Automatic nuclear cataract grading using image gradients

Everything real about unreal artificial intelligence in diabetic retinopathy and in ocular pathologies

Artificial Intelligence is a multidisciplinary field with the aim of building platforms that can make machines act, perceive, reason intelligently and whose goal is to automate activities that presently require human intelligence. From the cornea to the retina, artificial intelligence (AI) is expected to help ophthalmologists diagnose and treat ocular diseases. In ophthalmology, computerized analytics are being viewed as efficient and more objective ways to interpret the series of images and come to a conclusion. AI can be used to diagnose and grade diabetic retinopathy, glaucoma, age-related macular degeneration, cataracts, IOL power calculation, retinopathy of prematurity and keratoconus. This review article intends to discuss various aspects of artificial intelligence in ophthalmology.

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.

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

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.

Cataract grading systems: a review of past and present

PURPOSE OF REVIEW

To provide a comprehensive summary of past cataract grading systems, how they have shaped current grading systems, and the developing technologies that are being used to assess and grade cataracts.

RECENT FINDINGS

This summary of cataract grading systems examines the development and limitations that existed in past grading systems and how they have shaped the grading systems of present time. The Lens Opacities Classification System III (LOCS III) system is currently used both clinically and for research purposes. Recent advancements in imaging technologies have allowed researchers to create automatic systems that can locate lens landmarks and provide cataract grading scores that correlate well with LOCS III clinical grades. Utilizing existing technologies, researchers demonstrate that fundus photography and optical coherence tomography can be used as cataract grading tools. Lastly, deep learning has proved to be a powerful tool that can provide objective and reproducible cataract grading scores.

SUMMARY

Cataract grading schemes have provided ophthalmologists with a way to communicate clinical findings and to compare new developments in diagnostic technologies. As technologies advance, cataract grading can become more objective and standardized, allowing for improved patient care.