Current and future roles of artificial intelligence in retinopathy of prematurity
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916670284496896 |
|---|---|
| author | 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 Mohammad Shariful Acharya, U. Rajendra |
| author_facet | 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 Mohammad Shariful Acharya, U. Rajendra |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09975 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Current and future roles of artificial intelligence in retinopathy of prematurity 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 Mohammad Shariful Acharya, U. Rajendra Image and Video Processing Computer Vision and Pattern Recognition J.3.2; J.3.3 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. |
| title | Current and future roles of artificial intelligence in retinopathy of prematurity |
| topic | Image and Video Processing Computer Vision and Pattern Recognition J.3.2; J.3.3 |
| url | https://arxiv.org/abs/2402.09975 |