Intraocular lens diopter calculation system based on machine learning

Abstract

The invention relates to an intraocular lens diopter calculation system based on machine learning. The intraocular lens diopter calculation system comprises a prediction model, an input module, a calculation module, an additional module and an output module. The calculation module is used for acquiring a diopter number of an intraocular lens to be implanted (IOL) by taking target equivalent spherical power as an ideal value and taking preoperative information of a cataract patient as input on the basis of a prediction model; the additional module is used for providing a plurality of differentsimulated intraocular lens diopters to the calculation module, and the calculation module generates postoperative optometry equivalent spherical lenses corresponding to the different simulated intraocular lens diopters. The artificial intelligence active learning data characteristics are fully utilized, the error calculation capacity is autonomously optimized, the diopter of the intraocular lens needing to be implanted in cataract surgery is accurately calculated, eyeball biological parameters of all dimensions are matched, and the eyeball prediction accuracy of the extreme eyeball biologicalparameters is higher.

Description

technical field [0001] The invention relates to the field of artificial intelligence medical data analysis, in particular to a system for calculating the diopter of intraocular lens based on machine learning, which can realize the calculation of the intraocular lens implanted by the patient through the input preoperative information of cataract patients and information about the intraocular lens to be implanted Diopter and postoperative spherical equivalent. Background technique [0002] Cataract surgery has changed from vision restoration surgery to refractive surgery. Accurately calculating the diopter of intraocular lenses suitable for cataract patients and accurately predicting the postoperative refractive status is an extremely important step. In 2019, a multi-center large-sample study showed that 17.9% of patients had prediction errors exceeding 0.5D. The influence on the diopter of the intraocular lens mainly comes from two aspects: the calculation formula of the int...

AI Technical Summary

Problems solved by technology

The formula also has strict requirements on inspection instruments, and does not further explain the artificial intelligence technology used

Although the artificial intelligence intraocular lens formula can independently optimize the calculation results of different biological parameters through data learning, it is limited by domestic medical conditions. At present, the mainstream domestic ocular biological parameter inspection instruments such as A-ultrasound and IOLMaster cannot meet the optimal requirements of the above formulas.

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