ABSTRACT
We selected and discussed 10 articles in Acta Ophthalmologica since 1923 that changed clinical ophthalmology and treatment protocols, or provided novel findings and perspectives. We are aware that the selection of articles may be debatable and we invite readers to suggest other significant Acta articles. For historians, the article archive of Acta Ophthalmologica is located in Copenhagen.
Subject(s)
Ophthalmology , Periodicals as TopicABSTRACT
This article provides a comprehensive and up-to-date overview of the repositories that contain color fundus images. We analyzed them regarding availability and legality, presented the datasets' characteristics, and identified labeled and unlabeled image sets. This study aimed to complete all publicly available color fundus image datasets to create a central catalog of available color fundus image datasets.
ABSTRACT
The artificial intelligence (AI) systems used for diagnosing ophthalmic diseases have significantly progressed in recent years. The diagnosis of difficult eye conditions, such as cataracts, diabetic retinopathy, age-related macular degeneration, glaucoma, and retinopathy of prematurity, has become significantly less complicated as a result of the development of AI algorithms, which are currently on par with ophthalmologists in terms of their level of effectiveness. However, in the context of building AI systems for medical applications such as identifying eye diseases, addressing the challenges of safety and trustworthiness is paramount, including the emerging threat of adversarial attacks. Research has increasingly focused on understanding and mitigating these attacks, with numerous articles discussing this topic in recent years. As a starting point for our discussion, we used the paper by Ma et al. "Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems". A literature review was performed for this study, which included a thorough search of open-access research papers using online sources (PubMed and Google). The research provides examples of unique attack strategies for medical images. Unfortunately, unique algorithms for attacks on the various ophthalmic image types have yet to be developed. It is a task that needs to be performed. As a result, it is necessary to build algorithms that validate the computation and explain the findings of artificial intelligence models. In this article, we focus on adversarial attacks, one of the most well-known attack methods, which provide evidence (i.e., adversarial examples) of the lack of resilience of decision models that do not include provable guarantees. Adversarial attacks have the potential to provide inaccurate findings in deep learning systems and can have catastrophic effects in the healthcare industry, such as healthcare financing fraud and wrong diagnosis.
ABSTRACT
The purpose of the retrospective, population-based study was to assess the relationship between the risk of central retinal artery occlusion (CRAO) and the level of air pollutants. This study identified 2.272 cases of newly diagnosed CRAO registered in the Polish National Health Service database. The study authors gathered hourly ambient concentrations of particulate matter-PM 2.5, PM 10, benzene, carbon monoxide, nitrogen dioxide, ozone, and sulfur dioxide from pollution monitoring stations. Data on average daily temperature and atmospheric pressure were also obtained. In the statistical analyses, single- and multi-factor Poisson negative binomial regression models were carried out, controlling also for ambient temperature and atmospheric pressure with seasonality set at a level of 4. This study has shown a positive association between CRAO onset and short-term, daily changes in PM 10, NO2, SO2, O3, and CO concentrations, as well as with air temperature, in the days preceding the diagnosis.
