Abstract
Any clouding of the lens of the eye causing distortion of its transparency and scattering of transmitted light is called a cataract. The solution to this pathological condition affecting the quality of life is phacoemulsification of the cloudy contents of the human lens and its replacement with an artificial intraocular lens (IOL). The surgeon has the opportunity to influence the refractive state of the eye for optimal postoperative visual acuity by choosing an appropriate IOL. In addition, a certain group of patients has an ametropia called corneal astigmatism, which needs to be corrected with a so-called toric IOL, in which the key criterion for the success of the correction is its angular position in the capsular bag. Deviation from the intended angular position of the implanted lens can lead to refractive surprise, i.e. the patient's postoperative visual acuity does not reach 100% of its own potential. In clinical practice, however, there is no conventional technique or tool for retrospectively determining the postoperative angular position to the required 1° accuracy. Thus, the intent of this paper was to create a custom design for automating IOL detection using postoperative patient images from the Verion reference unit and a specific lens model, the SN6ATx. The problem addressed is the automation of finding the optical part of the intraocular artificial lens using machine vision technologies based on convolutional operations on the image. The study presents the actual approach and methodology of the self-developed solution.
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Change history
07 December 2022
In the originally published version of chapter 2, there was an error in the affiliation of the author Michal Hruska. This has been corrected.
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Fus, M., Pavlicek, J., Pitrova, S., Hruska, M. (2022). Proposal for Determining the Angular Position of Artificial Intraocular Lens in the Human Eye. In: Babkin, E., Barjis, J., Malyzhenkov, P., Merunka, V. (eds) Model-Driven Organizational and Business Agility. MOBA 2022. Lecture Notes in Business Information Processing, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-17728-6_2
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