Literature DB >> 19210329

Digital image analysis of plus disease in retinopathy of prematurity.

Tariq Aslam1, Brian Fleck, Niall Patton, Manuel Trucco, Hind Azegrouz.   

Abstract

An accurate assessment of retinopathy of prematurity (ROP) is essential in ensuring correct and timely treatment of this potentially blinding condition. Current modes of assessment are based upon clinical grading by expert examination of retinal changes. However, this may be subjective, unreliable and difficult and there has been significant interest in alternative means of measurement. These have been made possible through technological advancements in image capture and analysis as well as progress in clinical research, highlighting the specific importance of plus disease in ROP. Progress in these two fields has highlighted the potential for digital image analysis of plus disease to be used as an objective, reliable and valid measurement of ROP. The potential for clinical and scientific advancement through this method is argued and demonstrated in this article. Along with the potential benefits, there are significant challenges such as in image capture, segmentation, measurement of vessel width and tortuosity; these are also addressed. After discussing and explaining the challenges involved, the research articles addressing digital image analysis of ROP are critically reviewed. Benefits and limitations of the currently published techniques for digital ROP assessment are discussed with particular reference to the validity and reliability of outcome measures. Finally, the general limitations of current methods of analysis are discussed and more diverse potential areas of development are discussed.

Entities:  

Mesh:

Year:  2009        PMID: 19210329     DOI: 10.1111/j.1755-3768.2008.01448.x

Source DB:  PubMed          Journal:  Acta Ophthalmol        ISSN: 1755-375X            Impact factor:   3.761


  5 in total

1.  Automated identification of retinopathy of prematurity by image-based deep learning.

Authors:  Yan Tong; Wei Lu; Qin-Qin Deng; Changzheng Chen; Yin Shen
Journal:  Eye Vis (Lond)       Date:  2020-08-01

2.  Measurement of retinal vascular tortuosity and its application to retinal pathologies.

Authors:  Geoff Dougherty; Michael J Johnson; Matthew D Wiers
Journal:  Med Biol Eng Comput       Date:  2009-12-11       Impact factor: 2.602

3.  Enhancing Image Characteristics of Retinal Images of Aggressive Posterior Retinopathy of Prematurity Using a Novel Software, (RetiView).

Authors:  Chaitra Jayadev; Anand Vinekar; Poornima Mohanachandra; Samit Desai; Amit Suveer; Shwetha Mangalesh; Noel Bauer; Bhujang Shetty
Journal:  Biomed Res Int       Date:  2015-07-09       Impact factor: 3.411

4.  Artificial Intelligence: Quo Vadis?

Authors:  Marco A Zarbin
Journal:  Transl Vis Sci Technol       Date:  2020-01-29       Impact factor: 3.283

5.  Automatic Grading of Retinal Blood Vessel in Deep Retinal Image Diagnosis.

Authors:  Debasis Maji; Arif Ahmed Sekh
Journal:  J Med Syst       Date:  2020-09-01       Impact factor: 4.460

  5 in total

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