Dataset Information

Double crescentic edge separation for the management of cap-lenticular adhesion in small incision lenticule extraction.

ABSTRACT: We describe a modified technique of lenticule extraction for the management of cap-lenticular adhesions (CLAs). In cases where the lenticule edge could not be delineated, a Sinskey hook was introduced through the cap side-cut with the hook facing up (toward the cap), advanced to the periphery of lenticule at 3'o clock (for right-handed surgeons) and used to nudge the underside of the cap in the region of lenticule side-cut. The diagnosis of CLA was confirmed on observing a crescentic gap between the lenticule-side cut and the rolled lenticule edge. The gap was enlarged to create a crescentic area of separation spanning 2-3 clock hours. A similar crescentic area of separation was created on the opposite side (9'o clock). A microforceps was used to segmentally separate the lenticule from both edges toward the midline followed by lenticule extraction. Our technique was successfully applied in 11 cases of CLA with no complications.

SUBMITTER: Titiyal JS

PROVIDER: S-EPMC7350488 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Double crescentic edge separation for the management of cap-lenticular adhesion in small incision lenticule extraction.

Titiyal Jeewan S JS Kaur Manpreet M Shaikh Farin F Sahay Pranita P

Indian journal of ophthalmology 20200501 5

We describe a modified technique of lenticule extraction for the management of cap-lenticular adhesions (CLAs). In cases where the lenticule edge could not be delineated, a Sinskey hook was introduced through the cap side-cut with the hook facing up (toward the cap), advanced to the periphery of lenticule at 3'o clock (for right-handed surgeons) and used to nudge the underside of the cap in the region of lenticule side-cut. The diagnosis of CLA was confirmed on observing a crescentic gap between ...[more]

PMID: 32317475

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Project description:BackgroundSmall-incision lenticule extraction (SMILE) is a surgical procedure for the refractive correction of myopia and astigmatism, which has been reported as safe and effective. However, over- and under-correction still occur after SMILE. The necessity of nomograms is emphasized to achieve optimal refractive results. Ophthalmologists diagnose nomograms by analyzing the preoperative refractive data with their individual knowledge which they accumulate over years of experience. Our aim was to predict the nomograms of sphere, cylinder, and astigmatism axis for SMILE accurately by applying machine learning algorithm.MethodsWe retrospectively analyzed the data of 3,034 eyes composed of four categorical features and 28 numerical features selected from 46 features. The multiple linear regression, decision tree, AdaBoost, XGBoost, and multi-layer perceptron were employed in developing the nomogram models for sphere, cylinder, and astigmatism axis. The scores of the root-mean-square error (RMSE) and accuracy were evaluated and compared. Subsequently, the feature importance of the best models was calculated.ResultsAdaBoost achieved the highest performance with RMSE of 0.1378, 0.1166, and 5.17 for the sphere, cylinder, and astigmatism axis, respectively. The accuracies of which error below 0.25 D for the sphere and cylinder nomograms and 25° for the astigmatism axis nomograms were 0.969, 0.976, and 0.994, respectively. The feature with the highest importance was preoperative manifest refraction for all the cases of nomograms. For the sphere and cylinder nomograms, the following highly important feature was the surgeon.ConclusionsAmong the diverse machine learning algorithms, AdaBoost exhibited the highest performance in the prediction of the sphere, cylinder, and astigmatism axis nomograms for SMILE. The study proved the feasibility of applying artificial intelligence (AI) to nomograms for SMILE. Also, it may enhance the quality of the surgical result of SMILE by providing assistance in nomograms and preventing the misdiagnosis in nomograms.

Project description:PurposeTo quantitatively evaluate outcomes after small incision lenticule extraction (SMILE) combined with allogeneic intrastromal lenticule inlay for hyperopia with astigmatism.MethodsIt's a retrospective cohort study. Twenty-four eyes of 15 patients with more than 0.75 diopters (D) of astigmatism in hyperopic eyes were enrolled in this study. The hyperopic eye with astigmatism was first treated with SMILE to correct astigmatism; then a lenticule was extracted from a donor myopic eye and subsequently implanted into the hyperopic eye with astigmatism. Patients were examined preoperatively and 1 day, 1 week, 1,3 months and 1 year after surgery. The main outcome measures were the uncorrected and corrected distance visual acuity (UDVA and CDVA), uncorrected near visual acuity (UNVA), spherical equivalent (SE), corneal topography, anterior segment optical coherence topography (OCT) and ocular response analyzer (ORA) parameters: corneal hysteresis (CH) and corneal resistance factor (CRF). Repeated-measures analyses of variance (ANOVA) and post hoc tests were used to analyze data of different follow-up visits.ResultsThe mean preoperative cylinder was 1.95±1.04(D). The UDVA (from 0.37±0.23 to 0.09±0.09), UNVA (from 0.49±0.21 to 0.08±0.06), SE (from +7.42±3.12 to -0.75±0.79) and astigmatism (+1.95±1.04 to -0.65±0.63) postoperatively were obviously better than those before surgery. Five eyes (26.3%) gained one line of CDVA, and 3 eyes (15.8%) gained two lines of CDVA one year after surgery compared with preoperative levels. The average corneal curvature was changed from (43.19±4.37) D to (49.19±3.87) D one year after surgery. The anterior segment OCT images of corneas with lenticule inlays at each follow-up visit showed that the implanted lenticule was shaped like a crescent in the corneal stroma. The CH and CRF didn't change significantly after surgery (p = 0.189 and p = 0.107respectively).ConclusionsSMILE combined with intrastromal lenticule inlay can be used to correct high hyperopia with astigmatism with good safety, efficacy and reproducibility.

Dataset Information

Double crescentic edge separation for the management of cap-lenticular adhesion in small incision lenticule extraction.

Publications

Double crescentic edge separation for the management of cap-lenticular adhesion in small incision lenticule extraction.

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