Automated Explainable Multidimensional Deep Learning Platform of Retinal Images for Retinopathy of Prematurity Screening

Wang, Ji; Ji, Jie; Zhang, Mingzhi; Lin, Jianwei; Gong, Weifen; Cen, Ling-Ping; Lu, Yamei; Huang, Xuelin; Huang, Dingguo; Li, Taiping; Ng, Tsz Kin; Pang, Chi Pui

doi:10.1001/jamanetworkopen.2021.8758

Cited by 39 publications

(48 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has been effectively applied in the detection of Alzheimer’s disease 11 , intracranial diseases 12 , arrhythmia 13 , skin cancer 14 , lung cancer 4 , mesothelioma 15 , lymph node metastases of breast cancer 16 , and colorectal cancer 17 . In retinal diseases, deep learning algorithms for AI-assisted diagnoses have been applied to screen for DR 18 , 19 , AMD 20 , 21 , retinopathy of prematurity 22 , 23 , glaucoma 24 , and papilledema 25 . These AI-assisted diagnosis systems mostly focus on the detection of a single retinal disease.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks

et al. 2021

Self Cite

View full text Add to dashboard Cite

Retinal fundus diseases can lead to irreversible visual impairment without timely diagnoses and appropriate treatments. Single disease-based deep learning algorithms had been developed for the detection of diabetic retinopathy, age-related macular degeneration, and glaucoma. Here, we developed a deep learning platform (DLP) capable of detecting multiple common referable fundus diseases and conditions (39 classes) by using 249,620 fundus images marked with 275,543 labels from heterogenous sources. Our DLP achieved a frequency-weighted average F1 score of 0.923, sensitivity of 0.978, specificity of 0.996 and area under the receiver operating characteristic curve (AUC) of 0.9984 for multi-label classification in the primary test dataset and reached the average level of retina specialists. External multihospital test, public data test and tele-reading application also showed high efficiency for multiple retinal diseases and conditions detection. These results indicate that our DLP can be applied for retinal fundus disease triage, especially in remote areas around the world.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Generally, CAM could indicate the proper size but a less precise domain for lesion identification. In contrast, DeepSHAP could depict specific fine lesions, 33 but more dispersed. The combination of the two techniques can provide a heatmap of lesions in specific domains, meeting the requirement for distinguishing maculopathy from retinopathy.…”

Section: Discussionmentioning

confidence: 85%

Automated multidimensional deep learning platform for referable diabetic retinopathy detection: a multicentre, retrospective study

Lin

Wang

et al. 2022

BMJ Open

Self Cite

View full text Add to dashboard Cite

ObjectiveTo develop and validate a real-world screening, guideline-based deep learning (DL) system for referable diabetic retinopathy (DR) detection.DesignThis is a multicentre platform development study based on retrospective, cross-sectional data sets. Images were labelled by two-level certificated graders as the ground truth. According to the UK DR screening guideline, a DL model based on colour retinal images with five-dimensional classifiers, namely image quality, retinopathy, maculopathy gradability, maculopathy and photocoagulation, was developed. Referable decisions were generated by integrating the output of all classifiers and reported at the image, eye and patient level. The performance of the DL was compared with DR experts.SettingDR screening programmes from three hospitals and the Lifeline Express Diabetic Retinopathy Screening Program in China.Participants83 465 images of 39 836 eyes from 21 716 patients were annotated, of which 53 211 images were used as the development set and 30 254 images were used as the external validation set, split based on centre and period.Main outcomesAccuracy, F1 score, sensitivity, specificity, area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), Cohen’s unweighted κ and Gwet’s AC1 were calculated to evaluate the performance of the DL algorithm.ResultsIn the external validation set, the five classifiers achieved an accuracy of 0.915–0.980, F1 score of 0.682–0.966, sensitivity of 0.917–0.978, specificity of 0.907–0.981, AUROC of 0.9639–0.9944 and AUPRC of 0.7504–0.9949. Referable DR at three levels was detected with an accuracy of 0.918–0.967, F1 score of 0.822–0.918, sensitivity of 0.970–0.971, specificity of 0.905–0.967, AUROC of 0.9848–0.9931 and AUPRC of 0.9527–0.9760. With reference to the ground truth, the DL system showed comparable performance (Cohen’s κ: 0.86–0.93; Gwet’s AC1: 0.89–0.94) with three DR experts (Cohen’s κ: 0.89–0.96; Gwet’s AC1: 0.91–0.97) in detecting referable lesions.ConclusionsThe automatic DL system for detection of referable DR based on the UK guideline could achieve high accuracy in multidimensional classifications. It is suitable for large-scale, real-world DR screening.

show abstract

“…Thirty full-text articles were assessed for eligibility and 12 studies were included in the systematic review. [ 12 13 21 22 23 24 25 26 27 28 29 30 31 ] Fifty studies were excluded due to no test of diagnostic performance,[ 32 33 34 35 36 37 38 39 ] no classification task,[ 40 41 42 ] no internal validation,[ 23 43 ] no AI algorithm,[ 44 ] and not based on standard clinical care. [ 45 ]…”

Section: R Esultsmentioning

confidence: 99%

“…All twelve studies obtained retrospective images as part of routine clinical care or from local screening programs. Seven of these studies collected images from China,[ 22 24 25 26 28 29 31 ] one from India, one from North America,[ 12 ] one from America and Mexico sites,[ 30 ] one from America and Nepal,[ 21 ] and one study included images from New Zealand. [ 13 ] Date range for image collection among all studies varied from July 2011 to June 2020.…”

Section: R Esultsmentioning

confidence: 99%

“…Three studies specified their inclusion criteria[ 25 26 31 ] and five other studies specified their exclusion criteria. [ 12 13 21 28 29 ] Poor quality images were excluded in five studies[ 12 13 28 29 31 ] and image augmentation occurred in seven studies. [ 13 21 25 27 28 29 30 ] These characteristics are summarized in Table 1 .…”

Section: R Esultsmentioning

confidence: 99%

See 1 more Smart Citation

Performance of deep-learning artificial intelligence algorithms in detecting retinopathy of prematurity

Bai

Carty

Dai

2022

Saudi Journal of Ophthalmology

View full text Add to dashboard Cite

PURPOSE: Artificial intelligence (AI) offers considerable promise for retinopathy of prematurity (ROP) screening and diagnosis. The development of deep-learning algorithms to detect the presence of disease may contribute to sufficient screening, early detection, and timely treatment for this preventable blinding disease. This review aimed to systematically examine the literature in AI algorithms in detecting ROP. Specifically, we focused on the performance of deep-learning algorithms through sensitivity, specificity, and area under the receiver operating curve (AUROC) for both the detection and grade of ROP. METHODS: We searched Medline OVID, PubMed, Web of Science, and Embase for studies published from January 1, 2012, to September 20, 2021. Studies evaluating the diagnostic performance of deep-learning models based on retinal fundus images with expert ophthalmologists' judgment as reference standard were included. Studies which did not investigate the presence or absence of disease were excluded. Risk of bias was assessed using the QUADAS-2 tool. RESULTS: Twelve studies out of the 175 studies identified were included. Five studies measured the performance of detecting the presence of ROP and seven studies determined the presence of plus disease. The average AUROC out of 11 studies was 0.98. The average sensitivity and specificity for detecting ROP was 95.72% and 98.15%, respectively, and for detecting plus disease was 91.13% and 95.92%, respectively. CONCLUSION: The diagnostic performance of deep-learning algorithms in published studies was high. Few studies presented externally validated results or compared performance to expert human graders. Large scale prospective validation alongside robust study design could improve future studies.

show abstract

Automated Explainable Multidimensional Deep Learning Platform of Retinal Images for Retinopathy of Prematurity Screening

Cited by 39 publications

References 33 publications

Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks

Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks

Automated multidimensional deep learning platform for referable diabetic retinopathy detection: a multicentre, retrospective study

Performance of deep-learning artificial intelligence algorithms in detecting retinopathy of prematurity

Contact Info

Product

Resources

About