Sharafi Sayed Mehran, Ebrahimiadib Nazanin, Roohipourmoallai Ramak, Dastjani Farahani Afsar, Imani Fooladi Marjan, Gharehbaghi Golnaz, Khalili Pour Elias
Translational Ophthalmology Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran.
Ophthalmology Department, College of Medicine, University of Florida, Gainesville, FL, USA.
Int J Retina Vitreous. 2025 Apr 11;11(1):43. doi: 10.1186/s40942-025-00668-3.
Plus disease is characterized by abnormal changes in retinal vasculature of premature infants. Presence of Plus disease is an important criterion for identifying treatment-requiring cases in Retinopathy of Prematurity (ROP). However, diagnosis of Plus disease has been shown to be subjective and there is wide variability in the classification of Plus disease by ROP experts, which is mainly because experts have different cut-points for distinguishing the levels of vascular abnormality. This suggests that a continuous Plus disease severity score may reflect more accurately the behavior of expert clinicians and may better standardize the classification. The effect of using quantitative methods and computer-based image analysis to improve the objectivity of Plus disease diagnosis have been well established. Nevertheless, the current methods are based on categorical classifications of the disease severity and lack the compatibility with the continuous nature of the abnormal changes in retinal vasculatures. In this study, we developed a computer-based method that performs a quantitative analysis of vascular characteristics associated with Plus disease and utilizes them to build a regression model that outputs a continuous spectrum of Plus severity. We evaluated the proposed method against the consensus diagnosis made by four ROP experts on 76 posterior ROP images. The findings of our study indicate that our approach demonstrated a relatively acceptable level of accuracy in evaluating the severity of Plus disease, which is comparable to the diagnostic abilities of experts.
附加病变以早产儿视网膜血管系统的异常变化为特征。附加病变的存在是识别早产儿视网膜病变(ROP)中需要治疗病例的重要标准。然而,附加病变的诊断已被证明具有主观性,ROP专家对附加病变的分类存在很大差异,这主要是因为专家在区分血管异常程度时有不同的切点。这表明连续的附加病变严重程度评分可能更准确地反映专家临床医生的判断行为,并可能更好地规范分类。使用定量方法和基于计算机的图像分析来提高附加病变诊断客观性的效果已经得到充分证实。然而,目前的方法基于疾病严重程度的分类,与视网膜血管异常变化的连续性不兼容。在本研究中,我们开发了一种基于计算机的方法,对与附加病变相关的血管特征进行定量分析,并利用这些特征建立一个回归模型,输出连续的附加病变严重程度谱。我们根据四位ROP专家对76张ROP后部图像的共识诊断对所提出的方法进行了评估。我们的研究结果表明,我们的方法在评估附加病变严重程度方面表现出相对可接受的准确性水平,与专家的诊断能力相当。