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Automated detection of nine infantile fundus diseases and conditions in retinal images using a deep learning system

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Abstract

Purpose

We developed an Infant Retinal Intelligent Diagnosis System (IRIDS), an automated system to aid early diagnosis and monitoring of infantile fundus diseases and health conditions to satisfy urgent needs of ophthalmologists.

Methods

We developed IRIDS by combining convolutional neural networks and transformer structures, using a dataset of 7697 retinal images (1089 infants) from four hospitals. It identifies nine fundus diseases and conditions, namely, retinopathy of prematurity (ROP) (mild ROP, moderate ROP, and severe ROP), retinoblastoma (RB), retinitis pigmentosa (RP), Coats disease, coloboma of the choroid, congenital retinal fold (CRF), and normal. IRIDS also includes depth attention modules, ResNet-18 (Res-18), and Multi-Axis Vision Transformer (MaxViT). Performance was compared to that of ophthalmologists using 450 retinal images. The IRIDS employed a five-fold cross-validation approach to generate the classification results.

Results

Several baseline models achieved the following metrics: accuracy, precision, recall, F1-score (F1), kappa, and area under the receiver operating characteristic curve (AUC) with best values of 94.62% (95% CI, 94.34%-94.90%), 94.07% (95% CI, 93.32%-94.82%), 90.56% (95% CI, 88.64%-92.48%), 92.34% (95% CI, 91.87%-92.81%), 91.15% (95% CI, 90.37%-91.93%), and 99.08% (95% CI, 99.07%-99.09%), respectively. In comparison, IRIDS showed promising results compared to ophthalmologists, demonstrating an average accuracy, precision, recall, F1, kappa, and AUC of 96.45% (95% CI, 96.37%-96.53%), 95.86% (95% CI, 94.56%-97.16%), 94.37% (95% CI, 93.95%-94.79%), 95.03% (95% CI, 94.45%-95.61%), 94.43% (95% CI, 93.96%-94.90%), and 99.51% (95% CI, 99.51%-99.51%), respectively, in multi-label classification on the test dataset, utilizing the Res-18 and MaxViT models. These results suggest that, particularly in terms of AUC, IRIDS achieved performance that warrants further investigation for the detection of retinal abnormalities.

Conclusions

IRIDS identifies nine infantile fundus diseases and conditions accurately. It may aid non-ophthalmologist personnel in underserved areas in infantile fundus disease screening. Thus, preventing severe complications. The IRIDS serves as an example of artificial intelligence integration into ophthalmology to achieve better outcomes in predictive, preventive, and personalized medicine (PPPM / 3PM) in the treatment of infantile fundus diseases.

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Data availability

These authors do not have permission to share data except the first author.

Code availability

The source code used in this study is available upon request from the first author only.

Abbreviations

AI:

Artificial intelligence

AUC:

Area under the receiver operating characteristic curve

CNNs:

Convolutional neural networks

CRF:

Congenital retinal fold

DA:

Depth attention

DL:

Deep Learning

EPMA:

European Association for Predictive, Preventive and Personalized Medicine

eps:

Epsilon

F1:

F1 score

IRIDS:

Infant Retinal Intelligent Diagnosis System

Max ViT:

Multi-Axis Vision Transformer

PPPM/3PM:

Predictive, preventive, and personalized medicine

RB:

Retinoblastoma

ROC:

Receiver operating characteristic

ROP:

Retinopathy of prematurity

RP:

Retinitis pigmentosa

Res-18:

ResNet-18

SGD:

Stochastic gradient descent

T-SNE:

T-distributed stochastic neighbor embedding

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Funding

This study was supported by National Natural Science Foundation of China (No. 82271103, 82301269, 82301226, 62376164, 62106153, U22A2024), Sanming Project of Medicine in Shenzhen (No. SZSM202311018), Guangdong Basic and Applied Basic Research Foundation (No. 2022A1515012326, 22201910240002529), Shenzhen Medical Research Fund (No. C2301005), Shenzhen Key Medical Discipline Construction Fund (No. SZXK038), Shenzhen Fund for Guangdong Provincial High Level Clinical Key Specialties (No. SZGSP014), Shenzhen Science and Technology R&D Fund Project (No. JCYJ20220530153607015), China Ophthalmology New Technology Incubation Project.

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Author notes

  1. Yaling Liu, Hai Xie and Xinyu Zhao contributed equally and are considered co–first authors.

  2. Sifan Zhang, Baiying Lei, and Guoming Zhang contributed equally to this work.

Authors and Affiliations

  1. Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040, China

    Yaling Liu, Xinyu Zhao, Jiannan Tang, Zhen Yu, Zhenquan Wu, Ruyin Tian, Yi Chen, Miaohong Chen, Yueshanyi Du, Tingyi Chen, Yarou Hu & Guoming Zhang

  2. National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China

    Hai Xie & Baiying Lei

  3. Guizhou Medical University, Guiyang, Guizhou, China

    Yi Chen, Miaohong Chen, Tingyi Chen, Sifan Zhang & Guoming Zhang

  4. Retina Service, Ines and Fred Yeatts Retina Research Laboratory, Angiogenesis Laboratory, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA

    Dimitrios P. Ntentakis

  5. Southern University of Science and Technology School of Medicine, Shenzhen, China

    Sifan Zhang

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Contributions

GZ has full access to all data in the study and is responsible for the integrity of the data and the accuracy of the data analysis. YL, HX, and XZ contributed equally and are considered co-first authors. Concept and design: YL, HX, and XZ. Acquisition, analysis, or interpretation of data: YL, HX, XZ, and SZ. Drafting of the manuscript: YL and HX. Critical revision of the manuscript for important intellectual content: GZ, JT, XZ, and DPN. Statistical analysis: YL and JT. Obtained funding: GZ. Administrative, technical, or material support: ZY, ZW, RT, YC, MC, YD, TC, YH, and BL. Supervision: GZ.

Corresponding authors

Correspondence to Sifan Zhang, Baiying Lei or Guoming Zhang.

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This diagnostic study was approved by the Ethics Committee of Shenzhen Eye Hospital. All institutions abided by the tenets of the Declaration of Helsinki.

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Liu, Y., Xie, H., Zhao, X. et al. Automated detection of nine infantile fundus diseases and conditions in retinal images using a deep learning system. EPMA Journal 15, 39–51 (2024). https://doi.org/10.1007/s13167-024-00350-y

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