Does Field of View Matter? Assessing Its Impact on Plus Disease Diagnosis in Retinopathy of Prematurity Publisher



Sharafi SM ; Amanollahi M ; Ebrahimiadib N ; Nowroozzadeh MH ; Fooladi MI ; Bazvand F ; Mohammadi N ; Rajabi MB ; Riaziesfahani H ; Khalili Pour E
Source: International Journal of Retina and Vitreous Published:2025

Abstract

Purpose: This 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. Methods: Fundus 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. Results: Seventy-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. Conclusions: FOV 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. © The Author(s) 2025.