ArticleInternational journal of retina and vitreous2025
Does field of view matter? Assessing its impact on Plus disease diagnosis in retinopathy of prematurity.
Article in International journal of retina and vitreous, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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10 authors.
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Abstract
purposeThis study investigates the impact of field of view (FOV) on the diagnosis of Plus disease in retinopathy of prematurity (ROP), focusing on how different FOVs influence expert assessments.
methodsFundus images from 91 ROP infants were captured using a RetCam, each including the optic disk (OD) and a ridge/demarcation line. Cropped versions with a radius of three disc diameters (3DD) centered on the OD were automatically extracted using a computer-based algorithm. Images were categorized into five (normal, pre-Plus, Plus1, Plus2, Plus3), three (non-Plus, pre-Plus, Plus), or two groups (non-Plus, Plus). Five experts graded both entire-view and 3DD images. Inter-expert reliability was assessed using Cronbach’s alpha.
resultsSeventy-four 3DD and 77 entire-view images were graded. Cronbach’s alpha values were 0.933 (entire-view) and 0.942 (3DD) in the 5-level grading system, indicating slightly higher consistency among experts for 3DD images. Notable diagnostic shifts were observed when comparing entire-view and 3DD images, with 25.0% of Plus1 diagnoses shifting to pre-Plus and 16.9% of pre-Plus diagnoses shifting to normal in the 5-level grading system. Two experts showed substantial changes in their grades, with one expert upscaling 33.7% and downscaling 16.9% of diagnoses, and the other upscaling 21.7% and downscaling 15.7% when assessing entire-view compared to 3DD images.
conclusionsFOV significantly affects the diagnosis of Plus disease, with peripheral retinal features influencing expert judgments. Restricting the FOV to a 3DD area improved inter-expert reliability but led to notable diagnostic shifts. These findings highlight the need for standardized imaging protocols and the potential role of AI in reducing diagnostic variability in ROP.
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