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Scientific Reports Aug 2022Clinical discrimination of posterior uveitis entities remains a challenge. This exploratory, cross-sectional study investigated the green (GEFC) and red emission...
Clinical discrimination of posterior uveitis entities remains a challenge. This exploratory, cross-sectional study investigated the green (GEFC) and red emission fluorescent components (REFC) of retinal and choroidal lesions in posterior uveitis to facilitate discrimination of the different entities. Eyes were imaged by color fundus photography, spectrally resolved fundus autofluorescence (Color-FAF) and optical coherence tomography. Retinal/choroidal lesions' intensities of GEFC (500-560 nm) and REFC (560-700 nm) were determined, and intensity-normalized Color-FAF images were compared for birdshot chorioretinopathy, ocular sarcoidosis, acute posterior multifocal placoid pigment epitheliopathy (APMPPE), and punctate inner choroidopathy (PIC). Multivariable regression analyses were performed to reveal possible confounders. 76 eyes of 45 patients were included with a total of 845 lesions. Mean GEFC/REFC ratios were 0.82 ± 0.10, 0.92 ± 0.11, 0.86 ± 0.10, and 1.09 ± 0.19 for birdshot chorioretinopathy, sarcoidosis, APMPPE, and PIC lesions, respectively, and were significantly different in repeated measures ANOVA (p < 0.0001). Non-pigmented retinal/choroidal lesions, macular neovascularizations, and fundus areas of choroidal thinning featured predominantly GEFC, and pigmented retinal lesions predominantly REFC. Color-FAF imaging revealed involvement of both, short- and long-wavelength emission fluorophores in posterior uveitis. The GEFC/REFC ratio of retinal and choroidal lesions was significantly different between distinct subgroups. Hence, this novel imaging biomarker could aid diagnosis and differentiation of posterior uveitis entities.
Topics: Birdshot Chorioretinopathy; Coloring Agents; Cross-Sectional Studies; Fluorescein Angiography; Humans; Optical Imaging; Sarcoidosis; Tomography, Optical Coherence; Uveitis, Posterior
PubMed: 36038591
DOI: 10.1038/s41598-022-18048-4 -
Indian Journal of Ophthalmology Mar 2019Serpiginous choroiditis (SC) is an asymmetrically bilateral inflammation of the choroid that leads to loss of choriocapillaris atrophy or loss of overlying retinal... (Review)
Review
Serpiginous choroiditis (SC) is an asymmetrically bilateral inflammation of the choroid that leads to loss of choriocapillaris atrophy or loss of overlying retinal pigment epithelium. Over the last few decades, SC has passed through a long evolution of nomenclature, etiologies and morphological variations. Initially diagnosed in patients with tuberculosis and syphilis, SC was predominantly considered as autoimmune process. With the advancement of molecular diagnosis, a new aspect of infectious subtypes of SC has emerged out. The terminologies such as serpiginous-like choroiditis (SLC) and multifocal serpiginoid choroiditis are now used to denote the subtypes of SC which are associated with infectious etiologies especially tuberculosis. In a country endemic for tuberculosis such as India, it is very important to differentiate between classic SC and SLC before initiating aggressive immunomodulatory therapy. Also, management of paradoxical worsening of the clinical condition with antitubercular treatment is another challenge in SLC and ophthalmologists should be aware of such situations. With advent of newer imaging modalities, monitoring the patient with choroiditis and identification of complications such as choroidal neovascular membrane have become much easier. This article aims to review the existing literature on SC with a special emphasis on management of SC and SLC.
Topics: Choroid; Choroiditis; Diagnosis, Differential; Fluorescein Angiography; Fundus Oculi; Humans; Multifocal Choroiditis; Retinal Pigment Epithelium; Tomography, Optical Coherence
PubMed: 30777946
DOI: 10.4103/ijo.IJO_822_18 -
Journal Francais D'ophtalmologie Nov 2021
Topics: COVID-19; Humans; SARS-CoV-2; White Dot Syndromes
PubMed: 34625310
DOI: 10.1016/j.jfo.2021.07.004 -
Eye (London, England) Jan 2021The aim of this review was to identify the imaging methods at our disposal to optimally manage posterior uveitis at the present time. The focus was put on methods that... (Review)
Review
The aim of this review was to identify the imaging methods at our disposal to optimally manage posterior uveitis at the present time. The focus was put on methods that have become available since the 1990s, some 30 years after fluorescein angiography had revolutionized imaging of posterior uveitis in particular imaging of the retinal vascular structures in the 1960s. We have focussed our review on precise imaging methods that have been standardized and validated and can be used universally thanks to commercially produced and available instruments for the diagnosis and follow-up of posterior uveitis. The first part of this imaging review will deal with noninvasive imaging methods, focusing on fundus autofluorescence and optical coherence tomography as well as recent developments in imaging of the posterior segment.
Topics: Fluorescein Angiography; Humans; Optical Imaging; Retinal Vessels; Tomography, Optical Coherence; Uveitis; Uveitis, Posterior
PubMed: 32678354
DOI: 10.1038/s41433-020-1063-1 -
Eye (London, England) Jan 2021The aim of this review was to identify the imaging methods at our disposal to optimally manage posterior uveitis at the present time. The focus was put on methods that... (Review)
Review
The aim of this review was to identify the imaging methods at our disposal to optimally manage posterior uveitis at the present time. The focus was put on methods that have become available since the 1990s, some 30 years after fluorescein angiography had revolutionised imaging of posterior uveitis in particular imaging of the retinal vascular structures in the 1960s. We have focussed our review on precise imaging methods that have been standardised and validated and can be used universally thanks to commercially produced and available instruments for the diagnosis and follow-up of posterior uveitis. The second part of this imaging review will deal with invasive imaging methods and in particular ocular angiography.
Topics: Diagnostic Tests, Routine; Fluorescein Angiography; Humans; Retinal Vessels; Uveitis; Uveitis, Posterior
PubMed: 32778739
DOI: 10.1038/s41433-020-1072-0 -
Saudi Journal of Ophthalmology :... 2022Posterior uveitis is sight-threatening disease entity that can be caused by infectious and non-infectious entities. Vision loss in posterior uveitis can be following...
Interpreting posterior uveitis by integrating indocyanine green angiography, optical coherence tomography, and optical coherence tomography angiography data: A narrative review.
Posterior uveitis is sight-threatening disease entity that can be caused by infectious and non-infectious entities. Vision loss in posterior uveitis can be following complications such as cystoid macular edema, epiretinal membrane, artery and vein occlusions, vasculitis, papillitis, choroidal neovascular membrane, retinal neovascularization, tractional retinal detachment, vitreous hemorrhage, glaucoma, cataract, among others. Diagnosis of posterior uveitic entities have been revolutionized following introduction of choroidal imaging with techniques such as indocyanine green angiography (ICGA), optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA). Med Line search and PubMed search was performed pertaining to causes of posterior uveitis, ICGA in posterior uveitis, OCT in posterior uveitis, OCTA in posterior uveitis, retinal and choroidal vascular changes in posterior uveitis, quantification of choriocapillaris lesion area in posterior uveitis, subfoveal choroidal thickness in posterior uveitis, quantification of choriocapillaris in posterior uveitis, vascular indices for quantification of choriocapillaris. This review article highlights various changes in the choroid and the quantification of choroid using various parameters in ICGA, OCT and OCTA.
PubMed: 36618566
DOI: 10.4103/sjopt.sjopt_69_22 -
American Journal of Ophthalmology Aug 2021To determine classification criteria for serpiginous choroiditis.
PURPOSE
To determine classification criteria for serpiginous choroiditis.
DESIGN
Machine learning of cases with serpiginous choroiditis and 8 other posterior uveitides.
METHODS
Cases of posterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the infectious posterior uveitides / panuveitides. The resulting criteria were evaluated on the validation set.
RESULTS
One thousand sixty-eight cases of posterior uveitides, including 122 cases of serpiginous choroiditis, were evaluated by machine learning. Key criteria for serpiginous choroiditis included (1) choroiditis with an ameboid or serpentine shape; (2) characteristic imaging on fluorescein angiography or fundus autofluorescence; (3) absent to mild anterior chamber and vitreous inflammation; and (4) the exclusion of tuberculosis. Overall accuracy for posterior uveitides was 93.9% in the training set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rates for serpiginous choroiditis were 0% in both the training set and the validation set.
CONCLUSIONS
The criteria for serpiginous choroiditis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research.
Topics: Adult; Choroid; Female; Fluorescein Angiography; Fundus Oculi; Humans; Machine Learning; Male; Middle Aged; White Dot Syndromes
PubMed: 33845013
DOI: 10.1016/j.ajo.2021.03.038 -
American Journal of Ophthalmology Aug 2021To determine classification criteria for birdshot chorioretinitis.
PURPOSE
To determine classification criteria for birdshot chorioretinitis.
DESIGN
Machine learning of cases with birdshot chorioretinitis and 8 other posterior uveitides.
METHODS
Cases of posterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the infectious posterior uveitides / panuveitides. The resulting criteria were evaluated on the validation set.
RESULTS
One thousand sixty-eight cases of posterior uveitides, including 207 cases of birdshot chorioretinitis, were evaluated by machine learning. Key criteria for birdshot chorioretinitis included a multifocal choroiditis with (1) the characteristic appearance of a bilateral multifocal choroiditis with cream-colored or yellow-orange, oval or round choroidal spots ("birdshot" spots); (2) absent to mild anterior chamber inflammation; and (3) absent to moderate vitreous inflammation; or multifocal choroiditis with positive HLA-A29 testing and either classic "birdshot spots" or characteristic imaging on indocyanine green angiography. Overall accuracy for posterior uveitides was 93.9% in the training set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rates for birdshot chorioretinitis were 10% in the training set and 0% in the validation set.
CONCLUSIONS
The criteria for birdshot chorioretinitis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research.
Topics: Birdshot Chorioretinopathy; Choroid; Consensus; Female; Fluorescein Angiography; Fundus Oculi; Humans; Machine Learning; Male; Middle Aged; Retina
PubMed: 33845003
DOI: 10.1016/j.ajo.2021.03.059 -
American Journal of Ophthalmology Aug 2021To determine classification criteria for tubercular uveitis. (Comparative Study)
Comparative Study
PURPOSE
To determine classification criteria for tubercular uveitis.
DESIGN
Machine learning of cases with tubercular uveitis and 14 other uveitides.
METHODS
Cases of noninfectious posterior uveitis or panuveitis, and of infectious posterior uveitis or panuveitis, were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were analyzed by anatomic class, and each class was split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated on the validation sets.
RESULTS
Two hundred seventy-seven cases of tubercular uveitis were evaluated by machine learning against other uveitides. Key criteria for tubercular uveitis were a compatible uveitic syndrome, including (1) anterior uveitis with iris nodules, (2) serpiginous-like tubercular choroiditis, (3) choroidal nodule (tuberculoma), (4) occlusive retinal vasculitis, and (5) in hosts with evidence of active systemic tuberculosis, multifocal choroiditis; and evidence of tuberculosis, including histologically or microbiologically confirmed infection, positive interferon-γ release assay test, or positive tuberculin skin test. The overall accuracy of the diagnosis of tubercular uveitis vs other uveitides in the validation set was 98.2% (95% confidence interval 96.5, 99.1). The misclassification rates for tubercular uveitis were training set, 3.4%; and validation set, 3.6%.
CONCLUSIONS
The criteria for tubercular uveitis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research.
Topics: Adult; Female; Humans; Machine Learning; Male; Middle Aged; Retrospective Studies; Tuberculin Test; Tuberculosis, Ocular; Uveitis; Young Adult
PubMed: 33845014
DOI: 10.1016/j.ajo.2021.03.040 -
Romanian Journal of Ophthalmology 2022Anterior uveitis is the most common extra-articular manifestation in children diagnosed with Juvenile idiopathic arthritis (JIA). It is typically a non-granulomatous,...
Anterior uveitis is the most common extra-articular manifestation in children diagnosed with Juvenile idiopathic arthritis (JIA). It is typically a non-granulomatous, chronic, and asymptomatic uveitis. The lack of acute symptoms often delays the diagnosis with the incidence of severe ocular complications. Chorioretinitis lesions have been described in only 1% of cases. The absence of fundus changes can be explained by the impossibility of performing fundoscopy through the cloudy ocular media, secondary to inflammation. A 7-year-old female with a 3-month history of painless reduced vision came to have an eye examination. An initial diagnosis of bilateral anterior granulomatous uveitis complicated with glaucoma and cataract was formulated. Because of the concomitant diagnosis of COVID-19 disease (same day as the eye examination), the child was hospitalized in a hometown COVID-19 patient ward, so both local and general treatment, monitorization, and investigations were discontinued. The following eye examination revealed the persistence of anterior uveitis, inflammatory glaucoma, cataract, and the appearance of band keratopathy. Fundoscopy revealed numerous disseminated lesions of choroiditis. Further examinations established JIA-associated uveitis diagnosis, so systemic corticosteroids were initiated followed by Methotrexate and Adalimumab. . BVA = best visual acuity, CVA = corrected visual acuity, CS = corticosteroids, IOP = Intraocular pressure, JIA = Juvenile idiopathic arthritis, JIA-U = Juvenile idiopathic arthritis associated uveitis, LE = left eye, MTX = Methotrexate, OU = both eyes, OCT = Optical Coherence Tomography, RE = right eye, TNF = tumor necrosis factor.
Topics: Arthritis, Juvenile; COVID-19; Cataract; Child; Female; Glaucoma; Humans; Methotrexate; Uveitis; Uveitis, Anterior; Uveitis, Posterior
PubMed: 35935079
DOI: 10.22336/rjo.2022.36