Calculating the Human Eye—Basics on Biometry

Scholtz, Sibylle; Cayless, Alan; Langenbucher, Achim

doi:10.1007/978-3-030-38234-6_7

Cited by 8 publications

(12 citation statements)

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“…However, we also have to take into consideration that most of the lens power calculation concepts involve estimation of an ELP, which does not match the anatomical position of the IOL in the eye (Olsen & Hoffmann 2014; Scholtz et al. 2021). The ELP is back‐calculated from the preoperative biometric data, the power of the inserted lens and the postoperative refraction, and typically covers all systematic errors of biometry, lens power‐labelling errors or errors in refractometry (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…However, we also have to take into consideration that most of the lens power calculation concepts involve estimation of an ELP, which does not match the anatomical position of the IOL in the Age refers to the patient age at the timepoint of the biometric measurement before cataract surgery, R to the average corneal curvature (mean value of corneal curvature in the flat and steep meridian), ACD to the phakic anterior chamber depth as the distance between the front corneal apex and the front lens apex, LT to the central thickness of the crystalline lens, CCT to the central corneal thickness, CD to the horizontal corneal diameter, PD to the pupil diameter, CWX and CWY to the horizontal and vertical component of the CW chord, CWX cor to the CWX where the sign was flipped for left eyes, and LEQ to the axial position of the equator plane of the intraocular lens considered as half the distance between the anterior and posterior apex of the intraocular lens. eye (Olsen & Hoffmann 2014;Scholtz et al 2021). The ELP is back-calculated from the preoperative biometric data, the power of the inserted lens and the postoperative refraction, and typically covers all systematic errors of biometry, lens power-labelling errors or errors in refractometry (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…2020; Scholtz et al. 2021), Hoffer Q (Hoffer 1980; Hoffer 1993), Holladay1 (Holladay et al. 1988) or SRKT (Retzlaff et al.…”

Section: Introductionmentioning

confidence: 99%

“…2008; Scholtz et al. 2021). Modern lens power calculation concepts use the ALP value as a baseline and add slight (mostly constant) amounts of shift to consider the individual characteristics of IOL designs in terms of haptic and optic concept, refractive index and shape factor (Olsen 2007).…”

Section: Introductionmentioning

confidence: 99%

“…Most of the classical IOL power calculation formulae such as Haigis (Haigis et al 2000;Olsen 2007;Savini et al 2020;Scholtz et al 2021), Hoffer Q (Hoffer 1980;Hoffer 1993), Holla-day1 (Holladay et al 1988) or SRKT (Retzlaff et al 1990) mainly differbeside conversion of corneal radius of curvature to dioptric power with a keratometer index, offset values or limiters for input parameters-in different prediction models for the axial IOL position. This parameter is typically defined as the effective lens position (ELP), which does not necessarily coincide with the physical position of the IOL front surface (Olsen 2006), the IOL back surface or the image-side principal plane of the lens (Olsen & Hoffmann 2014).…”

Section: Introductionmentioning

confidence: 99%

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Prediction of the axial lens position after cataract surgery using deep learning algorithms and multilinear regression

Langenbucher

Szentmáry

Cayless

et al. 2022

Acta Ophthalmologica

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Background The prediction of anatomical axial intraocular lens position (ALP) is one of the major challenges in cataract surgery. The purpose of this study was to develop and test prediction algorithms for ALP based on deep learning strategies. Methods We evaluated a large data set of 1345 biometric measurements from the IOLMaster 700 before and after cataract surgery. The target parameter was the intraocular lens (IOL) equator plane at half the distance between anterior and posterior apex. The relevant input parameters from preoperative biometry were extracted using a principal component analysis. A selection of neural network algorithms was tested using a 5‐fold cross‐validation procedure to avoid overfitting. The results were then compared with a traditional multilinear regression in terms of root mean squared prediction error (RMSE). Results Corneal radius of curvature, axial length, anterior chamber depth, corneal thickness, lens thickness and patient age were identified as effective predictive parameters, whereas pupil size, horizontal corneal diameter and Chang–Waring chord did not enhance the model. From the tested algorithms, the Gaussian prediction regression and the Support Vector Machine algorithms performed best (RMSE = 0.2805 and 0.2731 mm), outperforming the multilinear prediction model (0.3379 mm). The mean absolute prediction error yielded 0.1998, 0.1948 and 0.2415 mm for the respective models. Conclusion Modern prediction techniques may have the potential to outperform traditional multilinear regression techniques as they can deal easily with nonlinearities between input and output parameters. However, in all cases a cross‐validation is mandatory to avoid overfitting and misinterpretation of the results.

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“…However, we also have to take into consideration that most of the lens power calculation concepts involve estimation of an ELP, which does not match the anatomical position of the IOL in the eye (Olsen & Hoffmann 2014; Scholtz et al. 2021). The ELP is back‐calculated from the preoperative biometric data, the power of the inserted lens and the postoperative refraction, and typically covers all systematic errors of biometry, lens power‐labelling errors or errors in refractometry (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…However, we also have to take into consideration that most of the lens power calculation concepts involve estimation of an ELP, which does not match the anatomical position of the IOL in the Age refers to the patient age at the timepoint of the biometric measurement before cataract surgery, R to the average corneal curvature (mean value of corneal curvature in the flat and steep meridian), ACD to the phakic anterior chamber depth as the distance between the front corneal apex and the front lens apex, LT to the central thickness of the crystalline lens, CCT to the central corneal thickness, CD to the horizontal corneal diameter, PD to the pupil diameter, CWX and CWY to the horizontal and vertical component of the CW chord, CWX cor to the CWX where the sign was flipped for left eyes, and LEQ to the axial position of the equator plane of the intraocular lens considered as half the distance between the anterior and posterior apex of the intraocular lens. eye (Olsen & Hoffmann 2014;Scholtz et al 2021). The ELP is back-calculated from the preoperative biometric data, the power of the inserted lens and the postoperative refraction, and typically covers all systematic errors of biometry, lens power-labelling errors or errors in refractometry (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…2020; Scholtz et al. 2021), Hoffer Q (Hoffer 1980; Hoffer 1993), Holladay1 (Holladay et al. 1988) or SRKT (Retzlaff et al.…”

Section: Introductionmentioning

confidence: 99%