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Chu Y, Hu S, Li Z, Yang X, Liu H, Yi X, Qi X. Image Analysis-Based Machine Learning for the Diagnosis of Retinopathy of Prematurity: A Meta-analysis and Systematic Review. Ophthalmol Retina 2024; 8:678-687. [PMID: 38237772 DOI: 10.1016/j.oret.2024.01.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 01/02/2024] [Accepted: 01/09/2024] [Indexed: 02/17/2024]
Abstract
TOPIC To evaluate the performance of machine learning (ML) in the diagnosis of retinopathy of prematurity (ROP) and to assess whether it can be an effective automated diagnostic tool for clinical applications. CLINICAL RELEVANCE Early detection of ROP is crucial for preventing tractional retinal detachment and blindness in preterm infants, which has significant clinical relevance. METHODS Web of Science, PubMed, Embase, IEEE Xplore, and Cochrane Library were searched for published studies on image-based ML for diagnosis of ROP or classification of clinical subtypes from inception to October 1, 2022. The quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies was used to determine the risk of bias (RoB) of the included original studies. A bivariate mixed effects model was used for quantitative analysis of the data, and the Deek's test was used for calculating publication bias. Quality of evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation. RESULTS Twenty-two studies were included in the systematic review; 4 studies had high or unclear RoB. In the area of indicator test items, only 2 studies had high or unclear RoB because they did not establish predefined thresholds. In the area of reference standards, 3 studies had high or unclear RoB. Regarding applicability, only 1 study was considered to have high or unclear applicability in terms of patient selection. The sensitivity and specificity of image-based ML for the diagnosis of ROP were 93% (95% confidence interval [CI]: 0.90-0.94) and 95% (95% CI: 0.94-0.97), respectively. The area under the receiver operating characteristic curve (AUC) was 0.98 (95% CI: 0.97-0.99). For the classification of clinical subtypes of ROP, the sensitivity and specificity were 93% (95% CI: 0.89-0.96) and 93% (95% CI: 0.89-0.95), respectively, and the AUC was 0.97 (95% CI: 0.96-0.98). The classification results were highly similar to those of clinical experts (Spearman's R = 0.879). CONCLUSIONS Machine learning algorithms are no less accurate than human experts and hold considerable potential as automated diagnostic tools for ROP. However, given the quality and high heterogeneity of the available evidence, these algorithms should be considered as supplementary tools to assist clinicians in diagnosing ROP. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Yihang Chu
- Central South University of Forestry and Technology, Changsha, Hunan, China; State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Shipeng Hu
- Central South University of Forestry and Technology, Changsha, Hunan, China
| | - Zilan Li
- Department of Biochemistry, McGill University, Montreal, Quebec, Canada
| | - Xiao Yang
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Hui Liu
- Central South University of Forestry and Technology, Changsha, Hunan, China.
| | - Xianglong Yi
- Department of Ophthalmology, The First Affiliated Hospital of Xinjiang Medical University, Urumchi, China.
| | - Xinwei Qi
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
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Chen S, Zhao X, Wu Z, Cao K, Zhang Y, Tan T, Lam CT, Xu Y, Zhang G, Sun Y. Multi-risk factors joint prediction model for risk prediction of retinopathy of prematurity. EPMA J 2024; 15:261-274. [PMID: 38841619 PMCID: PMC11147992 DOI: 10.1007/s13167-024-00363-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 04/17/2024] [Indexed: 06/07/2024]
Abstract
Purpose Retinopathy of prematurity (ROP) is a retinal vascular proliferative disease common in low birth weight and premature infants and is one of the main causes of blindness in children.In the context of predictive, preventive and personalized medicine (PPPM/3PM), early screening, identification and treatment of ROP will directly contribute to improve patients' long-term visual prognosis and reduce the risk of blindness. Thus, our objective is to establish an artificial intelligence (AI) algorithm combined with clinical demographics to create a risk model for ROP including treatment-requiring retinopathy of prematurity (TR-ROP) infants. Methods A total of 22,569 infants who underwent routine ROP screening in Shenzhen Eye Hospital from March 2003 to September 2023 were collected, including 3335 infants with ROP and 1234 infants with TR-ROP among ROP infants. Two machine learning methods of logistic regression and decision tree and a deep learning method of multi-layer perceptron were trained by using the relevant combination of risk factors such as birth weight (BW), gestational age (GA), gender, whether multiple births (MB) and mode of delivery (MD) to achieve the risk prediction of ROP and TR-ROP. We used five evaluation metrics to evaluate the performance of the risk prediction model. The area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUCPR) were the main measurement metrics. Results In the risk prediction for ROP, the BW + GA demonstrated the optimal performance (mean ± SD, AUCPR: 0.4849 ± 0.0175, AUC: 0.8124 ± 0.0033). In the risk prediction of TR-ROP, reasonable performance can be achieved by using GA + BW + Gender + MD + MB (AUCPR: 0.2713 ± 0.0214, AUC: 0.8328 ± 0.0088). Conclusions Combining risk factors with AI in screening programs for ROP could achieve risk prediction of ROP and TR-ROP, detect TR-ROP earlier and reduce the number of ROP examinations and unnecessary physiological stress in low-risk infants. Therefore, combining ROP-related biometric information with AI is a cost-effective strategy for predictive diagnostic, targeted prevention, and personalization of medical services in early screening and treatment of ROP.
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Affiliation(s)
- Shaobin Chen
- Faculty of Applied Sciences, Macao Polytechnic University, Gomes Street, Macao, China
| | - Xinyu Zhao
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, 518040 China
| | - Zhenquan Wu
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, 518040 China
| | - Kangyang Cao
- Faculty of Applied Sciences, Macao Polytechnic University, Gomes Street, Macao, China
| | - Yulin Zhang
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, 518040 China
| | - Tao Tan
- Faculty of Applied Sciences, Macao Polytechnic University, Gomes Street, Macao, China
| | - Chan-Tong Lam
- Faculty of Applied Sciences, Macao Polytechnic University, Gomes Street, Macao, China
| | - Yanwu Xu
- School of Future Technology, South China University of Technology, Guangzhou, Guangzhou; Pazhou Lab, China
| | - Guoming Zhang
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, 518040 China
| | - Yue Sun
- Faculty of Applied Sciences, Macao Polytechnic University, Gomes Street, Macao, China
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, 5612 AP The Netherlands
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Burt SS, Woodward M, Ni S, Jackson J, Coyner AS, Ostmo SR, Liang G, Bayhaqi Y, Jia Y, Huang D, Chiang MF, Young BK, Jian Y, Campbell JP. Isolated Retinal Neovascularization in Retinopathy of Prematurity: Clinical Associations and Prognostic Implications. Ophthalmol Retina 2024:S2468-6530(24)00234-3. [PMID: 38735640 DOI: 10.1016/j.oret.2024.04.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 05/14/2024]
Abstract
OBJECTIVE Isolated retinal neovascularization (IRNV) is a common finding in patients with stage 2 and 3 retinopathy of prematurity (ROP). This study aimed to further classify the clinical course and significance of these lesions (previously described as "popcorn" based on clinical appearance) in patients with ROP as visualized with ultrawidefield OCT (UWF-OCT). DESIGN Single center, retrospective case series. PARTICIPANTS Images were collected from 136 babies in the Oregon Health and Science University neonatal intensive care unit. METHODS A prototype UWF-OCT device captured en face scans (>140°), which were reviewed for the presence of IRNV along with standard zone, stage, and plus classification. In a cross-sectional analysis we compared demographics and the clinical course of eyes with and without IRNV. Longitudinally, we compared ROP severity using a clinician-assigned vascular severity score (VSS) and compared the risk of progression among eyes with and without IRNV using multivariable logistic regression. MAIN OUTCOME MEASURES Differences in clinical demographics and disease progression between patients with and without IRNV. RESULTS Of the 136 patients, 60 developed stage 2 or worse ROP during their disease course, 22 of whom had IRNV visualized on UWF-OCT (37%). On average, patients with IRNV had lower birth weights (BWs) (660.1 vs. 916.8 g, P = 0.001), gestational age (GA) (24.9 vs. 26.1 weeks, P = 0.01), and were more likely to present with ROP in zone I (63.4% vs. 15.8%, P < 0.001). They were also more likely to progress to stage 3 (68.2% vs. 13.2%, P < 0.001) and receive treatment (54.5% vs. 15.8%, P = 0.002). Eyes with IRNV had a higher peak VSS (5.61 vs. 3.73, P < 0.001) and averaged a higher VSS throughout their disease course. On multivariable logistic regression, IRNV was independently associated with progression to stage 3 (P = 0.02) and requiring treatment (P = 0.03), controlling for GA, BW, and initial zone 1 disease. CONCLUSIONS In this single center study, we found that IRNV occurs in higher risk babies and was an independent risk factor for ROP progression and treatment. These findings may have implications for OCT-based ROP classifications in the future. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Spencer S Burt
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
| | - Mani Woodward
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
| | - Shuibin Ni
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
| | - John Jackson
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
| | - Aaron S Coyner
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
| | - Susan R Ostmo
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
| | - Guangru Liang
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
| | - Yakub Bayhaqi
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
| | - Yali Jia
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
| | - David Huang
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
| | - Michael F Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland; National Library of Medicine, National Institutes of Health, Bethesda, Maryland
| | - Benjamin K Young
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
| | - Yifan Jian
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
| | - J Peter Campbell
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon.
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Conte L, Amodeo I, De Nunzio G, Raffaeli G, Borzani I, Persico N, Griggio A, Como G, Cascio D, Colnaghi M, Mosca F, Cavallaro G. Congenital diaphragmatic hernia: automatic lung and liver MRI segmentation with nnU-Net, reproducibility of pyradiomics features, and a machine learning application for the classification of liver herniation. Eur J Pediatr 2024; 183:2285-2300. [PMID: 38416256 PMCID: PMC11035462 DOI: 10.1007/s00431-024-05476-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 02/29/2024]
Abstract
Prenatal assessment of lung size and liver position is essential to stratify congenital diaphragmatic hernia (CDH) fetuses in risk categories, guiding counseling, and patient management. Manual segmentation on fetal MRI provides a quantitative estimation of total lung volume and liver herniation. However, it is time-consuming and operator-dependent. In this study, we utilized a publicly available deep learning (DL) segmentation system (nnU-Net) to automatically contour CDH-affected fetal lungs and liver on MRI sections. Concordance between automatic and manual segmentation was assessed by calculating the Jaccard coefficient. Pyradiomics standard features were then extracted from both manually and automatically segmented regions. The reproducibility of features between the two groups was evaluated through the Wilcoxon rank-sum test and intraclass correlation coefficients (ICCs). We finally tested the reliability of the automatic-segmentation approach by building a ML classifier system for the prediction of liver herniation based on support vector machines (SVM) and trained on shape features computed both in the manual and nnU-Net-segmented organs. We compared the area under the classifier receiver operating characteristic curve (AUC) in the two cases. Pyradiomics features calculated in the manual ROIs were partly reproducible by the same features calculated in nnU-Net segmented ROIs and, when used in the ML procedure, to predict liver herniation (both AUC around 0.85). Conclusion: Our results suggest that automatic MRI segmentation is feasible, with good reproducibility of pyradiomics features, and that a ML system for liver herniation prediction offers good reliability. Trial registration: https://clinicaltrials.gov/ct2/show/NCT04609163?term=NCT04609163&draw=2&rank=1 ; Clinical Trial Identification no. NCT04609163. What is Known: • Magnetic resonance imaging (MRI) is crucial for prenatal congenital diaphragmatic hernia (CDH) assessment. It enables the quantification of the total lung volume and the extent of liver herniation, which are essential for stratifying the severity of CDH, guiding counseling, and patient management. • The manual segmentation of MRI scans is a time-consuming process that is heavily reliant upon the skill set of the operator. What is New: • MRI lung and liver automatic segmentation using the deep learning nnU-Net system is feasible, with good Jaccard coefficient values and satisfactory reproducibility of pyradiomics features compared to manual results. • A feasible ML system for predicting liver herniation could improve prenatal assessments and CDH patient management.
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Affiliation(s)
- Luana Conte
- Department of Mathematics and Physics "E. De Giorgi", Laboratory of Biomedical Physics and Environment, Università del Salento, Lecce, Italy
- Advanced Data Analysis in Medicine (ADAM), Laboratory of Interdisciplinary Research Applied to Medicine (DReAM), Local Health Authority (ASL) Lecce and Università del Salento, Lecce, Italy
| | - Ilaria Amodeo
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Giorgio De Nunzio
- Department of Mathematics and Physics "E. De Giorgi", Laboratory of Biomedical Physics and Environment, Università del Salento, Lecce, Italy.
- Advanced Data Analysis in Medicine (ADAM), Laboratory of Interdisciplinary Research Applied to Medicine (DReAM), Local Health Authority (ASL) Lecce and Università del Salento, Lecce, Italy.
| | - Genny Raffaeli
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Clinical Sciences and Community Health, Università Degli Studi Di Milano, Milan, Italy
| | - Irene Borzani
- Pediatric Radiology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Nicola Persico
- Department of Clinical Sciences and Community Health, Università Degli Studi Di Milano, Milan, Italy
- Department of Obstetrics and Gynecology, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Alice Griggio
- ASST Fatebenefratelli Sacco, Ospedale Macedonio Melloni, Milan, Italy
| | - Giuseppe Como
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Donato Cascio
- Department of Physics and Chemistry, Università Degli Studi Di Palermo, Palermo, Italy
| | - Mariarosa Colnaghi
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Fabio Mosca
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Clinical Sciences and Community Health, Università Degli Studi Di Milano, Milan, Italy
| | - Giacomo Cavallaro
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
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Parmar UPS, Surico PL, Singh RB, Romano F, Salati C, Spadea L, Musa M, Gagliano C, Mori T, Zeppieri M. Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:527. [PMID: 38674173 PMCID: PMC11052176 DOI: 10.3390/medicina60040527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 03/12/2024] [Accepted: 03/21/2024] [Indexed: 04/28/2024]
Abstract
Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighting its potential to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. Herein, we elucidate the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their application in ophthalmology, underscoring the significance of AI-driven solutions in addressing the complexity and variability of retinal diseases. Furthermore, we delve into the specific applications of AI in retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), Macular Neovascularization, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), Retinitis Pigmentosa, Stargardt disease, best vitelliform macular dystrophy, and sickle cell retinopathy. We focus on the current landscape of AI technologies, including various AI models, their performance metrics, and clinical implications. Furthermore, we aim to address challenges and pitfalls associated with the integration of AI in clinical practice, including the "black box phenomenon", biases in data representation, and limitations in comprehensive patient assessment. In conclusion, this review emphasizes the collaborative role of AI alongside healthcare professionals, advocating for a synergistic approach to healthcare delivery. It highlights the importance of leveraging AI to augment, rather than replace, human expertise, thereby maximizing its potential to revolutionize healthcare delivery, mitigate healthcare disparities, and improve patient outcomes in the evolving landscape of medicine.
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Affiliation(s)
| | - Pier Luigi Surico
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
- Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Rohan Bir Singh
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Francesco Romano
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Carlo Salati
- Department of Ophthalmology, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy
| | - Leopoldo Spadea
- Eye Clinic, Policlinico Umberto I, “Sapienza” University of Rome, 00142 Rome, Italy
| | - Mutali Musa
- Department of Optometry, University of Benin, Benin City 300238, Edo State, Nigeria
| | - Caterina Gagliano
- Faculty of Medicine and Surgery, University of Enna “Kore”, Piazza dell’Università, 94100 Enna, Italy
- Eye Clinic, Catania University, San Marco Hospital, Viale Carlo Azeglio Ciampi, 95121 Catania, Italy
| | - Tommaso Mori
- Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
- Department of Ophthalmology, University of California San Diego, La Jolla, CA 92122, USA
| | - Marco Zeppieri
- Department of Ophthalmology, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy
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Sharafi SM, Ebrahimiadib N, Roohipourmoallai R, Farahani AD, Fooladi MI, Khalili Pour E. Automated diagnosis of plus disease in retinopathy of prematurity using quantification of vessels characteristics. Sci Rep 2024; 14:6375. [PMID: 38493272 PMCID: PMC10944526 DOI: 10.1038/s41598-024-57072-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 03/14/2024] [Indexed: 03/18/2024] Open
Abstract
The condition known as Plus disease is distinguished by atypical alterations in the retinal vasculature of neonates born prematurely. It has been demonstrated that the diagnosis of Plus disease is subjective and qualitative in nature. The utilization of quantitative methods and computer-based image analysis to enhance the objectivity of Plus disease diagnosis has been extensively established in the literature. This study presents the development of a computer-based image analysis method aimed at automatically distinguishing Plus images from non-Plus images. The proposed methodology conducts a quantitative analysis of the vascular characteristics linked to Plus disease, thereby aiding physicians in making informed judgments. A collection of 76 posterior retinal images from a diverse group of infants who underwent screening for Retinopathy of Prematurity (ROP) was obtained. A reference standard diagnosis was established as the majority of the labeling performed by three experts in ROP during two separate sessions. The process of segmenting retinal vessels was carried out using a semi-automatic methodology. Computer algorithms were developed to compute the tortuosity, dilation, and density of vessels in various retinal regions as potential discriminative characteristics. A classifier was provided with a set of selected features in order to distinguish between Plus images and non-Plus images. This study included 76 infants (49 [64.5%] boys) with mean birth weight of 1305 ± 427 g and mean gestational age of 29.3 ± 3 weeks. The average level of agreement among experts for the diagnosis of plus disease was found to be 79% with a standard deviation of 5.3%. In terms of intra-expert agreement, the average was 85% with a standard deviation of 3%. Furthermore, the average tortuosity of the five most tortuous vessels was significantly higher in Plus images compared to non-Plus images (p ≤ 0.0001). The curvature values based on points were found to be significantly higher in Plus images compared to non-Plus images (p ≤ 0.0001). The maximum diameter of vessels within a region extending 5-disc diameters away from the border of the optic disc (referred to as 5DD) exhibited a statistically significant increase in Plus images compared to non-Plus images (p ≤ 0.0001). The density of vessels in Plus images was found to be significantly higher compared to non-Plus images (p ≤ 0.0001). The classifier's accuracy in distinguishing between Plus and non-Plus images, as determined through tenfold cross-validation, was found to be 0.86 ± 0.01. This accuracy was observed to be higher than the diagnostic accuracy of one out of three experts when compared to the reference standard. The implemented algorithm in the current study demonstrated a commendable level of accuracy in detecting Plus disease in cases of retinopathy of prematurity, exhibiting comparable performance to that of expert diagnoses. By engaging in an objective analysis of the characteristics of vessels, there exists the possibility of conducting a quantitative assessment of the disease progression's features. The utilization of this automated system has the potential to enhance physicians' ability to diagnose Plus disease, thereby offering valuable contributions to the management of ROP through the integration of traditional ophthalmoscopy and image-based telemedicine methodologies.
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Affiliation(s)
- Sayed Mehran Sharafi
- Retinopathy of Prematurity Department, Retina Ward, Farabi Eye Hospital, Tehran University of Medical Sciences, South Kargar Street, Qazvin Square, Tehran, Iran
| | - Nazanin Ebrahimiadib
- Ophthalmology Department, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Ramak Roohipourmoallai
- Department of Ophthalmology, Morsani College of Medicine, University of South Florida, Tempa, FL, USA
| | - Afsar Dastjani Farahani
- Retinopathy of Prematurity Department, Retina Ward, Farabi Eye Hospital, Tehran University of Medical Sciences, South Kargar Street, Qazvin Square, Tehran, Iran
| | - Marjan Imani Fooladi
- Clinical Pediatric Ophthalmology Department, UPMC, Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Elias Khalili Pour
- Retinopathy of Prematurity Department, Retina Ward, Farabi Eye Hospital, Tehran University of Medical Sciences, South Kargar Street, Qazvin Square, Tehran, Iran.
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Slidsborg C, Fielder A, Hartnett ME. Editorial: Identification of novel biomarkers for retinopathy of prematurity in preterm infants by use of innovative technologies and artificial intelligence. Front Pediatr 2024; 12:1382858. [PMID: 38510080 PMCID: PMC10951073 DOI: 10.3389/fped.2024.1382858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 02/07/2024] [Indexed: 03/22/2024] Open
Affiliation(s)
- Carina Slidsborg
- Department of Ophthalmology, Copenhagen University Hospital, Rigshospitalet, Glostrup, Denmark
| | - Alistair Fielder
- Division of Optometry & Visual Science, City, University of London, London, United Kingdom
| | - M. Elizabeth Hartnett
- Department of Ophthalmology, Byers Eye Institute at Stanford University, Palo Alto, CA, United States
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Alfaar AS, Parlak M, Hassanain O, Abdelmaksoud E, Wolf A. The incidence of retinopathy of prematurity in neonates in Germany in 2019; a nationwide epidemiological cohort study. Eur J Pediatr 2024; 183:827-834. [PMID: 38030929 PMCID: PMC10912137 DOI: 10.1007/s00431-023-05229-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/16/2023] [Accepted: 09/18/2023] [Indexed: 12/01/2023]
Abstract
Retinopathy of prematurity (ROP) is a leading cause of childhood blindness in preterm infants. The incidence of ROP varies widely across countries, with rates as high as 30% in some regions. This study investigated the incidence, risk factors, treatment, and mortality of ROP patients in Germany. Data were extracted from the German Federal Statistical Office (Destatis) diagnosis-related group (DRG) and Institute for the Remuneration System in Hospitals (InEK) databases. Patients with a secondary diagnosis of ROP (ICD-10 code H35.1) in the first 28 days of life were included. Data were extracted for patients admitted between January 1, 2019 and December 31, 2019. The diagnoses and procedures were determined using the German version of the International Classification of Diseases (ICD-10-GM) and the German procedure coding system (OPS). The codes 5-154.xx, 5-155.xx, 8-020.xx, 5-156.9, 6-003.(c&d), 6-007.(2&8) were utilised to denote different ocular treatments. Patient Clinical Complexity Levels were extracted and used to compare ROP with non-ROP patients. A total of 1326 patients with ROP were identified. The incidence of ROP is estimated to be 17.04 per 10,000 live births. The incidence was highest in infants with birth weights less than 500 g and decreased with increasing birth weight. The most common risk factors for ROP were low birth weight, male sex, and prematurity. Of the infants with ROP, 7.2% required ocular treatment. The most common treatment was intraocular injections, followed by photocoagulation. No surgical treatment was required for any of the infants during the study period. The mortality rate for infants with ROP was 60.33 per 10,000. This is higher than the overall neonatal death rate of 24.2 per 10,000. CONCLUSIONS This study found that the incidence of ROP in Germany is similar to that in other developed countries. The study also found that the mortality rate for infants with ROP is higher than the overall neonatal death rate. These findings highlight the importance of early detection and treatment of ROP in preterm infants. WHAT IS KNOWN • ROP is a severe eye condition often affecting preterm infants. • Previous data are limited in scope and generalizability. WHAT IS NEW • Based on a national database, our study found ROP incidence to be 17.04 per 10,000 new births, higher in males (17.71) than in females (16.34). • 7.2% of ROP cases required ocular treatment, inversely correlated with birth weight. • High rates of multimorbidity such as neonatal jaundice (84.69%), respiratory distress syndrome (80.84%), and apnea (78.88%) were observed.
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Affiliation(s)
- Ahmad Samir Alfaar
- Department of Ophthalmology, Ulm University Hospital, Ulm, Germany.
- International Medical Neuroscience, Ophthalmology, Charité Medical University, Mittelalee 4, Augustenburger Platz 1, 13353, Berlin, Germany.
- St. Paul Eye Unit, The Royal Liverpool University Hospital, Liverpool, UK.
| | - Melih Parlak
- Department of Ophthalmology, Ulm University Hospital, Ulm, Germany
| | - Omneya Hassanain
- Clinical Research Department, Children's Cancer Hospital Egypt (CCHE-57357), Cairo, Egypt
| | - Eman Abdelmaksoud
- Pediatrics Department, Saint John Regional Hospital, Saint John, New Brunswick, Canada
| | - Armin Wolf
- Department of Ophthalmology, Ulm University Hospital, Ulm, Germany
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9
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Feng W, Huang Q, Ma T, Ju L, Ge Z, Chen Y, Zhao P. Development and validation of a semi-supervised deep learning model for automatic retinopathy of prematurity staging. iScience 2024; 27:108516. [PMID: 38269093 PMCID: PMC10805639 DOI: 10.1016/j.isci.2023.108516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 07/03/2023] [Accepted: 11/20/2023] [Indexed: 01/26/2024] Open
Abstract
Retinopathy of prematurity (ROP) is currently one of the leading causes of infant blindness worldwide. Recently significant progress has been made in deep learning-based computer-aided diagnostic methods. However, deep learning often requires a large amount of annotated data for model optimization, but this requires long hours of effort by experienced doctors in clinical scenarios. In contrast, a large number of unlabeled images are relatively easy to obtain. In this paper, we propose a new semi-supervised learning framework to reduce annotation costs for automatic ROP staging. We design two consistency regularization strategies, prediction consistency loss and semantic structure consistency loss, which can help the model mine useful discriminative information from unlabeled data, thus improving the generalization performance of the classification model. Extensive experiments on a real clinical dataset show that the proposed method promises to greatly reduce the labeling requirements in clinical scenarios while achieving good classification performance.
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Affiliation(s)
- Wei Feng
- Beijing Airdoc Technology Co., Ltd, Beijing 100089, China
- Faculty of Engineering, Monash University, Melbourne, VIC 3000, Australia
| | - Qiujing Huang
- Department of Ophthalmology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200092, China
- Department of Ophthalmology, Rainbow Children’s Clinic, Shanghai 200010, China
| | - Tong Ma
- Beijing Airdoc Technology Co., Ltd, Beijing 100089, China
| | - Lie Ju
- Beijing Airdoc Technology Co., Ltd, Beijing 100089, China
- Faculty of Engineering, Monash University, Melbourne, VIC 3000, Australia
| | - Zongyuan Ge
- Faculty of Engineering, Monash University, Melbourne, VIC 3000, Australia
| | - Yuzhong Chen
- Beijing Airdoc Technology Co., Ltd, Beijing 100089, China
| | - Peiquan Zhao
- Department of Ophthalmology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200092, China
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10
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Beam K, Sharma P, Levy P, Beam AL. Artificial intelligence in the neonatal intensive care unit: the time is now. J Perinatol 2024; 44:131-135. [PMID: 37443271 DOI: 10.1038/s41372-023-01719-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 06/24/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023]
Abstract
Artificial intelligence (AI) has the potential to revolutionize the neonatal intensive care unit (NICU) care by leveraging the large-scale, high-dimensional data that are generated by NICU patients. There is an emerging recognition that the confluence of technological progress, commercialization pathways, and rich data sets provides a unique opportunity for AI to make a lasting impact on the NICU. In this perspective article, we discuss four broad categories of AI applications in the NICU: imaging interpretation, prediction modeling of electronic health record data, integration of real-time monitoring data, and documentation and billing. By enhancing decision-making, streamlining processes, and improving patient outcomes, AI holds the potential to transform the quality of care for vulnerable newborns, making the excitement surrounding AI advancements well-founded and the potential for significant positive change stronger than ever before.
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Affiliation(s)
- Kristyn Beam
- Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Puneet Sharma
- Division of Newborn Medicine, Department of Pediatrics Boston Children's Hospital, Boston, MA, USA
| | - Phil Levy
- Division of Newborn Medicine, Department of Pediatrics Boston Children's Hospital, Boston, MA, USA
| | - Andrew L Beam
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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11
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Subramaniam A, Orge F, Douglass M, Can B, Monteoliva G, Fried E, Schbib V, Saidman G, Peña B, Ulacia S, Acevedo P, Rollins AM, Wilson DL. Image harmonization and deep learning automated classification of plus disease in retinopathy of prematurity. J Med Imaging (Bellingham) 2023; 10:061107. [PMID: 37794884 PMCID: PMC10546198 DOI: 10.1117/1.jmi.10.6.061107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 07/25/2023] [Accepted: 09/11/2023] [Indexed: 10/06/2023] Open
Abstract
Purpose Retinopathy of prematurity (ROP) is a retinal vascular disease affecting premature infants that can culminate in blindness within days if not monitored and treated. A disease stage for scrutiny and administration of treatment within ROP is "plus disease" characterized by increased tortuosity and dilation of posterior retinal blood vessels. The monitoring of ROP occurs via routine imaging, typically using expensive instruments ($50 to $140 K) that are unavailable in low-resource settings at the point of care. Approach As part of the smartphone-ROP program to enable referrals to expert physicians, fundus images are acquired using smartphone cameras and inexpensive lenses. We developed methods for artificial intelligence determination of plus disease, consisting of a preprocessing pipeline to enhance vessels and harmonize images followed by deep learning classification. A deep learning binary classifier (plus disease versus no plus disease) was developed using GoogLeNet. Results Vessel contrast was enhanced by 90% after preprocessing as assessed by the contrast improvement index. In an image quality evaluation, preprocessed and original images were evaluated by pediatric ophthalmologists from the US and South America with years of experience diagnosing ROP and plus disease. All participating ophthalmologists agreed or strongly agreed that vessel visibility was improved with preprocessing. Using images from various smartphones, harmonized via preprocessing (e.g., vessel enhancement and size normalization) and augmented in physically reasonable ways (e.g., image rotation), we achieved an area under the ROC curve of 0.9754 for plus disease on a limited dataset. Conclusions Promising results indicate the potential for developing algorithms and software to facilitate the usage of cell phone images for staging of plus disease.
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Affiliation(s)
- Ananya Subramaniam
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
| | - Faruk Orge
- Case Medical Center University Hospitals, Department of Ophthalmology, Cleveland, Ohio, United States
| | - Michael Douglass
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
| | - Basak Can
- Case Medical Center University Hospitals, Department of Ophthalmology, Cleveland, Ohio, United States
| | | | - Evelin Fried
- Hospital Italiano de San Justo Agustin Rocca, Buenos Aires, Argentina
| | - Vanina Schbib
- Hospital de Niños Sor Maria Ludovica, Buenos Aires, Argentina
| | | | - Brenda Peña
- Centro Integral de Salud Visual Daponte, Buenos Aires, Argentina
| | - Soledad Ulacia
- Mineserio de Salud Argentina, Ministry of Public Works Building, Buenos Aires, Argentina
| | | | - Andrew M. Rollins
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
| | - David L. Wilson
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
- Case Western Reserve University, Department of Radiology, Cleveland, Ohio, United States
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12
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Daich Varela M, Sen S, De Guimaraes TAC, Kabiri N, Pontikos N, Balaskas K, Michaelides M. Artificial intelligence in retinal disease: clinical application, challenges, and future directions. Graefes Arch Clin Exp Ophthalmol 2023; 261:3283-3297. [PMID: 37160501 PMCID: PMC10169139 DOI: 10.1007/s00417-023-06052-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/20/2023] [Accepted: 03/24/2023] [Indexed: 05/11/2023] Open
Abstract
Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans.
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Affiliation(s)
- Malena Daich Varela
- UCL Institute of Ophthalmology, London, UK
- Moorfields Eye Hospital, London, UK
| | | | | | | | - Nikolas Pontikos
- UCL Institute of Ophthalmology, London, UK
- Moorfields Eye Hospital, London, UK
| | | | - Michel Michaelides
- UCL Institute of Ophthalmology, London, UK.
- Moorfields Eye Hospital, London, UK.
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13
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Rao DP, Savoy FM, Tan JZE, Fung BPE, Bopitiya CM, Sivaraman A, Vinekar A. Development and validation of an artificial intelligence based screening tool for detection of retinopathy of prematurity in a South Indian population. Front Pediatr 2023; 11:1197237. [PMID: 37794964 PMCID: PMC10545957 DOI: 10.3389/fped.2023.1197237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 08/29/2023] [Indexed: 10/06/2023] Open
Abstract
Purpose The primary objective of this study was to develop and validate an AI algorithm as a screening tool for the detection of retinopathy of prematurity (ROP). Participants Images were collected from infants enrolled in the KIDROP tele-ROP screening program. Methods We developed a deep learning (DL) algorithm with 227,326 wide-field images from multiple camera systems obtained from the KIDROP tele-ROP screening program in India over an 11-year period. 37,477 temporal retina images were utilized with the dataset split into train (n = 25,982, 69.33%), validation (n = 4,006, 10.69%), and an independent test set (n = 7,489, 19.98%). The algorithm consists of a binary classifier that distinguishes between the presence of ROP (Stages 1-3) and the absence of ROP. The image labels were retrieved from the daily registers of the tele-ROP program. They consist of per-eye diagnoses provided by trained ROP graders based on all images captured during the screening session. Infants requiring treatment and a proportion of those not requiring urgent referral had an additional confirmatory diagnosis from an ROP specialist. Results Of the 7,489 temporal images analyzed in the test set, 2,249 (30.0%) images showed the presence of ROP. The sensitivity and specificity to detect ROP was 91.46% (95% CI: 90.23%-92.59%) and 91.22% (95% CI: 90.42%-91.97%), respectively, while the positive predictive value (PPV) was 81.72% (95% CI: 80.37%-83.00%), negative predictive value (NPV) was 96.14% (95% CI: 95.60%-96.61%) and the AUROC was 0.970. Conclusion The novel ROP screening algorithm demonstrated high sensitivity and specificity in detecting the presence of ROP. A prospective clinical validation in a real-world tele-ROP platform is under consideration. It has the potential to lower the number of screening sessions required to be conducted by a specialist for a high-risk preterm infant thus significantly improving workflow efficiency.
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Affiliation(s)
- Divya Parthasarathy Rao
- Artificial Intelligence Research and Development, Remidio Innovative Solutions Inc., Glen Allen, VA, United States
| | - Florian M. Savoy
- Artificial Intelligence Research and Development, Medios Technologies Pvt. Ltd., Singapore, Singapore
| | - Joshua Zhi En Tan
- Artificial Intelligence Research and Development, Medios Technologies Pvt. Ltd., Singapore, Singapore
| | - Brian Pei-En Fung
- Artificial Intelligence Research and Development, Medios Technologies Pvt. Ltd., Singapore, Singapore
| | - Chiran Mandula Bopitiya
- Artificial Intelligence Research and Development, Medios Technologies Pvt. Ltd., Singapore, Singapore
| | - Anand Sivaraman
- Artificial Intelligence Research and Development, Remidio Innovative Solutions Pvt. Ltd., Bangalore, India
| | - Anand Vinekar
- Department of Pediatric Retina, Narayana Nethralaya Eye Institute, Bangalore, India
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14
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Liu YF, Ji YK, Fei FQ, Chen NM, Zhu ZT, Fei XZ. Research progress in artificial intelligence assisted diabetic retinopathy diagnosis. Int J Ophthalmol 2023; 16:1395-1405. [PMID: 37724288 PMCID: PMC10475636 DOI: 10.18240/ijo.2023.09.05] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 06/14/2023] [Indexed: 09/20/2023] Open
Abstract
Diabetic retinopathy (DR) is one of the most common retinal vascular diseases and one of the main causes of blindness worldwide. Early detection and treatment can effectively delay vision decline and even blindness in patients with DR. In recent years, artificial intelligence (AI) models constructed by machine learning and deep learning (DL) algorithms have been widely used in ophthalmology research, especially in diagnosing and treating ophthalmic diseases, particularly DR. Regarding DR, AI has mainly been used in its diagnosis, grading, and lesion recognition and segmentation, and good research and application results have been achieved. This study summarizes the research progress in AI models based on machine learning and DL algorithms for DR diagnosis and discusses some limitations and challenges in AI research.
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Affiliation(s)
- Yun-Fang Liu
- Department of Ophthalmology, First People's Hospital of Huzhou, Huzhou University, Huzhou 313000, Zhejiang Province, China
| | - Yu-Ke Ji
- Eye Hospital, Nanjing Medical University, Nanjing 210000, Jiangsu Province, China
| | - Fang-Qin Fei
- Department of Endocrinology, First People's Hospital of Huzhou, Huzhou University, Huzhou 313000, Zhejiang Province, China
| | - Nai-Mei Chen
- Department of Ophthalmology, Huai'an Hospital of Huai'an City, Huai'an 223000, Jiangsu Province, China
| | - Zhen-Tao Zhu
- Department of Ophthalmology, Huai'an Hospital of Huai'an City, Huai'an 223000, Jiangsu Province, China
| | - Xing-Zhen Fei
- Department of Endocrinology, First People's Hospital of Huzhou, Huzhou University, Huzhou 313000, Zhejiang Province, China
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15
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Liu Y, Du Y, Wang X, Zhao X, Zhang S, Yu Z, Wu Z, Ntentakis DP, Tian R, Chen Y, Wang C, Yao X, Li R, Heng PA, Zhang G. An Artificial Intelligence System for Screening and Recommending the Treatment Modalities for Retinopathy of Prematurity. Asia Pac J Ophthalmol (Phila) 2023; 12:468-476. [PMID: 37851564 DOI: 10.1097/apo.0000000000000638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 08/01/2023] [Indexed: 10/20/2023] Open
Abstract
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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Affiliation(s)
- Yaling Liu
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Yueshanyi Du
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
- Guizhou Medical University, Guiyang, Guizhou, China
| | - Xi Wang
- Zhejiang Lab, Hangzhou, China
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, China
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, Palo Alto, CA
| | - Xinyu Zhao
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Sifan Zhang
- Southern University of Science and Technology School of Medicine, Shenzhen, China
| | - Zhen Yu
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Zhenquan Wu
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Dimitrios P Ntentakis
- Retina Service, Ines and Fred Yeatts Retina Research Laboratory, Boston, MA
- Angiogenesis Laboratory, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA
| | - Ruyin Tian
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Yi Chen
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Cui Wang
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Xue Yao
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, Palo Alto, CA
| | - Pheng-Ann Heng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, China
| | - Guoming Zhang
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
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16
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Ruamviboonsuk P, Ruamviboonsuk V, Tiwari R. Recent evidence of economic evaluation of artificial intelligence in ophthalmology. Curr Opin Ophthalmol 2023; 34:449-458. [PMID: 37459289 DOI: 10.1097/icu.0000000000000987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
PURPOSE OF REVIEW Health economic evaluation (HEE) is essential for assessing value of health interventions, including artificial intelligence. Recent approaches, current challenges, and future directions of HEE of artificial intelligence in ophthalmology are reviewed. RECENT FINDINGS Majority of recent HEEs of artificial intelligence in ophthalmology were for diabetic retinopathy screening. Two models, one conducted in the rural USA (5-year period) and another in China (35-year period), found artificial intelligence to be more cost-effective than without screening for diabetic retinopathy. Two additional models, which compared artificial intelligence with human screeners in Brazil and Thailand for the lifetime of patients, found artificial intelligence to be more expensive from a healthcare system perspective. In the Thailand analysis, however, artificial intelligence was less expensive when opportunity loss from blindness was included. An artificial intelligence model for screening retinopathy of prematurity was cost-effective in the USA. A model for screening age-related macular degeneration in Japan and another for primary angle close in China did not find artificial intelligence to be cost-effective, compared with no screening. The costs of artificial intelligence varied widely in these models. SUMMARY Like other medical fields, there is limited evidence in assessing the value of artificial intelligence in ophthalmology and more appropriate HEE models are needed.
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Affiliation(s)
- Paisan Ruamviboonsuk
- Department of Ophthalmology, Rajavithi Hospital, College of Medicine, Rangsit University
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17
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Patel SN, Al-Khaled T, Kang KB, Jonas KE, Ostmo S, Ventura CV, Martinez-Castellanos MA, Anzures RGAS, Campbell JP, Chiang MF, Chan RVP. Characterization of Errors in Retinopathy of Prematurity Diagnosis by Ophthalmologists-in-Training in Middle-Income Countries. J Pediatr Ophthalmol Strabismus 2023; 60:344-352. [PMID: 36263934 DOI: 10.3928/01913913-20220609-02] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
PURPOSE To characterize common errors in the diagnosis of retinopathy of prematurity (ROP) among ophthalmologistsin-training in middle-income countries. METHODS In this prospective cohort study, 200 ophthalmologists-in-training from programs in Brazil, Mexico, and the Philippines participated. A secure web-based educational system was developed using a repository of more than 2,500 unique image sets of ROP, and a reference standard diagnosis was established by combining the clinical diagnosis and the image-based diagnosis by multiple experts. Twenty web-based cases of wide-field retinal images were presented, and ophthalmologists-in-training were asked to diagnose plus disease, zone, stage, and category for each eye. Trainees' responses were compared to the consensus reference standard diagnosis. Main outcome measures were frequency and types of diagnostic errors were analyzed. RESULTS The error rate in the diagnosis of any category of ROP was between 48% and 59% for all countries. The error rate in identifying type 2 or pre-plus disease was 77%, with a tendency for overdiagnosis (27% underdiagnosis vs 50% overdiagnosis; mean difference: 23.4; 95% CI: 12.1 to 34.7; P = .005). Misdiagnosis of treatment-requiring ROP as type 2 ROP was most commonly associated with incorrectly identifying plus disease (plus disease error rate = 18% with correct category diagnosis vs 69% when misdiagnosed; mean difference: 51.0; 95% CI: 49.3 to 52.7; P = .003). CONCLUSIONS Ophthalmologists-in-training from middle-income countries misdiagnosed ROP more than half of the time. Identification of plus disease was the salient factor leading to incorrect diagnosis. These findings emphasize the need for improved access to ROP education to improve competency in diagnosis among ophthalmologists-in-training in middle-income countries. [J Pediatr Ophthalmol Strabismus. 2023;60(5):344-352.].
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Bai A, Dai S, Hung J, Kirpalani A, Russell H, Elder J, Shah S, Carty C, Tan Z. Multicenter Validation of Deep Learning Algorithm ROP.AI for the Automated Diagnosis of Plus Disease in ROP. Transl Vis Sci Technol 2023; 12:13. [PMID: 37578427 PMCID: PMC10431208 DOI: 10.1167/tvst.12.8.13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 06/30/2023] [Indexed: 08/15/2023] Open
Abstract
Purpose Retinopathy of prematurity (ROP) is a sight-threatening vasoproliferative retinal disease affecting premature infants. The detection of plus disease, a severe form of ROP requiring treatment, remains challenging owing to subjectivity, frequency, and time intensity of retinal examinations. Recent artificial intelligence (AI) algorithms developed to detect plus disease aims to alleviate these challenges; however, they have not been tested against a diverse neonatal population. Our study aims to validate ROP.AI, an AI algorithm developed from a single cohort, against a multicenter Australian cohort to determine its performance in detecting plus disease. Methods Retinal images captured during routine ROP screening from May 2021 to February 2022 across five major tertiary centers throughout Australia were collected and uploaded to ROP.AI. AI diagnostic output was compared with one of five ROP experts. Sensitivity, specificity, negative predictive value, and area under the receiver operator curve were determined. Results We collected 8052 images. The area under the receiver operator curve for the diagnosis of plus disease was 0.75. ROP.AI achieved 84% sensitivity, 43% specificity, and 96% negative predictive value for the detection of plus disease after operating point optimization. Conclusions ROP.AI was able to detect plus disease in an external, multicenter cohort despite being trained from a single center. Algorithm performance was demonstrated without preprocessing or augmentation, simulating real-world clinical applicability. Further training may improve generalizability for clinical implementation. Translational Relevance These results demonstrate ROP.AI's potential as a screening tool for the detection of plus disease in future clinical practice and provides a solution to overcome current diagnostic challenges.
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Affiliation(s)
- Amelia Bai
- Department of Ophthalmology, Queensland Children's Hospital, South Brisbane, Queensland, Australia
- Centre for Children's Health Research, South Brisbane, Queensland, Australia
- School of Medical Science, Griffith University, Southport, Queensland, Australia
| | - Shuan Dai
- Department of Ophthalmology, Queensland Children's Hospital, South Brisbane, Queensland, Australia
- School of Medical Science, Griffith University, Southport, Queensland, Australia
- University of Queensland, St Lucia, Queensland, Australia
| | - Jacky Hung
- Centre for Children's Health Research, South Brisbane, Queensland, Australia
| | - Aditi Kirpalani
- Department of Ophthalmology, Gold Coast University Hospital, Southport, Queensland, Australia
| | - Heather Russell
- Department of Ophthalmology, Gold Coast University Hospital, Southport, Queensland, Australia
- Bond University, Robina, Queensland, Australia
| | - James Elder
- Department of Ophthalmology, Royal Women's Hospital, Parkville, Victoria, Australia
- University of Melbourne, Parkville, Victoria, Australia
| | - Shaheen Shah
- Mater Misericordiae, South Brisbane, Queensland, Australia
| | - Christopher Carty
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland, Griffith University, Southport, Australia
- Department of Orthopaedics, Children's Health Queensland Hospital and Health Service, Queensland Children's Hospital, South Brisbane, Australia
| | - Zachary Tan
- Aegis Ventures, Sydney, New South Wales, Australia
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Ramanathan A, Athikarisamy SE, Lam GC. Artificial intelligence for the diagnosis of retinopathy of prematurity: A systematic review of current algorithms. Eye (Lond) 2023; 37:2518-2526. [PMID: 36577806 PMCID: PMC10397194 DOI: 10.1038/s41433-022-02366-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 11/23/2022] [Accepted: 12/09/2022] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND/OBJECTIVES With the increasing survival of premature infants, there is an increased demand to provide adequate retinopathy of prematurity (ROP) services. Wide field retinal imaging (WFDRI) and artificial intelligence (AI) have shown promise in the field of ROP and have the potential to improve the diagnostic performance and reduce the workload for screening ophthalmologists. The aim of this review is to systematically review and provide a summary of the diagnostic characteristics of existing deep learning algorithms. SUBJECT/METHODS Two authors independently searched the literature, and studies using a deep learning system from retinal imaging were included. Data were extracted, assessed and reported using PRISMA guidelines. RESULTS Twenty-seven studies were included in this review. Nineteen studies used AI systems to diagnose ROP, classify the staging of ROP, diagnose the presence of pre-plus or plus disease, or assess the quality of retinal images. The included studies reported a sensitivity of 71%-100%, specificity of 74-99% and area under the curve of 91-99% for the primary outcome of the study. AI techniques were comparable to the assessment of ophthalmologists in terms of overall accuracy and sensitivity. Eight studies evaluated vascular severity scores and were able to accurately differentiate severity using an automated classification score. CONCLUSION Artificial intelligence for ROP diagnosis is a growing field, and many potential utilities have already been identified, including the presence of plus disease, staging of disease and a new automated severity score. AI has a role as an adjunct to clinical assessment; however, there is insufficient evidence to support its use as a sole diagnostic tool currently.
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Affiliation(s)
- Ashwin Ramanathan
- Department of Paediatrics, Perth Children's Hospital, Perth, Australia
| | - Sam Ebenezer Athikarisamy
- Department of Neonatology, Perth Children's Hospital, Perth, Australia.
- School of Medicine, University of Western Australia, Crawley, Australia.
| | - Geoffrey C Lam
- Department of Ophthalmology, Perth Children's Hospital, Perth, Australia
- Centre for Ophthalmology and Visual Science, University of Western Australia, Crawley, Australia
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Zhang R, Dong L, Li R, Zhang K, Li Y, Zhao H, Shi J, Ge X, Xu X, Jiang L, Shi X, Zhang C, Zhou W, Xu L, Wu H, Li H, Yu C, Li J, Ma J, Wei W. Automatic retinoblastoma screening and surveillance using deep learning. Br J Cancer 2023; 129:466-474. [PMID: 37344582 PMCID: PMC10403507 DOI: 10.1038/s41416-023-02320-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 05/17/2023] [Accepted: 06/12/2023] [Indexed: 06/23/2023] Open
Abstract
BACKGROUND Retinoblastoma is the most common intraocular malignancy in childhood. With the advanced management strategy, the globe salvage and overall survival have significantly improved, which proposes subsequent challenges regarding long-term surveillance and offspring screening. This study aimed to apply a deep learning algorithm to reduce the burden of follow-up and offspring screening. METHODS This cohort study includes retinoblastoma patients who visited Beijing Tongren Hospital from March 2018 to January 2022 for deep learning algorism development. Clinical-suspected and treated retinoblastoma patients from February 2022 to June 2022 were prospectively collected for prospective validation. Images from the posterior pole and peripheral retina were collected, and reference standards were made according to the consensus of the multidisciplinary management team. A deep learning algorithm was trained to identify "normal fundus", "stable retinoblastoma" in which specific treatment is not required, and "active retinoblastoma" in which specific treatment is required. The performance of each classifier included sensitivity, specificity, accuracy, and cost-utility. RESULTS A total of 36,623 images were included for developing the Deep Learning Assistant for Retinoblastoma Monitoring (DLA-RB) algorithm. In internal fivefold cross-validation, DLA-RB achieved an area under curve (AUC) of 0.998 (95% confidence interval [CI] 0.986-1.000) in distinguishing normal fundus and active retinoblastoma, and 0.940 (95% CI 0.851-0.996) in distinguishing stable and active retinoblastoma. From February 2022 to June 2022, 139 eyes of 103 patients were prospectively collected. In identifying active retinoblastoma tumours from all clinical-suspected patients and active retinoblastoma from all treated retinoblastoma patients, the AUC of DLA-RB reached 0.991 (95% CI 0.970-1.000), and 0.962 (95% CI 0.915-1.000), respectively. The combination between ophthalmologists and DLA-RB significantly improved the accuracy of competent ophthalmologists and residents regarding both binary tasks. Cost-utility analysis revealed DLA-RB-based diagnosis mode is cost-effective in both retinoblastoma diagnosis and active retinoblastoma identification. CONCLUSIONS DLA-RB achieved high accuracy and sensitivity in identifying active retinoblastoma from the normal and stable retinoblastoma fundus. It can be used to surveil the activity of retinoblastoma during follow-up and screen high-risk offspring. Compared with referral procedures to ophthalmologic centres, DLA-RB-based screening and surveillance is cost-effective and can be incorporated within telemedicine programs. CLINICAL TRIAL REGISTRATION This study was registered on ClinicalTrials.gov (NCT05308043).
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Affiliation(s)
- Ruiheng Zhang
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Li Dong
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Ruyue Li
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Kai Zhang
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Yitong Li
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Hongshu Zhao
- Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Jitong Shi
- Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Xin Ge
- Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Xiaolin Xu
- Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Libin Jiang
- Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Xuhan Shi
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Chuan Zhang
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Wenda Zhou
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Liangyuan Xu
- Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Haotian Wu
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Heyan Li
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Chuyao Yu
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Jing Li
- Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Jianmin Ma
- Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
| | - Wenbin Wei
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
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deCampos-Stairiker MA, Coyner AS, Gupta A, Oh M, Shah PK, Subramanian P, Venkatapathy N, Singh P, Kalpathy-Cramer J, Chiang MF, Chan RVP, Campbell JP. Epidemiologic Evaluation of Retinopathy of Prematurity Severity in a Large Telemedicine Program in India Using Artificial Intelligence. Ophthalmology 2023; 130:837-843. [PMID: 37030453 PMCID: PMC10524227 DOI: 10.1016/j.ophtha.2023.03.026] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 03/08/2023] [Accepted: 03/29/2023] [Indexed: 04/08/2023] Open
Abstract
PURPOSE Epidemiological changes in retinopathy of prematurity (ROP) depend on neonatal care, neonatal mortality, and the ability to carefully titrate and monitor oxygen. We evaluate whether an artificial intelligence (AI) algorithm for assessing ROP severity in babies can be used to evaluate changes in disease epidemiology in babies from South India over a 5-year period. DESIGN Retrospective cohort study. PARTICIPANTS Babies (3093) screened for ROP at neonatal care units (NCUs) across the Aravind Eye Care System (AECS) in South India. METHODS Images and clinical data were collected as part of routine tele-ROP screening at the AECS in India over 2 time periods: August 2015 to October 2017 and March 2019 to December 2020. All babies in the original cohort were matched 1:3 by birthweight (BW) and gestational age (GA) with babies in the later cohort. We compared the proportion of eyes with moderate (type 2) or treatment-requiring (TR) ROP, and an AI-derived ROP vascular severity score (from retinal fundus images) at the initial tele-retinal screening exam for all babies in a district, VSS), in the 2 time periods. MAIN OUTCOME MEASURES Differences in the proportions of type 2 or worse and TR-ROP cases, and VSS between time periods. RESULTS Among BW and GA matched babies, the proportion [95% confidence interval {CI}] of babies with type 2 or worse and TR-ROP decreased from 60.9% [53.8%-67.7%] to 17.1% [14.0%-20.5%] (P < 0.001) and 16.8% [11.9%-22.7%] to 5.1% [3.4%-7.3%] (P < 0.001), over the 2 time periods. Similarly, the median [interquartile range] VSS in the population decreased from 2.9 [1.2] to 2.4 [1.8] (P < 0.001). CONCLUSIONS In South India, over a 5-year period, the proportion of babies developing moderate to severe ROP has dropped significantly for babies at similar demographic risk, strongly suggesting improvements in primary prevention of ROP. These results suggest that AI-based assessment of ROP severity may be a useful epidemiologic tool to evaluate temporal changes in ROP epidemiology. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found after the references.
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Affiliation(s)
| | - Aaron S Coyner
- Ophthalmology, Oregon Health & Science University, Portland, Oregon
| | - Aditi Gupta
- Ophthalmology, Oregon Health & Science University, Portland, Oregon
| | - Minn Oh
- Ophthalmology, Oregon Health & Science University, Portland, Oregon
| | - Parag K Shah
- Pediatric Retina and Ocular Oncology, Aravind Eye Hospital, Coimbatore, India
| | - Prema Subramanian
- Pediatric Retina and Ocular Oncology, Aravind Eye Hospital, Coimbatore, India
| | | | - Praveer Singh
- Ophthalmology, University of Colorado, Aurora, Colorado; Radiology, MGH/Harvard Medical School, Charlestown, Massachusetts
| | - Jayashree Kalpathy-Cramer
- Ophthalmology, University of Colorado, Aurora, Colorado; Radiology, MGH/Harvard Medical School, Charlestown, Massachusetts
| | - Michael F Chiang
- National Eye Institute, National Institute of Health, Bethesda, Maryland; National Library of Medicine, National Institute of Health, Bethesda, Maryland
| | - R V Paul Chan
- Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - J Peter Campbell
- Ophthalmology, Oregon Health & Science University, Portland, Oregon.
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Mellak Y, Achim A, Ward A, Nicholson L, Descombes X. A machine learning framework for the quantification of experimental uveitis in murine OCT. BIOMEDICAL OPTICS EXPRESS 2023; 14:3413-3432. [PMID: 37497491 PMCID: PMC10368067 DOI: 10.1364/boe.489271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/11/2023] [Accepted: 05/22/2023] [Indexed: 07/28/2023]
Abstract
This paper presents methods for the detection and assessment of non-infectious uveitis, a leading cause of vision loss in working age adults. In the first part, we propose a classification model that can accurately predict the presence of uveitis and differentiate between different stages of the disease using optical coherence tomography (OCT) images. We utilize the Grad-CAM visualization technique to elucidate the decision-making process of the classifier and gain deeper insights into the results obtained. In the second part, we apply and compare three methods for the detection of detached particles in the retina that are indicative of uveitis. The first is a fully supervised detection method, the second is a marked point process (MPP) technique, and the third is a weakly supervised segmentation that produces per-pixel masks as output. The segmentation model is used as a backbone for a fully automated pipeline that can segment small particles of uveitis in two-dimensional (2-D) slices of the retina, reconstruct the volume, and produce centroids as points distribution in space. The number of particles in retinas is used to grade the disease, and point process analysis on centroids in three-dimensional (3-D) shows clustering patterns in the distribution of the particles on the retina.
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Affiliation(s)
- Youness Mellak
- Université Côte d’Azur, INRIA, CNRS, I3S, Sophia Antipolis, France
| | - Alin Achim
- University of Bristol, Bristol, United Kingdom
| | - Amy Ward
- University of Bristol, Bristol, United Kingdom
| | | | - Xavier Descombes
- Université Côte d’Azur, INRIA, CNRS, I3S, Sophia Antipolis, France
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Kitaguchi Y, Hayakawa R, Kawashima R, Matsushita K, Tanaka H, Kawasaki R, Fujino T, Usui S, Shimojyo H, Okazaki T, Nishida K. Deep-learning approach to detect childhood glaucoma based on periocular photograph. Sci Rep 2023; 13:10141. [PMID: 37349526 PMCID: PMC10287677 DOI: 10.1038/s41598-023-37389-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 06/21/2023] [Indexed: 06/24/2023] Open
Abstract
Childhood glaucoma is one of the major causes of blindness in children, however, its diagnosis is of great challenge. The study aimed to demonstrate and evaluate the performance of a deep-learning (DL) model for detecting childhood glaucoma based on periocular photographs. Primary gaze photographs of children diagnosed with glaucoma with appearance features (corneal opacity, corneal enlargement, and/or globe enlargement) were retrospectively collected from the database of a single referral center. DL framework with the RepVGG architecture was used to automatically recognize childhood glaucoma from photographs. The average receiver operating characteristic curve (AUC) of fivefold cross-validation was 0.91. When the fivefold result was assembled, the DL model achieved an AUC of 0.95 with a sensitivity of 0.85 and specificity of 0.94. The DL model showed comparable accuracy to the pediatric ophthalmologists and glaucoma specialists in diagnosing childhood glaucoma (0.90 vs 0.81, p = 0.22, chi-square test), outperforming the average of human examiners in the detection rate of childhood glaucoma in cases without corneal opacity (72% vs. 34%, p = 0.038, chi-square test), with a bilateral corneal enlargement (100% vs. 67%, p = 0.03), and without skin lesions (87% vs. 64%, p = 0.02). Hence, this DL model is a promising tool for diagnosing missed childhood glaucoma cases.
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Affiliation(s)
- Yoshiyuki Kitaguchi
- Department of Ophthalmology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
| | - Rina Hayakawa
- Division of Health Science, Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Rumi Kawashima
- Department of Ophthalmology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Kenji Matsushita
- Department of Ophthalmology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Hisashi Tanaka
- Division of Health Science, Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Ryo Kawasaki
- Department of Ophthalmology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
- Artificial Intelligence Center for Medical Research and Application, Osaka University Hospital, Suita, Osaka, Japan
| | - Takahiro Fujino
- Department of Ophthalmology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Shinichi Usui
- Department of Ophthalmology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Hiroshi Shimojyo
- Department of Ophthalmology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Tomoyuki Okazaki
- Department of Ophthalmology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Kohji Nishida
- Department of Ophthalmology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
- Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Suita, Osaka, Japan
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24
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Gomes RFT, Schuch LF, Martins MD, Honório EF, de Figueiredo RM, Schmith J, Machado GN, Carrard VC. Use of Deep Neural Networks in the Detection and Automated Classification of Lesions Using Clinical Images in Ophthalmology, Dermatology, and Oral Medicine-A Systematic Review. J Digit Imaging 2023; 36:1060-1070. [PMID: 36650299 PMCID: PMC10287602 DOI: 10.1007/s10278-023-00775-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 01/03/2023] [Accepted: 01/04/2023] [Indexed: 01/19/2023] Open
Abstract
Artificial neural networks (ANN) are artificial intelligence (AI) techniques used in the automated recognition and classification of pathological changes from clinical images in areas such as ophthalmology, dermatology, and oral medicine. The combination of enterprise imaging and AI is gaining notoriety for its potential benefits in healthcare areas such as cardiology, dermatology, ophthalmology, pathology, physiatry, radiation oncology, radiology, and endoscopic. The present study aimed to analyze, through a systematic literature review, the application of performance of ANN and deep learning in the recognition and automated classification of lesions from clinical images, when comparing to the human performance. The PRISMA 2020 approach (Preferred Reporting Items for Systematic Reviews and Meta-analyses) was used by searching four databases of studies that reference the use of IA to define the diagnosis of lesions in ophthalmology, dermatology, and oral medicine areas. A quantitative and qualitative analyses of the articles that met the inclusion criteria were performed. The search yielded the inclusion of 60 studies. It was found that the interest in the topic has increased, especially in the last 3 years. We observed that the performance of IA models is promising, with high accuracy, sensitivity, and specificity, most of them had outcomes equivalent to human comparators. The reproducibility of the performance of models in real-life practice has been reported as a critical point. Study designs and results have been progressively improved. IA resources have the potential to contribute to several areas of health. In the coming years, it is likely to be incorporated into everyday life, contributing to the precision and reducing the time required by the diagnostic process.
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Affiliation(s)
- Rita Fabiane Teixeira Gomes
- Graduate Program in Dentistry, School of Dentistry, Federal University of Rio Grande Do Sul, Barcelos 2492/503, Bairro Santana, Porto Alegre, RS, CEP 90035-003, Brazil.
| | - Lauren Frenzel Schuch
- Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Piracicaba, Brazil
| | - Manoela Domingues Martins
- Graduate Program in Dentistry, School of Dentistry, Federal University of Rio Grande Do Sul, Barcelos 2492/503, Bairro Santana, Porto Alegre, RS, CEP 90035-003, Brazil
- Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Piracicaba, Brazil
| | | | - Rodrigo Marques de Figueiredo
- Technology in Automation and Electronics Laboratory - TECAE Lab, University of Vale Do Rio Dos Sinos - UNISINOS, São Leopoldo, Brazil
| | - Jean Schmith
- Technology in Automation and Electronics Laboratory - TECAE Lab, University of Vale Do Rio Dos Sinos - UNISINOS, São Leopoldo, Brazil
| | - Giovanna Nunes Machado
- Technology in Automation and Electronics Laboratory - TECAE Lab, University of Vale Do Rio Dos Sinos - UNISINOS, São Leopoldo, Brazil
| | - Vinicius Coelho Carrard
- Graduate Program in Dentistry, School of Dentistry, Federal University of Rio Grande Do Sul, Barcelos 2492/503, Bairro Santana, Porto Alegre, RS, CEP 90035-003, Brazil
- Department of Epidemiology, School of Medicine, TelessaúdeRS-UFRGS, Federal University of Rio Grande Do Sul, Porto Alegre, RS, Brazil
- Department of Oral Medicine, Otorhinolaryngology Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil
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25
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Jayanna S, Padhi TR, Nedhina EK, Agarwal K, Jalali S. Color fundus imaging in retinopathy of prematurity screening: Present and future. Indian J Ophthalmol 2023; 71:1777-1782. [PMID: 37203030 PMCID: PMC10391467 DOI: 10.4103/ijo.ijo_2913_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023] Open
Abstract
Advent of pediatric handheld fundus cameras like RetCam, 3netra Forus, and Phoenix ICON pediatric retinal camera has aided in effective screening of retinopathy of prematurity (ROP), especially in countries with limited number of trained specialists. Recent advent of various smartphone-based cameras has made pediatric fundus photography furthermore affordable and portable. Future advances like ultra-wide field fundus cameras, trans-pars-planar illumination pediatric fundus camera, artificial intelligence, deep learning algorithm, and handheld SS-OCTA can help in more accurate imaging and documentation. This article summarizes the features of existing and upcoming imaging modalities in detail, including their features, advantages, challenges, and effectiveness, which can help in implementation of telescreening as a standard screening protocol for ROP across developing as well as developed countries.
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Affiliation(s)
- Sushma Jayanna
- Srimati Kanuri Santhamma Center for Vitreoretinal Diseases, Kallam Anji Reddy Campus, LV Prasad Eye Institute, Hyderabad, Telangana; Newborn Eye Health Alliance (NEHA), L. V. Prasad Eye Institute Network; Child Sight Institute, L. V. Prasad Eye Institute Network, Bhubaneshwar, Odissa, India
| | - Tapas R Padhi
- Department of Vitreo Retina, Mithun Tulsi Chanrai Campus, LV Prasad Eye Institute, Bhubaneshwar, Odissa, India
| | - E K Nedhina
- Department of Vitreo Retina, Nethra Jyothi Advanced Eye Care, Taliparamba, Kannur, Kerala, India
| | - Komal Agarwal
- Srimati Kanuri Santhamma Center for Vitreoretinal Diseases, Kallam Anji Reddy Campus, LV Prasad Eye Institute, Hyderabad, Telangana; Newborn Eye Health Alliance (NEHA), L. V. Prasad Eye Institute Network; Child Sight Institute, L. V. Prasad Eye Institute Network, Bhubaneshwar, Odissa, India
| | - Subhadra Jalali
- Srimati Kanuri Santhamma Center for Vitreoretinal Diseases, Kallam Anji Reddy Campus, LV Prasad Eye Institute, Hyderabad, Telangana; Newborn Eye Health Alliance (NEHA), L. V. Prasad Eye Institute Network; Child Sight Institute, L. V. Prasad Eye Institute Network, Bhubaneshwar, Odissa, India
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Chioma R, Sbordone A, Patti ML, Perri A, Vento G, Nobile S. Applications of Artificial Intelligence in Neonatology. APPLIED SCIENCES 2023; 13:3211. [DOI: 10.3390/app13053211] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/17/2023]
Abstract
The development of artificial intelligence methods has impacted therapeutics, personalized diagnostics, drug discovery, and medical imaging. Although, in many situations, AI clinical decision-support tools may seem superior to rule-based tools, their use may result in additional challenges. Examples include the paucity of large datasets and the presence of unbalanced data (i.e., due to the low occurrence of adverse outcomes), as often seen in neonatal medicine. The most recent and impactful applications of AI in neonatal medicine are discussed in this review, highlighting future research directions relating to the neonatal population. Current AI applications tested in neonatology include tools for vital signs monitoring, disease prediction (respiratory distress syndrome, bronchopulmonary dysplasia, apnea of prematurity) and risk stratification (retinopathy of prematurity, intestinal perforation, jaundice), neurological diagnostic and prognostic support (electroencephalograms, sleep stage classification, neuroimaging), and novel image recognition technologies, which are particularly useful for prompt recognition of infections. To have these kinds of tools helping neonatologists in daily clinical practice could be something extremely revolutionary in the next future. On the other hand, it is important to recognize the limitations of AI to ensure the proper use of this technology.
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Affiliation(s)
- Roberto Chioma
- Department of Life Sciences and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Annamaria Sbordone
- Department of Life Sciences and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Maria Letizia Patti
- Department of Life Sciences and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Alessandro Perri
- Department of Life Sciences and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Giovanni Vento
- Department of Life Sciences and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Stefano Nobile
- Department of Life Sciences and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
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27
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Li P, Liu J. Quantitative Analysis of Vascular Abnormalities in Full-Term Infants With Mild Familial Exudative Vitreoretinopathy. Transl Vis Sci Technol 2023; 12:16. [PMID: 36930137 PMCID: PMC10036951 DOI: 10.1167/tvst.12.3.16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023] Open
Abstract
Purpose Our goal was to build a system that combined deep convolutional neural networks (DCNNs) and feature extraction algorithms, which automatically extracted and quantified vascular abnormalities in posterior pole retinal images of full-term infants clinically diagnosed with mild familial exudative retinopathy (FEVR). Methods Using posterior pole retinal images taken from 4628 full-term infants with a total of 9256 eyes, we created data sets, trained DCNNs, and performed tests and comparisons. With the segmented images, our system extracted peripapillary vascular densities, mean tortuosities, and maximum diameter ratios within the region of interest. We also compared them with normal eyes statistically. Results In the test data set, the trained system obtained a sensitivity of 0.78 and a specificity of 0.98 for vascular segmentation, with 0.94 and 0.99 for optic disc, respectively. While in the comparison data set, compared with normal, we found a significant increase in vascular densities in retinal images with mild FEVR (5.3211% ± 0.7600% vs. 4.5998% ± 0.6586%) and a significant increase in the maximum diameter ratios (1.8805 ± 0.3197 vs. 1.5087 ± 0.2877), while the mean tortuosities significantly decreased (2.1018 ± 0.2933 [104 cm-3] vs. 3.3344 ± 0.3890 [104 cm-3]). All values were statistically significantly different. Conclusions Our system could automatically segment the posterior pole retinal images and extract from vascular features associated with mild FEVR. Quantitative analysis of these parameters may help ophthalmologists in the early detection of FEVR. Translational Relevance This system may contribute to the early detection of FEVR and facilitate the promotion of artificial intelligence-assisted diagnostic techniques in clinical applications.
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Affiliation(s)
- Peng Li
- College of Electronic and Information Engineering, Tongji University, Shanghai, China
- Department of Electronic and Information Engineering, Tongji Zhejiang College, Jiaxing, China
| | - Jia Liu
- Optometry Center, Jiaxing Maternity and Child Health Care Hospital, Jiaxing, China
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Xie H, Liu Y, Lei H, Song T, Yue G, Du Y, Wang T, Zhang G, Lei B. Adversarial learning-based multi-level dense-transmission knowledge distillation for AP-ROP detection. Med Image Anal 2023; 84:102725. [PMID: 36527770 DOI: 10.1016/j.media.2022.102725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 10/31/2022] [Accepted: 12/02/2022] [Indexed: 12/13/2022]
Abstract
The Aggressive Posterior Retinopathy of Prematurity (AP-ROP) is the major cause of blindness for premature infants. The automatic diagnosis method has become an important tool for detecting AP-ROP. However, most existing automatic diagnosis methods were with heavy complexity, which hinders the development of the detecting devices. Hence, a small network (student network) with a high imitation ability is exactly needed, which can mimic a large network (teacher network) with promising diagnostic performance. Also, if the student network is too small due to the increasing gap between teacher and student networks, the diagnostic performance will drop. To tackle the above issues, we propose a novel adversarial learning-based multi-level dense knowledge distillation method for detecting AP-ROP. Specifically, the pre-trained teacher network is utilized to train multiple intermediate-size networks (i.e., teacher-assistant networks) and one student network by dense transmission mode, where the knowledge from all upper-level networks is transmitted to the current lower-level network. To ensure that two adjacent networks can distill the abundant knowledge, the adversarial learning module is leveraged to enforce the lower-level network to generate the features that are similar to those of the upper-level network. Extensive experiments demonstrate that our proposed method can realize the effective knowledge distillation from the teacher to student networks. We achieve a promising knowledge distillation performance for our private dataset and a public dataset, which can provide a new insight for devising lightweight detecting systems of fundus diseases for practical use.
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Affiliation(s)
- Hai Xie
- 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
| | - Yaling Liu
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Haijun Lei
- Guangdong Province Key Laboratory of Popular High-performance Computers, School of Computer and Software Engineering, Shenzhen University, Shenzhen, China
| | - Tiancheng Song
- Shenzhen Silan Zhichuang Technology Co., Ltd., Shenzhen, China
| | - Guanghui Yue
- 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
| | - Yueshanyi Du
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Tianfu Wang
- 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
| | - Guoming Zhang
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China.
| | - Baiying Lei
- 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.
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Eilts SK, Pfeil JM, Poschkamp B, Krohne TU, Eter N, Barth T, Guthoff R, Lagrèze W, Grundel M, Bründer MC, Busch M, Kalpathy-Cramer J, Chiang MF, Chan RVP, Coyner AS, Ostmo S, Campbell JP, Stahl A. Assessment of Retinopathy of Prematurity Regression and Reactivation Using an Artificial Intelligence-Based Vascular Severity Score. JAMA Netw Open 2023; 6:e2251512. [PMID: 36656578 PMCID: PMC9857423 DOI: 10.1001/jamanetworkopen.2022.51512] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
IMPORTANCE One of the biggest challenges when using anti-vascular endothelial growth factor (VEGF) agents to treat retinopathy of prematurity (ROP) is the need to perform long-term follow-up examinations to identify eyes at risk of ROP reactivation requiring retreatment. OBJECTIVE To evaluate whether an artificial intelligence (AI)-based vascular severity score (VSS) can be used to analyze ROP regression and reactivation after anti-VEGF treatment and potentially identify eyes at risk of ROP reactivation requiring retreatment. DESIGN, SETTING, AND PARTICIPANTS This prognostic study was a secondary analysis of posterior pole fundus images collected during the multicenter, double-blind, investigator-initiated Comparing Alternative Ranibizumab Dosages for Safety and Efficacy in Retinopathy of Prematurity (CARE-ROP) randomized clinical trial, which compared 2 different doses of ranibizumab (0.12 mg vs 0.20 mg) for the treatment of ROP. The CARE-ROP trial screened and enrolled infants between September 5, 2014, and July 14, 2016. A total of 1046 wide-angle fundus images obtained from 19 infants at predefined study time points were analyzed. The analyses of VSS were performed between January 20, 2021, and November 18, 2022. INTERVENTIONS An AI-based algorithm assigned a VSS between 1 (normal) and 9 (most severe) to fundus images. MAIN OUTCOMES AND MEASURES Analysis of VSS in infants with ROP over time and VSS comparisons between the 2 treatment groups (0.12 mg vs 0.20 mg of ranibizumab) and between infants who did and did not receive retreatment for ROP reactivation. RESULTS Among 19 infants with ROP in the CARE-ROP randomized clinical trial, the median (range) postmenstrual age at first treatment was 36.4 (34.7-39.7) weeks; 10 infants (52.6%) were male, and 18 (94.7%) were White. The mean (SD) VSS was 6.7 (1.9) at baseline and significantly decreased to 2.7 (1.9) at week 1 (P < .001) and 2.9 (1.3) at week 4 (P < .001). The mean (SD) VSS of infants with ROP reactivation requiring retreatment was 6.5 (1.9) at the time of retreatment, which was significantly higher than the VSS at week 4 (P < .001). No significant difference was found in VSS between the 2 treatment groups, but the change in VSS between baseline and week 1 was higher for infants who later required retreatment (mean [SD], 7.8 [1.3] at baseline vs 1.7 [0.7] at week 1) vs infants who did not (mean [SD], 6.4 [1.9] at baseline vs 3.0 [2.0] at week 1). In eyes requiring retreatment, higher baseline VSS was correlated with earlier time of retreatment (Pearson r = -0.9997; P < .001). CONCLUSIONS AND RELEVANCE In this study, VSS decreased after ranibizumab treatment, consistent with clinical disease regression. In cases of ROP reactivation requiring retreatment, VSS increased again to values comparable with baseline values. In addition, a greater change in VSS during the first week after initial treatment was found to be associated with a higher risk of later ROP reactivation, and high baseline VSS was correlated with earlier retreatment. These findings may have implications for monitoring ROP regression and reactivation after anti-VEGF treatment.
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Affiliation(s)
- Sonja K. Eilts
- Department of Ophthalmology, University Medicine Greifswald, Greifswald, Germany
| | - Johanna M. Pfeil
- Department of Ophthalmology, University Medicine Greifswald, Greifswald, Germany
| | - Broder Poschkamp
- Department of Ophthalmology, University Medicine Greifswald, Greifswald, Germany
| | - Tim U. Krohne
- Department of Ophthalmology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Nicole Eter
- Department of Ophthalmology, University of Muenster Medical Center, Muenster, Germany
| | - Teresa Barth
- Department of Ophthalmology, University of Regensburg, Regensburg, Germany
| | - Rainer Guthoff
- Department of Ophthalmology, Faculty of Medicine, University of Düsseldorf, Düsseldorf, Germany
| | - Wolf Lagrèze
- Eye Center, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Milena Grundel
- Department of Ophthalmology, University Medicine Greifswald, Greifswald, Germany
| | | | - Martin Busch
- Department of Ophthalmology, University Medicine Greifswald, Greifswald, Germany
| | - Jayashree Kalpathy-Cramer
- Center for Clinical Data Science, Massachusetts General Hospital, Brigham and Women’s Hospital, Boston
| | - Michael F. Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
- National Library of Medicine, National Institutes of Health, Bethesda, Maryland
| | - R. V. Paul Chan
- Department of Ophthalmology, University of Illinois Chicago, Chicago
| | - Aaron S. Coyner
- Casey Eye Institute, Oregon Health & Science University, Portland
| | - Susan Ostmo
- Casey Eye Institute, Oregon Health & Science University, Portland
| | | | - Andreas Stahl
- Department of Ophthalmology, University Medicine Greifswald, Greifswald, Germany
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Comprehensive Review on the Use of Artificial Intelligence in Ophthalmology and Future Research Directions. Diagnostics (Basel) 2022; 13:diagnostics13010100. [PMID: 36611392 PMCID: PMC9818832 DOI: 10.3390/diagnostics13010100] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/12/2022] [Accepted: 12/26/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Having several applications in medicine, and in ophthalmology in particular, artificial intelligence (AI) tools have been used to detect visual function deficits, thus playing a key role in diagnosing eye diseases and in predicting the evolution of these common and disabling diseases. AI tools, i.e., artificial neural networks (ANNs), are progressively involved in detecting and customized control of ophthalmic diseases. The studies that refer to the efficiency of AI in medicine and especially in ophthalmology were analyzed in this review. MATERIALS AND METHODS We conducted a comprehensive review in order to collect all accounts published between 2015 and 2022 that refer to these applications of AI in medicine and especially in ophthalmology. Neural networks have a major role in establishing the demand to initiate preliminary anti-glaucoma therapy to stop the advance of the disease. RESULTS Different surveys in the literature review show the remarkable benefit of these AI tools in ophthalmology in evaluating the visual field, optic nerve, and retinal nerve fiber layer, thus ensuring a higher precision in detecting advances in glaucoma and retinal shifts in diabetes. We thus identified 1762 applications of artificial intelligence in ophthalmology: review articles and research articles (301 pub med, 144 scopus, 445 web of science, 872 science direct). Of these, we analyzed 70 articles and review papers (diabetic retinopathy (N = 24), glaucoma (N = 24), DMLV (N = 15), other pathologies (N = 7)) after applying the inclusion and exclusion criteria. CONCLUSION In medicine, AI tools are used in surgery, radiology, gynecology, oncology, etc., in making a diagnosis, predicting the evolution of a disease, and assessing the prognosis in patients with oncological pathologies. In ophthalmology, AI potentially increases the patient's access to screening/clinical diagnosis and decreases healthcare costs, mainly when there is a high risk of disease or communities face financial shortages. AI/DL (deep learning) algorithms using both OCT and FO images will change image analysis techniques and methodologies. Optimizing these (combined) technologies will accelerate progress in this area.
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Luo Z, Ding X, Hou N, Wan J. A Deep-Learning-Based Collaborative Edge-Cloud Telemedicine System for Retinopathy of Prematurity. SENSORS (BASEL, SWITZERLAND) 2022; 23:276. [PMID: 36616874 PMCID: PMC9824555 DOI: 10.3390/s23010276] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/22/2022] [Accepted: 12/22/2022] [Indexed: 06/17/2023]
Abstract
Retinopathy of prematurity is an ophthalmic disease with a very high blindness rate. With its increasing incidence year by year, its timely diagnosis and treatment are of great significance. Due to the lack of timely and effective fundus screening for premature infants in remote areas, leading to an aggravation of the disease and even blindness, in this paper, a deep learning-based collaborative edge-cloud telemedicine system is proposed to mitigate this issue. In the proposed system, deep learning algorithms are mainly used for classification of processed images. Our algorithm is based on ResNet101 and uses undersampling and resampling to improve the data imbalance problem in the field of medical image processing. Artificial intelligence algorithms are combined with a collaborative edge-cloud architecture to implement a comprehensive telemedicine system to realize timely screening and diagnosis of retinopathy of prematurity in remote areas with shortages or a complete lack of expert medical staff. Finally, the algorithm is successfully embedded in a mobile terminal device and deployed through the support of a core hospital of Guangdong Province. The results show that we achieved 75% ACC and 60% AUC. This research is of great significance for the development of telemedicine systems and aims to mitigate the lack of medical resources and their uneven distribution in rural areas.
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Affiliation(s)
- Zeliang Luo
- College of Electro-Mechanical Engineering, Zhuhai City Polytechnic, Zhuhai 519090, China
| | - Xiaoxuan Ding
- Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China
| | - Ning Hou
- Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China
| | - Jiafu Wan
- Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China
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Barriada RG, Masip D. An Overview of Deep-Learning-Based Methods for Cardiovascular Risk Assessment with Retinal Images. Diagnostics (Basel) 2022; 13:diagnostics13010068. [PMID: 36611360 PMCID: PMC9818382 DOI: 10.3390/diagnostics13010068] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/19/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Cardiovascular diseases (CVDs) are one of the most prevalent causes of premature death. Early detection is crucial to prevent and address CVDs in a timely manner. Recent advances in oculomics show that retina fundus imaging (RFI) can carry relevant information for the early diagnosis of several systemic diseases. There is a large corpus of RFI systematically acquired for diagnosing eye-related diseases that could be used for CVDs prevention. Nevertheless, public health systems cannot afford to dedicate expert physicians to only deal with this data, posing the need for automated diagnosis tools that can raise alarms for patients at risk. Artificial Intelligence (AI) and, particularly, deep learning models, became a strong alternative to provide computerized pre-diagnosis for patient risk retrieval. This paper provides a novel review of the major achievements of the recent state-of-the-art DL approaches to automated CVDs diagnosis. This overview gathers commonly used datasets, pre-processing techniques, evaluation metrics and deep learning approaches used in 30 different studies. Based on the reviewed articles, this work proposes a classification taxonomy depending on the prediction target and summarizes future research challenges that have to be tackled to progress in this line.
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Ji Y, Liu S, Hong X, Lu Y, Wu X, Li K, Li K, Liu Y. Advances in artificial intelligence applications for ocular surface diseases diagnosis. Front Cell Dev Biol 2022; 10:1107689. [PMID: 36605721 PMCID: PMC9808405 DOI: 10.3389/fcell.2022.1107689] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 12/05/2022] [Indexed: 01/07/2023] Open
Abstract
In recent years, with the rapid development of computer technology, continual optimization of various learning algorithms and architectures, and establishment of numerous large databases, artificial intelligence (AI) has been unprecedentedly developed and applied in the field of ophthalmology. In the past, ophthalmological AI research mainly focused on posterior segment diseases, such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinal vein occlusion, and glaucoma optic neuropathy. Meanwhile, an increasing number of studies have employed AI to diagnose ocular surface diseases. In this review, we summarize the research progress of AI in the diagnosis of several ocular surface diseases, namely keratitis, keratoconus, dry eye, and pterygium. We discuss the limitations and challenges of AI in the diagnosis of ocular surface diseases, as well as prospects for the future.
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Affiliation(s)
- Yuke Ji
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Sha Liu
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Xiangqian Hong
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Yi Lu
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Xingyang Wu
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Kunke Li
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China,*Correspondence: Yunfang Liu, ; Keran Li, ; Kunke Li,
| | - Keran Li
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China,*Correspondence: Yunfang Liu, ; Keran Li, ; Kunke Li,
| | - Yunfang Liu
- Department of Ophthalmology, First Affiliated Hospital of Huzhou University, Huzhou, China,*Correspondence: Yunfang Liu, ; Keran Li, ; Kunke Li,
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Cole E, Valikodath NG, Al-Khaled T, Bajimaya S, KC S, Chuluunbat T, Munkhuu B, Jonas KE, Chuluunkhuu C, MacKeen LD, Yap V, Hallak J, Ostmo S, Wu WC, Coyner AS, Singh P, Kalpathy-Cramer J, Chiang MF, Campbell JP, Chan RVP. Evaluation of an Artificial Intelligence System for Retinopathy of Prematurity Screening in Nepal and Mongolia. OPHTHALMOLOGY SCIENCE 2022; 2:100165. [PMID: 36531583 PMCID: PMC9754980 DOI: 10.1016/j.xops.2022.100165] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/19/2022] [Accepted: 04/19/2022] [Indexed: 05/09/2023]
Abstract
PURPOSE To evaluate the performance of a deep learning (DL) algorithm for retinopathy of prematurity (ROP) screening in Nepal and Mongolia. DESIGN Retrospective analysis of prospectively collected clinical data. PARTICIPANTS Clinical information and fundus images were obtained from infants in 2 ROP screening programs in Nepal and Mongolia. METHODS Fundus images were obtained using the Forus 3nethra neo (Forus Health) in Nepal and the RetCam Portable (Natus Medical, Inc.) in Mongolia. The overall severity of ROP was determined from the medical record using the International Classification of ROP (ICROP). The presence of plus disease was determined independently in each image using a reference standard diagnosis. The Imaging and Informatics for ROP (i-ROP) DL algorithm was trained on images from the RetCam to classify plus disease and to assign a vascular severity score (VSS) from 1 through 9. MAIN OUTCOME MEASURES Area under the receiver operating characteristic curve and area under the precision-recall curve for the presence of plus disease or type 1 ROP and association between VSS and ICROP disease category. RESULTS The prevalence of type 1 ROP was found to be higher in Mongolia (14.0%) than in Nepal (2.2%; P < 0.001) in these data sets. In Mongolia (RetCam images), the area under the receiver operating characteristic curve for examination-level plus disease detection was 0.968, and the area under the precision-recall curve was 0.823. In Nepal (Forus images), these values were 0.999 and 0.993, respectively. The ROP VSS was associated with ICROP classification in both datasets (P < 0.001). At the population level, the median VSS was found to be higher in Mongolia (2.7; interquartile range [IQR], 1.3-5.4]) as compared with Nepal (1.9; IQR, 1.2-3.4; P < 0.001). CONCLUSIONS These data provide preliminary evidence of the effectiveness of the i-ROP DL algorithm for ROP screening in neonatal populations in Nepal and Mongolia using multiple camera systems and are useful for consideration in future clinical implementation of artificial intelligence-based ROP screening in low- and middle-income countries.
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Key Words
- Artificial intelligence
- BW, birth weight
- DL, deep learning
- Deep learning
- GA, gestational age
- ICROP, International Classification of Retinopathy of Prematurity
- IQR, interquartile range
- LMIC, low- and middle-income country
- Mongolia
- Nepal
- ROP, retinopathy of prematurity
- RSD, reference standard diagnosis
- Retinopathy of prematurity
- TR, treatment-requiring
- VSS, vascular severity score
- i-ROP, Imaging and Informatics for Retinopathy of Prematurity
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Affiliation(s)
- Emily Cole
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, Illinois
| | - Nita G. Valikodath
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, Illinois
| | - Tala Al-Khaled
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, Illinois
| | | | - Sagun KC
- Helen Keller International, Kathmandu, Nepal
| | | | - Bayalag Munkhuu
- National Center for Maternal and Child Health, Ulaanbaatar, Mongolia
| | - Karyn E. Jonas
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, Illinois
| | | | - Leslie D. MacKeen
- The Hospital for Sick Children, Toronto, Canada
- Phoenix Technology Group, Pleasanton, California
| | - Vivien Yap
- Department of Pediatrics, Weill Cornell Medical College, New York, New York
| | - Joelle Hallak
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, Illinois
| | - Susan Ostmo
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Wei-Chi Wu
- Chang Gung Memorial Hospital, Taoyuan, Taiwan, and Chang Gung University, College of Medicine, Taoyuan, Taiwan
| | - Aaron S. Coyner
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | | | | | - Michael F. Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - J. Peter Campbell
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - R. V. Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, Illinois
- Correspondence: R. V. Paul Chan, MD, MSc, MBA, Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1905 West Taylor Street, Chicago, IL 60612.
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Cole ED, Park SH, Kim SJ, Kang KB, Valikodath NG, Al-Khaled T, Patel SN, Jonas KE, Ostmo S, Coyner A, Berrocal A, Drenser KA, Nagiel A, Horowitz JD, Lee TC, Kalpathy-Cramer J, Chiang MF, Campbell JP, Chan RVP. Variability in Plus Disease Diagnosis using Single and Serial Images. Ophthalmol Retina 2022; 6:1122-1129. [PMID: 35659941 DOI: 10.1016/j.oret.2022.05.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 05/21/2022] [Accepted: 05/23/2022] [Indexed: 01/06/2023]
Abstract
PURPOSE To assess changes in retinopathy of prematurity (ROP) diagnosis in single and serial retinal images. DESIGN Cohort study. PARTICIPANTS Cases of ROP recruited from the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) consortium evaluated by 7 graders. METHODS Seven ophthalmologists reviewed both single and 3 consecutive serial retinal images from 15 cases with ROP, and severity was assigned as plus, preplus, or none. Imaging data were acquired during routine ROP screening from 2011 to 2015, and a reference standard diagnosis was established for each image. A secondary analysis was performed using the i-ROP deep learning system to assign a vascular severity score (VSS) to each image, ranging from 1 to 9, with 9 being the most severe disease. This score has been previously demonstrated to correlate with the International Classification of ROP. Mean plus disease severity was calculated by averaging 14 labels per image in serial and single images to decrease noise. MAIN OUTCOME MEASURES Grading severity of ROP as defined by plus, preplus, or no ROP. RESULTS Assessment of serial retinal images changed the grading severity for > 50% of the graders, although there was wide variability. Cohen's kappa ranged from 0.29 to 1.0, which showed a wide range of agreement from slight to perfect by each grader. Changes in the grading of serial retinal images were noted more commonly in cases of preplus disease. The mean severity in cases with a diagnosis of plus disease and no disease did not change between single and serial images. The ROP VSS demonstrated good correlation with the range of expert classifications of plus disease and overall agreement with the mode class (P = 0.001). The VSS correlated with mean plus disease severity by expert diagnosis (correlation coefficient, 0.89). The more aggressive graders tended to be influenced by serial images to increase the severity of their grading. The VSS also demonstrated agreement with disease progression across serial images, which progressed to preplus and plus disease. CONCLUSIONS Clinicians demonstrated variability in ROP diagnosis when presented with both single and serial images. The use of deep learning as a quantitative assessment of plus disease has the potential to standardize ROP diagnosis and treatment.
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Affiliation(s)
- Emily D Cole
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Shin Hae Park
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois; Department of Ophthalmology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sang Jin Kim
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon; Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kai B Kang
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Nita G Valikodath
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Tala Al-Khaled
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | | | - Karyn E Jonas
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Susan Ostmo
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Aaron Coyner
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Audina Berrocal
- Bascom Palmer Eye Institute, University of Miami, Miami, Florida
| | - Kimberly A Drenser
- Department of Ophthalmology, Beaumont Eye Institute, Royal Oak, Michigan
| | - Aaron Nagiel
- Stein Eye Institute, University of California Los Angeles, Los Angeles, California
| | - Jason D Horowitz
- Department of Ophthalmology, Columbia University, New York, New York
| | - Thomas C Lee
- Roski Eye Institute, Department of Ophthalmology, Keck School of Medicine of the University of Southern California, Los Angeles, California
| | | | - Michael F Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - J Peter Campbell
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - R V Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois.
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Almadhi NH, Dow ER, Paul Chan RV, Alsulaiman SM. Multimodal Imaging, Tele-Education, and Telemedicine in Retinopathy of Prematurity. Middle East Afr J Ophthalmol 2022; 29:38-50. [PMID: 36685346 PMCID: PMC9846956 DOI: 10.4103/meajo.meajo_56_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 05/25/2022] [Accepted: 09/25/2022] [Indexed: 01/24/2023] Open
Abstract
Retinopathy of prematurity (ROP) is a disease that affects retinal vasculature in premature infants and remains one of the leading causes of blindness in childhood worldwide. ROP screening can encounter some difficulties such as the lack of specialists and services in rural areas. The evolution of technology has helped address these issues and led to the emergence of state-of-the-art multimodal digital imaging devices such fundus cameras with its variable properties, optical coherence tomography (OCT), OCT angiography, and fluorescein angiography which has helped immensely in the process of improving ROP care and understanding the disease pathophysiology. Computer-based imaging analysis and deep learning have recently been demonstrating promising outcomes in regard to ROP diagnosis. Telemedicine is considered an acceptable alternative to clinical examination when optimal circumstances for ROP screening in certain areas are lacking, and the expansion of these programs has been reported. Tele-education programs in ROP have the potential to improve the quality of training to physicians to optimize ROP care.
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Affiliation(s)
- Nada H. Almadhi
- Vitreoretinal division, King Khaled Eye Specialist Hospital, Riyadh, Saudi Arabia
| | - Eliot R. Dow
- Department of Ophthalmology, Jules Stein Eye Institute, University of California, Los Angeles, USA
| | - R. V. Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois, Chicago, Illinois, USA
| | - Sulaiman M. Alsulaiman
- Vitreoretinal division, King Khaled Eye Specialist Hospital, Riyadh, Saudi Arabia,Address for correspondence: Dr. Sulaiman M. Alsulaiman, Vitreoretinal Division, King Khaled Eye Specialist Hospital, P.O. Box: 7191, Riyadh 11462, Saudi Arabia. E-mail:
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Tan Z, Isaacs M, Zhu Z, Simkin S, He M, Dai S. Retinopathy of prematurity screening: A narrative review of current programs, teleophthalmology, and diagnostic support systems. Saudi J Ophthalmol 2022; 36:283-295. [PMID: 36276257 PMCID: PMC9583350 DOI: 10.4103/sjopt.sjopt_220_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/04/2021] [Accepted: 11/12/2021] [Indexed: 01/24/2023] Open
Abstract
PURPOSE Neonatal care in middle-income countries has improved over the last decade, leading to a "third epidemic" of retinopathy of prematurity (ROP). Without concomitant improvements in ROP screening infrastructure, reduction of ROP-associated visual loss remains a challenge worldwide. The emergence of teleophthalmology screening programs and artificial intelligence (AI) technologies represents promising methods to address this growing unmet demand in ROP screening. An improved understanding of current ROP screening programs may inform the adoption of these novel technologies in ROP care. METHODS A critical narrative review of the literature was carried out. Publications that were representative of established or emerging ROP screening programs in high-, middle-, and low-income countries were selected for review. Screening programs were reviewed for inclusion criteria, screening frequency and duration, modality, and published sensitivity and specificity. RESULTS Screening inclusion criteria, including age and birth weight cutoffs, showed significant heterogeneity globally. Countries of similar income tend to have similar criteria. Three primary screening modalities including binocular indirect ophthalmoscopy (BIO), wide-field digital retinal imaging (WFDRI), and teleophthalmology were identified and reviewed. BIO has documented limitations in reduced interoperator agreement, scalability, and geographical access barriers, which are mitigated in part by WFDRI. Teleophthalmology screening may address limitations in ROP screening workforce distribution and training. Opportunities for AI technologies were identified in the context of these limitations, including interoperator reliability and possibilities for point-of-care diagnosis. CONCLUSION Limitations in the current ROP screening include scalability, geographical access, and high screening burden with low treatment yield. These may be addressable through increased adoption of teleophthalmology and AI technologies. As the global incidence of ROP continues to increase, implementation of these novel modalities requires greater consideration.
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Affiliation(s)
- Zachary Tan
- Centre for Eye Research Australia, University of Melbourne, Melbourne, Brisbane, Australia,Department of Clinical Medicine, Faculty of Medicine, University of Queensland, Brisbane, Australia
| | - Michael Isaacs
- Department of Clinical Medicine, Faculty of Medicine, University of Queensland, Brisbane, Australia,Department of Ophthalmology, Queensland Children's Hospital, Brisbane, Australia
| | - Zhuoting Zhu
- Centre for Eye Research Australia, University of Melbourne, Melbourne, Brisbane, Australia
| | - Samantha Simkin
- Department of Ophthalmology, The University of Auckland, Auckland, New Zealand
| | - Mingguang He
- Centre for Eye Research Australia, University of Melbourne, Melbourne, Brisbane, Australia
| | - Shuan Dai
- Department of Clinical Medicine, Faculty of Medicine, University of Queensland, Brisbane, Australia,Department of Ophthalmology, Queensland Children's Hospital, Brisbane, Australia,Address for correspondence: Dr. Shuan Dai, Assoc. Prof. Shuan Dai, Faculty of Medicine, The University of Queensland, Brisbane, Australia. E-mail:
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Scanzera AC, Shorter E, Kinnaird C, Valikodath N, Al-Khaled T, Cole E, Kravets S, Hallak JA, McMahon T, Chan RVP. Optometrist's perspectives of Artificial Intelligence in eye care. JOURNAL OF OPTOMETRY 2022; 15 Suppl 1:S91-S97. [PMID: 36137899 PMCID: PMC9732481 DOI: 10.1016/j.optom.2022.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 04/02/2022] [Accepted: 06/17/2022] [Indexed: 06/16/2023]
Abstract
PURPOSE The application of artificial intelligence (AI) in diagnosing and managing ocular disease has gained popularity as research highlights the utilization of AI to improve personalized medicine and healthcare outcomes. The objective of this study is to describe current optometric perspectives of AI in eye care. METHODS Members of the American Academy of Optometry were sent an electronic invitation to complete a 17-item survey. Survey items assessed perceived advantages and concerns regarding AI using a 5-point Likert scale ranging from "strongly agree" to "strongly disagree." RESULTS A total of 400 optometrists completed the survey. The mean number of years since optometry school completion was 25 ± 15.1. Most respondents reported familiarity with AI (66.8%). Though half of optometrists had concerns about the diagnostic accuracy of AI (53.0%), most believed it would improve the practice of optometry (72.0%). Optometrists reported their willingness to incorporate AI into practice increased from 53.3% before the COVID-19 pandemic to 65.5% after onset of the pandemic (p<0.001). CONCLUSION In this study, optometrists are optimistic about the use of AI in eye care, and willingness to incorporate AI in clinical practice also increased after the onset of the COVID-19 pandemic.
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Affiliation(s)
- Angelica C Scanzera
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1855 W. Taylor Street, Chicago, IL 60612, United States.
| | - Ellen Shorter
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1855 W. Taylor Street, Chicago, IL 60612, United States
| | - Charles Kinnaird
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1855 W. Taylor Street, Chicago, IL 60612, United States
| | - Nita Valikodath
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1855 W. Taylor Street, Chicago, IL 60612, United States
| | - Tala Al-Khaled
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1855 W. Taylor Street, Chicago, IL 60612, United States
| | - Emily Cole
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1855 W. Taylor Street, Chicago, IL 60612, United States
| | - Sasha Kravets
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1855 W. Taylor Street, Chicago, IL 60612, United States
| | - Joelle A Hallak
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1855 W. Taylor Street, Chicago, IL 60612, United States
| | - Timothy McMahon
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1855 W. Taylor Street, Chicago, IL 60612, United States
| | - R V Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1855 W. Taylor Street, Chicago, IL 60612, United States
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Wu Q, Hu Y, Mo Z, Wu R, Zhang X, Yang Y, Liu B, Xiao Y, Zeng X, Lin Z, Fang Y, Wang Y, Lu X, Song Y, Ng WWY, Feng S, Yu H. Development and Validation of a Deep Learning Model to Predict the Occurrence and Severity of Retinopathy of Prematurity. JAMA Netw Open 2022; 5:e2217447. [PMID: 35708686 PMCID: PMC10881218 DOI: 10.1001/jamanetworkopen.2022.17447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 04/29/2022] [Indexed: 01/18/2023] Open
Abstract
Importance Retinopathy of prematurity (ROP) is the leading cause of childhood blindness worldwide. Prediction of ROP before onset holds great promise for reducing the risk of blindness. Objective To develop and validate a deep learning (DL) system to predict the occurrence and severity of ROP before 45 weeks' postmenstrual age. Design, Setting, and Participants This retrospective prognostic study included 7033 retinal photographs of 725 infants in the training set and 763 retinal photographs of 90 infants in the external validation set, along with 46 characteristics for each infant. All images of both eyes from the same infant taken at the first screening were labeled according to the final diagnosis made between the first screening and 45 weeks' postmenstrual age. The DL system was developed using retinal photographs from the first ROP screening and clinical characteristics before or at the first screening in infants born between June 3, 2017, and August 28, 2019. Exposures Two models were specifically designed for predictions of the occurrence (occurrence network [OC-Net]) and severity (severity network [SE-Net]) of ROP. Five-fold cross-validation was applied for internal validation. Main Outcomes and Measures Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity to evaluate the performance in ROP prediction. Results This study included 815 infants (450 [55.2%] boys) with mean birth weight of 1.91 kg (95% CI, 1.87-1.95 kg) and mean gestational age of 33.1 weeks (95% CI, 32.9-33.3 weeks). In internal validation, mean AUC, accuracy, sensitivity, and specificity were 0.90 (95% CI, 0.88-0.92), 52.8% (95% CI, 49.2%-56.4%), 100% (95% CI, 97.4%-100%), and 37.8% (95% CI, 33.7%-42.1%), respectively, for OC-Net to predict ROP occurrence and 0.87 (95% CI, 0.82-0.91), 68.0% (95% CI, 61.2%-74.8%), 100% (95% CI, 93.2%-100%), and 46.6% (95% CI, 37.3%-56.0%), respectively, for SE-Net to predict severe ROP. In external validation, the AUC, accuracy, sensitivity, and specificity were 0.94, 33.3%, 100%, and 7.5%, respectively, for OC-Net, and 0.88, 56.0%, 100%, and 35.3%, respectively, for SE-Net. Conclusions and Relevance In this study, the DL system achieved promising accuracy in ROP prediction. This DL system is potentially useful in identifying infants with high risk of developing ROP.
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Affiliation(s)
- Qiaowei Wu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Department of Ophthalmology, General Hospital of Central Theater Command, Wuhan, China
| | - Yijun Hu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zhenyao Mo
- Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
| | - Rong Wu
- Department of Ophthalmology, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Xiayin Zhang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yahan Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Baoyi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yu Xiao
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Xiaomin Zeng
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zhanjie Lin
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Ying Fang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yijin Wang
- Department of Neonatology, Second Nanning People’s Hospital, Nanning, China
| | - Xiaohe Lu
- Department of Ophthalmology, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Yanping Song
- Department of Ophthalmology, General Hospital of Central Theater Command, Wuhan, China
| | - Wing W. Y. Ng
- Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
| | - Songfu Feng
- Department of Ophthalmology, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Honghua Yu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
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Dong L, He W, Zhang R, Ge Z, Wang YX, Zhou J, Xu J, Shao L, Wang Q, Yan Y, Xie Y, Fang L, Wang H, Wang Y, Zhu X, Wang J, Zhang C, Wang H, Wang Y, Chen R, Wan Q, Yang J, Zhou W, Li H, Yao X, Yang Z, Xiong J, Wang X, Huang Y, Chen Y, Wang Z, Rong C, Gao J, Zhang H, Wu S, Jonas JB, Wei WB. Artificial Intelligence for Screening of Multiple Retinal and Optic Nerve Diseases. JAMA Netw Open 2022; 5:e229960. [PMID: 35503220 PMCID: PMC9066285 DOI: 10.1001/jamanetworkopen.2022.9960] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
IMPORTANCE The lack of experienced ophthalmologists limits the early diagnosis of retinal diseases. Artificial intelligence can be an efficient real-time way for screening retinal diseases. OBJECTIVE To develop and prospectively validate a deep learning (DL) algorithm that, based on ocular fundus images, recognizes numerous retinal diseases simultaneously in clinical practice. DESIGN, SETTING, AND PARTICIPANTS This multicenter, diagnostic study at 65 public medical screening centers and hospitals in 19 Chinese provinces included individuals attending annual routine medical examinations and participants of population-based and community-based studies. EXPOSURES Based on 120 002 ocular fundus photographs, the Retinal Artificial Intelligence Diagnosis System (RAIDS) was developed to identify 10 retinal diseases. RAIDS was validated in a prospective collected data set, and the performance between RAIDS and ophthalmologists was compared in the data sets of the population-based Beijing Eye Study and the community-based Kailuan Eye Study. MAIN OUTCOMES AND MEASURES The performance of each classifier included sensitivity, specificity, accuracy, F1 score, and Cohen κ score. RESULTS In the prospective validation data set of 208 758 images collected from 110 784 individuals (median [range] age, 42 [8-87] years; 115 443 [55.3%] female), RAIDS achieved a sensitivity of 89.8% (95% CI, 89.5%-90.1%) to detect any of 10 retinal diseases. RAIDS differentiated 10 retinal diseases with accuracies ranging from 95.3% to 99.9%, without marked differences between medical screening centers and geographical regions in China. Compared with retinal specialists, RAIDS achieved a higher sensitivity for detection of any retinal abnormality (RAIDS, 91.7% [95% CI, 90.6%-92.8%]; certified ophthalmologists, 83.7% [95% CI, 82.1%-85.1%]; junior retinal specialists, 86.4% [95% CI, 84.9%-87.7%]; and senior retinal specialists, 88.5% [95% CI, 87.1%-89.8%]). RAIDS reached a superior or similar diagnostic sensitivity compared with senior retinal specialists in the detection of 7 of 10 retinal diseases (ie, referral diabetic retinopathy, referral possible glaucoma, macular hole, epiretinal macular membrane, hypertensive retinopathy, myelinated fibers, and retinitis pigmentosa). It achieved a performance comparable with the performance by certified ophthalmologists in 2 diseases (ie, age-related macular degeneration and retinal vein occlusion). Compared with ophthalmologists, RAIDS needed 96% to 97% less time for the image assessment. CONCLUSIONS AND RELEVANCE In this diagnostic study, the DL system was associated with accurately distinguishing 10 retinal diseases in real time. This technology may help overcome the lack of experienced ophthalmologists in underdeveloped areas.
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Affiliation(s)
- Li Dong
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Wanji He
- Beijing Airdoc Technology Co, Ltd, Beijing, China
| | - Ruiheng Zhang
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Zongyuan Ge
- eResearch Centre, Monash University, Melbourne, Victoria, Australia
- ECSE, Faculty of Engineering, Monash University, Melbourne, Victoria, Australia
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Jinqiong Zhou
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Jie Xu
- Beijing Institute of Ophthalmology, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Lei Shao
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Qian Wang
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Yanni Yan
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Ying Xie
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
- Department of Ophthalmology, Shanxi Provincial People's Hospital, Taiyuan, China
| | - Lijian Fang
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
- Department of Ophthalmology, Beijing Liangxiang Hospital, Capital Medical University, Beijing, China
| | - Haiwei Wang
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
- Department of Ophthalmology, Fuxing Hospital, Capital Medical University, Beijing, China
| | - Yenan Wang
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
- Department of Ophthalmology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xiaobo Zhu
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
- Department of Ophthalmology, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Jinyuan Wang
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Chuan Zhang
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Heng Wang
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Yining Wang
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Rongtian Chen
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Qianqian Wan
- Department of Ophthalmology, the Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jingyan Yang
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Wenda Zhou
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Heyan Li
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Xuan Yao
- Beijing Airdoc Technology Co, Ltd, Beijing, China
| | - Zhiwen Yang
- Beijing Airdoc Technology Co, Ltd, Beijing, China
| | | | - Xin Wang
- Beijing Airdoc Technology Co, Ltd, Beijing, China
| | - Yelin Huang
- Beijing Airdoc Technology Co, Ltd, Beijing, China
| | - Yuzhong Chen
- Beijing Airdoc Technology Co, Ltd, Beijing, China
| | - Zhaohui Wang
- iKang Guobin Healthcare Group Co, Ltd, Beijing, China
| | - Ce Rong
- iKang Guobin Healthcare Group Co, Ltd, Beijing, China
| | - Jianxiong Gao
- iKang Guobin Healthcare Group Co, Ltd, Beijing, China
| | | | - Shouling Wu
- Department of Cardiology, Kailuan General Hospital, Tangshan, Hebei, China
| | - Jost B Jonas
- Department of Ophthalmology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Institute of Molecular and Clinical Ophthalmology Basel, Switzerland
| | - Wen Bin Wei
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
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Wang SK, Korot E, Zaidi M, Ji MH, Al-Moujahed A, Callaway NF, Kumm J, Moshfeghi DM. Modeling absolute zone size in retinopathy of prematurity in relation to axial length. Sci Rep 2022; 12:4717. [PMID: 35304549 PMCID: PMC8933429 DOI: 10.1038/s41598-022-08680-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 03/09/2022] [Indexed: 11/09/2022] Open
Abstract
Treatment outcomes in retinopathy of prematurity (ROP) are closely correlated with the location (i.e. zone) of disease, with more posterior zones having poorer outcomes. The most posterior zone, Zone I, is defined as a circle centered on the optic nerve with radius twice the distance from nerve to fovea, or subtending an angle of 30 degrees. Because the eye enlarges and undergoes refractive changes during the period of ROP screening, the absolute area of Zone I according to these definitions may likewise change. It is possible that these differences may confound accurate assessment of risk in patients with ROP. In this study, we estimated the area of Zone I in relation to different ocular parameters to determine how variability in the size and refractive power of the eye may affect zoning. Using Gaussian optics, a model was constructed to calculate the absolute area of Zone I as a function of corneal power, anterior chamber depth, lens power, lens thickness, and axial length (AL), with Zone I defined as a circle with radius set by a 30-degree visual angle. Our model predicted Zone I area to be most sensitive to changes in AL; for example, an increase of AL from 14.20 to 16.58 mm at postmenstrual age 32 weeks was calculated to expand the area of Zone I by up to 72%. These findings motivate several hypotheses which upon future testing may help optimize treatment decisions for ROP.
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Affiliation(s)
- Sean K Wang
- Department of Ophthalmology, Byers Eye Institute, Horngren Family Vitreoretinal Center, Stanford University School of Medicine, 2452 Watson Court, Rm. 2277, Palo Alto, CA, 94303, USA
| | - Edward Korot
- Department of Ophthalmology, Byers Eye Institute, Horngren Family Vitreoretinal Center, Stanford University School of Medicine, 2452 Watson Court, Rm. 2277, Palo Alto, CA, 94303, USA
| | - Moosa Zaidi
- Department of Ophthalmology, Byers Eye Institute, Horngren Family Vitreoretinal Center, Stanford University School of Medicine, 2452 Watson Court, Rm. 2277, Palo Alto, CA, 94303, USA
| | - Marco H Ji
- Department of Ophthalmology, Jones Eye Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Ahmad Al-Moujahed
- Department of Ophthalmology, Byers Eye Institute, Horngren Family Vitreoretinal Center, Stanford University School of Medicine, 2452 Watson Court, Rm. 2277, Palo Alto, CA, 94303, USA
| | - Natalia F Callaway
- Department of Ophthalmology, Byers Eye Institute, Horngren Family Vitreoretinal Center, Stanford University School of Medicine, 2452 Watson Court, Rm. 2277, Palo Alto, CA, 94303, USA
| | - Jochen Kumm
- Department of Ophthalmology, Byers Eye Institute, Horngren Family Vitreoretinal Center, Stanford University School of Medicine, 2452 Watson Court, Rm. 2277, Palo Alto, CA, 94303, USA
| | - Darius M Moshfeghi
- Department of Ophthalmology, Byers Eye Institute, Horngren Family Vitreoretinal Center, Stanford University School of Medicine, 2452 Watson Court, Rm. 2277, Palo Alto, CA, 94303, USA.
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42
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Johnson SJ. Assessing the Value of Artificial Intelligence in Retinopathy of Prematurity Screening-Be Specific With Regard to the Data. JAMA Ophthalmol 2022; 140:409-410. [PMID: 35297956 DOI: 10.1001/jamaophthalmol.2022.0222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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43
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Federated learning for multi-center collaboration in ophthalmology: implications for clinical diagnosis and disease epidemiology. Ophthalmol Retina 2022; 6:650-656. [PMID: 35304305 PMCID: PMC9357070 DOI: 10.1016/j.oret.2022.03.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 02/10/2022] [Accepted: 03/04/2022] [Indexed: 11/24/2022]
Abstract
OBJECTIVE OR PURPOSE To utilize a deep learning (DL) model trained via federated learning (FL), a method of collaborative training without sharing patient data, to delineate institutional differences in clinician diagnostic paradigms and disease epidemiology in retinopathy of prematurity (ROP). DESIGN Evaluation of a diagnostic test or technology SUBJECTS, PARTICIPANTS, AND/OR CONTROLS: 5,245 patients with wide-angle retinal imaging from the neonatal intensive care units of 7 institutions as part of the Imaging and Informatics in ROP (i-ROP) study. Images were labeled with the clinical diagnosis of plus disease (plus, pre-plus, no plus) that was documented in the chart, and a reference standard diagnosis (RSD) determined by three image-based ROP graders and the clinical diagnosis. METHODS, INTERVENTION OR TESTING Demographics (birthweight [BW], gestational age [GA]), and clinical diagnoses for all eye exams were recorded from each institution. Using a FL approach, a DL model for plus disease classification was trained using only the clinical labels. The three class probabilities were then converted into a vascular severity score (VSS) for each eye exam, as well as an "institutional VSS" in which the average of the VSS values assigned to patients' higher severity ("worse") eyes at each exam was calculated for each institution. MAIN OUTCOME MEASURES We compared demographics, clinical diagnosis of plus disease, and institutional VSS between institutions using the McNemar Bowker test, two-proportion Z test and one-way ANOVA with post-hoc analysis by Tukey-Kramer test. Single regression analysis was performed to explore the relationship between demographics and VSS. RESULTS We found that the proportion of patients diagnosed with pre-plus disease varied significantly between institutions (p<0.00l). Using the DL-derived VSS trained on the data from all institutions using FL, we observed differences in the institutional VSS, as well as level of vascular severity diagnosed as no plus (p<0.001) across institutions. A significant, inverse relationship between the institutional VSS and the mean GA was found (p=0.049, adjusted R2=0.49). CONCLUSIONS A DL-derived ROP VSS developed without sharing data between institutions using FL identified differences in the clinical diagnosis of plus disease, and overall levels of ROP severity between institutions. FL may represent a method to standardize clinical diagnosis and provide objective measurement of disease for image-based diseases.
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Tan Z, Zhu Z, He Z, He M. Artificial Intelligence in Ophthalmology. Artif Intell Med 2022. [DOI: 10.1007/978-981-19-1223-8_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Wang Z, Keane PA, Chiang M, Cheung CY, Wong TY, Ting DSW. Artificial Intelligence and Deep Learning in Ophthalmology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Jansen LG, Schultz T, Holz FG, Finger RP, Wintergerst MWM. [Smartphone-based fundus imaging: applications and adapters]. Ophthalmologe 2021; 119:112-126. [PMID: 34913992 DOI: 10.1007/s00347-021-01536-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/08/2021] [Indexed: 12/28/2022]
Abstract
BACKGROUND Smartphone-based fundus imaging (SBFI) is an innovative and low-cost alternative for color fundus photography. Since the first reports on this topic more than 10 years ago a large number of studies on different adapters and clinical applications have been published. OBJECTIVE The aim of this review article is to provide an overview on the development of SBFI and adapters and clinical applications published so far. MATERIAL AND METHODS A literature search was performed using the MEDLINE and Science Citation Index Expanded databases without time restrictions. RESULTS Overall, 11 adapters were included and compared in terms of exemplary image material, field of view, acquisition costs, weight, software, application range, smartphone compatibility and certification. Previously published SBFI applications are screening for diabetic retinopathy, glaucoma and retinopathy of prematurity as well as the application in emergency medicine, pediatrics and medical education/teaching. Image quality of conventional retinal cameras is in general superior to SBFI. First approaches on automatic detection of diabetic retinopathy through SBFI are promising and the use of automatic image processing algorithms enables the generation of wide-field image montages. CONCLUSION SBFI is a versatile, mobile, low-cost alternative to conventional equipment for color fundus photography. In addition, it facilitates the delegation of ophthalmological examinations to assistance personnel in telemedical settings, could simplify retinal documentation, improve teaching, and improve ophthalmological care, particularly in countries with low and middle incomes.
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Affiliation(s)
- Linus G Jansen
- Klinik für Augenheilkunde, Universitätsklinikum Bonn, Ernst-Abbe-Str. 2, 53127, Bonn, Deutschland
| | - Thomas Schultz
- Institut für Informatik II, Universität Bonn, Friedrich-Hirzebruch-Allee 5, 53115, Bonn, Deutschland.,Bonn-Aachen International Center for Information Technology (B-IT), Universität Bonn, Friedrich-Hirzebruch-Allee 5, 53115, Bonn, Deutschland
| | - Frank G Holz
- Klinik für Augenheilkunde, Universitätsklinikum Bonn, Ernst-Abbe-Str. 2, 53127, Bonn, Deutschland
| | - Robert P Finger
- Klinik für Augenheilkunde, Universitätsklinikum Bonn, Ernst-Abbe-Str. 2, 53127, Bonn, Deutschland
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Sato T, Enoki T, Karasawa Y, Someya H, Taguchi M, Harimoto K, Takayama K, Kanda T, Ito M, Takeuchi M. Inflammatory Factors of Macular Atrophy in Eyes With Neovascular Age-Related Macular Degeneration Treated With Aflibercept. Front Immunol 2021; 12:738521. [PMID: 34721402 PMCID: PMC8548619 DOI: 10.3389/fimmu.2021.738521] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 09/17/2021] [Indexed: 12/14/2022] Open
Abstract
Background Neovascular age-related macular degeneration (nAMD) is a leading cause of blindness in older people. Low-grade inflammation is well-known as one of the pathogenic mechanisms in nAMD. Anti-vascular endothelial growth factor (VEGF) therapy is the first-line treatment for nAMD, although macula atrophy (MA) developed under anti-VEGF therapy causes irreversible visual function impairment and is recognized as a serious disorder. Here, we show specific expression patterns of aqueous humor (AH) cytokines in nAMD eyes developing MA under intravitreal injection of aflibercept (IVA) as an anti-VEGF antibody and present predictive cytokines as biomarkers for the incidence of MA in nAMD eyes under IVA treatment. Methods Twenty-eight nAMD patients received three consecutive monthly IVA, followed by a pro re nata regimen for 2 years. AH specimens were collected before first IVA (pre-IVA) and before third IVA (post-IVA). AH cytokine levels, visual acuity (VA), and central retinal thickness (CRT) were measured. Results Two-year incidence of MA was 21.4%. In nAMD eyes developing MA [MA (+) group], pre-IVA levels of monocyte chemoattractant protein-1 (MCP-1), macrophage inflammatory protein (MIP)-1β, VEGF and post-IVA level of MCP-1 were higher than those in nAMD eyes without MA [MA (−) group]. In hierarchical cluster analysis, pre-IVA MCP-1 and VEGF were grouped into the same subcluster, as were post-IVA MCP-1 and CRT. In principal component analysis, principal component loading (PCL) of pre-IVA interferon-γ-inducible protein 10 (IP-10) was 0.61, but PCL of post-IVA IP-10 decreased to −0.09. In receiver operating characteristic analysis and Kaplan–Meier curves, pre-IVA MCP-1, MIP-1β, and VEGF and post-IVA interleukin-6, MCP-1, and MIP-1β were detected as predictive factors for MA incidence. In 2-year clinical course, changes of VA in groups with high levels of pre-IVA MIP-1β (over 39.9 pg/ml) and VEGF (over 150.4 pg/ml) were comparable to those in MA (+) group. Conclusion Substantial loss of IP-10 effects and persistent inflammation contribute to incidence of MA, and screening of AH cytokine levels could be a useful method to predict MA incidence in nAMD eyes under anti-VEGF therapy.
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Affiliation(s)
- Tomohito Sato
- Department of Ophthalmology, National Defense Medical College, Tokorozawa, Japan
| | | | - Yoko Karasawa
- Department of Ophthalmology, National Defense Medical College, Tokorozawa, Japan
| | - Hideaki Someya
- Department of Ophthalmology, National Defense Medical College, Tokorozawa, Japan
| | - Manzo Taguchi
- Department of Ophthalmology, National Defense Medical College, Tokorozawa, Japan
| | - Kozo Harimoto
- Department of Ophthalmology, National Defense Medical College, Tokorozawa, Japan
| | - Kei Takayama
- Department of Ophthalmology, National Defense Medical College, Tokorozawa, Japan
| | - Takayuki Kanda
- Department of Ophthalmology, National Defense Medical College, Tokorozawa, Japan
| | - Masataka Ito
- Department of Developmental Anatomy and Regenerative Biology, National Defense Medical College, Tokorozawa, Japan
| | - Masaru Takeuchi
- Department of Ophthalmology, National Defense Medical College, Tokorozawa, Japan
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Chen JS, Coyner AS, Ostmo S, Sonmez K, Bajimaya S, Pradhan E, Valikodath N, Cole ED, Al-Khaled T, Chan RVP, Singh P, Kalpathy-Cramer J, Chiang MF, Campbell JP. Deep Learning for the Diagnosis of Stage in Retinopathy of Prematurity: Accuracy and Generalizability across Populations and Cameras. Ophthalmol Retina 2021; 5:1027-1035. [PMID: 33561545 PMCID: PMC8364291 DOI: 10.1016/j.oret.2020.12.013] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 12/02/2020] [Accepted: 12/16/2020] [Indexed: 12/23/2022]
Abstract
PURPOSE Stage is an important feature to identify in retinal images of infants at risk of retinopathy of prematurity (ROP). The purpose of this study was to implement a convolutional neural network (CNN) for binary detection of stages 1, 2, and 3 in ROP and to evaluate its generalizability across different populations and camera systems. DESIGN Diagnostic validation study of CNN for stage detection. PARTICIPANTS Retinal fundus images obtained from preterm infants during routine ROP screenings. METHODS Two datasets were used: 5943 fundus images obtained by RetCam camera (Natus Medical, Pleasanton, CA) from 9 North American institutions and 5049 images obtained by 3nethra camera (Forus Health Incorporated, Bengaluru, India) from 4 hospitals in Nepal. Images were labeled based on the presence of stage by 1 to 3 expert graders. Three CNN models were trained using 5-fold cross-validation on datasets from North America alone, Nepal alone, and a combined dataset and were evaluated on 2 held-out test sets consisting of 708 and 247 images from the Nepali and North American datasets, respectively. MAIN OUTCOME MEASURES Convolutional neural network performance was evaluated using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), sensitivity, and specificity. RESULTS Both the North American- and Nepali-trained models demonstrated high performance on a test set from the same population: AUROC, 0.99; AUPRC, 0.98; sensitivity, 94%; and AUROC, 0.97; AUPRC, 0.91; and sensitivity, 73%; respectively. However, the performance of each model decreased to AUROC of 0.96 and AUPRC of 0.88 (sensitivity, 52%) and AUROC of 0.62 and AUPRC of 0.36 (sensitivity, 44%) when evaluated on a test set from the other population. Compared with the models trained on individual datasets, the model trained on a combined dataset achieved improved performance on each respective test set: sensitivity improved from 94% to 98% on the North American test set and from 73% to 82% on the Nepali test set. CONCLUSIONS A CNN can identify accurately the presence of ROP stage in retinal images, but performance depends on the similarity between training and testing populations. We demonstrated that internal and external performance can be improved by increasing the heterogeneity of the training dataset features of the training dataset, in this case by combining images from different populations and cameras.
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Affiliation(s)
- Jimmy S Chen
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Aaron S Coyner
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon
| | - Susan Ostmo
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Kemal Sonmez
- Cancer Early Detection Advanced Research Center, Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon
| | | | - Eli Pradhan
- Tilganga Institute of Ophthalmology, Kathmandu, Nepal
| | - Nita Valikodath
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Emily D Cole
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Tala Al-Khaled
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - R V Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Praveer Singh
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts; Center for Clinical Data Science, Massachusetts General Hospital and Brigham and Women's Hospital, Boston, Massachusetts
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts; Center for Clinical Data Science, Massachusetts General Hospital and Brigham and Women's Hospital, Boston, Massachusetts
| | - Michael F Chiang
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon; Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon
| | - J Peter Campbell
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon.
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Nuzzi R, Boscia G, Marolo P, Ricardi F. The Impact of Artificial Intelligence and Deep Learning in Eye Diseases: A Review. Front Med (Lausanne) 2021; 8:710329. [PMID: 34527682 PMCID: PMC8437147 DOI: 10.3389/fmed.2021.710329] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 07/23/2021] [Indexed: 12/21/2022] Open
Abstract
Artificial intelligence (AI) is a subset of computer science dealing with the development and training of algorithms that try to replicate human intelligence. We report a clinical overview of the basic principles of AI that are fundamental to appreciating its application to ophthalmology practice. Here, we review the most common eye diseases, focusing on some of the potential challenges and limitations emerging with the development and application of this new technology into ophthalmology.
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Affiliation(s)
- Raffaele Nuzzi
- Ophthalmology Unit, A.O.U. City of Health and Science of Turin, Department of Surgical Sciences, University of Turin, Turin, Italy
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Nazir T, Nawaz M, Rashid J, Mahum R, Masood M, Mehmood A, Ali F, Kim J, Kwon HY, Hussain A. Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model. SENSORS 2021; 21:s21165283. [PMID: 34450729 PMCID: PMC8398326 DOI: 10.3390/s21165283] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 07/22/2021] [Accepted: 07/23/2021] [Indexed: 02/06/2023]
Abstract
Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems require experts to manually analyze images to recognize diseases. However, due to the challenging nature of the screening method and lack of trained human resources, devising effective screening-oriented treatment is an expensive task. Automated systems are trying to cope with these challenges; however, these methods do not generalize well to multiple diseases and real-world scenarios. To solve the aforementioned issues, we propose a new method comprising two main steps. The first involves dataset preparation and feature extraction and the other relates to improving a custom deep learning based CenterNet model trained for eye disease classification. Initially, we generate annotations for suspected samples to locate the precise region of interest, while the other part of the proposed solution trains the Center Net model over annotated images. Specifically, we use DenseNet-100 as a feature extraction method on which the one-stage detector, CenterNet, is employed to localize and classify the disease lesions. We evaluated our method over challenging datasets, namely, APTOS-2019 and IDRiD, and attained average accuracy of 97.93% and 98.10%, respectively. We also performed cross-dataset validation with benchmark EYEPACS and Diaretdb1 datasets. Both qualitative and quantitative results demonstrate that our proposed approach outperforms state-of-the-art methods due to more effective localization power of CenterNet, as it can easily recognize small lesions and deal with over-fitted training data. Our proposed framework is proficient in correctly locating and classifying disease lesions. In comparison to existing DR and DME classification approaches, our method can extract representative key points from low-intensity and noisy images and accurately classify them. Hence our approach can play an important role in automated detection and recognition of DR and DME lesions.
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Affiliation(s)
- Tahira Nazir
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Marriam Nawaz
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Junaid Rashid
- Department of Computer Science and Engineering, Kongju National University, Gongju 31080, Chungcheongnam-do, Korea;
- Correspondence: (J.R.); (H.-Y.K.)
| | - Rabbia Mahum
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Momina Masood
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Awais Mehmood
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Farooq Ali
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Jungeun Kim
- Department of Computer Science and Engineering, Kongju National University, Gongju 31080, Chungcheongnam-do, Korea;
| | - Hyuk-Yoon Kwon
- Research Center for Electrical and Information Technology, Department of Industrial Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea
- Correspondence: (J.R.); (H.-Y.K.)
| | - Amir Hussain
- Centre of AI and Data Science, Edinburgh Napier University, Edinburgh EH11 4DY, UK;
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