Cataract Severity Research Articles

Automatic Cataract Severity Detection and Grading Using Deep Learning

Cataracts are an eye condition that causes the eye’s lens to become cloudy and is a significant cause of vision loss worldwide. Accurate and timely detection and diagnosis of cataracts can prevent vision loss. However, poor medical care and expensive treatments prevented cataract patients from receiving appropriate treatment on time. Therefore, an inexpensive system that diagnoses cataracts at an early stage needs to be developed. This study proposes an automatic method for detecting and classifying cataracts in their earliest stages by combining a deep learning (DL) model with the 2D‐discrete Fourier transform (DFT) spectrum of fundus images. The proposed method calculates the spectrogram of fundus images using a 2D‐DFT and uses this calculated spectrogram as an input to the DL model for feature extraction. After feature extraction, the classification task is performed by a softmax classifier. This study collected fundus images from various open‐source databases that are freely available on the Internet and classified them into four classes based on an ophthalmologist’s assessment. All the collected fundus images from various datasets with open access are unsuitable for cataract diagnosis. Consequently, a module for identifying the fundus images of good and poor quality is also incorporated into this method. The experimental results show that the proposed system can outperform previous state‐of‐the‐art works by a significant margin compared to a benchmark of four‐class accuracy and achieves the four‐class accuracy of 93.10%.

Innovative Technologies in the Monitoring of the Age-Related Cataract

Literature review is devoted to modern high-technology instrumental methods of the lens evaluation, which have been developed to improve efficiency of the diagnostics and management of cataract in ophthalmological clinical practice. The slit-lamp examination of lens and retroillumination images are used for subjective assessment of cataract according to LOCS III grading system. Currently objective methods of lens evaluation have practical value and provide information about early age-related changes of crystalline lens with the quantitative assessment of the severity of opacities including the optical and ultrasound examinations. Optical methods include densitometry using Scheimpflug images obtained from applications to quantify the localization of lens opacities and their progression rate. Another method is the intensity distribution analysis of the wave front of the optical aberrations according to grading cataract severity with the objective scatter index (OSI) obtained by double-pass technology. Moreover the optical quantitative assessment of lens opacities is carried out by the Shack—Hartmann wave front sensor to identify the grade of nuclear opalescence (N-type) with the Lens Opacities Classification System III. This method is very helpful in nuclear cataract. Swept source optical coherent tomography (SS-OCT) with variable length of wave and high frequency detector is used for the density measurements of nuclear and cortical opacities of lens imaging in micro- and macro dimensional scale. Ultrasound biomicroscopy is the most informative ultrasonic diagnostic method for the evaluation of acoustic density and early age-related changes of lens. Current high-informative methods of quantitative assessment of the optical density of lens are helpful in determination of indications for surgery, the optimization of phacoemulsification settings and prevention of intraoperative complications. The use of high-technologic methods demonstrated the effectiveness of eye drops 0,005 % pirenoxine in early lens opacities management.

Feasibility assessment of the Eye Scan Ultrasound System for cataract characterization and optimal phacoemulsification energy estimation: protocol for a pilot, nonblinded and monocentre study

BackgroundCataracts are lens opacifications that are responsible for more than half of blindness cases worldwide, and the only treatment is surgical intervention. Phacoemulsification surgery, the most frequently performed cataract surgery in developed countries, has associated risks, some of which are related to excessive phacoemulsification energy levels and times. The protocol proposed in herein will be used to evaluate the feasibility of a new experimental medical device, the Eye Scan Ultrasound System (ESUS), for the automatic classification of cataract type and severity and quantitative estimation of the optimal phacoemulsification energy.MethodsThe pilot study protocol will be used to evaluate the feasibility and safety of the ESUS in clinical practice. The study will be conducted in subjects with age-related cataracts and on healthy subjects as controls. The procedures include data acquisition with the experimental ESUS, classification based on the Lens Opacity Classification System III (LOCS III, comparator) using a slit lamp, contrast sensitivity test, optical coherence tomography, specular microscopy and surgical parameters.ESUS works in A-scan pulse-echo mode, with a central frequency of 20 MHz. From the collected signals, acoustic parameters will be extracted and used for automatic cataract characterization and optimal phacoemulsification energy estimation.The study includes two phases. The data collected in the first phase (40 patients, 2 eyes per patient) will be used to train the ESUS algorithms, while the data collected in the second phase (10 patients, 2 eyes per patient) will be used to assess the classification performance. System safety will be monitored during the study.DiscussionThe present pilot study protocol will evaluate the feasibility and safety of the ESUS for use in clinical practice, and the results will support a larger clinical study for the efficacy assessment of the ESUS as a diagnostic tool. Ultimately, the ESUS is expected to represent a valuable tool for surgical planning by reducing complications associated with excessive levels of phacoemulsification energy and surgical times, which will have a positive impact on healthcare systems and society. The study is not yet recruiting.Trial registrationClinicalTrials.gov identifier NCT04461912, registered on July 8, 2020.

Inhibitory effect of lanosterol on cataractous lens of cynomolgus monkeys using a subconjunctival drug release system.

ABSTRACTBackgroundTo evaluate the effect of lanosterol on cataractous lens of cynomolgus monkeys using a subconjunctival drug release system.MethodsNine elder cynomolgus monkeys were used, consisting of three monkeys without cataract as controls, three monkeys with naturally occurring cortical cataract, and three monkeys with nuclear cataract as intervention groups. Nanoparticulated thermogel with lanosterol and fluorescein was administered by subconjunctival injection in the monkeys with cataract. Fluorescence changes of injected thermogel and cataract progression were observed. Lanosterol concentration in aqueous humor, solubility changes in lens proteins, and oxidative stress levels were analyzed in the lenses of the control and intervention groups.ResultsInjected thermogel showed decreased fluorescence during follow up. Lanosterol concentration in aqueous humor increased in the first 2 weeks and then gradually decreased, which was in accordance with the changes in cortical lens clarity. However, lenses with nuclear opacification showed little change. In the cortical region of lenses with cortical cataract, solubility of α-crystallin was significantly increased after administration of lanosterol, as well as the reduction of oxidative stress.ConclusionsWe demonstrated the effect of lanosterol on cataract progression based on in vivo models of primates. Lanosterol showed a short-term and reliable reversal effect on reducing cataract severity in cortical cataract in the early stages, possibly due to the increase in the solubility of lens proteins and changes in the oxidative stress status. Lanosterol administration using subconjunctival drug release system could be a promising nonsurgical approach for future clinical studies of cataract prevention and treatment.

Levels of Trace Elements in the Lens, Aqueous Humour, and Plasma of Cataractous Patients-A Narrative Review.

Cataracts are one of the most common causes of effective vision loss. Although most cases of cataracts are related to the ageing process, identifying modifiable risk factors can prevent their onset or progression. Many studies have suggested that micro and macroelement levels, not only in blood serum but also in the lens and aqueous humour, may affect the risk of the occurrence and severity of cataracts. This systematic review aims to summarise existing scientific reports concerning the importance of trace elements in cataractogenesis. Many authors have pointed out elevated or decreased levels of particular elements in distinct ocular compartments. However, it is not known if these alterations directly affect the increased risk of cataract occurrence. Further studies are needed to show whether changes in the levels of these elements are correlated with cataract severity and type. Such information would be useful for determining specific recommendations for micronutrient supplementation in preventing cataractogenesis.

Relationship Between Cooking Fuel and Lens Opacities in South India: A 15-Year Prospective Cohort Study

To determine whether exposure to unclean cooking fuels was associated with subsequent cataract progression as reported in previous cross-sectional studies. Prospective cohort study. This is a secondary observational analysis of the community-based Antioxidants in Prevention of Cataracts trial (ClinicalTrials.gov ID NCT01664819). The exposure of interest was cooking fuel type, measured at baseline. Main outcome measures were baseline cataract severity and self-reported cataract surgery at a 15-year visit. Baseline and 15-year follow-up data were available for 798 and 579 participants, respectively. Wood or kerosene was used by 711 of 798 (89.1%) baseline participants, including 539 of 579 (93.1%) participants with complete follow-up. Cooking fuel type was not associated with cataract severity at baseline (P=.443). Of 8334 person-years of follow-up, 90 cataract surgeries were observed over 15 years (1.08 surgeries per 100 person-years; 95% CI=0.87-1.32). Use of wood or kerosene was not associated with 15-year incidence of cataract surgery relative to use of propane (adjusted P=.154). Cataract surgery was more common in older individuals (HR=1.1 per year, 95% CI=1.1-1.2, P < .001), those with baseline myopia (HR=2.1, 95% CI=1.2-3.5, P=.009), and women (HR=3.5, 95% CI=1.2-10.1, P=.019). This study found no association between unclean cooking fuels and cataract progression over a 15-year period. No other modifiable risk factors were associated with incident self-reported cataract surgery.

Optical Coherence Tomography Angiography to Estimate Early Retinal Blood Flow Changes after Uncomplicated Cataract Surgery.

Background: To investigate macular microvascular changes after uncomplicated phacoemulsification surgery according to the cataract severity grade. Methods: Retrospective, cross-sectional study involving 23 eyes of 23 patients who underwent elective cataract extraction. All patients underwent routine ophthalmologic examination, including optical coherence tomography angiography (OCTA) at baseline (preoperative visit, T0) and seven days postoperatively (T7). OCTA scans were obtained with the spectral domain system Cirrus 5000 (Carl Zeiss Meditec, Inc., Dublin, CA, USA), and 3 mm × 3 mm raster fovea-centered scans were obtained to evaluate the superficial capillary plexus (SCP) vessel density, perfusion density, and foveal avascular zone (FAZ) parameters. Results: SCP perfusion density significantly increased from 28.3 ± 5.73% to 33.74 ± 4.13% after the surgery (p < 0.001). Similarly, SCP vessel density significantly increased from 15.14 ± 3.41 mm−1 to 18.14 ± 2.57 mm−1 after surgery (p < 0.001). The mean preoperative FAZ area significantly increased from 0.27 ± 0.12 mm to 0.24 ± 0.11 mm seven days postoperatively (p = 0.008). When comparing softer and harder cataracts, no significant variations in SCP vessel density, as well as SCP perfusion density parameters and the FAZ area, perimeter, and circularity index, were noted before and after surgery. Conclusions: Macular SPC vessel density and macular SCP perfusion density increase after uncomplicated cataract surgery regardless of the cataract severity.

Artificial Intelligence for the Estimation of Visual Acuity Using Multi-Source Anterior Segment Optical Coherence Tomographic Images in Senile Cataract.

PurposeTo investigate an artificial intelligence (AI) model performance using multi-source anterior segment optical coherence tomographic (OCT) images in estimating the preoperative best-corrected visual acuity (BCVA) in patients with senile cataract.DesignRetrospective, cross-instrument validation study.SubjectsA total of 2,332 anterior segment images obtained using swept-source OCT, optical biometry for intraocular lens calculation, and a femtosecond laser platform in patients with senile cataract and postoperative BCVA ≥ 0.0 logMAR were included in the training/validation dataset. A total of 1,002 images obtained using optical biometry and another femtosecond laser platform in patients who underwent cataract surgery in 2021 were used for the test dataset.MethodsAI modeling was based on an ensemble model of Inception-v4 and ResNet. The BCVA training/validation dataset was used for model training. The model performance was evaluated using the test dataset. Analysis of absolute error (AE) was performed by comparing the difference between true preoperative BCVA and estimated preoperative BCVA, as ≥0.1 logMAR (AE≥0.1) or <0.1 logMAR (AE <0.1). AE≥0.1 was classified into underestimation and overestimation groups based on the logMAR scale.Outcome MeasurementsMean absolute error (MAE), root mean square error (RMSE), mean percentage error (MPE), and correlation coefficient between true preoperative BCVA and estimated preoperative BCVA.ResultsThe test dataset MAE, RMSE, and MPE were 0.050 ± 0.130 logMAR, 0.140 ± 0.134 logMAR, and 1.3 ± 13.9%, respectively. The correlation coefficient was 0.969 (p < 0.001). The percentage of cases with AE≥0.1 was 8.4%. The incidence of postoperative BCVA > 0.1 was 21.4% in the AE≥0.1 group, of which 88.9% were in the underestimation group. The incidence of vision-impairing disease in the underestimation group was 95.7%. Preoperative corneal astigmatism and lens thickness were higher, and nucleus cataract was more severe (p < 0.001, 0.007, and 0.024, respectively) in AE≥0.1 than that in AE <0.1. The longer the axial length and the more severe the cortical/posterior subcapsular opacity, the better the estimated BCVA than the true BCVA.ConclusionsThe AI model achieved high-level visual acuity estimation in patients with senile cataract. This quantification method encompassed both visual acuity and cataract severity of OCT image, which are the main indications for cataract surgery, showing the potential to objectively evaluate cataract severity.

Comparison of acquisition rate and agreement of axial length with two swept-source optical coherence tomographers and a partial coherence interferometer.

To assess the acquisition rate and agreement of axial length (AL) measurements with the OA-2000, Anterion, and IOLMaster 500 in cataractous patients. In total, 298 eyes of 191 cataractous patients were enrolled and scanned with the three devices in random order. The success rate of AL measurements per device was calculated and a chi-square test was utilized to identify the differences in acquisition rate between the three devices. Logistic regression analysis was applied to evaluate the association of different cataract types and severity with the AL measurement acquisition rate. Bland-Altman plots were mapped to appraise the agreement of AL values. AL measurements were successfully achieved in 288 eyes (96.64%) with the OA-2000, in 282 eyes (94.30%) with the Anteiron, and in 246 eyes (82.55%) with the IOLMaster 500. Significant differences in the acquisition rate were found between either of the SS-OCT devices and IOLMaster 500 by chi-square analysis (P < 0.001). No significant difference was noted between OA-2000 and Anterion. Increasing severity of posterior subcapsular cataract was associated with a higher failure rate with the IOLMaster 500. Bland-Altman analysis identified good agreement between the three biometers with narrow 95% limits of agreement. The OA-2000 and Anterion showed similarly higher acquisition rate of AL measurements than IOLMaster 500 in cataractous patients. Good agreement for AL values was found between the three biometers in cataractous patients.

Refractive Lens Exchange Surgery in Early-Onset High Myopia Patients With Partial Cataract.

PurposeBy reporting clinical characteristics and retinal image quality before and after refractive lens replacement surgery in early-onset high myopia (eoHM) patients presenting with partial cataract, we emphasized the need for an objective way to grade the severity of partial cataracts.MethodsThis retrospective, consecutive case series included six Chinese patients (nine eyes). Analysis of previous medical records, visual acuity, optometry, retinal image quality, and axial length (AXL) before surgery and after surgery was performed.ResultsFive females and one male (nine eyes) with a mean (± SD) age of 11.6 ± 7.9 years (range: 4–25 years) were included in this study. The preoperative spherical power ranged from −7.5 to −42 D. The mean follow-up time was 36 months (range: 24–48 months). Phacoemulsification was followed by in-the-bag implantation of intraocular lens. For patients who were under 6 years old, posterior capsulotomy + anterior vitrectomy were performed simultaneously. All surgeries were uneventful and no postoperative complications occurred during the entire follow-up period. All patients’ uncorrected visual acuity improved by ≥2 lines postoperatively(Snellen acuity). LogMAR best-corrected visual acuity was improved at 24-month (P = 0.042) and endpoint (P = 0.046) follow-ups. Modulation transfer function cutoff frequency (MTFcutoff) and objective scatter index (OSI) was significantly improved at 12-month (P = 0.025, P = 0.038), 24-month (P = 0.005, P = 0.007) and endpoint (P = 0.005, P = 0.008) follow-ups. Postoperative AXL remained stable during 2–4 year follow-ups (P > 0.05).ConclusionRefractive lens replacement surgery is safe and effective for improving functional vision in eoHM patients presenting with partial cataract. Retinal image quality could provide a useful and objective way to facilitate partial cataract severity evaluation and surgery decision making.

The Effect of Cataract on Color Vision Measurement with the Low-Vision Cambridge Colour Test: Providing an Adjustment Factor for Clinical Trials.

PurposeTo quantify the effect of cataract on color vision as measured by the low-vision Cambridge Colour Test (lvCCT; Cambridge Research Systems) and to understand whether different types and severities of cataract have different effects on color vision.DesignCohort study.ParticipantsPatients aged 18 to 95 undergoing routine cataract surgery at the Oxford Eye Hospital.MethodsThe lvCCT was performed to measure color sensitivity in both eyes both before and after surgery. The crystalline lens was examined and graded according to the Lens Opacities Classification System III to determine the type and severity of cataract. Measures of repeatability were performed for the data to explore test–retest bias using Bland–Altman analysis. The Wilcoxon signed-rank test was performed to assess the effect of cataract on color vision by comparing control and surgical test measurements. Three multiple linear regressions were performed to relate cataract grading or severity to color vision measurements.Main Outcome MeasuresColor discrimination along each of the protan, deutan, and tritan confusion lines.ResultsThe Wilcoxon signed-rank test showed a statistically significant difference in both the protan (P = 0.024) and tritan (P = 0.020) axes on comparison of control and surgical test measurements. As severity of cataract increased, color vision sensitivity was affected more greatly, and nuclear sclerotic cataract showed the most profound effect on color vision sensitivity in the lvCCT; however, the linear regression models showed that these observations did not reach statistical significance.ConclusionsCataract surgery has a statistically significant effect on color vision in both the protan and tritan axes. The effects of specific subtypes of cataract and different severities could not be elucidated because of the high prevalence of patients with mixed cataract. The lvCCT color sensitivity measurements are used regularly as outcome measures in clinical gene therapy trials involving vitreoretinal surgery, and vitrectomy accelerates cataract formation. Therefore, it is important to quantify the effect of cataract on color vision measurements so that it may be taken into account when used as an outcome measure in clinical trials. We were unable to derive a precise correction factor for cataract on color vision measurements.

Computer‐aided diagnosis of cataract severity using retinal fundus images and deep learning

AbstractCataracts are lenticular opacities that can appear in different parts of the lens in the eye and are a leading cause of blindness globally. Accurate detection and early‐stage diagnosis can prevent the cataract and improve the quality of life for cataract patients. However, clinical cataract detection and grading require the expertise of trained eye specialists, which may impede everyone's early intervention due to the underlying expenses. This article proposed a computer aid diagnosis method for cataract detection, which also grades the severity of cataracts from fundus retinal images such as normal, mild, moderate, and severe. The proposed method uses a hybrid approach in which various pre‐trained convolutional neural networks (AlexNet, VGGNet, ResNet) with transfer learning are used to extract features. These feature vectors of each network individually and in the fused form are applied on the support vector machine classifiers for 4‐stage cataract classification. This architecture also takes advantage of ensemble learning by applying a majority voting scheme on the predictions of these SVM classifiers. The fundus cataract images are obtained from several open‐access datasets and arranged into 4‐classes with the assistance of an eye specialist. Since all the collected images are not suitable for diagnosis, an image quality selection module is included with this method to determine the quality of fundus images. The proposed method achieved 96.25% 4‐class classification accuracy. According to the experimental results, the proposed method is effective for cataract classification and outperforms conventional methods.

Development of portable and robust cataract detection and grading system by analyzing multiple texture features for Tele-Ophthalmology.

This paper presents a low cost, robust, portable and automated cataract detection system which can detect the presence of cataract from the colored digital eye images and grade their severity. Ophthalmologists detect cataract through visual screening using ophthalmoscope and slit lamps. Conventionally a patient has to visit an ophthalmologist for eye screening and treatment follows the course. Developing countries lack the proper health infrastructure and face huge scarcity of trained medical professionals as well as technicians. The condition is not very satisfactory with the rural and remote areas of developed nations. To bridge this barrier between the patient and the availability of resources, current work focuses on the development of portable low-cost, robust cataract screening and grading system. Similar works use fundus and retinal images which use costly imaging modules and image based detection algorithms which use much complex neural network models. Current work derives its benefit from the advancements in digital image processing techniques. A set of preprocessing has been done on the colored eye image and later texture information in form of mean intensity, uniformity, standard deviation and randomness has been calculated and mapped with the diagnostic opinion of doctor for cataract screening of over 200 patients. For different grades of cataract severity edge pixel count was calculated as per doctor’s opinion and later these data are used for calculating the thresholds using hybrid k-means algorithm, for giving a decision on the presence of cataract and grade its severity. Low value of uniformity and high value of other texture parameters confirm the presence of cataract as clouding in eye lens causes the uniformity function to take lower value due to presence of coarse texture. Higher the edge pixel count value, this confirms the presence of starting of cataract as solidified regions in lens are nonuniform. Lower value corresponds to fully solidified region or matured cataract. Proposed algorithm was initially developed on MATLAB, and tested on over 300 patients in an eye camp. The system has shown more than 98% accuracy in detection and grading of cataract. Later a cloud based system was developed with 3D printed image acquisition module to manifest an automated, portable and efficient cataract detection system for Tele-Ophthalmology. The proposed system uses a very simple and efficient technique by mapping the diagnostic opinion of the doctor as well, giving very promising results which suggest its potential use in teleophthalmology applications to reduce the cost of delivering eye care services and increasing its reach effectively. Developed system is simple in design and easy to operate and suitable for mass screening of cataracts. Due to non-invasive and non-mydriatic and mountable nature of device, in person screening is not required. Hence, social distancing norms are easy to follow and device is very useful in COVID-19 like situation.

Cataract grading method based on deep convolutional neural networks and stacking ensemble learning

AbstractThe cataract is the most common cause of severe vision impairment or blindness worldwide. A periodical diagnosis is recommended in order to prevent cataract severity, where screening might be feasibly ensured through fundus images. In this paper, we propose a CAD system to efficiently grade the cataract from fundus images. For such a need, an ensemble learning framework is put forward where the knowledge of three convolutional deep neural networks is stacked in order to perform an efficient cataract prediction. The main contributions of this work are given as follows: (1) Preprocessing and data augmentation of fundus images are carried out to ensure the robustness of the cataract grading; (2) The well‐known DL architectures (Inception‐V3, MobileNet‐V2 and NasNet‐Mobile) are fine‐tuned and learned as base classifiers; (3) An Extreme Learning Machine (ELM) is performed as a meta classifier to stack the predictions of base classifiers. The evaluation is conducted using a dataset of 590 fundus images selected from two public databases. The suggested framework achieves 94.07% accuracy, 94.37% sensitivity, 93.62% specificity, 95.71% precision and 95.04% F‐measure for cataract grading. The experimentation proves that the proposed framework successfully grades fundus images into cataract severity. Moreover, stacking ensemble learning allows achieving a performance that significantly surpasses the ones realized by each DL architecture, applied separately.

An Assessment of Cataract Severity Based on Antioxidant Status and Ascorbic Acid Levels in Aqueous Humor.

Cataract is the leading cause of blindness throughout the world. Currently, the cataract severity evaluation is based on the subjective LOCS III guideline. To ameliorate the evaluation system and develop an objective and quantitative analysis, we investigated the relationships among aqueous humor total antioxidant capacity (AqTAC), ascorbic acid (AqAA) concentration, and cataract severity. In this study, we enrolled 130 cataract patients who underwent phacoemulsification between April 2019 and March 2020. The AqTAC and AqAA were measured by our own developed TAC assay and commercially available kit. Cataract severity was recorded by nuclear opalescence (NO) and cortical cataract (CC) degree according to LOCS III. Cumulative dissipated energy (CDE) during phacoemulsification was recorded to verify the severity of the cataract. As a result, we found a moderate correlation between AqTAC and CDE (p < 0.001). In addition, we found AqTAC independently associated with the CDE when analyzed by multivariate linear regression (p < 0.001). AqTAC also negatively correlated to cataract severity when measured by NO and CC (p = 0.012 in NO grade 3 vs. grade 1; p = 0.012 in CC grade 2 vs. grade 1; p < 0.001 in CC grade 3 vs. grade 1). We further found AqAA provided 71.9 ± 13.5% of AqTAC, and showed a high correlation (rho = 0.79, p < 0.001). In conclusion, we found a significant correlation between AqTAC/AqAA and cataract severity measured by CDE. The correlation was superior to the correlation between LOCS III and CDE. Aqueous humor TAC owns the potential to assess cataracts in an objective and quantitative way.

Safety and effectiveness of a new ophthalmic viscosurgical device: randomized, controlled study.

To evaluate the safety and effectiveness of a new dispersive ophthalmic viscosurgical device (OVD) (ClearVisc) compared with an approved dispersive OVD (Viscoat) when used in cataract surgery. 16 clinics in the United States. Prospective multicenter controlled randomized 1:1 (ClearVisc:Viscoat; stratified by site, age group, and cataract severity). Patients and examiners masked. Patients aged 45 years or older with age-related noncomplicated cataract considered amenable to treatment with standard phacoemulsification cataract extraction and intraocular lens (IOL) implantation were included. Patients were randomized to receive either ClearVisc or Viscoat using standard techniques. 5 postoperative visits occurred at 6 hours, 24 hours, 7 days, 1 month, and 3 months. The primary effectiveness outcome was the change in endothelial cell density (ECD) from baseline to 3 months. The primary safety end point was the proportion of patients who experienced at least 1 intraocular pressure (IOP) measurement ≥30 mm Hg at any follow-up visit. Noninferiority was tested. Inflammation and adverse events were evaluated. 372 patients were randomized: 184 patients in the ClearVisc group and 188 patients in the Viscoat group. ClearVisc was noninferior to Viscoat in mean percentage of ECD loss from baseline to 3 months (8.4% and 6.8%, respectively). ClearVisc was significantly noninferior to Viscoat in the proportion of patients with postoperative IOP ≥30 mm Hg at any follow-up visit (17.4% and 20.3%, respectively, P = .0002). ClearVisc dispersive OVD provides surgeons with a new option in the continuum of approved dispersive OVDs with beneficial properties as a surgical aid in cataract extraction and IOL implantation.

DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity

To develop deep learning models to perform automated diagnosis and quantitative classification of age-related cataract from anterior segment photographs. DeepLensNet was trained by applying deep learning models to the Age-Related Eye Disease Study (AREDS) dataset. A total of 18 999 photographs (6333 triplets) from longitudinal follow-up of 1137 eyes (576 AREDS participants). Deep learning models were trained to detect and quantify nuclear sclerosis (NS; scale 0.9-7.1) from 45-degree slit-lamp photographs and cortical lens opacity (CLO; scale 0%-100%) and posterior subcapsular cataract (PSC; scale 0%-100%) from retroillumination photographs. DeepLensNet performance was compared with that of 14 ophthalmologists and 24 medical students. Mean squared error (MSE). On the full test set, mean MSE for DeepLensNet was 0.23 (standard deviation [SD], 0.01) for NS, 13.1 (SD, 1.6) for CLO, and 16.6 (SD, 2.4) for PSC. On a subset of the test set (substantially enriched for positive cases of CLO and PSC), for NS, mean MSE for DeepLensNet was 0.23 (SD, 0.02), compared with 0.98 (SD, 0.24; P= 0.000001) for the ophthalmologists and 1.24 (SD, 0.34; P= 0.000005) for the medical students. For CLO, mean MSE was 53.5 (SD, 14.8), compared with 134.9 (SD, 89.9; P= 0.003) for the ophthalmologists and 433.6 (SD, 962.1; P= 0.0007) for the medical students. For PSC, mean MSE was 171.9 (SD, 38.9), compared with 176.8 (SD, 98.0; P= 0.67) for the ophthalmologists and 398.2 (SD, 645.4; P=0.18) for the medical students. In external validation on the Singapore Malay Eye Study (sampled to reflect the cataract severity distribution in AREDS), the MSE for DeepSeeNet was 1.27 for NS and 25.5 for PSC. DeepLensNet performed automated and quantitative classification of cataract severity for all 3 types of age-related cataract. For the 2 most common types (NS and CLO), the accuracy was significantly superior to that of ophthalmologists; for the least common type (PSC), it was similar. DeepLensNet may have wide potential applications in both clinical and research domains. In the future, such approaches may increase the accessibility of cataract assessment globally. The code and models are available at https://github.com/ncbi/deeplensnet.

Color vision assessment following cataract surgery using anomaloscope

Aim: To evaluate the impact of cataract on color vision detection using an anomaloscope.&#x0D; Methods: 12 patients aged 64–82 years with history of cataract surgery in one eye and mature cataracts in the other eye were examined for color vision using the Rayleigh and the Moreland anomaloscope tests.&#x0D; Results: Different patterns of blue and green color vision deterioration have been observed in the eye with cataract as compared to the IOL eye. A decrease in M1 to approximately 1 (the minimum of the Moreland test scale) was observed in 5 out of 12 participants, whereas an increase in M2 to 99 (the maximum of the Moreland test scale) was observed in 6 out of 12 subjects. R1 found in the Rayleigh test was significantly lower in the cataract eyes as compared to the IOL eyes (38.9 ± 2.3 vs. 43.4 ± 1.6; p &lt; 0.001).&#x0D; Conclusions: It has been shown that cataract causes a decrease in color sensitivity, especially affecting green and blue colors. Following IOL implantation, the previously deteriorated vision of green and blue was clearly improved. The most valuable parameters of the anomaloscope test, which enable to determine cataract severity, were M1, M2 and R1.

Aged Nrf2-Null Mice Develop All Major Types of Age-Related Cataracts

PurposeAge-related cataracts affect the majority of older adults and are a leading cause of blindness worldwide. Treatments that delay cataract onset or severity have the potential to delay cataract surgery, but require relevant animal models that recapitulate the major types of cataracts for their development. Unfortunately, few such models are available. Here, we report the lens phenotypes of aged mice lacking the critical antioxidant transcription factor Nfe2l2 (designated as Nrf2 −/−).MethodsThree independent cohorts of Nrf2 −/− and wild-type C57BL/6J mice were evaluated for cataracts using combinations of slit lamp imaging, photography of freshly dissected lenses, and histology. Mice were fed high glycemic diets, low glycemic diets, regular chow ad libitum, or regular chow with 30% caloric restriction.ResultsNrf2 −/− mice developed significant opacities between 11 and 15 months and developed advanced cortical, posterior subcapsular, anterior subcapsular, and nuclear cataracts. Cataracts occurred similarly in male mice fed high or low glycemic diets, and were also observed in 21-month male and female Nrf2 −/− mice fed ad libitum or 30% caloric restriction. Histological observation of 18-month cataractous lenses revealed significant disruption to fiber cell architecture and the retention of nuclei throughout the cortical region of the lens. However, fiber cell denucleation and initiation of lens differentiation was normal at birth, with the first abnormalities observed at 3 months.ConclusionsNrf2 −/− mice offer a tool to understand how defective antioxidant signaling causes multiple forms of cataract and may be useful for screening drugs to prevent or delay cataractogenesis in susceptible adults.

Modeling congenital cataract in vitro using patient-specific induced pluripotent stem cells

Congenital cataracts are the leading cause of childhood blindness. To date, surgical removal of cataracts is the only established treatment, but surgery is associated with multiple complications, which often lead to visual impairment. Therefore, mechanistic studies and drug-candidate screening have been intrigued by the aims of developing novel therapeutic strategies. However, these studies have been hampered by a lack of an appropriate human-disease model of congenital cataracts. Herein, we report the establishment of a human congenital cataract in vitro model through differentiation of patient-specific induced pluripotent stem cells (iPSCs) into regenerated lenses. The regenerated lenses derived from patient-specific iPSCs with known causative mutations of congenital cataracts (CRYBB2 [p. P24T] and CRYGD [p. Q155X]) showed obvious opacification that closely resembled that seen in patients’ cataracts in terms of opacification severity and disease course accordingly, as compared with lentoid bodies (LBs) derived from healthy individuals. Increased protein aggregation and decreased protein solubility corresponding to the patients’ cataract severity were observed in the patient-specific LBs and were attenuated by lanosterol treatment. Taken together, the in vitro model described herein, which recapitulates patient-specific clinical manifestations of congenital cataracts and protein aggregation in patient-specific LBs, provides a robust system for research on the pathological mechanisms of cataracts and screening of drug candidates for cataract treatment.