A Deep Learning Framework for Identifying Zone I in RetCam Images

Zhao, Jinfeng; Lei, Baiying; Wu, Zhenquan; Zhang, Yinsheng; Li, Yafeng; Wang, Li; Tian, Ruyin; Chen, Yi; Ma, Dahui; Wang, Jiantao; Wang, Tianfu; Chen, Guozhen; Zeng, Jing; Zhang, Guoming

doi:10.1109/access.2019.2930120

Cited by 25 publications

(9 citation statements)

References 32 publications

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“…Other research have shown similar success with ROP severity classification and deep learning. [ 76 77 78 79 ] Integration of AI into an ROP screening program will likely occur in the near future.…”

Section: Pediatricsmentioning

confidence: 99%

Artificial intelligence and machine learning in ophthalmology: A review

Srivastava

Tennant

Grewal

et al. 2022

Indian Journal of Ophthalmology

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Since the introduction of artificial intelligence (AI) in 1956 by John McCarthy, the field has propelled medicine, optimized efficiency, and led to technological breakthroughs in clinical care. As an important frontier in healthcare, AI has implications on every subspecialty within medicine. This review highlights the applications of AI in ophthalmology: a specialty that lends itself well to the integration of computer algorithms due to the high volume of digital imaging, data, and objective metrics such as central retinal thickness. The focus of this review is the use of AI in retina, cornea, anterior segment, and pediatrics.

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Section: Pediatricsmentioning

confidence: 99%

Artificial intelligence and machine learning in ophthalmology: A review

Srivastava

Tennant

Grewal

et al. 2022

Indian Journal of Ophthalmology

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“…Most studies to date have focused on computer-based systems to diagnose plus disease; however, there are a number of reports of using DL to grade ROP severity category or classify zone or stage specifically. 29 , 30 For example, a DL system called DeepROP achieved a sensitivity of 96.62% (95% confidence interval, 92.29%–98.89%) and a specificity of 99.32% (95% confidence interval, 96.29%–9.98%) for the detection of ROP (vs no ROP). 31 , 32 Zhao et al 30 reported the development of a DL system that can automatically draw the border of zone 1 on a fundus image as a diagnostic aid.…”

Section: The Development Of Ai Systems For Rop Diagnosismentioning

confidence: 99%

“… 29 , 30 For example, a DL system called DeepROP achieved a sensitivity of 96.62% (95% confidence interval, 92.29%–98.89%) and a specificity of 99.32% (95% confidence interval, 96.29%–9.98%) for the detection of ROP (vs no ROP). 31 , 32 Zhao et al 30 reported the development of a DL system that can automatically draw the border of zone 1 on a fundus image as a diagnostic aid. Mulay et al 29 were the first to report the identification of a peripheral ROP ridge (stage) directly in a fundus image.…”

Section: The Development Of Ai Systems For Rop Diagnosismentioning

confidence: 99%

Artificial Intelligence in Retinopathy of Prematurity Diagnosis

Scruggs

Chan

Kalpathy–Cramer

et al. 2020

Trans. Vis. Sci. Tech.

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Citation: Scruggs BA, Chan RVP, Kalpathy-Cramer J, Chiang MF, Campbell JP. Artificial intelligence in retinopathy of prematurity diagnosis. Trans Vis Sci Tech. 2020;9(2):5, https://doi.org/10.1167/tvst.9.2.5Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide. The diagnosis of ROP is subclassified by zone, stage, and plus disease, with each area demonstrating significant intra-and interexpert subjectivity and disagreement. In addition to improved efficiencies for ROP screening, artificial intelligence may lead to automated, quantifiable, and objective diagnosis in ROP. This review focuses on the development of artificial intelligence for automated diagnosis of plus disease in ROP and highlights the clinical and technical challenges of both the development and implementation of artificial intelligence in the real world.

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“…15,16 For ROP, previous studies with RetCam images have established the identification of automated ROP plus disease, identification of all zones (zones I, II, and III), ROP severity detection, etc. [17][18][19][20][21][22] Our previous study achieved the automated location of zone I in RetCam images 23 and the identification of aggressive ROP, thus introducing an effective tool for assessing ROP severity and treatment recommendations. 24,25 Although all the above systems have achieved a good diagnostic performance, nearly all of them lack information related to treatment.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Intelligence System for Screening and Recommending the Treatment Modalities for Retinopathy of Prematurity

Liu,

Du,

Wang

et al. 2023

Asia-Pacific Journal of Ophthalmology

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Purpose: The purpose of this study was to develop an artificial intelligence (AI) system for the identification of disease status and recommending treatment modalities for retinopathy of prematurity (ROP). Methods: This retrospective cohort study included a total of 24,495 RetCam images from 1075 eyes of 651 preterm infants who received RetCam examination at the Shenzhen Eye Hospital in Shenzhen, China, from January 2003 to August 2021. Three tasks included ROP identification, severe ROP identification, and treatment modalities identification (retinal laser photocoagulation or intravitreal injections). The AI system was developed to identify the 3 tasks, especially the treatment modalities of ROP. The performance between the AI system and ophthalmologists was compared using extra 200 RetCam images. Results: The AI system exhibited favorable performance in the 3 tasks, including ROP identification [area under the receiver operating characteristic curve (AUC), 0.9531], severe ROP identification (AUC, 0.9132), and treatment modalities identification with laser photocoagulation or intravitreal injections (AUC, 0.9360). The AI system achieved an accuracy of 0.8627, a sensitivity of 0.7059, and a specificity of 0.9412 for identifying the treatment modalities of ROP. External validation results confirmed the good performance of the AI system with an accuracy of 92.0% in all 3 tasks, which was better than 4 experienced ophthalmologists who scored 56%, 65%, 71%, and 76%, respectively. Conclusions: The described AI system achieved promising outcomes in the automated identification of ROP severity and treatment modalities. Using such algorithmic approaches as accessory tools in the clinic may improve ROP screening in the future.

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A Deep Learning Framework for Identifying Zone I in RetCam Images

Cited by 25 publications

References 32 publications

Artificial intelligence and machine learning in ophthalmology: A review

Artificial intelligence and machine learning in ophthalmology: A review

Artificial Intelligence in Retinopathy of Prematurity Diagnosis

An Artificial Intelligence System for Screening and Recommending the Treatment Modalities for Retinopathy of Prematurity

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