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Current Application of Digital Diagnosing Systems for Retinopathy of Prematurity
Yuekun Bao1, Wai-Kit Ming2, Zhi-Wei Mou3
1Department of Ophthalmology, the First Affiliated Hospital of Jinan University, Guangzhou, China; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
Insights
Digital image analysis aids in diagnosing retinopathy of prematurity (ROP), a leading cause of childhood blindness. Computer-based systems, including deep learning, offer accurate ROP detection for premature infants, enhancing remote ophthalmic support.
Area of Science:
- Ophthalmology
- Medical Imaging
- Computer Science
Background:
- Retinopathy of prematurity (ROP) is a major cause of childhood blindness in premature infants.
- Digital image analysis presents new diagnostic possibilities for ROP.
- Existing diagnostic methods require specialized expertise and infrastructure.
Purpose of the Study:
- To review the development of digital diagnostic systems for ROP.
- To guide software researchers and ophthalmologists in selecting ROP diagnostic software.
- To highlight the role of these systems in remote ophthalmic care.
Main Methods:
- Literature review of articles published between 1998 and 2020.
- Screening of databases including Pubmed and Google Scholar.
- Analysis and categorization of digital diagnosing systems for ROP.
Main Results:
- Telemedicine enables remote ROP image interpretation but needs local operator training.
- Computer-based systems using classic machine learning and deep learning (DL) have been developed.
- Automated ROP diagnosis systems based on DL show high accuracy; multiple instance learning requires further research.
Conclusions:
- Computer-based image analysis integrated with telemedicine facilitates ROP detection and timely treatment.
- Digital diagnostic systems improve ROP management in preterm infants.
- These technologies enhance the accessibility of ophthalmic care for premature infants.
Background And Objective:
Retinopathy of prematurity (ROP), a proliferative vascular eye disease, is one of the leading causes of blindness in childhood and prevails in premature infants with low-birth-weight. The recent progress in digital image analysis offers novel strategies for ROP diagnosis. This paper provides a comprehensive review on the development of digital diagnosing systems for ROP to software researchers. It may also be adopted as a guide to ophthalmologists for selecting the most suitable diagnostic software in the clinical setting, particularly for the remote ophthalmic support.
Methods:
We review the latest literatures concerning the application of digital diagnosing systems for ROP. The diagnosing systems are analyzed and categorized. Articles published between 1998 and 2020 were screened with the two searching engines Pubmed and Google Scholar.
Results:
Telemedicine is a method of remote image interpretation that can provide medical service to remote regions, and yet requires training to local operators. On the basis of image collection in telemedicine, computer-based image analytical systems for ROP were later developed. So far, the aforementioned systems have been mainly developed by virtue of classic machine learning, deep learning (DL) and multiple machine learning. During the past two decades, various computer-aided systems for ROP based on classic machine learning (e.g. RISA, ROPtool, CAIER) became available and have achieved satisfactory performance. Further, automated systems for ROP diagnosis based on DL are developed for clinical applications and exhibit high accuracy. Moreover, multiple instance learning is another method to establish an automated system for ROP detection besides DL, which, however, warrants further investigation in future.
Conclusion:
At present, the incorporation of computer-based image analysis with telemedicine potentially enables the detection, supervision and in-time treatment of ROP for the preterm babies.
