Abstract: A computer-implemented method for increasing a training data volume for a machine learning system for determining an initial refractive power value for an intraocular lens to be inserted is described. The method includes measuring a group of ophthalmological biometry data of a patient and determining an initial refractive power value for the intraocular lens to be inserted by a trained machine learning system. The measured ophthalmological biometry data and a postoperative target refraction value are used as input data for the trained machine learning system. The method also includes measuring a postoperative refractive results value, assigning the postoperative refractive results value to the measured ophthalmological biometry data of the patient, and determining an importance indicator value for the new training data record.

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 computer implemented method for determining a refractive power value of an intraocular lens to be inserted is described. The method includes measuring ophthalmological patient data, receiving a target refraction value, determining a first refractive power value of an intraocular lens to be inserted, with the measured ophthalmological patient data and the target refraction value being used as input data, determining, by means of a trained machine learning system, a second refractive power value of the intraocular lens to be inserted, the measured ophthalmological patient data and the received target refraction value being used as input data for the trained machine learning system, and determining the final refractive power value of the intraocular lens to be inserted from the first refractive power value and the second refractive power value by means of an individual boosting factor value.

Abstract: A method for training a machine learning system with an extended set of patient data is described. This method includes measuring patient data and assigning ground truth data, determining the number of data pairs E/A, determining whether the number of data pairs lies below a previously defined training data threshold value, and if this is the case, carrying out the following steps: selecting a physical-optical model; using data pairs E/A in order to determine corresponding second output vectors A? from input vectors E by means of the relation function R, determining a respective difference vector, modifying the input vectors by an ?-vector; determining third output vectors of the relation function; determining modified output vectors; and training a machine learning system by means of the modified data and the original data.

Abstract: A computer-implemented method for training a machine learning system to determine an expected offset for a physical postoperative lens position of an intraocular lens to be inserted. The method includes determining a plurality of theoretical positions in the eye of different intraocular lenses to be inserted, the determination including a respective use of a relation and a respective lens-specific constant for the plurality of the theoretical postoperative positions.

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: 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: 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 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.