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 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 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: 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: The invention is directed to an intraocular lens having an optical body, two haptic elements and first and second sets of a plurality of ropes corresponding to respective ones of the first and second haptic elements. The ropes are secured to the optical body and to the haptic element and have a severing sequence. The haptic elements each have a compressed state, a partly compressed state and an uncompressed state. In the compressed state, the first rope of the severing sequence is configured to deform the haptic element in a direction toward the optical body as a result of which the first rope is under a tensile stress and the rest of the ropes are stress-free and, by the ropes being severed successively in the severing sequence, the haptic element can be brought firstly to the partly compressed state. All the ropes are severed in the uncompressed state.

Abstract: The invention is directed to an intraocular lens having an optical body, two haptic elements and first and second sets of a plurality of ropes corresponding to respective ones of the first and second haptic elements. The ropes are secured to the optical body and to the haptic element and have a severing sequence. The haptic elements each have a compressed state, a partly compressed state and an uncompressed state. In the compressed state, the first rope of the severing sequence is configured to deform the haptic element in a direction toward the optical body as a result of which the first rope is under a tensile stress and the rest of the ropes are stress-free and, by the ropes being severed successively in the severing sequence, the haptic element can be brought firstly to the partly compressed state. All the ropes are severed in the uncompressed state.