Artificial intelligence applications and cataract management: A systematic review

Artificial intelligence (AI)-based applications exhibit the potential to improve the quality and efficiency of patient care in different fields, including cataract management. A systematic review of the different applications of AI-based software on all aspects of a cataract patient's management, from diagnosis to follow-up, was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. All selected articles were analyzed to assess the level of evidence according to the Oxford Centre for Evidence-Based Medicine 2011 guidelines, and the quality of evidence according to the Grading of Recommendations Assessment, Development and Evaluation system. Of the articles analyzed, 49 met the inclusion criteria. No data synthesis was possible for the heterogeneity of available data and the design of the available studies. The AI-driven diagnosis seemed to be comparable and, in selected cases, to even exceed the accuracy of experienced clinicians in classifying disease, supporting the operating room scheduling, and intraoperative and postoperative management of complications. Considering the heterogeneity of data analyzed, however, further randomized controlled trials to assess the efficacy and safety of AI application in the management of cataract should be highly warranted

Studies on machine learning-based aid for residency training and time difficulty in ophthalmology

兵庫県立大学大学院工学(博士)2023doctoral thesi

DIAGNOSE EYES DISEASES USING VARIOUS FEATURES EXTRACTION APPROACHES AND MACHINE LEARNING ALGORITHMS

Ophthalmic diseases like glaucoma, diabetic retinopathy, and cataracts are the main cause of visual impairment worldwide. With the use of the fundus images, it could be difficult for a clinician to detect eye diseases early enough. By other hand, the diagnoses of eye disease are prone to errors, challenging and labor-intensive. Thus, for the purpose of identifying various eye problems with the use of the fundus images, a system of automated ocular disease detection with computer-assisted tools is needed. Due to machine learning (ML) algorithms' advanced skills for image classification, this kind of system is feasible. An essential area of artificial intelligence)AI (is machine learning. Ophthalmologists will soon be able to deliver accurate diagnoses and support individualized healthcare thanks to the general capacity of machine learning to automatically identify, find, and grade pathological aspects in ocular disorders. This work presents a ML-based method for targeted ocular detection. The Ocular Disease Intelligent Recognition (ODIR) dataset, which includes 5,000 images of 8 different fundus types, was classified using machine learning methods. Various ocular diseases are represented by these classes. In this study, the dataset was divided into 70% training data and 30% test data, and preprocessing operations were performed on all images starting from color image conversion to grayscale, histogram equalization, BLUR, and resizing operation. The feature extraction represents the next phase in this study ,two algorithms are applied to perform the extraction of features which includes: SIFT(Scale-invariant feature transform) and GLCM(Gray Level Co-occurrence Matrix), ODIR dataset is then subjected to the classification techniques Naïve Bayes, Decision Tree, Random Forest, and K-nearest Neighbor. This study achieved the highest accuracy for binary classification (abnormal and normal) which is 75% (NB algorithm), 62% (RF algorithm), 53% (KNN algorithm), 51% (DT algorithm) and achieved the highest accuracy for multiclass classification (types of eye diseases) which is 88% (RF algorithm), 61% (KNN algorithm) 42% (NB algorithm), and 39% (DT algorithm)

Perceptual learning improves contrast sensitivity, visual acuity, and foveal crowding in amblyopia

BACKGROUND: Amblyopic observers present abnormal spatial interactions between a low-contrast sinusoidal target and high-contrast collinear flankers. It has been demonstrated that perceptual learning (PL) can modulate these low-level lateral interactions, resulting in improved visual acuity and contrast sensitivity. OBJECTIVE: We measured the extent and duration of generalization effects to various spatial tasks (i.e., visual acuity, Vernier acuity, and foveal crowding) through PL on the target's contrast detection. METHODS: Amblyopic observers were trained on a contrast-detection task for a central target (i.e., a Gabor patch) flanked above and below by two high-contrast Gabor patches. The pre- and post-learning tasks included lateral interactions at different target-to-flankers separations (i.e., 2, 3, 4, 8λ) and included a range of spatial frequencies and stimulus durations as well as visual acuity, Vernier acuity, contrast-sensitivity function, and foveal crowding. RESULTS: The results showed that perceptual training reduced the target's contrast-detection thresholds more for the longest target-to-flanker separation (i.e., 8λ). We also found generalization of PL to different stimuli and tasks: contrast sensitivity for both trained and untrained spatial frequencies, visual acuity for Sloan letters, and foveal crowding, and partially for Vernier acuity. Follow-ups after 5-7 months showed not only complete maintenance of PL effects on visual acuity and contrast sensitivity function but also further improvement in these tasks. CONCLUSION: These results suggest that PL improves facilitatory lateral interactions in amblyopic observers, which usually extend over larger separations than in typical foveal vision. The improvement in these basic visual spatial operations leads to a more efficient capability of performing spatial tasks involving high levels of visual processing, possibly due to the refinement of bottom-up and top-down networks of visual areas

Improving Eye Care Delivery Through Data Sharing Technology

Preventable blindness has massive social, economic, and societal impacts around the world. The Armenian EyeCare Project (AECP) is addressing this through a network of regional and subspecialty ophthalmological clinics, but current data collection, storage and sharing methods are inadequate. With the organization’s input we conducted focused research to determine current state and best practices, and synthesized this information to develop recommendations and implementation plans for Electronic Medical Record and teleconsultation systems which would improve data sharing for better patient care

SurgMAE: Masked Autoencoders for Long Surgical Video Analysis

There has been a growing interest in using deep learning models for processing long surgical videos, in order to automatically detect clinical/operational activities and extract metrics that can enable workflow efficiency tools and applications. However, training such models require vast amounts of labeled data which is costly and not scalable. Recently, self-supervised learning has been explored in computer vision community to reduce the burden of the annotation cost. Masked autoencoders (MAE) got the attention in self-supervised paradigm for Vision Transformers (ViTs) by predicting the randomly masked regions given the visible patches of an image or a video clip, and have shown superior performance on benchmark datasets. However, the application of MAE in surgical data remains unexplored. In this paper, we first investigate whether MAE can learn transferrable representations in surgical video domain. We propose SurgMAE, which is a novel architecture with a masking strategy based on sampling high spatio-temporal tokens for MAE. We provide an empirical study of SurgMAE on two large scale long surgical video datasets, and find that our method outperforms several baselines in low data regime. We conduct extensive ablation studies to show the efficacy of our approach and also demonstrate it's superior performance on UCF-101 to prove it's generalizability in non-surgical datasets as well