Cataract is one of the most common eye disorders that causes vision distortion. Accurate and timely detection of cataracts is the best way to control the risk and avoid blindness. Recently, artificial intelligence-based cataract detection systems have been received research attention. In this paper, a novel deep neural network, namely CataractNet , is proposed for automatic cataract detection in fundus images. The loss and activation functions are tuned to train the network with small kernels, fewer training parameters, and layers. Thus, the computational cost and average running time of CataractNet are significantly reduced compared to other pre-trained Convolutional Neural Network (CNN) models. The proposed network is optimized with the Adam optimizer. A total of 1130 cataract and non-cataract fundus images are collected and augmented to 4746 images to train the model. For avoiding the over-fitting problem, the dataset is extended through augmentation before model training. Experimental results prove that the proposed method outperforms the state-of-the-art cataract detection approaches with an average accuracy of 99.13%.

CataractNet: An Automated Cataract Detection System Using Deep Learning for Fundus Images

Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning

Deep learning automated diagnosis and quantitative classification of cataract type and severity: quantifying the effectiveness and usability of deep learning-assisted disease diagnosis models with 14 ophthalmologists and multi-center validations

Abstract Cataracts are the leading cause of visual impairment and blindness globally. Over the years, researchers have achieved significant progress in developing state-of-the-art machine learning techniques for automatic cataract classification and grading, aiming to prevent cataracts early and improve clinicians’ diagnosis efficiency. This survey provides a comprehensive survey of recent advances in machine learning techniques for cataract classification/grading based on ophthalmic images. We summarize existing literature from two research directions: conventional machine learning methods and deep learning methods. This survey also provides insights into existing works of both merits and limitations. In addition, we discuss several challenges of automatic cataract classification/grading based on machine learning techniques and present possible solutions to these challenges for future research. 

/pdf/machine-learning-for-cataract-classification-grading-on-1a5dz4h6.pdf

Machine Learning for Cataract Classification/Grading on Ophthalmic Imaging Modalities: A Survey

This study assessed the anterior chamber depth (ACD) and iridocorneal angle using a portable smart eye camera (SEC) compared to the conventional slit-lamp microscope and anterior-segment optical coherence tomography (AS-OCT). This retrospective case-control study included 170 eyes from 85 Japanese patients. The correlation between the ACD evaluations conducted with the SEC and conventional slit-lamp was high (r = 0.814). The correlation between the Van-Herick Plus grade obtained using two devices was also high (r = 0.919). A high kappa value was observed for the Van-Herick Plus grading (Kappa = 0.757). A moderate correlation was observed between the ACD measured using AS-OCT and the slit-lamp image acquired with the conventional slit-lamp microscope and SEC (r = 0.609 and 0.641). A strong correlation was observed between the trabecular-iris angle (TIA) measured using AS-OCT and Van-Herick Plus grade obtained with the conventional slit-lamp microscope and SEC (r = 0.702 and 0.764). Strong correlations of ACD evaluation and high kappa value of the Van-Herick Plus grading indicated the adequate subjective assessment function of the SEC. Moderate correlations between the ACD objective measurement and evaluation and strong correlation between the TIA and Van-Herick Plus grade suggested the good objective assessment function of the SEC. The SEC demonstrated adequate performance for ACD evaluation and angle estimation.

A Study Validating the Estimation of Anterior Chamber Depth and Iridocorneal Angle with Portable and Non-Portable Slit-Lamp Microscopy

Cataract constitutes half of the blindness cases worldwide; hence, detecting and treating cataracts in a timely manner are effective strategies for blindness prevention. Recently, methods of detecting cataracts through deep learning are flourishing; however, the task of improving the grading mechanism is still the priority in the research field. This study evaluates the classification capability of the automated nuclear cataract detection algorithm using ocular images captured by smartphone-based slit-lamp. The task of the algorithm is to automatically detect cataract severity in terms of the photometric appearance of the nuclear region of the crystalline lens of the eyes. The nuclear region of the ocular lens was localized by YOLOv3. Subsequently, the combination of a deep learning network, ShuffleNet, and a support vector machine (SVM) classifier was used to grade cataract severity, evaluating the gray conjugate features of the nuclear region. Using the trained algorithm, 819 anterior ocular images captured by smartphone-based slit-lamp were utilized to evaluate the algorithm’s performance. The accuracy was 93.5% with Kappa of 95.4% and F1 of 92.3%. The AUC was 0.9198. The proposed validation method could evaluate a cataract severity in 29 ms and the entire classification process in less than 1s. This study can improve the accuracy of the examination, reduce misdiagnosis rate and the difficulty of the doctor’s examination. The addition of scoring system can improve the quality of pictures obtained by non-ophthalmologists. The method is especially suitable for cataract screening in the underdeveloped areas or areas which are in shortage of ophthalmic resources. It can also improve the accessibility of ophthalmic medical treatment.

/pdf/unified-diagnosis-framework-for-automated-nuclear-cataract-4ayghttc4q.pdf

Unified Diagnosis Framework for Automated Nuclear Cataract Grading Based on Smartphone Slit-Lamp Images

Purpose A real-time automatic cataract-grading algorithm based on cataract video is proposed. Materials and methods In this retrospective study, we set the video of the eye lens section as the research target. A method is proposed to use YOLOv3 to assist in positioning, to automatically identify the position of the lens and classify the cataract after color space conversion. The data set is a cataract video file of 38 people's 76 eyes collected by a slit lamp. Data were collected using five random manner, the method aims to reduce the influence on the collection algorithm accuracy. The video length is within 10 s, and the classified picture data are extracted from the video file. A total of 1520 images are extracted from the image data set, and the data set is divided into training set, validation set and test set according to the ratio of 7:2:1. Results We verified it on the 76-segment clinical data test set and achieved the accuracy of 0.9400, with the AUC of 0.9880, and the F1 of 0.9388. In addition, because of the color space recognition method, the detection per frame can be completed within 29 microseconds and thus the detection efficiency has been improved significantly. Conclusion With the efficiency and effectiveness of this algorithm, the lens scan video is used as the research object, which improves the accuracy of the screening. It is closer to the actual cataract diagnosis and treatment process, and can effectively improve the cataract inspection ability of non-ophthalmologists. For cataract screening in poor areas, the accessibility of ophthalmology medical care is also increased.

/pdf/accv-automatic-classification-algorithm-of-cataract-video-1zh2ipbd3u.pdf

ACCV: automatic classification algorithm of cataract video based on deep learning.

Purpose As part of plans to provide help to people in remote and poor areas who have no medical resources, a portable slit-lamp based on a smartphone was proposed. This would help in early screening of cataract diseases. Methods This means a microlens is designed that would work with a phone's camera. The phone's photo taking function is used in capturing the image of the eyes lens to replace the observation system of the desktop slit-lamp. A simplified slit light band was designed. In order for the light source part to meet the portable requirements of the slit-lamp, the adjustable and diffused light functions of the ligaments were removed in this design. Furthermore, the images collected by the smartphone are uploaded to the deep learning cataract screening system, which can achieve real-time and effective screening of cataract. Results Unlike the desktop slit-lamp, which needs skilled personnel to operate, this device can be easily operated by less-skilled or inexperienced doctors. This eliminates the concerns of inaccurate diagnosis based on the use of unskilled professionals. Due to the portability, ease of use, and simplicity in obtaining crystal images of this device, it serves as a promising platform for nonhospital screening and telemedicine. Conclusions In this paper, we invented a small portable device for screening cataract. This device is to make screening and diagnosis of cataract in remote areas very fast and effective. It will also solve the problem of inadequate specialized doctors and equipment in those areas as well. Translational Relevance. Smartphones can be used with portable slit-lamps to capture the images of the lens.

/pdf/portable-handheld-slit-lamp-based-on-a-smartphone-camera-for-2qt7kef7w5.pdf

Portable Handheld Slit-Lamp Based on a Smartphone Camera for Cataract Screening.

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Unified Diagnosis Framework for Automated Nuclear Cataract Grading Based on Smartphone Slit-Lamp Images

ACCV: automatic classification algorithm of cataract video based on deep learning.

Portable Handheld Slit-Lamp Based on a Smartphone Camera for Cataract Screening.