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Lancet (London, England) Dec 2020Acute retinal vascular occlusions are common causes of visual impairment. Although both retinal artery occlusions and retinal vein occlusions are associated with... (Review)
Review
Acute retinal vascular occlusions are common causes of visual impairment. Although both retinal artery occlusions and retinal vein occlusions are associated with increased age and cardiovascular risk factors, their pathophysiology, systemic implications, and management differ substantially. Acute management of retinal artery occlusions involves a multidisciplinary approach including neurologists with stroke expertise, whereas treatment of retinal vein occlusions is provided by ophthalmologists. Optimisation of systemic risk factors by patients' primary care providers is an important component of the management of these two disorders.
Topics: Age Factors; Humans; Neurologists; Ophthalmologists; Retinal Artery Occlusion; Retinal Vein Occlusion; Risk Factors; Stroke
PubMed: 33308475
DOI: 10.1016/S0140-6736(20)31559-2 -
The British Journal of Dermatology May 2020A transition from a subtyping to a phenotyping approach in rosacea is underway, allowing individual patient management according to presenting features instead of...
BACKGROUND
A transition from a subtyping to a phenotyping approach in rosacea is underway, allowing individual patient management according to presenting features instead of categorization by predefined subtypes. The ROSacea COnsensus (ROSCO) 2017 recommendations further support this transition and align with guidance from other working groups.
OBJECTIVES
To update and extend previous global ROSCO recommendations in line with the latest research and continue supporting uptake of the phenotype approach in rosacea through clinical tool development.
METHODS
Nineteen dermatologists and two ophthalmologists used a modified Delphi approach to reach consensus on statements pertaining to critical aspects of rosacea diagnosis, classification and management. Voting was electronic and blinded.
RESULTS
Delphi statements on which the panel achieved consensus of ≥ 75% voting 'Agree' or 'Strongly agree' are presented. The panel recommends discussing disease burden with patients during consultations, using four questions to assist conversations. The primary treatment objective should be achievement of complete clearance, owing to previously established clinical benefits for patients. Cutaneous and ocular features are defined. Treatments have been reassessed in line with recent evidence and the prior treatment algorithm updated. Combination therapy is recommended to benefit patients with multiple features. Ongoing monitoring and dialogue should take place between physician and patients, covering defined factors to maximize outcomes. A prototype clinical tool (Rosacea Tracker) and patient case studies have been developed from consensus statements.
CONCLUSIONS
The current survey updates previous recommendations as a basis for local guideline development and provides clinical tools to facilitate a phenotype approach in practice and improve rosacea patient management. What's already known about this topic? A transition to a phenotype approach in rosacea is underway and is being recommended by multiple working groups. New research has become available since the previous ROSCO consensus, necessitating an update and extension of recommendations. What does this study add? We offer updated global recommendations for clinical practice that account for recent research, to continue supporting the transition to a phenotype approach in rosacea. We present prototype clinical tools to facilitate use of the phenotype approach in practice and improve management of patients with rosacea.
Topics: Combined Modality Therapy; Consensus; Cost of Illness; Humans; Ophthalmologists; Rosacea
PubMed: 31392722
DOI: 10.1111/bjd.18420 -
Tidsskrift For Den Norske Laegeforening... Aug 2019Dry eye disease is a frequent reason for patients to seek help. In our experience, it is an underdiagnosed and undertreated condition. (Review)
Review
Dry eye disease is a frequent reason for patients to seek help. In our experience, it is an underdiagnosed and undertreated condition.
Topics: Dry Eye Syndromes; General Practitioners; Humans; Meibomian Glands; Ophthalmologists; Optometrists
PubMed: 31429248
DOI: 10.4045/tidsskr.18.0752 -
The Lancet. Digital Health Aug 2021Medical artificial intelligence (AI) has entered the clinical implementation phase, although real-world performance of deep-learning systems (DLSs) for screening fundus...
BACKGROUND
Medical artificial intelligence (AI) has entered the clinical implementation phase, although real-world performance of deep-learning systems (DLSs) for screening fundus disease remains unsatisfactory. Our study aimed to train a clinically applicable DLS for fundus diseases using data derived from the real world, and externally test the model using fundus photographs collected prospectively from the settings in which the model would most likely be adopted.
METHODS
In this national real-world evidence study, we trained a DLS, the Comprehensive AI Retinal Expert (CARE) system, to identify the 14 most common retinal abnormalities using 207 228 colour fundus photographs derived from 16 clinical settings with different disease distributions. CARE was internally validated using 21 867 photographs and externally tested using 18 136 photographs prospectively collected from 35 real-world settings across China where CARE might be adopted, including eight tertiary hospitals, six community hospitals, and 21 physical examination centres. The performance of CARE was further compared with that of 16 ophthalmologists and tested using datasets with non-Chinese ethnicities and previously unused camera types. This study was registered with ClinicalTrials.gov, NCT04213430, and is currently closed.
FINDINGS
The area under the receiver operating characteristic curve (AUC) in the internal validation set was 0·955 (SD 0·046). AUC values in the external test set were 0·965 (0·035) in tertiary hospitals, 0·983 (0·031) in community hospitals, and 0·953 (0·042) in physical examination centres. The performance of CARE was similar to that of ophthalmologists. Large variations in sensitivity were observed among the ophthalmologists in different regions and with varying experience. The system retained strong identification performance when tested using the non-Chinese dataset (AUC 0·960, 95% CI 0·957-0·964 in referable diabetic retinopathy).
INTERPRETATION
Our DLS (CARE) showed satisfactory performance for screening multiple retinal abnormalities in real-world settings using prospectively collected fundus photographs, and so could allow the system to be implemented and adopted for clinical care.
FUNDING
This study was funded by the National Key R&D Programme of China, the Science and Technology Planning Projects of Guangdong Province, the National Natural Science Foundation of China, the Natural Science Foundation of Guangdong Province, and the Fundamental Research Funds for the Central Universities.
TRANSLATION
For the Chinese translation of the abstract see Supplementary Materials section.
Topics: Area Under Curve; Artificial Intelligence; Biomedical Technology; China; Deep Learning; Diabetic Retinopathy; Expert Systems; Fundus Oculi; Humans; Image Processing, Computer-Assisted; Mass Screening; Models, Biological; Ophthalmologists; Photography; ROC Curve; Retina; Retinal Diseases
PubMed: 34325853
DOI: 10.1016/S2589-7500(21)00086-8 -
Arquivos Brasileiros de Oftalmologia Jun 2018
Topics: Biomedical Technology; Humans; Internship and Residency; Ophthalmologists; Ophthalmology; Teaching
PubMed: 29924205
DOI: 10.5935/0004-2749.20180036 -
Asia-Pacific Journal of Ophthalmology...Recent advances in artificial intelligence have provided ophthalmologists with fast, accurate, and automated means for diagnosing and treating ocular conditions, paving... (Review)
Review
Recent advances in artificial intelligence have provided ophthalmologists with fast, accurate, and automated means for diagnosing and treating ocular conditions, paving the way to a modern and scalable eye care system. Compared to other ophthalmic disciplines, neuro-ophthalmology has, until recently, not benefitted from significant advances in the area of artificial intelligence. In this narrative review, we summarize and discuss recent advancements utilizing artificial intelligence for the detection of structural and functional optic nerve head abnormalities, and ocular movement disorders in neuro-ophthalmology.
Topics: Artificial Intelligence; Eye; Humans; Ophthalmologists; Ophthalmology; Optic Nerve
PubMed: 35533331
DOI: 10.1097/APO.0000000000000512 -
Eye (London, England) Jun 2020This article will review the best approaches to neuroimaging for specific ophthalmologic conditions and discuss characteristic radiographic findings. A review of the... (Review)
Review
This article will review the best approaches to neuroimaging for specific ophthalmologic conditions and discuss characteristic radiographic findings. A review of the current literature was performed to find recommendations for the best approaches and characteristic radiographic findings for various ophthalmologic conditions. Options for imaging continue to grow with modern advances in technology, and ophthalmologists should stay current on the various radiographic techniques available to them, focusing on their strengths and weaknesses for different clinical scenarios.
Topics: Humans; Neuroimaging; Ophthalmologists
PubMed: 31896804
DOI: 10.1038/s41433-019-0753-z -
Indian Journal of Ophthalmology Nov 2019
Topics: Clinical Competence; Delivery of Health Care; Education, Medical, Graduate; Eye Diseases; Humans; Ophthalmologists; Ophthalmology; Point-of-Care Systems
PubMed: 31638034
DOI: 10.4103/ijo.IJO_1922_19 -
Indian Journal of Ophthalmology Feb 2017
Topics: Clinical Competence; Education, Medical, Graduate; Humans; India; Internship and Residency; Ophthalmologists; Ophthalmology
PubMed: 28345558
DOI: 10.4103/ijo.IJO_177_17 -
Translational Vision Science &... Jan 2020
Topics: Algorithms; Artificial Intelligence; Humans; Ophthalmologists
PubMed: 32518707
DOI: 10.1167/tvst.9.2.2