Fast and Mobile Cataract Detection by Applying Line Laser Eye Illumination

Cataract is observed when the eye lens becomes opaque. This condition causes blurred vision and is the main cause of blindness worldwide. Cataract diagnosis is usually performed during ophthalmologist examination using a slit lamp, which requires expertise, is expensive and bulky. In this study, we present a small handheld illumination setup for cataract detection. Ex-vivo porcine eyes are investigated to determine whether colored line lasers, 450 nm (blue), 520 nm (green) and 650 nm (red), which shine obliquely into the eye, are principally suited for detection of the Y shaped suture cataract and of cold cataract, respecting exposure limits of EU guideline 2006/25/EC. Camera images of the cataract exhibited good results under illumination with all line lasers. Observations with the physician’s eye led to an even better diagnosis of cataract. Generally, green laser light illumination was the best choice for cataract detection. With red laser light illumination it was also possible, but least suitable for this purpose. With this method, line lasers are a good choice for cataract identification, as cataract can be detected quickly and without much effort. This type of line laser illumination of the eye is safe and both types of cataract are detectable with all wavelengths. For the human eye, a further development of this system is conceivable

CATRA: Interactive Measuring and Modeling of Cataracts

We introduce an interactive method to assess cataracts in the human eye by crafting an optical solution that measures the perceptual impact of forward scattering on the foveal region. Current solutions rely on highly-trained clinicians to check the back scattering in the crystallin lens and test their predictions on visual acuity tests. Close-range parallax barriers create collimated beams of light to scan through sub-apertures, scattering light as it strikes a cataract. User feedback generates maps for opacity, attenuation, contrast and sub-aperture point-spread functions. The goal is to allow a general audience to operate a portable high-contrast light-field display to gain a meaningful understanding of their own visual conditions. User evaluations and validation with modified camera optics are performed. Compiled data is used to reconstruct the individual's cataract-affected view, offering a novel approach for capturing information for screening, diagnostic, and clinical analysis.Alfred P. Sloan Foundation (Research Fellowship)United States. Defense Advanced Research Projects Agency (Young Faculty Award

Current roles of artificial intelligence in ophthalmology

Artificial intelligence (AI) studies are increasingly reporting successful results in the diagnosis and prognosis prediction of ophthalmological diseases as well as systemic disorders. The goal of this review is to detail how AI can be utilized in making diagnostic predictions to enhance the clinical setting. It is crucial to keep improving methods that emphasize clarity in AI models. This makes it possible to evaluate the information obtained from ocular imaging and easily incorporate it into therapeutic decision-making procedures. This will contribute to the wider acceptance and adoption of AI-based ocular imaging in healthcare settings combining advanced machine learning and deep learning techniques with new developments. Multiple studies were reviewed and evaluated, including AI-based algorithms, retinal images, fundus and optic nerve head (ONH) photographs, and extensive expert reviews. In these studies, carried out in various countries and laboratories of the world, it is seen those complex diagnoses, which can be detected systemic diseases from ophthalmological images, can be made much faster and with higher predictability, accuracy, sensitivity, and specificity, in addition to ophthalmological diseases, by comparing large numbers of images and teaching them to the computer. It is now clear that it can be taken advantage of AI to achieve diagnostic certainty. Collaboration between the fields of medicine and engineering foresees promising advances in improving the predictive accuracy and precision of future medical diagnoses achieved by training machines with this information. However, it is important to keep in mind that each new development requires new additions or updates to various social, psychological, ethical, and legal regulations

Effectiveness of Machine Learning Classifiers for Cataract Screening

Cataract is the leading cause of blindness and vision loss globally. The implementation of artificial intelligence (AI) in the healthcare industry has been on the rise in the past few decades and machine learning (ML) classifiers have shown to be able to diagnose patients with cataracts. A systematic review and meta-analysis were conducted to assess the diagnostic accuracy of these ML classifiers for cataracts currently published in the literature. Retrieved from nine articles, the pooled sensitivity was 94.8% and the specificity was 96.0% for adult cataracts. Additionally, an economic analysis was conducted to explore the cost-effectiveness of implementing ML to diagnostic eye camps in rural Nepal compared to traditional diagnostic eye camps. There was a total of 22,805 patients included in the decision tree, and the ML-based eye camp was able to identify 31 additional cases of cataracts, and 2546 additional cases of non-cataract

A computer-aided diagnosis system of nuclear cataract

10.1109/TBME.2010.2041454IEEE Transactions on Biomedical Engineering5771690-169

A computer-aided diagnosis system of nuclear cataract via ranking

10.1007/978-3-642-04271-3_97Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)5762 LNCSPART 2803-81