Abstract: A computer-implemented method for determining the refractive power of an intraocular lens includes providing a physical model for determining refractive power and training a machine learning system with clinical ophthalmological training data and associated desired results to form a learning model for determining the refractive power. A loss function for training includes: a first component taking into account clinical ophthalmological training data and associated and desired results and a second component taking into account limitations of the physical model wherein a loss function component value is greater the further a predicted value of the refractive power during the training is from results of the physical model with the same clinical ophthalmological training data as input values. Moreover, the method includes providing ophthalmological data of a patient and predicting the refractive power of the intraocular lens to be used by means of the trained machine learning system.

Abstract: A method for operating a microscopy system includes irradiating a region segment of a first region by a light source with light at a first wavelength ?1 and a first luminous intensity L1, determining a substance-specific parameter within the region segment as a response to being irradiated by the light source, and repeating the steps for all region segments within the first region. In addition, the disclosure relates to a microscopy system, and a calibration method for a microscopy system.

Abstract: A treatment apparatus for a cataract treatment of an eye includes a modular intraocular lens including a first part, which includes a haptic configured to contact a capsular bag of the eye in a region of the equator of the capsular bag and thereby to span the capsular bag, and a second part, which includes an optic body, and has a convergence state, in which the second part contacts the first part, and a spaced-apart state, in which the second part is arranged spaced apart from the first part, a measurement system configured to determine, based on a first measurement, a position of the first part in the eye and in the spaced-apart state of the modular intraocular lens, and a controller configured to determine, based on the position of the first part, a refractive power of the optic body which is to be inserted into the eye.

Abstract: The invention relates to a computer-implemented method for a machine learning-supported processing pipeline for determining parameter values for an intraocular lens to be inserted. The method comprises providing a scan result of an eye. The scan result is an image of an anatomical structure of the eye. The method further comprises determining biometric data of the eye from the scan results of an eye and using a first, trained machine learning system for determining a final position of an intraocular lens to be inserted, ophthalmological data being used as input data for the first machine learning system. The method further comprises determining a first optical power of the intraocular lens to be inserted, which is based on a physical model in which the determined final position of the intraocular lens and the determined biometric data are used as input variables for the physical model.

Abstract: The invention relates to a computer-implemented method and a corresponding system for a machine-learning-supported determining of refractive power for a measure for correcting the eyesight of a patient. The method involves providing a scan result of an eye, wherein the scan result represents an image of an anatomical structure of the eye. The method also involves supplying the scan result as input data to a first machine-learning system in the form of a convolutional neural network, and using output values of the first machine-learning system as input data for a second machine-learning system in the form of a multi-layer perceptron, and a target refraction value for the second machine-learning system is used as an additional input value for the second machine-learning system. Finally, the method involves determining parameters for the measure for correcting the eyesight of a patient via an immediate and direct cooperation of the first machine-learning system and the second machine-learning system.

Abstract: A computer-implemented method for determining the refractive power of an intraocular lens to be inserted is presented. The method includes generating first training data for a machine learning system on the basis of a first physical model for a refractive power for an intraocular lens and training the machine learning system by means of the first training data generated, for the purposes of forming a first learning model for determining the refractive power. Furthermore, the method includes training the machine learning system, which was trained using the first training data, using clinical ophthalmological training data for forming a second learning model for determining the refractive power and providing ophthalmological data of a patient and an expected position of the intraocular lens to be inserted. Moreover, the method includes predicting the refractive power of the intraocular lens to be inserted by means of the trained machine learning system and the second learning model.

Abstract: The invention relates to an arrangement 10 with an OCT device 20 for scanning an object region volume 22 arranged in an object region 18 with an OCT scanning beam 21, comprising an item 24 which has a section 84 that is arrangeable in the object region 18 and is localizable in the object region volume 22 by means of the OCT device 20, and having a computing unit 60 connected to the OCT device 20 and having a computer program for determining a 3-D reconstruction of the object region volume 22 and determining the location of the portion of the object 24 in the object region volume 22 by processing OCT scan information obtained with the OCT device 20 by scanning the object region volume 22. According to the invention, the computer program has a calculation routine for determining a target area in the 3-D reconstruction of the object region volume 22, which routine determines a reference variable for the object 24 for the target area.

Abstract: The invention relates to an assembly (10) comprising an OCT device (20) for scanning an object region volume (22) arranged in an object region (18) using an OCT scanning beam (21), an object (24) with a section, which can be arranged in the object region (18) and which can be located in the object region volume (22) by means of the OCT device (20), in the object region volume (22), and a calculating unit (60) which is connected to the OCT device (20) and contains a computer program for ascertaining a 3D reconstruction of the object region volume (22) and for ascertaining the position of the section of the object (24) in the object region volume (22) by processing OCT scanning information obtained by scanning the object region volume (22) using the OCT device (20).

Abstract: The invention relates to a computer-assisted method for position determination for an intraocular lens supported by machine learning. The method comprises providing a scan result for an eye. The scan result here represents an image of an anatomical structure of the eye. The method further comprises use of a trained machine learning system for the direct determination of a final location of an intraocular lens to be fitted, wherein digital data of the scan of the eye is used as the input data for the machine learning system.

Abstract: A computer-implemented method for recognizing deviations from plan parameters during an ophthalmological operation is described, the method including: providing video sequences of cataract operations, the video sequences having been recorded by means of an image recording apparatus, training a machine learning system using the video sequences provided and also, in each case, a planned refractive power of an intraocular lens to be inserted during a cataract operation and a target refraction value following the cataract operation as training input data and associated prediction results in the form of an actual refraction value following the cataract operation to form a machine learning model for predicting the actual refraction value following the cataract operation, and persistently storing parameter values of the trained machine learning system.

Abstract: Provided is to a system with an interface for providing visualization data for visualizing at least one section of a patient's eye, comprising an OCT device for capturing OCT scanning data by scanning the section of the patient's eye by means of an OCT scanning beam and comprising a computer unit for processing the OCT scanning data into the visualization data within the scope of an image rectification algorithm, which is designed to output the visualization data at the interface.

Abstract: An eye surgery surgical system includes a visualization device for visualizing the position of at least one trocar point for a trocar on the sclera of a patient's eye. The trocar serves to introduce a surgical instrument configured for surgical interventions on the fundus of the patient's eye into the patient's eye. The eye surgery surgical system also includes a computer unit which is configured to provide the position of the at least one trocar point to the visualization device. Here, the computer unit contains a trocar point computation routine configured to calculate the position of the at least one trocar point from the location of at least one surgical site in a model of the patient's eye and from geometric data relating to at least one surgical instrument introducible into the patient's eye through the trocar.

Abstract: A method of determining at least one selection parameter for selecting an intraocular lens to be inserted into an eye; the method comprises reading, by a data processing system, data indicative of an axial position of at least a portion of an anterior surface of an at least partially empty capsular bag of the eye, relative to an optical axis of the eye. The method further comprises determining an axial position parameter, which is representative of the axial position of the portion of the anterior surface, depending on the data. The method further comprises determining the at least one selection parameter for selecting the intraocular lens depending on the determined position parameter.

Abstract: A system for performing a surgical procedure includes an OCT unit for scanning an object region volume with an OCT scanning beam and a control unit for setting the position of the object region volume. A surgical instrument having an effective section is arrangeable in an object region of the object region volume and is localizable in the object region volume with the OCT unit. A computer unit has a first computer program for determining the position of the effective section by processing scan information obtained by scanning the object region volume and a second computer program compares the scan information to reference data and provides a set value for the position of the effective section. The second computer program determines deviation information as to the spatial deviation of the effective section from the set value.

Abstract: A method of determining at least one selection parameter for selecting an intraocular lens to be inserted into an eye; the method comprises reading, by a data processing system, data indicative of an axial position of at least a portion of an anterior surface of an at least partially empty capsular bag of the eye, relative to an optical axis of the eye. The method further comprises determining an axial position parameter, which is representative of the axial position of the portion of the anterior surface, depending on the data. The method further comprises determining the at least one selection parameter for selecting the intraocular lens depending on the determined position parameter.

Abstract: A method of determining at least one selection parameter for selecting an intraocular lens to be inserted into an eye; the method comprises reading, by a data processing system, data indicative of an axial position of at least a portion of an anterior surface of an at least partially empty capsular bag of the eye, relative to an optical axis of the eye. The method further comprises determining an axial position parameter, which is representative of the axial position of the portion of the anterior surface, depending on the data. The method further comprises determining the at least one selection parameter for selecting the intraocular lens depending on the determined position parameter.

Abstract: The invention relates to an optical system for examining an eye by means of optical coherence tomography. The OCT system is designed in such a way that at least a first and a second state of the optical system can be selectively set by controlling the variable optical unit. In the first state, the OCT measurement beam has a measurement focus at an object distance from the objective, wherein the object distance has a value between 50 millimeters and 400 millimeters. In the second state, the measurement beam has defocusing at the same object distance, wherein the defocusing corresponds to a distance of a virtual or real focus from a position of the object distance that is greater than 100 millimeters.

Abstract: An optical filter system for florescence observation comprises an illumination light filter (I) and an observation light filter (O). The observation light filter has plural transmitting regions (D1O,D2O) allowing fluorescent light to traverse the observation light filter. Blocking regions (S0O,S1O) separate the transmitting regions. The illumination light filter has transmitting regions (D0I,D1I) where the observation light filter has a corresponding blocking region. The illumination light filter has blocking regions (S1I,S2I) where the observation light filter has a corresponding transmitting region. The plural transmitting regions of the illumination light filter hallow for an improved color impression in the normal light observation.

Abstract: A surgical microscope for imaging structures of an eye includes: a front optical unit, an illumination device which has an illumination-radiation-emitting illumination source and which illuminates the retina of the eye with an illumination spot via an illumination beam path which extends through the front optical unit, a camera and an adjustable camera optical unit disposed upstream thereof, an imaging beam path which extends through the front optical unit and the camera optical unit, and a control device which controls the camera optical unit and sets the latter in such a way that the retina of the eye in the region of the illumination spot is imaged on the camera. The control device varies a focusing state of the camera optical unit and, as a result thereof, records a plurality of images of the retina in the region of the illumination spot, the images being focused in different depth planes, and establishes a refractive value of the eye from these images.

Abstract: The invention relates to an optical system for examining an eye by means of optical coherence tomography. The OCT system is designed in such a way that at least a first and a second state of the optical system can be selectively set by controlling the variable optical unit. In the first state, the OCT measurement beam has a measurement focus at an object distance from the objective, wherein the object distance has a value between 50 millimeters and 400 millimeters. In the second state, the measurement beam has defocussing at the same object distance, wherein the defocussing corresponds to a distance of a virtual or real focus from a position of the object distance that is greater than 100 millimeters.