Current uses of artificial intelligence in the analysis of biofluid markers involved in corneal and ocular surface diseases: a systematic review

Pur, Daiana R.; Krance, Saffire H.; Pucchio, Aidan; Miranda, Rafael N; Felfeli, Tina

doi:10.1038/s41433-022-02307-9

Cited by 14 publications

(8 citation statements)

References 50 publications

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“…According to the location of vein occlusion, RVO is mainly divided into central retinal vein occlusion (CRVO) and branch retinal vein occlusion (BRVO), of which branch occlusion is the most common (Miao et al, 2022). In the early stage, the symptoms are characterized by a sudden loss of vision to varying degrees; mild patients may have no symptoms or only a little shadow (Pur et al, 2023), and with the progression of the disease, RVO patients have serious visual impairment (Zhang X. T et al, 2022;Sood et al, 2022). Typical fundus changes in RVO patients include retinal hemorrhage, tortuous retinal vein dilatation, extensive retinal capillary non-perfusion area, and macular edema (Irgat and (Altintas and Ilhan, 2023;Patil et al, 2023).…”

Section: Application Of Artificial Intelligence In Retinal Vein Occlu...mentioning

confidence: 99%

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

Liu

et al. 2023

Front. Cell Dev. Biol.

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As the only blood vessels that can directly be seen in the whole body, pathological changes in retinal vessels are related to the metabolic state of the whole body and many systems, which seriously affect the vision and quality of life of patients. Timely diagnosis and treatment are key to improving vision prognosis. In recent years, with the rapid development of artificial intelligence, the application of artificial intelligence in ophthalmology has become increasingly extensive and in-depth, especially in the field of retinal vascular diseases. Research study results based on artificial intelligence and fundus images are remarkable and provides a great possibility for early diagnosis and treatment. This paper reviews the recent research progress on artificial intelligence in retinal vascular diseases (including diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, retinopathy of prematurity, and age-related macular degeneration). The limitations and challenges of the research process are also discussed.

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Section: Application Of Artificial Intelligence In Retinal Vein Occlu...mentioning

confidence: 99%

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

Liu

et al. 2023

Front. Cell Dev. Biol.

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“…This human heterogeneity is likely to result in a decline in the accuracy of AI model verification and testing for clinical diagnosis and treatment. 5) Biases exist in AI model datasets ( Hung et al, 2022 ; Keel et al, 2018 ; Pur et al, 2022 ). The AI models are most likely to be successful when they are trained and validated using high-quality datasets.…”

Section: Limitations and Challengesmentioning

confidence: 99%

Advances in artificial intelligence applications for ocular surface diseases diagnosis

Hong

et al. 2022

Front. Cell Dev. Biol.

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In recent years, with the rapid development of computer technology, continual optimization of various learning algorithms and architectures, and establishment of numerous large databases, artificial intelligence (AI) has been unprecedentedly developed and applied in the field of ophthalmology. In the past, ophthalmological AI research mainly focused on posterior segment diseases, such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinal vein occlusion, and glaucoma optic neuropathy. Meanwhile, an increasing number of studies have employed AI to diagnose ocular surface diseases. In this review, we summarize the research progress of AI in the diagnosis of several ocular surface diseases, namely keratitis, keratoconus, dry eye, and pterygium. We discuss the limitations and challenges of AI in the diagnosis of ocular surface diseases, as well as prospects for the future.

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“…The advent of artificial intelligence in ophthalmology 1 neural network algorithms and deep learning strategies in keratoconus (KC) diagnosis, 2 genetic, 3 and molecular biologic tests 4 alter the natural history of KC. 5 – 7 This may allow us to think that KC is a different specialization of ophthalmology rather than just a clinical disorder.…”

Section: Introductionmentioning

confidence: 99%

Keratoconus: exploring fundamentals and future perspectives – a comprehensive systematic review

Niazi,

Gatzioufas,

Doroodgar

et al. 2024

Ophthalmol Eye Dis

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Background: New developments in artificial intelligence, particularly with promising results in early detection and management of keratoconus, have favorably altered the natural history of the disease over the last few decades. Features of artificial intelligence in different machine such as anterior segment optical coherence tomography, and femtosecond laser technique have improved safety, precision, effectiveness, and predictability of treatment modalities of keratoconus (from contact lenses to keratoplasty techniques). These options ingrained in artificial intelligence are already underway and allow ophthalmologist to approach disease in the most non-invasive way. Objectives: This study comprehensively describes all of the treatment modalities of keratoconus considering machine learning strategies. Design: A multidimensional comprehensive systematic narrative review. Data sources and methods: A comprehensive search was done in the five main electronic databases (PubMed, Scopus, Web of Science, Embase, and Cochrane), without language and time or type of study restrictions. Afterward, eligible articles were selected by screening the titles and abstracts based on main mesh keywords. For potentially eligible articles, the full text was also reviewed. Results: Artificial intelligence demonstrates promise in keratoconus diagnosis and clinical management, spanning early detection (especially in subclinical cases), preoperative screening, postoperative ectasia prediction after keratorefractive surgery, and guiding surgical decisions. The majority of studies employed a solitary machine learning algorithm, whereas minor studies assessed multiple algorithms that evaluated the association of various keratoconus staging and management strategies. Last but not least, AI has proven effective in guiding the implantation of intracorneal ring segments in keratoconus corneas and predicting surgical outcomes. Conclusion: The efficient and widespread clinical translation of machine learning models in keratoconus management is a crucial goal of potential future approaches to better visual performance in keratoconus patients. Trial registration: The article has been registered through PROSPERO, an international database of prospectively registered systematic reviews, with the ID: CRD42022319338

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Current uses of artificial intelligence in the analysis of biofluid markers involved in corneal and ocular surface diseases: a systematic review

Cited by 14 publications

References 50 publications

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

Advances in artificial intelligence applications for ocular surface diseases diagnosis

Keratoconus: exploring fundamentals and future perspectives – a comprehensive systematic review

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