Current and future roles of artificial intelligence in retinopathy of
  prematurity
Ali Jafarizadeh Shadi Farabi Maleki Parnia Pouya Navid Sobhi Mirsaeed Abdollahi Siamak Pedrammehr Chee Peng Lim Houshyar Asadi Roohallah Alizadehsani Ru-San Tan Sheikh Mohammad Shariful Islam U. Rajendra Acharya
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 89 original studies in this field (out of 1487 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.