MACHINE-LEARNING GESTÜTZTE PIPELINE ZUR DIMENSIONIERUNG EINER INTRAOKULARLINSE
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..
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Patent| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Europäisches Patentamt - (2022) vom: 30. Nov. Zur Gesamtaufnahme - year:2022 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
BURWINKEL HENDRIK [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Anmerkungen: |
Source: www.epo.org (no modifications made), First posted: 2022-11-30, Last update posted on www.tib.eu: 2023-10-31, Last updated: 2023-11-03 |
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| Patentnummer: |
EP4093329 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
EPA016694244 |
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| 245 | 1 | 0 | |a MACHINE-LEARNING GESTÜTZTE PIPELINE ZUR DIMENSIONIERUNG EINER INTRAOKULARLINSE |
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| 520 | |a 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. | ||
| 650 | 4 | |a G02C: Spectacles; sunglasses or goggles insofar as they have the same features as spectacles; contact lenses | |
| 650 | 4 | |a G16H: Healthcare informatics, i.e. information and communication technology [ict] specially adapted for the handling or processing of medical or healthcare data | |
| 650 | 4 | |a A61F: Filters implantable into blood vessels; prostheses; devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents; orthopaedic, nursing or contraceptive devices; fomentation; treatment or protection of eyes or ears; bandages, dressings or absorbent pads; first-aid kits (dental prosthetics a61c) | |
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