The Role of Telemedicine to Alleviate an Increasingly Burdened Healthcare System: Retinopathy of Prematurity

MBT: Model-Based Transformer for retinal optical coherence tomography image and video multi-classification

BACKGROUND AND OBJECTIVE

The detection of retinal diseases using optical coherence tomography (OCT) images and videos is a concrete example of a data classification problem. In recent years, Transformer architectures have been successfully applied to solve a variety of real-world classification problems. Although they have shown impressive discriminative abilities compared to other state-of-the-art models, improving their performance is essential, especially in healthcare-related problems.

METHODS

This paper presents an effective technique named model-based transformer (MBT). It is based on popular pre-trained transformer models, particularly, vision transformer, swin transformer for OCT image classification, and multiscale vision transformer for OCT video classification. The proposed approach is designed to represent OCT data by taking advantage of an approximate sparse representation technique. Then, it estimates the optimal features, and performs data classification.

RESULTS

The experiments are carried out using three real-world retinal datasets. The experimental results on OCT image and OCT video datasets show that the proposed method outperforms existing state-of-the-art deep learning approaches in terms of classification accuracy, precision, recall, and f1-score, kappa, AUC-ROC, and AUC-PR. It can also boost the performance of existing transformer models, including Vision transformer and Swin transformer for OCT image classification, and Multiscale Vision Transformers for OCT video classification.

CONCLUSIONS

This work presents an approach for the automated detection of retinal diseases. Although deep neural networks have proven great potential in ophthalmology applications, our findings demonstrate for the first time a new way to identify retinal pathologies using OCT videos instead of images. Moreover, our proposal can help researchers enhance the discriminative capacity of a variety of powerful deep learning models presented in published papers. This can be valuable for future directions in medical research and clinical practice.

Retinopathy of Prematurity Outcomes of Neonates Meeting Only a Single Screening Criterion: Proposal of the TWO-ROP Algorithm

PURPOSE

To assess the rates of retinopathy of prematurity (ROP) and treatment-warranted ROP in a modern set of patients meeting 0 or 1 of the current ROP screening criteria.

DESIGN

Retrospective cohort study.

METHODS

Single-center study of 9350 infants screened for ROP from 2009 to 2019. Rates of ROP and treatment-warranted ROP were evaluated in group 1 (birth weight [BW] <1500 g and gestational age [GA] ≥30 weeks), group 2 (BW ≥1500 g and GA <30 weeks), and group 3 (BW ≥1500 g and GA ≥30 weeks).

RESULTS

Of 7520 patients with reported BW and GA, 1612 (21.4%) patients met the inclusion criteria. The number of patients in groups 1, 2, and 3 was 466 (6.19%), 23 (0.31%), and 1123 (14.93%), respectively. The number of patients diagnosed with ROP was 20 (4.29%) in group 1, 1 (4.35%) in group 2, and 12 (1.07%) in group 3 (P < .001). The mean interval between birth and ROP diagnosis was 36.25 days (range 12-75 days) in group 1, 47 days in group 2, and 23.33 days (range 10-39 days) in group 3 (P = .05). No cases of stage 3, zone 1, or plus disease were recorded. No patients met the treatment criteria.

CONCLUSIONS

Patients meeting 1 screening criterion had a low rate of ROP (<5%), with no stage 3, zone 1, or plus disease. No patients required treatment. We propose a possible algorithm (TWO-ROP) in appropriate neonatal intensive care units, with an amendment in screening protocol for this low-risk population to include only an outpatient screening examination within 1 week of discharge, or at 40 weeks if inpatient, to decrease the inpatient ROP screening burden while maintaining safety. Further external validation of this protocol would be required.

Artificial intelligence in diabetic retinopathy: Bibliometric analysis

BACKGROUND

The use of artificial intelligence in diabetic retinopathy has become a popular research focus in the past decade. However, no scientometric report has provided a systematic overview of this scientific area.

AIMS

We utilized a bibliometric approach to identify and analyse the academic literature on artificial intelligence in diabetic retinopathy and explore emerging research trends, key authors, co-authorship networks, institutions, countries, and journals. We further captured the diabetic retinopathy conditions and technology commonly used within this area.

METHODS

Web of Science was used to collect relevant articles on artificial intelligence use in diabetic retinopathy published between January 1, 2012, and December 31, 2022 . All the retrieved titles were screened for eligibility, with one criterion that they must be in English. All the bibliographic information was extracted and used to perform a descriptive analysis. Bibliometrix (R tool) and VOSviewer (Leiden University) were used to construct and visualize the annual numbers of publications, journals, authors, countries, institutions, collaboration networks, keywords, and references.

RESULTS

In total, 931 articles that met the criteria were collected. The number of annual publications showed an increasing trend over the last ten years. Investigative Ophthalmology & Visual Science (58/931), IEEE Access (54/931), and Computers in Biology and Medicine (23/931) were the most journals with most publications. China (211/931), India (143/931, USA (133/931), and South Korea (44/931) were the most productive countries of origin. The National University of Singapore (40/931), Singapore Eye Research Institute (35/931), and Johns Hopkins University (34/931) were the most productive institutions. Ting D. (34/931), Wong T. (28/931), and Tan G. (17/931) were the most productive researchers.

CONCLUSION

This study summarizes the recent advances in artificial intelligence technology on diabetic retinopathy research and sheds light on the emerging trends, sources, leading institutions, and hot topics through bibliometric analysis and network visualization. Although this field has already shown great potential in health care, our findings will provide valuable clues relevant to future research directions and clinical practice.

Clinical Role of Smartphone Fundus Imaging in Diabetic Retinopathy and Other Neuro-retinal Diseases

Purpose: In today's life, many electronic gadgets have the potential to become invaluable health care devices in future. The gadgets in this category include smartphones, smartwatches, and others. Till now, smartphone role has been highlighted on many occasions in different areas, and they continue to possess immense role in clinical documentation, clinical consultation, and digitalization of ocular care. In last one decade, many treatable conditions including diabetic retinopathy, glaucoma, and other pediatric retinal diseases are being imaged using smartphones.Methods: To comprehend this cumulative knowledge, a detailed medical literature search was conducted on PubMed/Medline, Scopus, and Web of Science till February 2021.Results: The included literature revealed a definitive progress in posterior segment imaging. From simple torch light with smartphone examination to present day compact handy devices with artificial intelligence integrated software's have changed the very perspectives of ocular imaging in ophthalmology. The consistently reproducible results, constantly improving imaging techniques, and most importantly their affordable costs have renegotiated their role as effective screening devices in ophthalmology. Moreover, the obtained field of view, ocular safety, and their key utility in non-ophthalmic specialties are also growing.Conclusions: To conclude, smartphone imaging can now be considered as a quick, cost-effective, and digitalized tool for posterior segment screenings, however, their definite role in routine ophthalmic clinics is yet to be established.

Machine Learning Applications in Pediatric Ophthalmology

Purpose: To describe emerging applications of machine learning (ML) in pediatric ophthalmology with an emphasis on the diagnosis and treatment of disorders affecting visual development. Methods: Literature review of studies applying ML algorithms to problems in pediatric ophthalmology. Results: At present, the ML literature emphasizes applications in retinopathy of prematurity. However, there are increasing efforts to apply ML techniques in the diagnosis of amblyogenic conditions such as pediatric cataracts, strabismus, and high refractive error. Conclusions: A greater understanding of the principles governing ML will enable pediatric eye care providers to apply the methodology to unexplored challenges within the subspecialty.