Systems and methods automatically locate optical surfaces of an eye and automatically generate surface models of the optical surfaces. A method includes OCT scanning of an eye. Returning portions of a sample beam are processed to locate a point on the optical surface and first locations on the optical surface within a first radial distance of the point. A first surface model of the optical surface is generated based on the location of the point and the first locations. Returning portions of the sample beam are processed so as to detect second locations on the optical surface beyond the first radial distance and within a second radial distance from the point. A second surface model of the optical surface is generated based on the location of the point on the optical surface and the first and second locations on the optical surface.

Provided are an apparatus and a computer-readable storage medium having stored therein a program that can determine keratoconus with a simple configuration so as to allow the apparatus and the program to be distributed widely and contribute to early diagnosis of keratoconus. In a keratometer as a keratoconus determination apparatus, a prediction model for keratoconus is stored in a memory. The prediction model is a logistic regression model in which three parameters that are a steep meridian refractive power, a flat meridian refractive power, and a value indicating whether or not a subject eye has a with-the-rule astigmatism, are independent variables, and a probability of keratoconus is a dependent variable. A control unit substitutes the three parameter values into the prediction model, to obtain a probability of keratoconus. When the probability is greater than a cutoff value, the subject eye is determined to be suspected of having keratoconus.

Provided are an apparatus and a computer-readable storage medium having stored therein a program that can determine keratoconus with a simple configuration so as to allow the apparatus and the program to be distributed widely and contribute to early diagnosis of keratoconus. In a keratometer as a keratoconus determination apparatus, a prediction model for keratoconus is stored in a memory. The prediction model is a logistic regression model in which three parameters that are a steep meridian refractive power, a flat meridian refractive power, and a value indicating whether or not a subject eye has a with-the-rule astigmatism, are independent variables, and a probability of keratoconus is a dependent variable. A control unit substitutes the three parameter values into the prediction model, to obtain a probability of keratoconus. When the probability is greater than a cutoff value, the subject eye is determined to be suspected of having keratoconus.

An ophthalmic apparatus of an aspect example includes an image acquiring unit, a corneal shape estimating processor, and a first image correcting processor. The image acquiring unit is configured to acquire an anterior segment image constructed based on data collected from an anterior segment of a subject's eye by optical coherence tomography (OCT) scanning. The anterior segment image includes a missing part corresponding to a part of a cornea. The corneal shape estimating processor is configured to estimate a shape of the missing part of a cornea image by analyzing the anterior segment image acquired by the image acquiring unit. The first image correcting processor is configured to correct distortion of the anterior segment image based at least on the shape of the missing part estimated by the corneal shape estimating processor.

An ophthalmic apparatus of an aspect example includes an image acquiring unit, a corneal shape estimating processor, and a first image correcting processor. The image acquiring unit is configured to acquire an anterior segment image constructed based on data collected from an anterior segment of a subject's eye by optical coherence tomography (OCT) scanning. The anterior segment image includes a missing part corresponding to a part of a cornea. The corneal shape estimating processor is configured to estimate a shape of the missing part of a cornea image by analyzing the anterior segment image acquired by the image acquiring unit. The first image correcting processor is configured to correct distortion of the anterior segment image based at least on the shape of the missing part estimated by the corneal shape estimating processor.

Methods and systems for predicting the post-operative effective lens position (ELP) of a prosthetic intraoccular lens (IOL) in cataract surgery using novel pre-operative measurement(s) of the natural lens curvatures.

In a corneal measurement system, an optical element focuses an excitation light to an area of corneal tissue at a selected depth. In response, a fluorescing agent applied to the cornea generates a fluorescence emission. An aperture of a pinhole structure selectively transmits the fluorescence emission from the area of corneal tissue at the selected depth. A detector captures the selected fluorescence emission transmitted by the aperture and communicates information relating to a measurement of the selected fluorescence emission captured by the detector. A controller receives the information from the detector and determines a measurement of the fluorescing agent in the area of corneal tissue at the selected depth. The system may include a scan mechanism that causes the optical element to scan the cornea at a plurality of depths, and the controller may determine a measurement of the fluorescing agent in the cornea as a function of depth.

In a corneal measurement system, an optical element focuses an excitation light to an area of corneal tissue at a selected depth. In response, a fluorescing agent applied to the cornea generates a fluorescence emission. An aperture of a pinhole structure selectively transmits the fluorescence emission from the area of corneal tissue at the selected depth. A detector captures the selected fluorescence emission transmitted by the aperture and communicates information relating to a measurement of the selected fluorescence emission captured by the detector. A controller receives the information from the detector and determines a measurement of the fluorescing agent in the area of corneal tissue at the selected depth. The system may include a scan mechanism that causes the optical element to scan the cornea at a plurality of depths, and the controller may determine a measurement of the fluorescing agent in the cornea as a function of depth.

In an ophthalmologic apparatus, including: an objective lens that faces a subject's eye; an illumination optical system that irradiates a cornea of the subject's eye with illumination light through the objective lens; and a corneal measurement optical system including an interference image capturing camera that takes an image of a corneal reflection light, which is a reflection of the illumination light reflected from the cornea, through the objective lens, a numerical aperture G of the illumination optical system is larger than a numerical aperture g of the corneal measurement optical system.

In an ophthalmologic apparatus, including: an objective lens that faces a subject's eye; an illumination optical system that irradiates a cornea of the subject's eye with illumination light through the objective lens; and a corneal measurement optical system including an interference image capturing camera that takes an image of a corneal reflection light, which is a reflection of the illumination light reflected from the cornea, through the objective lens, a numerical aperture G of the illumination optical system is larger than a numerical aperture g of the corneal measurement optical system.