Automated Detection and Classification of Telemedical Retinopathy of Prematurity Images

Vijayalakshmi, C.; Sakthivel, P.; Vinekar, Anand

doi:10.1089/tmj.2019.0004

Cited by 16 publications

(10 citation statements)

References 21 publications

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“…Of the articles reviewed, the image preprocessing stages consisted of applying a mask over the retinal image, image resizing, colour normalisation, vessel segmentation, image enhancement, illumination adjustment and image augmentation techniques (flipping and rotation). In seven articles (38.9%), details of the preprocessing techniques applied were not described 23–28…”

Section: Resultsmentioning

confidence: 99%

Fairness and generalisability in deep learning of retinopathy of prematurity screening algorithms: a literature review

Nakayama,

Mitchell,

Ribeiro

et al. 2023

BMJ Open Ophth

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BackgroundRetinopathy of prematurity (ROP) is a vasoproliferative disease responsible for more than 30 000 blind children worldwide. Its diagnosis and treatment are challenging due to the lack of specialists, divergent diagnostic concordance and variation in classification standards. While artificial intelligence (AI) can address the shortage of professionals and provide more cost-effective management, its development needs fairness, generalisability and bias controls prior to deployment to avoid producing harmful unpredictable results. This review aims to compare AI and ROP study’s characteristics, fairness and generalisability efforts.MethodsOur review yielded 220 articles, of which 18 were included after full-text assessment. The articles were classified into ROP severity grading, plus detection, detecting treatment requiring, ROP prediction and detection of retinal zones.ResultsAll the article’s authors and included patients are from middle-income and high-income countries, with no low-income countries, South America, Australia and Africa Continents representation.Code is available in two articles and in one on request, while data are not available in any article. 88.9% of the studies use the same retinal camera. In two articles, patients’ sex was described, but none applied a bias control in their models.ConclusionThe reviewed articles included 180 228 images and reported good metrics, but fairness, generalisability and bias control remained limited. Reproducibility is also a critical limitation, with few articles sharing codes and none sharing data. Fair and generalisable ROP and AI studies are needed that include diverse datasets, data and code sharing, collaborative research, and bias control to avoid unpredictable and harmful deployments.

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Section: Resultsmentioning

confidence: 99%

Fairness and generalisability in deep learning of retinopathy of prematurity screening algorithms: a literature review

Nakayama,

Mitchell,

Ribeiro

et al. 2023

BMJ Open Ophth

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“…One study used AI-derived VSS and GA to predict TR-ROP with 100% sensitivity across multiple data sets [25]. This study also predicted that up to 51.3% fewer examinations could be required for low-risk infants without missing cases of TR-ROP.…”

Section: Validation and Comparison Studies Of Ai-based Quantitative M...mentioning

confidence: 99%

“…A diagnostic study conducted in India used retinal fundus images that were collected as part of a telemedicine screening program, from 32 to 33 weeks PMA to obtain an AI-based vascular severity score (VSS) [24]. Specifically, the study analyzed images using the iROP DL, an algorithm that primarily consists of a CNN with the implementation of a vessel segmentation network built from the backpropagating U-Net algorithm by Ronneberger et al [25]. A VSS is assigned to retinal fundus images, typically from the first examination, on a scale of 1–9 (higher number indicating increasing severity), which is calculated by AI.…”

Section: Building An Ai Risk Modelmentioning

confidence: 99%

“…Several validation and comparison studies of an AI-based quantitative VSS to predict occurrence of ROP have been conducted with promising results [24, 25, 31, 32, 33, 34]. A comparison study was conducted to determine how well a DL ROP algorithm-based VSS correlates to expert labels of disease in retinal fundus photos [32].…”

Section: Validation and Comparison Studies Of Ai-based Quantitative M...mentioning

confidence: 99%

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Application of Artificial Intelligence in the Early Detection of Retinopathy of Prematurity: Review of the Literature

Shah,

Slaney,

VerHage

et al. 2023

Neonatology

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Retinopathy of prematurity (ROP) is a potentially blinding disease in premature neonates that requires a skilled workforce for diagnosis, monitoring, and treatment. Artificial intelligence is a valuable tool that clinicians employ to reduce the screening burden on ophthalmologists and neonatologists and improve the detection of treatment-requiring ROP. Neural networks such as convolutional neural networks and deep learning (DL) systems are used to calculate a vascular severity score (VSS), an important component of various risk models. These DL systems have been validated in various studies, which are reviewed here. Most importantly, we discuss a promising study that validated a DL system that could predict the development of ROP despite a lack of clinical evidence of disease on the first retinal examination. Additionally, there is promise in utilizing these systems through telemedicine in more rural and resource-limited areas. This review highlights the value of these DL systems in early ROP diagnosis.

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“…Several other groups are also working on AI for ROP screening, [4][5][6][7] and once systems can provide reliable information on all of the signs needed to make a management decision at each screening episode, together with the availability of lower-cost imaging devices, the door is open for a dramatic change in how ROP screening is delivered and by whom. 8 In 2019, the World Health Organization and United Nations Children's Fund recommended ROP screening and treatment as standard of care in their "Survive and Thrive" policy.…”

mentioning

confidence: 99%

Artificial Intelligence for ROP Screening and to Assess Quality of Care: Progress and Challenges

2021

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Automated Detection and Classification of Telemedical Retinopathy of Prematurity Images

Cited by 16 publications

References 21 publications

Fairness and generalisability in deep learning of retinopathy of prematurity screening algorithms: a literature review

Fairness and generalisability in deep learning of retinopathy of prematurity screening algorithms: a literature review

Application of Artificial Intelligence in the Early Detection of Retinopathy of Prematurity: Review of the Literature

Artificial Intelligence for ROP Screening and to Assess Quality of Care: Progress and Challenges

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