Abstract: A method for detecting a penetration depth of riboflavin in a cornea includes acquiring an optical section of RGB colors and converting it into an image in a CIEL*a*b* color space, extracting a luminance component L* in the CIEL*a*b* color space, performing binarization and Blob analysis to obtain a non-corneal-region-free image; based on the non-corneal-region-free image and the luminance component L* image, extracting boundary points of anterior and posterior corneal surfaces and performing curve fitting, so as to obtain anterior and posterior corneal surface curves; performing image fusion on the anterior and posterior corneal surface curves into the CIEL*a*b* color space to obtain a cornea-only CIEL*a*b* color image; in the cornea-only CIEL*a*b* color image, locating a riboflavin-penetrating region and partitioning the riboflavin-penetrating region into riboflavin-penetrating sites, and performing curve fitting and noise reduction on the riboflavin-penetrating sites to obtain riboflavin the penetration de

Abstract: A method for early diagnosis of keratoconus based on multi-modal data considers a mutual relationship between both eyes using four refractive maps for corneas of the both eyes and absolute corneal elevation data, and combines the deep convolutional network method, the traditional support vector machine (SVM) method in machine learning, and the elevation map enhancement method with adjustable best-fit-sphere (BFS) to identify sensitivity and specificity of a focus and balance the sensitivity and specificity. With multi-dimensional comprehensive judgment of a keratoconus morbidity with a patient as a unit, combined with binocular data including both manual selection features and deep network learning from big data, the diagnosis method has higher robustness and accuracy.

Abstract: A method for early diagnosis of keratoconus based on multi-modal data considers a mutual relationship between both eyes using four refractive maps for corneas of the both eyes and absolute corneal elevation data, and combines the deep convolutional network method, the traditional support vector machine (SVM) method in machine learning, and the elevation map enhancement method with adjustable best-fit-sphere (BFS) to identify sensitivity and specificity of a focus and balance the sensitivity and specificity. With multi-dimensional comprehensive judgment of a keratoconus morbidity with a patient as a unit, combined with binocular data including both manual selection features and deep network learning from big data, the diagnosis method has higher robustness and accuracy.

Abstract: The present invention discloses a hand-held autonomous visual acuity measurement apparatus and a visual acuity measuring method. The invention comprises a corneal curvature measurement module comprising a first light source for emitting a first light beam and a first image collector for collecting an image formed from the first light beam after it has been reflected by cornea, the first image collector being movably provided in the corneal curvature measurement optical path of the corneal curvature measurement module, and the corneal curvature measurement module further comprises a first motor for driving the first image collector to move along the corneal curvature measurement optical path so that the first image collector moves to the imaging position.

Abstract: Disclosed is an optometry apparatus capable of measuring para-central defocus, comprising a diopter measurement module. The diopter measurement module comprises a first light source (1) emitting a first light beam. The optometry apparatus also comprises a scanning module for adjusting the position of a light spot formed on the retina by the first light beam. The scanning module comprises a first reflecting mirror (3) and a second reflecting mirror (5) rotatably positioned in sequence in the emergent optical path of the first light source (1). The rotation axis of the first reflecting mirror (3) and the rotation axis of the second reflecting mirror (5) form therebetween an angle greater than 0 degree and less than 180 degrees.

Abstract: The present invention discloses a hand-held autonomous visual acuity measurement apparatus and a visual acuity measuring method. The invention comprises a corneal curvature measurement module comprising a first light source for emitting a first light beam and a first image collector for collecting an image formed from the first light beam after it has been reflected by cornea, the first image collector being movably provided in the corneal curvature measurement optical path of the corneal curvature measurement module, and the corneal curvature measurement module further comprises a first motor for driving the first image collector to move along the corneal curvature measurement optical path so that the first image collector moves to the imaging position.

Abstract: Disclosed is an optometry apparatus capable of measuring para-central defocus, comprising a diopter measurement module. The diopter measurement module comprises a first light source (1) emitting a first light beam. The optometry apparatus also comprises a scanning module for adjusting the position of a light spot formed on the retina by the first light beam. The scanning module comprises a first reflecting mirror (3) and a second reflecting mirror (5) rotatably positioned in sequence in the emergent optical path of the first light source (1). The rotation axis of the first reflecting mirror (3) and the rotation axis of the second reflecting mirror (5) form therebetween an angle greater than 0 degree and less than 180 degrees.