Accuracy of intraocular lens formulas in combined phacovitrectomy.

PURPOSE: To assess the refractive accuracy of eight intraocular lens (IOL) formulas in eyes that underwent combined phacovitrectomy. METHODS: A retrospective chart review of 59 eyes that underwent uncomplicated phacovitrectomy between 2017 and 2020 at the Johns Hopkins Wilmer Eye Institute. Inclusion criteria were postoperative best corrected visual acuity of 20/40 or better within 6 months of surgery and IOL implantation in the capsular bag. The Barrett Universal II (BUII), Emmetropia Verifying Optical (EVOv2.0), Hill-Radial Basis Function (Hill-RBFv3.0), Hoffer Q, Holladay I, Kane, Ladas Super Formula (LSF), and SRK/T formulas were compared for accuracy in predicting postoperative spherical equivalents (SE) using Wilcoxon rank sum tests. Pearson's correlation coefficients were used to assess correlations between biometric parameters and errors for all formulas. RESULTS: Prediction errors of SE ranged from - 1.69 to 1.43 diopters (D), mean absolute errors (MAE) ranged from 0.39 to 0.47 D, and median absolute errors (MedAE) ranged from 0.23 to 0.37 D among all formulas. The BUII had the lowest mean error (- 0.043), MAE (0.39) and MedAE (0.23). The BUII also had the highest percentage of eyes with predicted error within ± 0.25 D (51%) and ± 0.50 D (83%). Based on MedAE however, no pairwise comparisons resulted in statistically significant differences. Axial length (AL) was positively correlated with the error from the Hoffer Q and Holladay I formulas (correlation coefficients = 0.34, 0.30, p values < 0.01, 0.02 respectively). CONCLUSION: While all eight IOL formulas had comparable accuracy in predicting refractive outcomes in eyes undergoing combined phacovitrectomy, the BUII and Kane formulas had a tendency to greater accuracy.

Artificial intelligence in cornea, refractive, and cataract surgery.

PURPOSE OF REVIEW: The subject of artificial intelligence has recently been responsible for the advancement of many industries including aspects of medicine and many of its subspecialties. Within ophthalmology, artificial intelligence technology has found ways of improving the diagnostic and therapeutic processes in cornea, glaucoma, retina, and cataract surgery. As demands on the modern ophthalmologist grow, artificial intelligence can be utilized to help address increased demands of modern medicine and ophthalmology by adding to the physician's clinical and surgical acumen. The purpose of this review is to highlight the integration of artificial intelligence into ophthalmology in recent years in the areas of cornea, refractive, and cataract surgery. RECENT FINDINGS: Within the realms of cornea, refractive, and cataract surgery, artificial intelligence has played a major role in identifying ways of improving diagnostic detection. In keratoconus, artificial intelligence algorithms may help with the early detection of keratoconus and other ectatic disorders. In cataract surgery, artificial intelligence may help improve the performance of intraocular lens (IOL) calculation formulas. Further, with its potential integration into automated refraction devices, artificial intelligence can help provide an improved framework for IOL formula optimization that is more accurate and customized to a specific cataract surgeon. SUMMARY: The future of artificial intelligence in ophthalmology is a promising prospect. With continued advancement of mathematical and computational algorithms, corneal disease processes can be diagnosed sooner and IOL calculations can be made more accurate.

Optimization of intraocular lens constant improves refractive outcomes in combined endothelial keratoplasty and cataract surgery.

PURPOSE: To evaluate the accuracy of intraocular lens (IOL) power calculations with A-constant optimization in Descemet's stripping automated endothelial keratoplasty (DSAEK) combined with cataract extraction and intraocular lens implantation (DSAEK triple procedure). DESIGN: Retrospective case series. PARTICIPANTS: Thirty eyes of 22 patients with Fuchs' endothelial dystrophy who underwent the DSAEK triple procedure performed by a single surgeon. METHODS: Prediction errors were calculated retrospectively for consecutive DSAEK triple procedures. These prediction errors then were used to determine an IOL constant for this cohort of patients. The new optimized IOL constant subsequently was compared with the manufacturer's IOL constant, allowing evaluation and quantification of refractive benefits of optimization. MAIN OUTCOMES MEASURES: The error in diopters (D) of the predicted refraction with the manufacturer's and optimized IOL constants. RESULTS: Optimization of the A constant decreased the mean absolute error (MAE) from 1.09 ± 0.63 D (range, 0.12-2.41 D) to 0.61 ± 0.4 D (range, 0-1.58 D; P = 0.004). Comparing the intended and final postoperative refractions calculated with the original manufacturer's constant and the optimized constant, 20% versus 43% of all eyes were in the less than 0.5-D range and 50% versus 83% of all eyes were in the less than 1.0-D range of the target refraction. Furthermore, optimization decreased the number of eyes that were more than 1.0 D from the target refraction from 50% to 17%. CONCLUSIONS: Optimization of the IOL constant showed significantly improved accuracy of predicted postoperative refraction compared with the manufacturer's IOL constant, which may help improve the postoperative refractive outcomes in patients undergoing the DSAEK triple procedure.

Refractive accuracy in eyes undergoing combined cataract extraction and Descemet membrane endothelial keratoplasty.

PURPOSE: To evaluate the refractive accuracy of current intraocular lens (IOL) formulas and propose a modification in calculation of corneal power in eyes undergoing combined cataract extraction and Descemet membrane endothelial keratoplasty (DMEK). DESIGN: Retrospective cohort study. METHODS: Patients with Fuchs endothelial corneal dystrophy undergoing uncomplicated combined cataract surgery and DMEK at a single institution were included. The Hoffer Q, SRK/T, Holladay I, Barrett Universal II and Haigis formulas were compared. A modified corneal power was calculated using a thick lens equation based on anterior and posterior corneal radii and corneal thickness from Pentacam imaging. Error calculations were adjusted based on the difference in optical biometry and the modified corneal power. Mean absolute error (MAE) for each formula was compared between the corneal power modification and optical biometry corneal power. RESULTS: In 86 eyes, the mean error ranged from 0.90 D for the Barrett Universal II formula to -0.10 D for the Haigis formula, with 4 of 5 formulas resulting in a mean hyperopic error. The corneal power modification resulted in a significantly lower MAE for the Hoffer Q (0.82 D), Holladay I (0.85 D), SRK/T (0.85 D) and Barrett Universal II (0.90 D) formulas compared with optical biometry corneal power for the Hoffer Q (1.02 D; p<0.005), Holladay I (0.97 D; p<0.005), SRK/T (0.93 D; p<0.01) and Barrett Universal II (1.16 D; p<0.005) formulas. CONCLUSIONS: All formulas except the Haigis formula resulted in a hyperopic error. The corneal power modification significantly reduced error in four out of five IOL formulas.

Accuracy of Intraocular Lens Formulas in Eyes With Keratoconus.

PURPOSE: To evaluate the refractive accuracy of current intraocular lens (IOL) formulas in eyes with keratoconus. DESIGN: Retrospective case series. METHODS: Preoperative optical biometry, Pentacam topography, and postoperative outcomes were collected from eyes with keratoconus that had uncomplicated cataract surgery between 2014 and 2018 at a single institution. Exclusion criteria include postoperative best-corrected spectacle visual acuity worse than 20/40, multifocal lens, prior ophthalmic surgeries, and prior ocular trauma. The Hoffer Q, SRK/T, Holladay I, Holladay II, Haigis, and Barrett Universal II formulas were analyzed in each eye stratified by keratoconus severity. RESULTS: A total of 73 eyes were included. All formulas had a positive mean predicted error ranging from 0.10 to 4.38 diopters (D). The Barrett Universal II formula had the lowest median absolute error for stage I (n = 46, 0.445 D) and II (n = 22, 0.445 D) eyes, and the highest percentage of eyes with predicted error within ±0.50 D for both stage I (52%) and II (50%) eyes. In stage III eyes (n = 5), the Haigis formula had the lowest median predicated error (1.90 D) and the highest percentage of eyes with predicted error within ±0.50 D (40%). Corneal power measured by optical biometers was higher than measurements by Pentacam keratometry. CONCLUSIONS: All formulas tend to have a hyperopic surprise. The Barrett Universal II formula was the most accurate for mild to moderate disease. Pentacam keratometry may help avoid hyperopic outcomes.

A Review of Machine Learning Techniques for Keratoconus Detection and Refractive Surgery Screening.

Various machine learning techniques have been developed for keratoconus detection and refractive surgery screening. These techniques utilize inputs from a range of corneal imaging devices and are built with automated decision trees, support vector machines, and various types of neural networks. In general, these techniques demonstrate very good differentiation of normal and keratoconic eyes, as well as good differentiation of normal and form fruste keratoconus. However, it is difficult to directly compare these studies, as keratoconus represents a wide spectrum of disease. More importantly, no public dataset exists for research purposes. Despite these challenges, machine learning in keratoconus detection and refractive surgery screening is a burgeoning field of study, with significant potential for continued advancement as imaging devices and techniques become more sophisticated.

Intraocular lens power calculation after corneal refractive surgery.

PURPOSE OF REVIEW: Corneal refractive procedures have become increasingly popular over the past decade, allowing patients to have excellent uncorrected visual acuity and spectacle independence. As these individuals mature, many will eventually undergo cataract surgery. With the advances in modern cataract surgery and lens implant technology, particularly presbyopic intraocular lens implants, patients and physicians have greater expectations regarding visual outcomes and independence from glasses after cataract surgery. Therefore, it is important to understand methods to accurately determine intraocular lens power calculation after keratorefractive procedures to avoid refractive surprises and patient dissatisfaction. RECENT FINDINGS: In this review article, we provide an overview of intraocular lens power determination after corneal refractive surgery, highlighting sources of errors and potential methods to improve the accuracy of the lens power estimation. SUMMARY: Newer methods to address errors in intraocular lens power calculations after keratorefractive surgery represent a paradigm shift from the previous gold standard of the clinical history method. Understanding the advantages and limitations of the various methods may be beneficial in obtaining more accurate estimations of the intraocular lens power after corneal refractive surgery, resulting in improved visual outcomes.

Laser flare-cell photometry: methodology and clinical applications.

Diagnosis and management of intraocular inflammation involves the assessment of cells and protein levels ("flare") in the aqueous humor. These factors are difficult to quantify precisely on clinical examination alone. Laser flare-cell photometry provides an automated technique to quantify these factors objectively, and it has been used in a variety of research and clinical situations to assess anterior segment inflammation. Any new technique requires evaluation to determine accuracy and reproducibility of measured values, and initial applications require critical appraisal to assess the value of the technique. Both in vitro and in vivo studies of laser flare-cell photometry have been performed to determine its validity and utility as a research and clinical tool. This article reviews published studies that describe the technique of laser flare-cell photometry; it provides new in vitro data that supplements information on the capabilities of this technique and factors that influence photometry results, and it reviews representative publications that have used laser flare-cell photometry for study of specific disease entities. This information can help clinicians and researchers to become familiar with the strengths and limitations of laser flare-cell photometry, to identify appropriate future uses for this technique, and to use it and interpret its results appropriately. Laser flare-cell photometry offers an opportunity to improve upon current techniques of inflammation assessment and should not be considered simply an objective surrogate for clinical grading of cells and flare at the slit-lamp biomicroscope. Its research applications and utility for monitoring patients with uveitis have not yet been fully explored.

A 3-D "Super Surface" Combining Modern Intraocular Lens Formulas to Generate a "Super Formula" and Maximize Accuracy.

IMPORTANCE: Cataract surgery is the most common eye surgery. Calculating the most accurate power of the intraocular lens (IOL) is a critical factor in optimizing patient outcomes. OBJECTIVES: To develop a graphical method for displaying IOL calculation formulas in 3 dimensions, and to describe a method that uses the most accurate and current information on IOL formulas, adjustments, and lens design to create one "super surface" and develop an IOL "super formula." DESIGN, SETTING, AND PARTICIPANTS: A numerical computing environment was used to create 3-D surfaces of IOL formulas: Hoffer Q, Holladay I, Holladay I with Koch adjustment, Haigis, and SRK/T. The surfaces were then analyzed to determine where the IOL powers calculated by each formula differed by more than 0.5, 1.0, and 1.5 diopters (D) from each of the other formulas. Next, based on the current literature and empirical knowledge, a super surface was rendered that incorporated the ideal portions from 4 of the 5 formulas to generate a super formula. Last, IOL power values of a set of 100 eyes from consecutive patients at an eye institute were calculated using the 5 formulas and super formula. The study was performed from December 11, 2014, to April 20, 2015. Analysis was conducted from February 18 to May 6, 2015. MAIN OUTCOMES AND MEASURES: Intraocular lens power value in diopters and the magnitude of disparity between an existing individual IOL formula and our super formula. RESULTS: In the 100 eyes tested, the super formula localized to the correct portion of the super surface 100% of the time and thus chose the most appropriate IOL power value. The individual formulas deviated from the optimal super formula IOL power values by more than 0.5 D 30% of the time in Hoffer Q, 16% in Holladay I, 22% in Holladay I with Koch adjustment, 48% in Haigis, and 24% in SRK/T. CONCLUSIONS AND RELEVANCE: A novel method was developed to represent IOL formulas in 3 dimensions. An IOL super formula was formulated that incorporates the ideal segments from each of the existing formulas and uses the ideal IOL formula for an individual eye. The expectation is that this method will broaden the conceptual understanding of IOL calculations, improve clinical outcomes for patients, and stimulate further progress in IOL formula research.