary of a nucleus of the lens structure; (1b) extracting features from the test image based on the defined contour of the lens structure in the test image; and (1c) determining the grade of nuclear cataract in the test image based on the extracted features and a grading model.

Abstract: A method for detecting disc haemorrhages in a retinal fundus image. The method includes (a) identifying a ring-shaped region of interest in the retinal fundus image encompassing the optic disc boundary; (b) removing blood vessel regions in the identified region of interest; (c) detecting candidate disc haemorrhages from the removed blood vessels regions in the identified region of interest; and (d) screening the candidate disc haemorrhages. The detected disc haemorrhages may be used to aid in the detection of glaucoma.

Abstract: A method for determining the position of an optic cup boundary in a 2D retinal image. The method includes detecting kinks in blood vessels at an estimated boundary of the optic cup and the optic disc, and determining the position of the optic cup boundary in the 2D retinal image based on the detected kinks. The determined optic cup boundary may be used for determining a CDR which may in turn be used for determining a risk of contracting glaucomatous.

Abstract: A two-dimensional retinal fundus image of the retinal fundus of an eye is processed by optic disc segmentation (2) followed by cup segmentation 4. Data derived from the optic disc segmentation (i.e. the output of the disc segmentation (2) and/or data derived from the output of the optic disc segmentation step, e.g. by a smoothing operation 3) and data derived from the out-put of the optic cup segmentation (i.e. the output of the cup segmentation (4) and/or data derived from the output of the optic disc segmentation, e.g. by a smoothing operation 5) are fed (6) to an adaptive model which has been trained to generate from such inputs a value indicative of cup-to-disc ratio (CDR) of the eye. The CDR is indicative of glaucoma. Thus, the method can be used to screen patients for glaucoma.

Abstract: A method performed by a computer system for detecting opacity in an image of the lens of an eye. The method includes detecting a region of interest in a picture of the lens, and processing the region of interest to produce a modified image using an algorithm which emphasizes opacity associated with a cortical cataract relative to opacity caused by other types of opacity, such as opacity caused by posterior sub-capsular cataracts (PSC). The modified image may be used for grading the level of cortical opacity, by measuring, in the modified image, the proportion of opacity in at least one area of the region of interest.