research-article

Deep Learning-assisted Retinopathy of Prematurity (ROP) Screening

Authors:

Shorya AzadAuthors Info & Claims

ACM Transactions on Computing for Healthcare, Volume 4, Issue 3

Article No.: 18, Pages 1 - 32

https://doi.org/10.1145/3596223

Published: 08 September 2023 Publication History

Abstract

Retinopathy of prematurity (ROP) is a leading cause of blindness in premature infants worldwide, particularly in developing countries. In this research, we propose a Deep Convolutional Neural Network (DCNN) and image processing-based approach for the automatic detection of retinal features, including the optical disc (OD) and retinal blood vessels (BV), as well as disease classification using a rule-based method for ROP patients. Our DCNN model uses YOLO-v5 for OD detection and either Pix2Pix or a U-Net for BV segmentation.

We trained our DCNN models on publicly available fundus image datasets of size 1,117 and 288 for OD detection and BV segmentation, respectively. We evaluated our approach on a dataset of 439 preterm neonatal retinal images, testing for ROP Zone and 6 BV masks. Our proposed system achieved excellent results, with the OD detection module achieving an overall accuracy of 98.94% (when IoU 0.5) and the BV segmentation module achieving an accuracy of 96.69% and a Dice coefficient between 0.60 and 0.64. Moreover, our system accurately diagnosed ROP in Zone-1 with 88.23% accuracy. Our approach offers a promising solution for accurate ROP screening and diagnosis, particularly in low-resource settings, where it has the potential to improve healthcare outcomes.

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Deep Learning-assisted Retinopathy of Prematurity (ROP) Screening

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cover image ACM Transactions on Computing for Healthcare

ACM Transactions on Computing for Healthcare Volume 4, Issue 3

July 2023

128 pages

EISSN:2637-8051

DOI:10.1145/3623485

Editors:
Insup Lee
University of Pennsylvania, USA
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Gang Zhou
William and Mary, USA

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Publication History

Published: 08 September 2023

Online AM: 22 May 2023

Accepted: 18 April 2023

Revised: 14 March 2023

Received: 31 March 2022

Published in HEALTH Volume 4, Issue 3

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Peng YXu HZhao LZhu WShi FWang MZhou YFeng KChen X(2024)Automatic zoning for retinopathy of prematurity with a key area location systemBiomedical Optics Express10.1364/BOE.50611915:2(725)Online publication date: 16-Jan-2024
https://doi.org/10.1364/BOE.506119
Selvamarilakshmi DVani RAravinth SThenappan SBegam RGangarani N(2024)Integrating Machine Learning for Early Diagnosis and Prognostic Assessment of Retinopathy of Premature Babies2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)10.1109/ACCAI61061.2024.10602334(1-7)Online publication date: 9-May-2024
https://doi.org/10.1109/ACCAI61061.2024.10602334
Raja Sankari VUmapathy S(2024)Computer-aided diagnosis of early-stage Retinopathy of Prematurity in neonatal fundus images using artificial intelligenceBiomedical Physics & Engineering Express10.1088/2057-1976/ad91ba11:1(015015)Online publication date: 21-Nov-2024
https://doi.org/10.1088/2057-1976/ad91ba
Almeida JKubicek JPenhaker MCerny MAugustynek MVarysova ABansal ATimkovic J(2024)Enhancing ROP plus form diagnosis: An automatic blood vessel segmentation approach for newborn fundus imagesResults in Engineering10.1016/j.rineng.2024.10305424(103054)Online publication date: Dec-2024
https://doi.org/10.1016/j.rineng.2024.103054

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