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<p>Retinopathy of Prematurity (ROP) represents a critical ophthalmological pathology affecting premature infants, with established associations to low birth weight (BW) and early gestational age (GA). Elevated risk of severe ROP, which can result in irreversible vision loss, is observed in infants exhibiting lower BW and GA. This research investigates the development of an automated diagnostic system designed to classify Plus disease, a marker of abnormal retinal vascularity, and ROP staging, a determinant of disease progression. Specifically, the model facilitates binary classification of Plus disease (Plus/Normal) and multi-class classification of ROP stage (Stage 0, 1, 2, 3) using a meticulously curated dataset of retinal fundus images. The proposed model demonstrates high diagnostic accuracy, achieving 0.996 for Plus disease detection and 0.98 for ROP stage classification. These results suggest potential clinical utility for automated ROP screening methodologies in supporting timely diagnosis and intervention in similar settings, pending multi-center validation, which could help reduce the incidence of vision impairment in preterm populations.</p>

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Automated diagnosis of plus form and early stages of ROP using deep learning models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月4日

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  • Mahdi Vahidmoghadam,
  • Parisa Ghorbani,
  • Mohammad Javad Ahmadi,
  • Esmaeil Asadi Khameneh,
  • Babak Tavassoli,
  • Hamid D. Taghirad

摘要

Retinopathy of Prematurity (ROP) represents a critical ophthalmological pathology affecting premature infants, with established associations to low birth weight (BW) and early gestational age (GA). Elevated risk of severe ROP, which can result in irreversible vision loss, is observed in infants exhibiting lower BW and GA. This research investigates the development of an automated diagnostic system designed to classify Plus disease, a marker of abnormal retinal vascularity, and ROP staging, a determinant of disease progression. Specifically, the model facilitates binary classification of Plus disease (Plus/Normal) and multi-class classification of ROP stage (Stage 0, 1, 2, 3) using a meticulously curated dataset of retinal fundus images. The proposed model demonstrates high diagnostic accuracy, achieving 0.996 for Plus disease detection and 0.98 for ROP stage classification. These results suggest potential clinical utility for automated ROP screening methodologies in supporting timely diagnosis and intervention in similar settings, pending multi-center validation, which could help reduce the incidence of vision impairment in preterm populations.