Descemet membrane endothelial keratoplasty in a patient with iris-fixated intraocular lens and prior radial keratotomy: a case report.

BACKGROUND: Anterior segment surgeries such as cataract surgery, intraocular lens (IOL) repositioning, and radial keratotomy (RK) may hasten endothelial dysfunction, particularly in the context of pre-existing Fuchs dystrophy, necessitating future corneal transplantation. CASE PRESENTATION: A 68-year-old woman with a history of RK with associated irregular astigmatism in both eyes and iris-fixated intraocular lens (IF-IOL) in the left eye presented with six months of decreased vision in the left eye. She was found to have Fuchs dystrophy and underwent DMEK surgery. She had an uncomplicated postoperative course, with uncorrected visual acuity improving to 20/20 three months after surgery. CONCLUSION: To our knowledge, this is the first reported case of a highly successful DMEK surgery in a patient with prior RK and IF-IOL.

Gender differences in refraction prediction error of five formulas for cataract surgery.

OBJECTIVES: To evaluate gender differences in optical biometry measurements and lens power calculations. METHODS: Eight thousand four hundred thirty-one eyes of five thousand five hundred nineteen patients who underwent cataract surgery at University of Michigan's Kellogg Eye Center were included in this retrospective study. Data including age, gender, optical biometry, postoperative refraction, implanted intraocular lens (IOL) power, and IOL formula refraction predictions were gathered and/or calculated utilizing the Sight Outcomes Research Collaborative (SOURCE) database and analyzed. RESULTS: There was a statistical difference between every optical biometry measure between genders. Despite lens constant optimization, mean signed prediction errors (SPEs) of modern IOL formulas differed significantly between genders, with predictions skewed more hyperopic for males and myopic for females for all 5 of the modern IOL formulas tested. Optimization of lens constants by gender significantly decreased prediction error for 2 of the 5 modern IOL formulas tested. CONCLUSIONS: Gender was found to be an independent predictor of refraction prediction error for all 5 formulas studied. Optimization of lens constants by gender can decrease refraction prediction error for certain modern IOL formulas.

Perioperative Prophylactic Antibiotics in 1,250 Orbital Surgeries.

PURPOSE: Intravenous antibiotic prophylaxis is used for many clean-contaminated surgeries or clean surgeries with an implant, but its value for clean orbital surgery has not been determined. This study investigated infection risks and adverse effects related to antibiotics in patients undergoing orbital surgery. METHODS: A prospective, nonrandomized comparative case series of all patients undergoing orbital surgery with participating surgeons between October 1, 2013, and March 1, 2015. Types of surgery, antibiotic regimens, corticosteroid use, antibiotic side effects, and surgical site infections (SSIs) were entered into an electronic database and subsequently analyzed. Cases in which patients received postoperative oral antibiotics were analyzed separately. RESULTS: Of 1,250 consecutive orbital surgeries, 1,225 met inclusion criteria. A total of 1208 patients were included in the primary analysis: 603 received no antibiotic prophylaxis (group A), and 605 received a single dose of intravenous antibiotic (group B). Five patients (0.42%) developed an SSI, 3 in group A and 2 in group B. The difference in SSI rates was not statistically significant between the 2 groups (p = 0.66). Antibiotic prophylaxis, alloplastic implants, paranasal sinus entry, and corticosteroid use were not associated with differences in SSI rates. All SSIs resolved on a single course of oral antibiotics; an implant was removed in 1 case. There were no complications associated with a single dose of intravenous prophylaxis. However, 12% of 17 patients (group C) who received 1 week of oral postoperative prophylactic antibiotics developed antibiotic-related complications (diarrhea, renal injury), yielding a number needed to harm of 8.5. CONCLUSIONS: In this large series, antibiotic prophylaxis does not appear to have reduced the already low incidence of SSI following orbital surgery. Given the detriments of systemic antibiotics, the rarity of infections related to orbital surgery, and the efficacy of treating such infections should they occur, patients undergoing orbital surgery should be educated to the early symptoms of postoperative infection and followed closely, but do not routinely require perioperative antibiotics.

Neovascularization against autologous SLET donor grafts despite successful corneal epithelialization and graft adherence.

PURPOSE: To report a case of neovascularization against autologous grafts after simple limbal epithelial transplantation (SLET) despite successful corneal epithelialization, as well as its subsequent regression without intervention. METHODS: A case report and review of the literature. RESULTS: A 52-year-old woman underwent uncomplicated autologous SLET for asymmetric limbal stem cell deficiency (LSCD) in the left eye. One month after the surgery, the patient had successful adherence of the graft and corneal epithelialization; however, new neovascularization developed in the left eye towards the graft sites. With only a slow taper of topical prednisolone acetate and polymyxin b/trimethoprim, the neovascularization regressed to ghost vessels over the following three months with improvement of her LSCD symptoms and increased clarity of her cornea. CONCLUSION: The limbus does not enjoy relative immune privilege like other parts of the eye; therefore, autologous limbal stem cell transplantation (along with the minimal immune response generated) is valuable for restoration of the ocular surface. Here, we describe neovascularization against autologous donor grafts despite an otherwise uncomplicated surgery and expected epithelialization course. Inflammation-mediated angiogenesis likely initiated the neovascularization, suggesting that immune mediators of inflammation may be inadvertently part of the graft tissue in bilateral LSCD.

Comparison of Machine and Human Expert Evaluation of Capsulorrhexis Creation Performance Through Analysis of Surgical Video Recordings.

Purpose: Achieving competency in cataract surgery is an essential component of ophthalmology residency training. Video-based analysis of surgery can change training through its objective, reliable, and timely assessment of resident performance. Methods: Using the Image Labeler application in MATLAB, the capsulorrhexis step of 208 surgical videos, recorded at the University of Michigan, was annotated for subjective and objective analysis. Two expert surgeons graded the creation of the capsulorrhexis based on the International Council of Ophthalmology's Ophthalmology Surgical Competency Assessment Rubric:Phacoemulsification (ICO-OSCAR:phaco) rating scale and a custom rubric (eccentricity, roundness, size, centration) that focuses on the objective aspects of this step. The annotated rhexis frames were run through an automated analysis to obtain objective scores for these components. The subjective scores were compared using both intra and inter-rater analyses to assess the consistency of a human-graded scale. The subjective and objective scores were compared using intraclass correlation methods to determine relative agreement. Results: All rhexes were graded as 4/5 or 5/5 by both raters for both items 4 and 5 of the ICO-OSCAR:phaco rating scale. Only roundness scores were statistically different between the subjective graders (mean difference = -0.149, p-value = 0.0023). Subjective scores were highly correlated for all components (>0.6). Correlations between objective and subjective scores were low (0.09 to 0.39). Conclusion: Video-based analysis of cataract surgery presents significant opportunities, including the ability to asynchronously evaluate performance and provide longitudinal assessment. Subjective scoring between two raters was moderately correlated for each component.

CatStep: Automated Cataract Surgical Phase Classification and Boundary Segmentation Leveraging Inflated 3D-Convolutional Neural Network Architectures and BigCat.

Objective: Accurate identification of surgical phases during cataract surgery is essential for improving surgical feedback and performance analysis. Time spent in each surgical phase is an indicator of performance, and segmenting out specific phases for further analysis can simplify providing both qualitative and quantitative feedback on surgical maneuvers. Study Design: Retrospective surgical video analysis. Subjects: One hundred ninety cataract surgical videos from the BigCat dataset (comprising nearly 4 million frames, each labeled with 1 of 11 nonoverlapping surgical phases). Methods: Four machine learning architectures were developed for segmentation of surgical phases. Models were trained using cataract surgical videos from the BigCat dataset. Main Outcome Measures: Models were evaluated using metrics applied to frame-by-frame output and, uniquely in this work, metrics applied to phase output. Results: The final model, CatStep, a combination of a temporally sensitive model (Inflated 3D Densenet) and a spatially sensitive model (Densenet169), achieved an F1-score of 0.91 and area under the receiver operating characteristic curve of 0.95. Phase-level metrics showed considerable boundary segmentation performance with a median absolute error of phase start and end time of just 0.3 seconds and 0.1 seconds, respectively, a segmental F1-score @70 of 0.94, an oversegmentation score of 0.89, and a segmental edit score of 0.92. Conclusion: This study demonstrates the feasibility of high-performance automated surgical phase identification for cataract surgery and highlights the potential for improved surgical feedback and performance analysis. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Adaptive Tensor-Based Feature Extraction for Pupil Segmentation in Cataract Surgery.

Cataract surgery remains the only definitive treatment for visually significant cataracts, which are a major cause of preventable blindness worldwide. Successful performance of cataract surgery relies on stable dilation of the pupil. Automated pupil segmentation from surgical videos can assist surgeons in detecting risk factors for pupillary instability prior to the development of surgical complications. However, surgical illumination variations, surgical instrument obstruction, and lens material hydration during cataract surgery can limit pupil segmentation accuracy. To address these problems, we propose a novel method named adaptive wavelet tensor feature extraction (AWTFE). AWTFE is designed to enhance the accuracy of deep learning-powered pupil recognition systems. First, we represent the correlations among spatial information, color channels, and wavelet subbands by constructing a third-order tensor. We then utilize higher-order singular value decomposition to eliminate redundant information adaptively and estimate pupil feature information. We evaluated the proposed method by conducting experiments with state-of-the-art deep learning segmentation models on our BigCat dataset consisting of 5,700 annotated intraoperative images from 190 cataract surgeries and a public CaDIS dataset. The experimental results reveal that the AWTFE method effectively identifies features relevant to the pupil region and improved the overall performance of segmentation models by up to 2.26% (BigCat) and 3.31% (CaDIS). Incorporation of the AWTFE method led to statistically significant improvements in segmentation performance (P < 1.29 × 10-10 for each model) and yielded the highest-performing model overall (Dice coefficients of 94.74% and 96.71% for the BigCat and CaDIS datasets, respectively). In performance comparisons, the AWTFE consistently outperformed other feature extraction methods in enhancing model performance. In addition, the proposed AWTFE method significantly improved pupil recognition performance by up to 2.87% in particularly challenging phases of cataract surgery.

Evaluation of Methods for Detection and Semantic Segmentation of the Anterior Capsulotomy in Cataract Surgery Video.

Background: The capsulorhexis is one of the most important and challenging maneuvers in cataract surgery. Automated analysis of the anterior capsulotomy could aid surgical training through the provision of objective feedback and guidance to trainees. Purpose: To develop and evaluate a deep learning-based system for the automated identification and semantic segmentation of the anterior capsulotomy in cataract surgery video. Methods: In this study, we established a BigCat-Capsulotomy dataset comprising 1556 video frames extracted from 190 recorded cataract surgery videos for developing and validating the capsulotomy recognition system. The proposed system involves three primary stages: video preprocessing, capsulotomy video frame classification, and capsulotomy segmentation. To thoroughly evaluate its efficacy, we examined the performance of a total of eight deep learning-based classification models and eleven segmentation models, assessing both accuracy and time consumption. Furthermore, we delved into the factors influencing system performance by deploying it across various surgical phases. Results: The ResNet-152 model employed in the classification step of the proposed capsulotomy recognition system attained strong performance with an overall Dice coefficient of 92.21%. Similarly, the UNet model with the DenseNet-169 backbone emerged as the most effective segmentation model among those investigated, achieving an overall Dice coefficient of 92.12%. Moreover, the time consumption of the system was low at 103.37 milliseconds per frame, facilitating its application in real-time scenarios. Phase-wise analysis indicated that the Phacoemulsification phase (nuclear disassembly) was the most challenging to segment (Dice coefficient of 86.02%). Conclusion: The experimental results showed that the proposed system is highly effective in intraoperative capsulotomy recognition during cataract surgery and demonstrates both high accuracy and real-time capabilities. This system holds significant potential for applications in surgical performance analysis, education, and intraoperative guidance systems.

Development of Crystalline Corneal Opacities (Steroid Keratopathy) in Dogs After Treatment With Ophthalmic Corticosteroids.

PURPOSE: To retrospectively evaluate and describe the relationship between the use of topical corticosteroids and the development of crystalline corneal opacities (steroid keratopathy) in a colony of research Beagles and Beagle-derived dogs. METHODS: Medical records of 73 purpose-bred Beagles and Beagle-derived dogs were reviewed from June 2012 to May 2021. All dogs were treated with topical ophthalmic corticosteroids for at least 21 days. In addition to regular ophthalmic examination, some dogs also had a systemic lipid profile (n = 6) performed to work up further and characterize the crystalline corneal opacities. Globes of 3 dogs were examined histopathologically. RESULTS: Axial stromal crystalline corneal opacities were appreciated in 25 eyes of 14 dogs after a median of 141 days after initiating treatment (35-396 days). Multiple corticosteroids were used, including neomycin-polymyxin b-dexamethasone 0.1% ophthalmic ointment, prednisolone acetate 1% ophthalmic suspension, and difluprednate 0.05% ophthalmic emulsion (Durezol). Resolution of corneal opacity was documented in 4 of 25 eyes when ophthalmic corticosteroids were discontinued after a median of 406.5 days (271-416 days). Histopathologic examination revealed a dense band of acellular material, poorly staining with periodic acid-Schiff, subtending the corneal epithelium, and being surrounded by spindle cells. CONCLUSIONS: This case series documents the onset of steroid keratopathy in Beagles and Beagle-derived dogs after treatment with ophthalmic corticosteroids. Clinical resolution of steroid keratopathy lesions may be possible after discontinuation of ophthalmic corticosteroids.

Automatic Classification of Slit-Lamp Photographs by Imaging Illumination.

PURPOSE: The aim of this study was to facilitate deep learning systems in image annotations for diagnosing keratitis type by developing an automated algorithm to classify slit-lamp photographs (SLPs) based on illumination technique. METHODS: SLPs were collected from patients with corneal ulcer at Kellogg Eye Center, Bascom Palmer Eye Institute, and Aravind Eye Care Systems. Illumination techniques were slit beam, diffuse white light, diffuse blue light with fluorescein, and sclerotic scatter (ScS). Images were manually labeled for illumination and randomly split into training, validation, and testing data sets (70%:15%:15%). Classification algorithms including MobileNetV2, ResNet50, LeNet, AlexNet, multilayer perceptron, and k-nearest neighborhood were trained to distinguish 4 type of illumination techniques. The algorithm performances on the test data set were evaluated with 95% confidence intervals (CIs) for accuracy, F1 score, and area under the receiver operator characteristics curve (AUC-ROC), overall and by class (one-vs-rest). RESULTS: A total of 12,132 images from 409 patients were analyzed, including 41.8% (n = 5069) slit-beam photographs, 21.2% (2571) diffuse white light, 19.5% (2364) diffuse blue light, and 17.5% (2128) ScS. MobileNetV2 achieved the highest overall F1 score of 97.95% (CI, 97.94%-97.97%), AUC-ROC of 99.83% (99.72%-99.9%), and accuracy of 98.98% (98.97%-98.98%). The F1 scores for slit beam, diffuse white light, diffuse blue light, and ScS were 97.82% (97.80%-97.84%), 96.62% (96.58%-96.66%), 99.88% (99.87%-99.89%), and 97.59% (97.55%-97.62%), respectively. Slit beam and ScS were the 2 most frequently misclassified illumination. CONCLUSIONS: MobileNetV2 accurately labeled illumination of SLPs using a large data set of corneal images. Effective, automatic classification of SLPs is key to integrating deep learning systems for clinical decision support into practice workflows.

Severe Spontaneous Tilt of Scleral-Fixated Intraocular Lenses.

PURPOSE: To report and evaluate a multicenter series of 18 cases of severe, spontaneous IOL tilt involving the flanged intrascleral haptic fixation technique (FISHF). DESIGN: Clinical study with historical controls. METHODS: We report a cross-sectional study of 46 FISHF cases using the CT Lucia 602 IOL at a single academic center over a period of 24 weeks to determine the incidence of severe rotisserie-style rotational tilt. These rates were then compared with the same time-frame the prior year to help determine if this is a new phenomenon. Additional cases of severe tilt were solicited from another 4 academic centers. RESULTS: Among 46 FISHF cases at a single center, 5 developed severe tilt. No clear pattern in surgical technique, ocular history, or ocular anatomy was evident in these cases compared with controls, although the involved IOLs clustered within a narrow diopter range, indicative of a batch effect. In the same 24-week interval the year before, 33 FISHF cases were performed, none of which exhibited severe rotational tilt. In our multicenter dataset, 18 cases of tilt were identified. Surgeons included fellow and early-career physicians as well as surgeons with multiple years of experience with the Yamane technique. A variety of surgical approaches for FISHF were represented. In at least 8 of the cases, haptic rotation and/or dehiscence at the optic-haptic junction were documented. CONCLUSIONS: The identification of haptic rotation and dehiscence intraoperatively in several cases may reflect a new stability issue involving the optic-haptic junction.

Corneal innervation as a window to peripheral neuropathies.

The cornea receives the densest sensory innervation of the body, which is exclusively from small-fiber nociceptive (pain-sensing) neurons. These are similar to those in the skin of the legs, the standard location for neurodiagnostic skin biopsies used to diagnose small-fiber peripheral polyneuropathies. Many cancer chemotherapy agents cause dose-related, therapy-limiting, sensory-predominant polyneuropathy. Because corneal innervation can be detected non-invasively, it is a potential surrogate biomarker for skin biopsy measurements. Therefore, we compared hindpaw-skin and cornea innervation in mice treated with neurotoxic chemotherapy. Paclitaxel (0, 5, 10, or 20 mg/kg) was administered to C57/Bl6 mice and peri-mortem cornea and skin biopsies were immunolabeled to reveal and permit quantitation of innervation. Both tissues demonstrated dose-dependent, highly correlated (r = 0.66) nerve fiber damage. These findings suggest that the quantification of corneal nerves may provide a useful surrogate marker for skin peripheral innervation.

Orbital development as a function of age in indigenous North American skeletons.

PURPOSE: Infants with orbital hemangiomas and vascular malformations often develop expanded orbits or regional hyperostosis. Treatment in these cases depends, in part, on the stage of orbital development at the time of intervention; yet, orbital development with respect to age is not well-known. The authors sought to determine the rate of orbital development and the age of orbital maturation in a single ethnic population. METHODS: Skeletons recovered in North America and housed at the Peabody Museum of Archaeology and Ethnology, Harvard University, were inspected. The age of specimen was determined by dentition. Orbital volume was measured using 1-mm glass beads and a graduated cylinder. Linear measurements were taken with calipers and paper rulers. The measurements were plotted against age, and statistical analysis was performed. Relevant literature was reviewed. RESULTS: Of the hundreds of skeletons examined, 42 were sufficiently intact for orbital measurement. The specimens represented a period of up to 1000 years. Thirty-two were pediatric (defined prenatal to 18 years) and 10 were adults. Mean adult orbital volume was 26.2 ml. Based on the regression analysis, 60% of adult orbital volume was achieved at 4.35 years, 75% at 9.36 years, and 90% at 17.13 years of age. Linear dimensions progressively increased with age. CONCLUSIONS: This largest direct-measure study of pediatric orbital volume suggests that orbital growth continually decelerates from birth until maturity at 22 years. With 50% of orbital growth occurring by 16 months of age, surgeons removing periocular vascular anomalies after that age should consider concurrent skeletal management.

Prophylactic postoperative antibiotics for enucleation and evisceration.

PURPOSE: To investigate the necessity and usefulness of prophylactic postoperative antibiotics in patients undergoing enucleation or ocular evisceration. METHODS: A retrospective, multicenter, comparative case series was designed. After obtaining Institutional Review Board authorization, a medical records' review was conducted. Demographics, indication for surgery, surgical technique, postoperative antibiotic dosing, and postoperative course were evaluated. Records were grouped according to antibiotic protocols, and presence or absence of postoperative wound infection (orbital cellulitis) was recorded. Rates of postoperative infection were analyzed statistically. RESULTS: Between 1996 and 2011, 666 evisceration or enucleation surgeries were conducted at 4 institutions. Six hundred forty-eight records were available for analysis, of which 4 were excluded due to insufficient follow-up data. All the remaining 644 patients received a single, perioperative, intravenous dose of antibiotics. Five hundred seventy-eight patients (90%) received an orbital implant, while 66 (10%) did not. Three hundred eighty-one patients (59%) received postoperative antibiotics, and 263 patients (41%) did not. Two cases were identified with signs suggestive of infection, but no culture-positive infections were found, and no patient was admitted to the hospital for management. Of the 2 suspicious cases, 1 was found in the group that received postoperative antibiotics (group 1) and 1 in the group that did not receive postoperative antibiotics (group 2). No statistically significant difference in postoperative infection rate was noted between the 2 groups (p=0.52). While patients with infectious indications for surgery were more likely to receive postoperative antibiotics (p<0.001), there was no statistically significant difference in rates of infection among patients with infectious indications for surgery based on receiving or not receiving postoperative antibiotics (p=0.79), and no patients with infectious indications for surgery not receiving postoperative antibiotics developed a postoperative infection. CONCLUSIONS: This study demonstrates the clinical safety of withholding postoperative prophylactic antibiotics in orbital surgery even when implanting alloplastic material in a sterile field. Furthermore, Centers for Disease Control and Prevention guidelines mandate cessation of postoperative antibiotics within 24 hours of surgery. Surgeons are cautioned not to generalize these results to nonsterile surgery such as sinonasal or nasolacrimal surgery.

Comparison of methodologies in volumetric orbitometry.

PURPOSE: The rate at which the orbit matures is not well-documented. Limiting this pursuit are the difficulties inherent in measuring orbital volumes accurately. This study compared 3 common methods of determining orbital volume and sought to identify an accurate, practical manner for doing so. METHODS: The volume of 1 orbit of 8 human cadaver heads was independently measured using 3 different methods: 1) CT was performed, and images were analyzed with 3-dimensional (3D) volumetric software; 2) The same orbits were then exenterated and a silicone cast was taken. The cast volumes were measured by water displacement; 3) The orbits were then filled with 1-mm glass beads that were transferred to a graduated cylinder where their volume was determined. The data were analyzed statistically. RESULTS: Intraobserver agreements were good for both beads and casts. Interobserver agreements were good for both beads and CT (p > 0.05). Values obtained using the bead method were equal to values obtained using the cast method (p > 0.05). However, agreement between direct (orbital fillers and casts) and indirect measurements (radiographic techniques) was not satisfactory (p < 0.05). CONCLUSIONS: Independent of method, determining orbital volume is inherently difficult owing to the hyperbolic parabola that is the orbit entrance; all methods require estimation. Glass beads and casts yielded more reproducible values but can only be used in cadavers. CT measurement is prone to error due to the variability of methodologies used but allows access to enormous testing populations. Interstudy comparison is currently not possible. CT volumetric software with strict universal standards for estimating the anterior limit of the orbit appears to be the best method of studying human orbital volumes.

Evaluation of the Nallasamy formula: a stacking ensemble machine learning method for refraction prediction in cataract surgery.

AIMS: To develop a new intraocular lens power selection method with improved accuracy for general cataract patients receiving Alcon SN60WF lenses. METHODS AND ANALYSIS: A total of 5016 patients (6893 eyes) who underwent cataract surgery at University of Michigan's Kellogg Eye Center and received the Alcon SN60WF lens were included in the study. A machine learning-based method was developed using a training dataset of 4013 patients (5890 eyes), and evaluated on a testing dataset of 1003 patients (1003 eyes). The performance of our method was compared with that of Barrett Universal II, Emmetropia Verifying Optical (EVO), Haigis, Hoffer Q, Holladay 1, PearlDGS and SRK/T. RESULTS: Mean absolute error (MAE) of the Nallasamy formula in the testing dataset was 0.312 Dioptres and the median absolute error (MedAE) was 0.242 D. Performance of existing methods were as follows: Barrett Universal II MAE=0.328 D, MedAE=0.256 D; EVO MAE=0.322 D, MedAE=0.251 D; Haigis MAE=0.363 D, MedAE=0.289 D; Hoffer Q MAE=0.404 D, MedAE=0.331 D; Holladay 1 MAE=0.371 D, MedAE=0.298 D; PearlDGS MAE=0.329 D, MedAE=0.258 D; SRK/T MAE=0.376 D, MedAE=0.300 D. The Nallasamy formula performed significantly better than seven existing methods based on the paired Wilcoxon test with Bonferroni correction (p<0.05). CONCLUSIONS: The Nallasamy formula (available at https://lenscalc.com/) outperformed the seven other formulas studied on overall MAE, MedAE, and percentage of eyes within 0.5 D of prediction. Clinical significance may be primarily at the population level.

Ray tracing intraocular lens calculation performance improved by AI-powered postoperative lens position prediction.

AIMS: To assess whether incorporating a machine learning (ML) method for accurate prediction of postoperative anterior chamber depth (ACD) improves cataract surgery refraction prediction performance of a commonly used ray tracing power calculation suite (OKULIX). METHODS AND ANALYSIS: A dataset of 4357 eyes of 4357 patients with cataract was gathered at the Kellogg Eye Center, University of Michigan. A previously developed machine learning (ML)-based method was used to predict the postoperative ACD based on preoperative biometry measured with the Lenstar LS900 optical biometer. Refraction predictions were computed with standard OKULIX postoperative ACD predictions and ML-based predictions of postoperative ACD. The performance of the ray tracing approach with and without ML-based ACD prediction was evaluated using mean absolute error (MAE) and median absolute error (MedAE) in refraction prediction as metrics. RESULTS: Replacing the standard OKULIX postoperative ACD with the ML-predicted ACD resulted in statistically significant reductions in both MAE (1.7% after zeroing mean error) and MedAE (2.1% after zeroing mean error). ML-predicted ACD substantially improved performance in eyes with short and long axial lengths (p<0.01). CONCLUSIONS: Using an ML-powered postoperative ACD prediction method improves the prediction accuracy of the OKULIX ray tracing suite by a clinically small but statistically significant amount, with the greatest effect seen in long eyes.

MAEPI and CIR: New Metrics for Robust Evaluation of the Prediction Performance of AI-Based IOL Formulas.

Purpose: To develop a class of new metrics for evaluating the performance of intraocular lens power calculation formulas robust to issues that can arise with AI-based methods. Methods: The dataset consists of surgical information and biometry measurements of 6893 eyes of 5016 cataract patients who received Alcon SN60WF lenses at University of Michigan's Kellogg Eye Center. We designed two types of new metrics: the MAEPI (Mean Absolute Error in Prediction of Intraocular Lens [IOL]) and the CIR (Correct IOL Rate) and compared the new metrics with traditional metrics including the mean absolute error (MAE), median absolute error, and standard deviation. We evaluated the new metrics with simulation analysis, machine learning (ML) methods, as well as existing IOL formulas (Barrett Universal II, Haigis, Hoffer Q, Holladay 1, PearlDGS, and SRK/T). Results: Results of traditional metrics did not accurately reflect the performance of overfitted ML formulas. By contrast, MAEPI and CIR discriminated between accurate and inaccurate formulas. The standard IOL formulas received low MAEPI and high CIR, which were consistent with the results of the traditional metrics. Conclusions: MAEPI and CIR provide a more accurate reflection of the real-life performance of AI-based IOL formula than traditional metrics. They should be computed in conjunction with conventional metrics when evaluating the performance of new and existing IOL formulas. Translational Relevance: The proposed new metrics would help cataract patients avoid the risks caused by inaccurate AI-based formulas, whose true performance cannot be determined by traditional metrics.

Optical Biometry Changes Throughout Childhood and Adolescence in Patients Wearing Ortho-K Lenses.

Background: Orthokeratology has been shown to suppress progressive myopia in some children. We examine the changes in optical biometry parameters in orthokeratology (Ortho-K) patients, in a retrospective longitudinal study at a tertiary eye care center in Ann Arbor, MI, USA. Methods: Optical biometry measurements obtained with the Lenstar LS 900 (Haag-Streit USA Inc, EyeSuite software version i9.1.0.0) were aggregated from 170 patients who had undergone Ortho-K for myopia correction between 5 and 20 years of age. Pre-intervention biometry measurements were compared with follow-up measurements done 6-18 months after initiation of Ortho-K. Linear mixed models were used to quantify associations in biometry changes with age of intervention allowing for correlation between measurements on two eyes of the same patient. Results: A total of 91 patients were included in the study. Axial length increased through the age of 15.7 ± 0.84 years for Ortho-K patients at our center. The growth curve in our Ortho-K population was comparable to previously published normal growth curves in Wuhan and Germany populations. Corneal thickness and keratometry decreased at a stable rate regardless of age of intervention (-7.9 µm, 95% CI [-10.2, -5.7], p < 0.001). Conclusion: In our population, Ortho-K did not appear to affect the overall trajectory of axial length progression when compared to normal growth curves, despite showing a previously described reduction in corneal thickness. As Ortho-K has been shown to have varying effects that differ from individual to individual, it continues to be important to reassess its effects on new populations to better understand its ideal uses.

Tensor-based Feature Extraction for Pupil Recognition in Cataract Surgery.

Cataract surgery remains the definitive treatment for cataracts, which are a major cause of preventable blindness worldwide. Adequate and stable dilation of the pupil are necessary for the successful performance of cataract surgery. Pupillary instability is a known risk factor for cataract surgery complications, and the accurate segmentation of the pupil from surgical video streams can enable the analysis of intraoperative pupil changes in cataract surgery. However, pupil segmentation performance can suffer due to variations in surgical illumination, obscuration of the pupil with surgical instruments, and hydration of the lens material intraoperatively. To overcome these challenges, we present a novel method called tensor-based pupil feature extraction (TPFE) to improve the accuracy of pupil recognition systems. We analyzed the efficacy of this approach with experiments performed on a dataset of 4,560 intraoperative annotated images from 190 cataract surgeries in human patients. Our results indicate that TPFE can identify features relevant to pupil segmentation and that pupil segmentation with state-of-the-art deep learning models can be significantly improved with the TPFE method.