Early Detection Of Cataract Research Articles

InceptionResNetV2 Based Eye Cataract Detection

An eye lens is a key source for visualizing things. Sometimes it may be cloudy, caused by cataracts, and disturb the visualization process. It is necessary to detect and treat the cataract in its early stages; otherwise, it may lead to blindness. For the early detection of eye cataracts, many solutions have been proposed in past years by experts. However, it requires an expert ophthalmologist or eye surgeon to detect cataracts in its early stages because early detection of cataracts is a challenging task. Now this is the era of digitalization and AI. Everyone is talking about AI. Many researchers use AI to propose a solution to this problem. This study also proposed an AI-based cataract detection solution. A binary class eye fundus images data set was used for this research work. The ”Cataract” data set is a famous dataset for detecting cataracts on KAGGLE. Some image augmentation techniques were used to enhance the data set proficiency. In this study, InceptionResNetV2 was applied with some hyper-parameter tuning. The proposed methodology achieved a training accuracy of 99% and a validation accuracy of 98%.

An Assessment of Contemporary Methods and Data-Enabled Approaches for Early Cataract Detection

Background: Cataracts are common causes of visual impairment. Preventing blindness requires an early and accurate diagnosis. This review examines current cataract diagnosis strategies, explores data-driven machine learning algorithms for early detection, investigates the use of artificial intelligence (AI) approaches, assesses improvements in cataract detection accuracy, identifies research gaps, and provides recommendations for future studies. Methods: We gathered labelled cataract and non-cataract fundus data from the Kaggle. Scholarly publications were sourced from reliable databases such as ProQuest, IEEE, ELSEVIER, Google Scholar, and PubMed. A detailed literature search with specific terms expanded the scope of this review. We included studies that used cataract and non-cataract fundus eye images from cross-sectional, retrospective, and prospective studies. The quality assessment used the AMSTAR tool, considering factors such as literature search comprehensiveness, study selection criteria, data extraction methodologies, and study validity (Table 1). Results: This study encompassed 130 research publications, focusing on machine learning models and clinical-based diagnostic approaches for early-stage cataract identification. The performance of machine-learning models is influenced by factors such as dataset noise and limited reliable data. Barriers to the successful implementation of AI for cataract diagnosis were identified. Conclusions: This review emphasises the obstacles hindering the broad application of AI in cataract diagnosis. Addressing these findings is vital for developing strategies to overcome these challenges and enhance cataract detection systems. To achieve improved accuracy and efficiency in cataract diagnosis, future research should prioritise efforts to enhance dataset availability and quality, reduce data noise, and refine machine-learning algorithms. Unlocking the full potential of AI and/or machine learning can lead to significant breakthroughs in cataract diagnosis, ultimately resulting in better patient outcomes and reduced visual impairments.

Design of A Cataract Detection System based on The Convolutional Neural Network

Cataract, a condition characterized by clouding of the eye's lens, leads to decreased vision and potentially blindness. In Indonesia, it is the predominant cause of blindness, accounting for 81.2% of cases. Given the rising life expectancy, the incidence of degenerative diseases like cataracts is expected to increase. This research aims to develop a cataract detection system capable of classifying eye images as either indicative of cataracts or normal. Utilizing Convolutional Neural Networks (CNN) and RGB-based image processing—including edge detection techniques such as Canny and Prewitt—the system identifies eye contours. This facilitates image segmentation to ascertain the eye's condition. Therefore, image collection and processing models play a crucial role in this study. The research findings indicate that the system functions effectively, with a 98% success rate in accurately processing normal eye images through the CNN model without detecting cataracts. When tested using grayscale imaging, cataract-afflicted eyes—characterized by red spots in the images—were also successfully identified by the CNN model. These test results demonstrate that the designed cataract detection system can accurately classify images into normal or cataract-afflicted eyes with high precision. This system shows promise for use in early cataract detection, potentially helping to reduce the prevalence of cataract-related blindness in Indonesia.

Enhancing Cataract Detection through Hybrid CNN Approach and Image Quadration: A Solution for Precise Diagnosis and Improved Patient Care

Cataracts, characterized by lens opacity, pose a significant global health concern, leading to blurred vision and potential blindness. Timely detection is crucial, particularly in regions with a shortage of ophthalmologists, where manual diagnosis is time-consuming. While deep learning and convolutional neural networks (CNNs) offer promising solutions, existing models often struggle with diverse datasets. This study introduces a hybrid CNN approach, training on both full retinal fundus images and quadrated parts (i.e., the fundus images divided into four segments). Majority voting is utilized to enhance accuracy, resulting in a superior performance of 97.12%, representing a 1.44% improvement. The hybrid model facilitates early cataract detection, aiding in preventing vision impairment. Integrated into applications, it supports ophthalmologists by providing rapid, cost-efficient predictions. Beyond cataract detection, this research addresses broader computer vision challenges, contributing to various applications. In conclusion, our proposed approach, combining CNNs and image quadration enhances cataract detection’s accuracy, robustness, and generalization. This innovation holds promise for improving patient care and aiding ophthalmologists in precise cataract diagnosis.

Effective Cataract Identification System using Deep Convolution Neural Network

INTRODUCTION: The paper introduces a novel approach for the early detection of cataracts using images captured using smartphones. Cataracts are a significant global eye disease that can lead to vision impairment in individuals aged 40 and above. In this article, we proposed a deep convolution neural network (CataractsNET) trained using an open dataset available in Github which includes images collected through google searches and images generated using standard augmentation mechanism.
 OBJECTIVES: The main objective of this paper is to design and implement a lightweight network model for cataract identification that outperforms other state-of-the-art network models in terms of accuracy, precision, recall, and F1 Score.
 METHODS: The proposed neural network model comprises nine layers, guaranteeing the extraction of significant details from the input images and achieving precise classification. The dataset primarily comprises cataract images sourced from a standardized dataset that is publicly available on GitHub, with 8000 training images and 1600 testing images.
 RESULTS: The proposed CataractsNET model achieved an accuracy of 96.20%, precision of 96.1%, recall of 97.6%, and F1 score of 96.1%. These results demonstrate that the proposed method outperforms other deep learning models like ResNet50 and VGG19.
 CONCLUSION: The paper concludes that identifying cataracts in the earlier stages is crucial for effective treatment and reducing the likelihood of experiencing blindness. The widespread use of smartphones makes this approach accessible to a broad audience, allowing individuals to check for cataracts and seek timely consultation with ophthalmologists for further diagnosis.

Cataract classification and grading in federated learning environment using CNN based Modified-ResNet-50

Cataract, a common eye disease, causes lens opacification, which can lead to blindness. Early cataract detection in a privacy-preserving approach has led us to investigate the concept of Federated Learning (FL) and its prominent technique, known as Federated Averaging (FedAVG). Federated learning has the potential to solve the privacy issues by allowing data servers to train their models natively and distribute them without invading patient confidentiality. This research introduces an interactive federated learning framework that permits multiple medical institutions to screen cataract from split lamp images utilising convolutional neural network (CNN) without sharing patient data, as well as grade normal, mild, moderate, and severe cataracts. The CNN is developed based on Modified-ResNet-50 and FedAVG technique could achieve relatively high accuracy. The experimental results demonstrate that the proposed modification reduces the processing time to a greater extent.

Cataract Progression dan Lens Drop after Pars Plana Vitrectomy and Gas Tamponade : A Case Report

Introduction : Vitreous Hemorrhage (VH) in proliferative diabetic retinopathy (PDR) is common cause of acute vision loss.If the retina cannot be visualized and the etiology of the VH is unknown, pars plana vitrectomy (PPV) is indicated. 1Intraocular gases like SF6 or C3F8 are one of the most useful adjuncts in vitreoretinal surgery. Exposure to intraocular gases additionally increases retrolental oxygen levels, resulting in development of lens opacities.
 Case Illustration : Patient female 52 y.o, came with sudden blurry vision in the left eye since two weeks ago. History of Diabetes Mellitus 10 years with insulin treatment. From the examination left eye visual acuity 1/60, zonulysis, VH, and negative fundus reflect. On ultrasonography found vitreous opacity. The patient was diagnosed with left eye VH ec advanced PDR and scheduled PPV with C3F8 gas tamponade. Patient difficult to maintain prone position after surgery. 2 month after the surgery the patient scheduled with phacoemulsification surgery because progressive lens opacity. While prepare to the second operation, the lens found drop to the vitreous. The second surgery done with lens evacuation and Intraocular lens implantation. Last follow up, best correction visual acuity was 6/18.
 Discussion : PPV and Gas tamponade are presdisposing lens opacity progression. Lens opacity after gas tamponade include longer intraocular longevity gas and patients who fail to maintain a prone position. Risk factors for zonulysis in this patient increase the occurrence of lens drop after PPV with gas tamponade.
 Conclusion : Regular slit-lamp examination needs to be performed for early detection of cataract after PPV with gas tamponade.

Cataract detection and visualization based on multi-scale deep features by RINet tuned with cyclic learning rate hyperparameter

The most common reason worldwide for blindness is cataract, the clouding of the lens where vision becomes increasingly blurry. Early detection of cataract is crucial for a better prognosis. This can be achieved with improved automated cataract detection and localization of the affected regions in the fundus images. This work proposes a supervised miniature U-Net (SMi-UNet) for feature extraction integrated with a convolutional neural network (RINet) explicitly designed for fundus images. The dataset used has been self-curated by collecting cataract images from multiple repositories.The proposed SMi-UNet allows a deeper network with significantly reduced parameters than conventional U-Net. The proposed RINet utilizes the features extracted by SMi-UNet for discriminating between a cataract and a normal fundus image. Further, the performance of the RINet is optimized using a cyclic learning rate (CLR) hyperparameter. CLR eliminates the need to find the best value of the learning rate and improves accuracy in a minimum number of epochs, making it suitable for edge devices. Further, to localize the prominent regions in the cataract images, colored heatmap techniques are applied at the last convolutional layer. These maps help visualize the affected areas with a hotter color.The exceptional performance of the proposed technique in cataract detection and its localizationhas been established by quantitative and qualitative data, demonstrating that it can be a valuabletool for early cataract detection. The obtained results were validated by an expert. The proposed RINet attains a classification accuracy of 96% and 93% with and without CLR respectively.

An Assessment of Contemporary Methods and Data-Enabled Approaches for Early Cataract Detection

Background: Cataracts are common causes of visual impairment. Preventing blindness requires an early and accurate diagnosis. This review examines current cataract diagnosis strategies, explores data-driven machine learning algorithms for early detection, investigates the use of artificial intelligence (AI) approaches, assesses improvements in cataract detection accuracy, identifies research gaps, and provides recommendations for future studies. Methods: We gathered labelled cataract and non-cataract fundus data from the Kaggle. Scholarly publications were sourced from reliable databases such as ProQuest, IEEE, ELSEVIER, Google Scholar, and PubMed. A detailed literature search with specific terms expanded the scope of this review. We included studies that used cataract and non-cataract fundus eye images from cross-sectional, retrospective, and prospective studies. The quality assessment used the AMSTAR tool, considering factors such as literature search comprehensiveness, study selection criteria, data extraction methodologies, and study validity (Table 1). Results: This study encompassed 130 research publications, focusing on machine learning models and clinical-based diagnostic approaches for early-stage cataract identification. The performance of machine-learning models is influenced by factors such as dataset noise and limited reliable data. Barriers to the successful implementation of AI for cataract diagnosis were identified. Conclusions: This review emphasises the obstacles hindering the broad application of AI in cataract diagnosis. Addressing these findings is vital for developing strategies to overcome these challenges and enhance cataract detection systems. To achieve improved accuracy and efficiency in cataract diagnosis, future research should prioritise efforts to enhance dataset availability and quality, reduce data noise, and refine machine-learning algorithms. Unlocking the full potential of AI and/or machine learning can lead to significant breakthroughs in cataract diagnosis, ultimately resulting in better patient outcomes and reduced visual impairments.

Automated cataract disease detection on anterior segment eye images using adaptive thresholding and fine tuned inception-v3 model

Early detection of cataracts plays a vital role in ensuring the prevention of vision loss. This paper aims to propose an algorithm that will act as an assistive measure in the process of automating cataract disease detection. The majority of the existing works are focused on the utilization of either fundus images, slit lamp images, or visible wavelength images captured using a DSLR camera. The novelty of this proposed algorithm is the capability to deliver equally accurate and precise performance using both normally captured visible wavelength images as well as medically captured anterior segment images which can, in turn, prove to be cost-effective as well. The image pre-processing techniques particularly adaptive thresholding hereby employed provides fast and accurate results on the input dataset fed to the CNN model which in turn is a fine-tuned version of Inception-v3. Here the training of the model has been done using visible wavelength images whereas the validation testing has been done using the anterior segment eye images medically obtained from a hospital. The proposed image pre-processing technique along with the model architecture ensures the achievement of a high classification accuracy of about 95%. Since the model deals with the examination of the anterior segment of the image, cases concerning nuclear cataract, cortical cataract, or a hybrid case involving the detection of both the aforementioned cataract types lie under the range of detection of our system.

Cataract Classification Based on Fundus Images Using Convolutional Neural Network

A cataract is a disease that attacks the eye's lens and makes it difficult to see. Cataracts can occur due to hydration of the lens (addition of fluid) or denaturation of proteins in the lens. Cataracts that are not treated properly can lead to blindness. Therefore, early detection needs to be done to provide appropriate treatment according to the level of cataracts experienced. In this study, a comparison of cataract classification based on fundus images using GoogleNet, MobileNet, ResNet, and the proposed Convolutional Neural Network was carried out. We compared four CNN architectures when implementing the Adam optimizer with a learning rate of 0.001. The data used are 399 datasets and augmented to 3200 data. This test's best and most stable results were obtained from the proposed CNN model with 92% accuracy, followed by MobileNet at 92%, ResNet at 93%, and GoogLeNet at 86%. We also make comparisons with previous research. Most of the previous studies only used two to three class categories. In this study, the system was improved by increasing system classifies into four categories: Normal, Immature, Mature, and Hypermature. In addition, the accuracy obtained is also quite good compared to previous studies using manual feature extraction. This study is expected to help medical staff to carry out early detection of cataracts to prevent the dangerous effect of cataracts and appropriate medical treatment. In the future, we want to expand the number of datasets to improve the classification accuracy of the cataract detection system.

Infrared slit light-based scanning type portable device for monitoring of cataract

Age-related cataracts are reported to be responsible for over 50% of world blindness and are considered a priority eye disease by the World Health Organization. Cataracts progress slowly and often have no subjective symptoms, so regular examination is required for early detection and treatment. We report on the design and development of a portable slit-light-based cataract detection device using near-infrared light and a scanning mirror. Imaging results were obtained by scanning cataract model eyes made from dimethylpolysiloxane and starch mixture and enucleated swine eye with induced cataracts. The fabricated system has adequate sensitivity to detect the smallest cataract formation and progress of cataract in the induced cataract eye and such measurements are useful for early detection of cataract. The results indicate that our fabricated slit-light device could be an easy-to-handle and low-cost tool for remote/routine detection and diagnosis of ocular diseases (like cataracts) without a need for an ophthalmologist.

A Novel Method for Cataract Detection and Segmentation Using Nakagami Distribution

Early detection of cataract is considered as an important solution to prevent vision loss. An automatic detection of cataract is proposed in this work with the help of histogram approach. In the beginning, noises occur in an image which is also referred to as impulse noise. To eliminate this noise a non-linear type of median filter is matched especially for the morphological filter. These filtering methods help to extract the content of the image by edge detection and segmentation. The quality of the image is evaluated the image enhancing can be obtained by a histogram approach. A normalization method can be used to enhance the image which is also called Contrast stretching. To make morphological functions effective a top-hat filter is used to segment the cataract part in the given image. Nakagami distributions are usually used for extracting required important information of ultrasound details by matching histograms from the radio frequency signals. The extracted information from the Nakagami distribution is obtained by parameter values. The recent techniques used to improve the given image quality in histogram modification method are done by Intentional Camera Movement (ICM) and Unintentional Camera Movement (UCM) to recognize the real image more precisely. In the proposed method the result shows the noise reduction and a better contrast in the output image through parameters values such as Mean Squared Error (MSE) obtained as 17.23 and Peak-Signal-to-Noise Ratio (PSNR) obtained as 35.8.

Efficient network selection for computer-aided cataract diagnosis under noisy environment

Background and objective:Computer-aided cataract diagnosis (CACD) methods play a crucial role in early detection of cataract. The existing CACD methods are suffering from performance diminution due to the presence of noise in digital fundus retinal images. The lack of robustness in CACD methods against noise is a serious concern since even the presence of small noise levels may degrade the performance of cataract detection. However, noise in fundus retinal images is unavoidable due to various processes involved in the acquisition or transmission. Hence, a robust CACD method against noisy conditions is required to diagnose the cataract accurately.Methods:In this paper, an efficient network selection based robust CACD method under additive white Gaussian noise (AWGN) is proposed. The presented method consists a set of locally- and globally-trained independent support vector networks with features extracted at various noise levels. A suitable network is then selected based on the noise level present in the input image. The automatic feature extraction technique using pre-trained convolutional neural network (CNN) is adopted to extract features from input fundus retinal images.Results:A good-quality fundus retinal image dataset is obtained from EyePACS dataset with the use of natural image quality evaluator (NIQE) score. The synthetic noisy fundus retinal images are then generated artificially from good-quality fundus retinal images using AWGN model for effective analysis. The analysis is carried out with existing CNN based CACD methods at different noise levels. From results it is obvious that the proposed CACD method is superior in exhibiting robust performance against AWGN than existing CNN based CACD methods.Conclusions:From the experimental results, it is clear that the proposed method show superior performance against noise when compared with existing methods in literature. The proposed method can be useful as a starting point to continue further research on CNN based robust CACD methods.

Computer Aided Diagnosis Based on Artificial Intelligence in Providing Support for Health Services in Rural Areas

The health service system in Indonesia is still a homework that needs to be addressed by the government. Both in terms of equity, regulation, and integration between offline and online systems. As for what is happening now, there are at least some obstacles that need to be addressed and addressed as soon as possible. On the other hand, health development should beseen as an investment to improve the quality of human resources and support economic development, and have an essential role in poverty reduction efforts. Today’s primary health development problems include the high disparity in health status between socioeconomic levels, regions, and urban and rural areas. In general, the health status of people with high socioeconomic levels in western Indonesia and urban areas tends to be better. On the other hand, the health status of people with low socioeconomic status in eastern Indonesia and rural areas is still lagging. The quality, equity, and affordability of health services are also still low. The quality of service is an obstacle because medical personnel is minimal and equipment is inadequate. In terms of numbers, the ratio of health workers to the population to be served is still low. The affordability of services is closely related to the number and distribution of healthfacilities. In terms of the limitations of medical equipment, this is due to the high price of the medical equipment. Besides, special training is also needed in medical equipment, which training takes quite a long time, and the material is quite complex. With these conditions, it is necessary to optimize technology to develop medical equipment, especially diagnostic support equipment that is cheap, easy to use, and has high accuracy. Our research aims to address optimizing image processing technology and artificial intelligence in developing technology to support health services in rural areas. We tried to implement several cases of health services that often occur in rural areas, namely early detection of cataracts, optimizing the use of ultrasound machines, optimizing the use of lowquality MRI machines in the diagnosis of Alzheimer’s, using microscopic images to identify leukocytes and using X-ray images to support the diagnosis. COVID19. Of the several modulesthat we have developed for health care support technology, the results show promisingprospects with a good level of accuracy, ease to use, and use modalities that already exist in the lowest health service units to reduce prices. It is hoped that this image processing and artificial intelligence-based health service support module can be used in rural areas to overcome disparities in health services in Indonesia.

The capability of early detection of eye diseases in the community by primary health facility worker

Cataract is one of the biggest causes of blindness in the world. As many as 2.4 million people suffered from cataracts in Indonesia in 2013. Therefore, early detection needs to be done to prevent further complications. However, health workers' limited ability in primary health care facilities has resulted in high cataract diagnosis rates. The purpose of this study was to determine the ability of early detection of eye diseases, especially cataracts by primary health care workers. This study was conducted using a quantitative descriptive method analyzing 158 medical records as a result of early detection of primary health care facilities in Sleman Regency, which were sent to the ophthalmologist team of the Department of Eye Health FKKMK UGM to be re-examined. The re-examination results by a team of ophthalmologists received 141 medical records that fulfilled the inclusion criteria, consisting of 61 patients confirmed by cataract diagnosis (43.3%) and 80 patients with a diagnosis of not cataract (56.7%). According to the study, primary health care workers' ability towards early detection of cataracts in Sleman Regency needed to be enhanced (4.3%).

Automated identification of cataract severity using retinal fundus images

ABSTRACT Cataract is the most prevalent cause of blindness worldwide, which accounts for more than 51% of overall blindness. The early detection of cataract can salvage impaired vision leading to blindness. Most of the existing cataract classification systems are based on traditional machine learning methods with hand-engineered features. The manual extraction of retinal features is generally a time-taking process and requires professional ophthalmologists. Convolutional neural network (CNN) is a widely accepted model for automatic feature extraction, but it necessitates a larger dataset to evade overfitting problems. Contrarily, classification using SVM has great generalisation power to elucidate small-sample dataset. Therefore, we proposed a hybrid model by integrating deep learning models and SVM for 4-class cataract classification. The transfer learning-based models (AlexNet, VGGNet, ResNet) are employed for automatic feature extraction and SVM performs as a recogniser. The proposed architecture evaluated on 8030 retinal images with strong feature extraction and classification techniques has achieved 95.65% of accuracy. The results of this study have verified that the proposed method outperforms conventional methods and can provide a reference for other retinal diseases.

Hereditary hyperferritinaemia-cataract syndrome (HHCS) - an underestimated condition: ferritin light chain variant spectrum in German families.

Background In hereditary hyperferritinaemia-cataract syndrome (HHCS), single nucleic acid alterations in the ferritin light chain (L-ferritin) iron response element (IRE) constitutively derepress ferritin synthesis, resulting in hyperferritinaemia, L-ferritin deposits in the lens of the eye and early bilateral cataract onset. Methods In this study, six German families with putative HHCS were analysed. Clinical diagnosis of HHCS was based on medical history, evaluation of ferritin serum levels, transferrin saturation and clinical ophthalmological examination. Diagnosis was confirmed by polymerase chain reaction (PCR)-based DNA sequencing of the L-ferritin IRE. Results Genetic analysis of the L-ferritin IRE revealed relevant single nucleic acid alterations in each of the affected families. Variants c.-168G > A, c.-168G > U and c.-167C > U were located in the C-bulge region; and variants c.-161C > U and c.-157G > A were located in the hexanucleotide loop of the L-ferritin IRE. Conclusions Family history of hyperferritinaemia and juvenile cataracts are strong indicators of HHCS. Genetic analysis of the L-ferritin IRE is a straightforward procedure to confirm the diagnosis. Accurate diagnosis of hyperferritinaemia can avoid unnecessary treatment by venesection, and focus attention on early cataract detection in offspring at risk.

Optical coherence elastography of cold cataract in porcine lens.

.Cataract is one of the most prevalent causes of blindness around the world. Understanding the mechanisms of cataract development and progression is important for clinical diagnosis and treatment. Cold cataract has proven to be a robust model for cataract formation that can be easily controlled in the laboratory. There is evidence that the biomechanical properties of the lens can be significantly changed by cataract. Therefore, early detection of cataract, as well as evaluation of therapies, could be guided by characterization of lenticular biomechanical properties. In this work, we utilized optical coherence elastography (OCE) to monitor the changes in biomechanical properties of ex vivo porcine lenses during formation of cold cataract. Elastic waves were induced in the porcine lenses by a focused micro air-pulse while the lenses were cooled, and the elastic wave velocity was translated to Young’s modulus of the lens. The results show an increase in the stiffness of the lens due to formation of the cold cataract (from to ). These results show a relation between lens opacity and stiffness and demonstrate that OCE can assess lenticular biomechanical properties and may be useful for detecting and potentially characterizing cataracts.

Abnormal High Signal Intensity Discovered in Eye Lenses in Routine Brain MRI; Correlation with Ophthalmologist Examination

Objectives: The aim of this study was to determine the possible role of brain magnetic resonance imaging (MRI) in cataract detection. Patients and Methods: This study was performed on patients who were referred to Shahid Faghihi hospital with a known disease for brain MRI. Patients were classified in two groups of case and controls. Cases were 44 eyes with abnormal signal of the lens and controls were 88 eyes with normal signal intensity of the lens. All patients underwent an ophthalmological examination and the presence of cataract and intraocular lens (IOL) was assessed in these patients. Average lens signal, average muscle signal, average fat signal, lens to muscle signal ratio, and average lens signal to average fat signal ratio were recorded. Results: The mean of the average lens signal in the case group was significantly higher than controls (370 versus 161, respectively) (P value < 0.0001). Sixteen (36.4%) out of 44 eyes in the case group had cataract, but 48 (54.5%) of 88 eyes in the control group had cataract (P value = 0.049). In the case group, 18.2% had IOL, and this was 6.8% in the controls (P value = 0.046). The mean of the average lens signal in eyes with cataract was 218.5 and in normal eyes, it was 249.1 (P value = 0.36). In eyes with IOL, the mean of the average lens signal was 347.5 and in normal eyes, this was 223.1 (P value = 0.036). Conclusion: Cataract in eyes with abnormal lens signal was significantly lower than eyes with normal lens average signal. The lens signal in eyes with cataract was similar to eyes without cataract. So, average lens signal in brain MRI does not lead to early detection of cataract.