Impact of extreme (flat and steep) keratometry on the safety and efficacy of small incision lenticule extraction (SMILE)

Luft, Nikolaus; Reinking, Franziska; Mayer, Wolfgang J.; Schworm, Benedikt; Kassumeh, Stefan; Priglinger, Siegfried; Dirisamer, Martin

doi:10.1038/s41598-021-97375-4

Cited by 5 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…11 Similarly, Luft et al showed noninferior outcomes for flat and steep corneas after SMILE. 12 In this study, all 3 groups had high efficacy and safety indexes with no significant differences between the groups and were comparable with previous studies. In all groups, at least 96% of patients had no change in or gained 1 or 2 lines in CDVA with 0% to 1% who lost 2 lines in CDVA, with no significant difference between all 3 groups.…”

Section: Discussionsupporting

confidence: 90%

See 1 more Smart Citation

Photorefractive keratectomy in flat, normal, and steep corneas

Levinger,

Arnon,

Pikkel

et al. 2024

J Cataract Refract Surg

View full text Add to dashboard Cite

Purpose: To compare photorefractive keratectomy (PRK) outcomes in patients with different corneal steepness. Setting: Care-Vision Laser Center, Tel-Aviv, Israel. Design: Retrospective comparative chart review. Methods: Patients included in this study underwent wavefront optimized myopic PRK between January 2013 and December 2019. Patients were divided into 3 groups based on the steepness of the cornea (steep keratometry) (flat: <42 diopters [D], normal: 42 to 46 D and steep: >46 D). One to one matching was performed to verify that baseline parameters (age, preoperative visual acuity and refractive error) were similar between groups. Primary outcome measures were: postoperative corrected/uncorrected distance visual acuity (CDVA/UDVA), efficacy index (postoperative UDVA/preoperative CDVA), safety index (postoperative CDVA/preoperative CDVA), spherical equivalence (SEQ) and cylinder. Results: After matching, 650 eyes were included in each group, which were similar in baseline parameters. There were no significant postoperative differences between flat, normal, and steep corneas in terms of safety index (1.02 vs 1.03 vs 1.03, P = .28), efficacy index (1.01 vs 1.01 vs 1.02, P = .57), logMAR CDVA (0.01 vs 0.02 vs 0.01, P = .76), logMAR UDVA (0.02 vs 0.03 vs 0.02, P = .68), %SEQ within 0.50 D (73.9% vs 74.2% vs 74.6%, P = .95) or 1.00 D of target (91.9% vs 92.5% vs 92.2%, P = .92), %cylinder within 0.50 D (82.8% vs 82.2% vs 81.4%, P = .81) or 1.00 D of target (96.9% vs 97.1% vs 97.2%, P = .95). Conclusions: No significant differences were found between flat, normal and steep corneas following wavefront-optimized myopic PRK. Thus, this procedure may be safely and effectively performed in both flat and steep corneas.

show abstract

Section: Discussionsupporting

confidence: 90%

“…[8][9][10] Studies evaluating steep and flat corneas before a SMILE procedure have showed comparable postoperative results. 11,12 The aforementioned studies focused on myopic LASIK or SMILE and not PRK.…”

mentioning

confidence: 99%

Photorefractive keratectomy in flat, normal, and steep corneas

Levinger,

Arnon,

Pikkel

et al. 2024

J Cataract Refract Surg

View full text Add to dashboard Cite

show abstract

“…The K value was used as the target metric for determining corneal curvature, which averages the flat meridian (K1) and steep meridian (K2) measured by keratometry. A flat cornea was defined as a K value of 42.0 D or below, a regular cornea as K value between 42.0 and 45.0 D, and a steep cornea as K value of 45.0 or greater [ 17 ]. This categorization has been adopted in previous studies to predict different postoperative prognoses according to corneal curves [ 17 , 18 ].…”

Section: Methodsmentioning

confidence: 99%

“…A flat cornea was defined as a K value of 42.0 D or below, a regular cornea as K value between 42.0 and 45.0 D, and a steep cornea as K value of 45.0 or greater [ 17 ]. This categorization has been adopted in previous studies to predict different postoperative prognoses according to corneal curves [ 17 , 18 ]. When analyzing the keratometry values, eight eyes were excluded from the original test set ( N = 142 eyes) due to missing values.…”

Section: Methodsmentioning

confidence: 99%

Deep Transfer Learning for Ethnically Distinct Populations: Prediction of Refractive Error Using Optical Coherence Tomography

Jain,

Yoo,

Ryu

et al. 2023

Ophthalmol Ther

View full text Add to dashboard Cite

Introduction The mismatch between training and testing data distribution causes significant degradation in the deep learning model performance in multi-ethnic scenarios. To reduce the performance differences between ethnic groups and image domains, we built a deep transfer learning model with adaptation training to predict uncorrected refractive errors using posterior segment optical coherence tomography (OCT) images of the macula and optic nerve. Methods Observational, cross-sectional, multicenter study design. We pre-trained a deep learning model on OCT images from the B&VIIT Eye Center (Seoul, South Korea) ( N = 2602 eyes of 1301 patients). OCT images from Poona Eye Care (Pune, India) were chronologically sorted into adaptation training data ( N = 60 eyes of 30 patients) for transfer learning and test data ( N = 142 eyes of 71 patients) for validation. Deep learning models were trained to predict spherical equivalent (SE) and mean keratometry ( K ) values via transfer learning for domain adaptation. Results Both adaptation models for SE and K were significantly better than those without adaptation ( P < 0.001). In myopia/hyperopia classification, the model trained on circular optic disc OCT images yielded the best performance (accuracy = 74.7%). It also performed best in estimating SE with the lowest mean absolute error (MAE) of 1.58 D. For classifying the degree of corneal curvature, the optic nerve vertical algorithm performed best (accuracy = 65.7%). The optic nerve horizontal model achieved the lowest MAE (1.85 D) when predicting the K value. Saliency maps frequently highlighted the retinal nerve fiber layers. Conclusions Adaptation training via transfer learning is an effective technique for estimating refractive errors and K values using macular and optic nerve OCT images from ethnically heterogeneous populations. Further studies with larger sample sizes and various data sources are needed to confirm the feasibility of the proposed algorithm. Supplementary Information The online version contains supplementary material available at 10.1007/s40123-023-00842-6.

show abstract