Samsung Life Public Welfare Foundation

Dong Hui Lim

Young Jun Kim

Yong Man Ro

Gyu Le Han

Hye Bin Lee

Jae Hyeong Chun

A system for and a method of analyzing a corneal lesion using an anterior segment image according to the present invention. The system includes: an image acquisition unit configured to acquire an anterior segment image from the eyeball of a subject, a feature extractor configured to extract feature information on a position and a cause of a lesion in the cornea from the anterior segment image by applying a convolution layer to the anterior segment image through machine learning on the basis of a database in which clinical information pre-acquired by analyzing positions and causes of lesions in the corneas of subjects is stored; and a result determination unit configured to identify a position of the cornea from the anterior segment image using the feature information and to analyze and determine the position and the cause of the lesion in the cornea from the position of the cornea.

System and method for analyzing corneal lesion using anterior ocular segment image, and computer-readable recording medium

Provided is a diagnosis system for anterior eye diseases. An image obtainer obtains an anterior eye image of an image obtainer configured to obtain an anterior eye image of a subject. A characteristic extractor extracts characteristic information of the obtained anterior eye image on the basis of a machine-learning model. A disease analyzer determines whether or not an anterior eye of the subject has a disease and classifying a disease class according to the extracted characteristic information.

Anterior eye disease diagnostic system and diagnostic method using same

Disclosed is a method of measuring a refractive error using a reflection image of a pupil for a visible ray, the method capable of diagnosing and self-examining a refractive error using only an image captured by a smartphone camera. The method may include obtaining an image by projecting a visible ray, using a flash, onto the retina of a subject and capturing, using a smartphone camera, a shape and location of the image reflected in the pupil of the subject, applying the image to a residual neural network (ResNet) model trained using data of an ImageNet based on clinical information into which result values of a refractive error of the subjects have been incorporated, and outputting the refractive error of the image and a measurement factor measured to determine the refractive error based on the shape and the location reflected in the pupil in a process of deriving the refractive error of the image.

Method for measuring anomalies of refraction using a reflection image of pupil in visible light

A vision assistance apparatus may include an image acquisition unit configured to acquire an image by capturing the scene of the front which a user watches, a sensor unit configured to acquire sensing information on objects located in front of the user, a control unit configured to analyze the image acquired by the image acquisition unit and generate a notification signal for the front scene through an analysis result of the image and the sensing information acquired by the sensor unit, and an output unit configured to provide the user with the notification signal generated by the control unit in the form of sound.

Growing community of inventors

Seoul, South Korea

Dong Hui Lim