Current and future roles of artificial intelligence in retinopathy of prematurity
Article
Jafarizadeh, Ali, Maleki, Shadi Farabi, Pouya, Parnia, Sobhi, Navid, Abdollahi, Mirsaeed, Pedrammehr, Siamak, Lim, Chee Peng, Asadi, Houshyar, Alizadehsani, Roohallah, Tan, Ru-San, Islam, Sheikh Mohammed Shariful and Acharya, U. Rajendra. 2025. "Current and future roles of artificial intelligence in retinopathy of prematurity." Artificial Intelligence Review. 58 (6). https://doi.org/10.1007/s10462-025-11153-6
Article Title | Current and future roles of artificial intelligence in retinopathy of prematurity |
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Article Category | Article |
Authors | Jafarizadeh, Ali, Maleki, Shadi Farabi, Pouya, Parnia, Sobhi, Navid, Abdollahi, Mirsaeed, Pedrammehr, Siamak, Lim, Chee Peng, Asadi, Houshyar, Alizadehsani, Roohallah, Tan, Ru-San, Islam, Sheikh Mohammed Shariful and Acharya, U. Rajendra |
Journal Title | Artificial Intelligence Review |
Journal Citation | 58 (6) |
Number of Pages | 55 |
Year | 2025 |
Publisher | Springer |
Place of Publication | Netherlands |
ISSN | 0269-2821 |
1573-7462 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s10462-025-11153-6 |
Web Address (URL) | https://link.springer.com/article/10.1007/s10462-025-11153-6 |
Abstract | Retinopathy of prematurity (ROP) is a severe condition affecting premature infants, leading to abnormal retinal blood vessel growth, retinal detachment, and potential blindness. While semi-automated systems have been used in the past to diagnose ROP-related plus disease by quantifying retinal vessel features, traditional machine learning (ML) models face challenges like accuracy and overfitting. Recent advancements in deep learning (DL), especially convolutional neural networks (CNNs), have significantly improved ROP detection and classification. The i-ROP deep learning (i-ROP-DL) system also shows promise in detecting plus disease, offering reliable ROP diagnosis potential. This research comprehensively examines the contemporary progress and challenges associated with using retinal imaging and artificial intelligence (AI) to detect ROP, offering valuable insights that can guide further investigation in this domain. Based on 84 original studies in this field (out of 2025 studies that were comprehensively reviewed), we concluded that traditional methods for ROP diagnosis suffer from subjectivity and manual analysis, leading to inconsistent clinical decisions. AI holds great promise for improving ROP management. This review explores AI’s potential in ROP detection, classification, diagnosis, and prognosis. |
Keywords | Artificial intelligence; Retinopathy of prematurity; Deep learning ; Convolutional neural networks; Machine learning; ROPtool |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified |
Byline Affiliations | Tabriz University of Medical Sciences, Iran |
Deakin University | |
Duke-NUS Medical School, Singapore | |
National Heart Centre, Singapore | |
George Institute for Global Health, Australia | |
University of Sydney | |
School of Mathematics, Physics and Computing | |
Centre for Health Research |
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https://research.usq.edu.au/item/zx203/current-and-future-roles-of-artificial-intelligence-in-retinopathy-of-prematurity
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