Corneal thickness measurement by secondary speckle tracking and image processing using machine-learning algorithms

Bennett, Aviya; Davidovitch, Elnatan; Beiderman, Yafim; Agadarov, Sergey; Beiderman, Yevgeny; Moshkovitz, Avital; Polat, Uri; Zalevsky, Zeev

doi:10.1117/1.jbo.24.12.126001

Cited by 5 publications

(4 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CNN and combined SVM-linear and CNN models alone are able to achieve up to 100% accuracy [ 79 ]. Corneal thickness can also be measured using an automated process via CNN, which involves a secondary speckle tracking (tracking of a laser beam speckle pattern backscattered from corneal-scleral border) and processing of the obtained data by a CNN [ 80 ]. This method was reliable and accurate with a 26 μm of mean fit error, which is comparable to existing pachymetry tools [ 80 ].…”

Section: Main Textmentioning

confidence: 99%

“…Corneal thickness can also be measured using an automated process via CNN, which involves a secondary speckle tracking (tracking of a laser beam speckle pattern backscattered from corneal-scleral border) and processing of the obtained data by a CNN [80]. This method was reliable and accurate with a 26 μm of mean fit error, which is comparable to existing pachymetry tools [80]. Diabetic peripheral neuropathy is one of the most prevalent complications of diabetes mellitus, and is found in more than 50% of diabetic patients [81].…”

Section: Endothelial Cell Countmentioning

confidence: 99%

See 1 more Smart Citation

Potential applications of artificial intelligence in image analysis in cornea diseases: a review

Tey,

Cheong,

Ang

2024

Eye and Vis

View full text Add to dashboard Cite

Artificial intelligence (AI) is an emerging field which could make an intelligent healthcare model a reality and has been garnering traction in the field of medicine, with promising results. There have been recent developments in machine learning and/or deep learning algorithms for applications in ophthalmology—primarily for diabetic retinopathy, and age-related macular degeneration. However, AI research in the field of cornea diseases is relatively new. Algorithms have been described to assist clinicians in diagnosis or detection of cornea conditions such as keratoconus, infectious keratitis and dry eye disease. AI may also be used for segmentation and analysis of cornea imaging or tomography as an adjunctive tool. Despite the potential advantages that these new technologies offer, there are challenges that need to be addressed before they can be integrated into clinical practice. In this review, we aim to summarize current literature and provide an update regarding recent advances in AI technologies pertaining to corneal diseases, and its potential future application, in particular pertaining to image analysis.

show abstract

Section: Main Textmentioning

confidence: 99%

Section: Endothelial Cell Countmentioning

confidence: 99%

Potential applications of artificial intelligence in image analysis in cornea diseases: a review

Tey,

Cheong,

Ang

2024

Eye and Vis

View full text Add to dashboard Cite

show abstract

“…The results showed that the determination of orbital stops is very accurate and the implementation is faster. 7 Shen incorporated the principle of image processing through a specially designed experimental setup. He meshed the image with a fine ink pen and recorded every moment with a digital camera.…”

Section: Related Workmentioning

confidence: 99%

“…A suggested configuration involves rapidly capturing pictures of incoming stops from smart track stops and then processing the images. The results showed that the determination of orbital stops is very accurate and the implementation is faster 7 . Shen incorporated the principle of image processing through a specially designed experimental setup.…”

Section: Related Workmentioning

confidence: 99%

(Retracted) Intelligent rail transit signal control system based on image processing technology

Huang

2022

J. Electron. Imag.

View full text Add to dashboard Cite

.With the rapid development of the intelligent rail field, the intelligent rail traffic signal control system plays an increasingly important role, bringing greater convenience to people’s travel. Under normal circumstances, the trains are dispatched strictly according to the schedule, but in the daily operation of urban rail transit, the service is inevitably affected by many unpredictable uncertain factors. To ensure the safety of vehicles and smooth traffic, we proposed an intelligent rail traffic signal control system based on image processing technology. When the system detects that there are too many people waiting, it automatically adjusts the display time of the signal lights in real time according to the image, thus ensuring more time for passengers to get on the train, so as to realize the intelligent control of the track. The experimental results of our paper show that the accuracy of the improved vehicle target detection and recognition algorithm is more than 90%, which is higher than that of the algorithm without the improvement. Applying the proposed algorithm to image processing improved the recognition rate and accuracy. Through the test of the intelligent rail traffic signal control system based on image processing, it was found that the system effectively identified the number of people waiting in the lane and automatically adjusted the train closing time. The time required was only 1 s, and the responsiveness reached 99% to ensure the safety of vehicles and smooth traffic.

show abstract

Microsaccades Tracking by Secondary Speckle Pattern Analysis

Shteinberg,

Agdarov,

Beiderman

et al. 2024

Journal of Biophotonics

View full text Add to dashboard Cite

Here we propose a not pupil‐dependent microsaccades tracking technique and a novel detection method. We present a proof of concept for detecting microsaccades using a non‐contact laser‐based photonic system recording and processing the temporal changes of speckle patterns scattered from an eye sclera. The data, simultaneously recorded by the speckle‐based tracker (SBT) and the video‐based eye tracker (Eyelink), was analyzed by the frequently used detection method of Engbert and Kliegl (E&K) and by advanced machine learning detection (MLD) techniques. We detected 93% of microsaccades in the SBT data out of microsaccades detected in the Eyelink data with the E&K method. By utilizing MLD, a precision of 86% was achieved. The findings of our study demonstrate a potential improvement in measuring tiny eye movements, such as microsaccades, using speckle‐based eye tracking and, thus, an alternative to video‐based eye tracking for detecting microsaccades.

show abstract

Corneal thickness measurement by secondary speckle tracking and image processing using machine-learning algorithms

Cited by 5 publications

References 38 publications

Potential applications of artificial intelligence in image analysis in cornea diseases: a review

Potential applications of artificial intelligence in image analysis in cornea diseases: a review

(Retracted) Intelligent rail transit signal control system based on image processing technology

Microsaccades Tracking by Secondary Speckle Pattern Analysis

Contact Info

Product

Resources

About