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Huang YP, Vadloori S, Kang EYC, Fukushima Y, Takahashi R, Wu WC. Computer-aided detection of retinopathy of prematurity severity assessment via vessel tortuosity measurement in preterm infants' fundus images. Eye (Lond) 2024:10.1038/s41433-024-03285-w. [PMID: 39097674 DOI: 10.1038/s41433-024-03285-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 07/12/2024] [Accepted: 07/23/2024] [Indexed: 08/05/2024] Open
Abstract
OBJECTIVE To develop a computer-aided diagnostic system for retinopathy of prematurity (ROP) disease using retinal vessel morphological features. METHODS A total of 200 fundus images from 136 preterm infants with stage 1 to 3 ROP were analysed. Two methods were developed to measure vessel tortuosity: the peak-and-valley method and the polynomial curve fitting method. Correlations between temporal artery tortuosity (TAT) and temporal vein tortuosity (TVT) with ROP severity were investigated, and vessel tortuosity relationships with vessel angles (TAA and TVA) and vessel widths (TAW and TVW). A separate dataset from Japan containing 126 images from 97 preterm patients was used for verification. RESULTS Both methods identified similar tortuosity in images without ROP and mild ROP cases. However, the polynomial curve fit method demonstrated enhanced tortuosity detection in stages 2 and 3 ROP compared to the peak and valley method. A strong positive correlation was revealed between ROP severity and increased arterial and venous tortuosity (P < 0.0001). A significant negative correlation between TAA and TAT (r = -0.485, P < 0.0001) and TVA and TVT (r = -0.281, P < 0.0001), and a significant positive correlation between TAW and TAT (r = 0.204, P value = 0.0040) were identified. Similar results were found in the test dataset from Japan. CONCLUSIONS ROP severity was associated with increased retinal tortuosity and retinal vessel width while displaying a decrease in retinal vascular angle. This quantitative analysis of retinal vessels provides crucial insights for advancing ROP diagnosis and understanding its progression.
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Grants
- CORPG3L0131, CMRPG3M0131~2, and CMRPG3L0151~3 Chang Gung Memorial Hospital (CGMH)
- CORPG3L0131, CMRPG3M0131~2, and CMRPG3L0151~3 Chang Gung Memorial Hospital (CGMH)
- NTUT-CGMH-110-01 and NTUT-CGMH-109-01 National Taipei University of Technology (NTUT)
- NTUT-CGMH-110-01 and NTUT-CGMH-109-01 National Taipei University of Technology (NTUT)
- NTUT-CGMH-110-01 and NTUT-CGMH-109-01 National Taipei University of Technology (NTUT)
- NTUT-CGMH-110-01 and NTUT-CGMH-109-01 National Taipei University of Technology (NTUT)
- NTUT-CGMH-110-01 and NTUT-CGMH-109-01 National Taipei University of Technology (NTUT)
- NTUT-CGMH-110-01 and NTUT-CGMH-109-01 National Taipei University of Technology (NTUT)
- MOST 109-2314-B-182A-019-MY3 Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)
- MOST 109-2314-B-182A-019-MY3 Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)
- MOST 109-2314-B-182A-019-MY3 Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)
- MOST 109-2314-B-182A-019-MY3 Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)
- MOST 109-2314-B-182A-019-MY3 Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)
- MOST 109-2314-B-182A-019-MY3 Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)
- CORPG3L0131, CMRPG3M0131~2, and CMRPG3L0151~3 Chang Gung Memorial Hospital, Linkou (Linkou Chang Gung Memorial Hospital)
- CORPG3L0131, CMRPG3M0131~2, and CMRPG3L0151~3 Chang Gung Memorial Hospital, Linkou (Linkou Chang Gung Memorial Hospital)
- CORPG3L0131, CMRPG3M0131~2, and CMRPG3L0151~3 Chang Gung Memorial Hospital, Linkou (Linkou Chang Gung Memorial Hospital)
- CORPG3L0131, CMRPG3M0131~2, and CMRPG3L0151~3 Chang Gung Memorial Hospital, Linkou (Linkou Chang Gung Memorial Hospital)
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Affiliation(s)
- Yo-Ping Huang
- Department of Electrical Engineering, National Penghu University of Science and Technology, Penghu, 88046, Taiwan.
- Department of Electrical Engineering, National Taipei University of Technology, Taipei, 10608, Taiwan.
- Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung, 41349, Taiwan.
| | - Spandana Vadloori
- Department of Electrical Engineering, National Penghu University of Science and Technology, Penghu, 88046, Taiwan
| | - Eugene Yu-Chuan Kang
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou, 33305, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, 33305, Taiwan
| | - Yoko Fukushima
- Department of Ophthalmology, Osaka University, Osaka, 565-0871, Japan
| | - Rie Takahashi
- Department of Ophthalmology, Fukuoka University, Fukuoka, 814-0180, Japan
| | - Wei-Chi Wu
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou, 33305, Taiwan.
- College of Medicine, Chang Gung University, Taoyuan, 33305, Taiwan.
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Timkovič J, Nowaková J, Kubíček J, Hasal M, Varyšová A, Kolarčík L, Maršolková K, Augustynek M, Snášel V. Retinal Image Dataset of Infants and Retinopathy of Prematurity. Sci Data 2024; 11:814. [PMID: 39043697 PMCID: PMC11266588 DOI: 10.1038/s41597-024-03409-7] [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: 05/02/2023] [Accepted: 05/23/2024] [Indexed: 07/25/2024] Open
Abstract
Retinopathy of prematurity (ROP) represents a vasoproliferative disease, especially in newborns and infants, which can potentially affect and damage the vision. Despite recent advances in neonatal care and medical guidelines, ROP still remains one of the leading causes of worldwide childhood blindness. The paper presents a unique dataset of 6,004 retinal images of 188 newborns, most of whom are premature infants. The dataset is accompanied by the anonymized patients' information from the ROP screening acquired at the University Hospital Ostrava, Czech Republic. Three digital retinal imaging camera systems are used in the study: Clarity RetCam 3, Natus RetCam Envision, and Phoenix ICON. The study is enriched by the software tool ReLeSeT which is aimed at automatic retinal lesion segmentation and extraction from retinal images. Consequently, this tool enables computing geometric and intensity features of retinal lesions. Also, we publish a set of pre-processing tools for feature boosting of retinal lesions and retinal blood vessels for building classification and segmentation models in ROP analysis.
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Affiliation(s)
- Juraj Timkovič
- University Hospital Ostrava, Clinic of Ophthalmology, Ostrava, 708 52, Czech Republic
- University of Ostrava, Faculty of Medicine, Department of Craniofacial Surgery, Ostrava, 703 00, Czech Republic
| | - Jana Nowaková
- VSB-Technical University of Ostrava, Faculty of Electrical Engineering and Computer Science, Department of Computer Science, Ostrava, 708 00, Czech Republic.
| | - Jan Kubíček
- VSB-Technical University of Ostrava, Faculty of Electrical Engineering and Computer Science, Department of Cybernetics and Biomedical Engineering, Ostrava, 708 00, Czech Republic
| | - Martin Hasal
- VSB-Technical University of Ostrava, Faculty of Electrical Engineering and Computer Science, Department of Computer Science, Ostrava, 708 00, Czech Republic
| | - Alice Varyšová
- VSB-Technical University of Ostrava, Faculty of Electrical Engineering and Computer Science, Department of Cybernetics and Biomedical Engineering, Ostrava, 708 00, Czech Republic
| | - Lukáš Kolarčík
- University Hospital Ostrava, Clinic of Ophthalmology, Ostrava, 708 52, Czech Republic
| | - Kristýna Maršolková
- University Hospital Ostrava, Clinic of Ophthalmology, Ostrava, 708 52, Czech Republic
| | - Martin Augustynek
- VSB-Technical University of Ostrava, Faculty of Electrical Engineering and Computer Science, Department of Cybernetics and Biomedical Engineering, Ostrava, 708 00, Czech Republic
| | - Václav Snášel
- VSB-Technical University of Ostrava, Faculty of Electrical Engineering and Computer Science, Department of Computer Science, Ostrava, 708 00, Czech Republic
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3
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Paradis H, Werdyani S, Zhai G, Gendron RL, Tabrizchi R, McGovern M, Jumper JM, Brinton D, Good WV. Genetic Variants of the Beta-Adrenergic Receptor Pathways as Both Risk and Protective Factors for Retinopathy of Prematurity. Am J Ophthalmol 2024; 263:179-187. [PMID: 38224928 DOI: 10.1016/j.ajo.2023.12.017] [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: 07/13/2023] [Revised: 12/21/2023] [Accepted: 12/28/2023] [Indexed: 01/17/2024]
Abstract
PURPOSE There is strong evidence that genetic factors influence retinopathy of prematurity (ROP), a neovascular eye disease. It has been previously suggested that polymorphisms in the genes involved in β-adrenergic receptor (ADRβ) pathways could protect against ROP. Antagonists for the ADRβ are actively tested in clinical trials for ROP treatment, but not without controversy and safety concerns. This study was designed to assess whether genetic variations in components of the ADRβ signaling pathways associate with risk of developing ROP. DESIGN An observational case-control targeted genetic analysis. METHODS A study was carried out in premature participants with (n = 30) or without (n = 34) ROP and full-term controls (n = 20), who were divided into a discovery cohort and a validation cohort. ROP was defined using International Classification of Retinopathy of Prematurity criteria (ICROP). Targeted sequencing of 20 genes in the ADRβ pathways was performed in the discovery cohort. Polymerase chain reaction (PCR)/restriction enzyme analysis for some of the discovered ROP-associated variants was performed for validation of the results using the validation cohort. RESULTS The discovery cohort revealed 543 bi-allelic variants within 20 genes of the ADRβ pathways. Ten single-nucleotide variants (SNVs) in 5 genes including protein kinase A regulatory subunit 1α (PRKAR1A), rap guanine exchange factor 3 (RAPGEF3), adenylyl cyclase 4 (ADCY4), ADCY7, and ADCY9 were associated with ROP (P < .05). The most significant SNV was found in PRKAR1A (P = .001). Multiple variants located in the 3'-untranslated region (3'UTR) of RAPGEF3 were also associated with ROP (P < .05). PCR/restriction enzyme analysis of the 3'UTR of RAPGEF3 methodologically validated these findings. CONCLUSION SNVs in PRKAR1A may represent protective factors whereas SNVs in RAPGEF3 may represent risk factors for ROP. PRKAR1α has previously been implicated in retinal vascular development whereas the RAPGEF3 product has a role in the maintenance of vascular barrier function, 2 processes important in ROP. Multicenter validation of these newly discovered risk factors could lead to valuable tools for predicting and preventing the development of severe ROP.
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Affiliation(s)
- Hélène Paradis
- From the Division of BioMedical Sciences (H.P., S.W., G.Z., R.L.G., R.T.), Faculty of Medicine, Memorial University, St. John's, Newfoundland, Canada
| | - Salem Werdyani
- From the Division of BioMedical Sciences (H.P., S.W., G.Z., R.L.G., R.T.), Faculty of Medicine, Memorial University, St. John's, Newfoundland, Canada
| | - Guangju Zhai
- From the Division of BioMedical Sciences (H.P., S.W., G.Z., R.L.G., R.T.), Faculty of Medicine, Memorial University, St. John's, Newfoundland, Canada
| | - Robert L Gendron
- From the Division of BioMedical Sciences (H.P., S.W., G.Z., R.L.G., R.T.), Faculty of Medicine, Memorial University, St. John's, Newfoundland, Canada
| | - Reza Tabrizchi
- From the Division of BioMedical Sciences (H.P., S.W., G.Z., R.L.G., R.T.), Faculty of Medicine, Memorial University, St. John's, Newfoundland, Canada
| | - Margaret McGovern
- Smith Kettlewell Eye Research Institute (M.M., W.V.G.), San Francisco, California, USA
| | | | - Daniel Brinton
- East Bay Retina Consultants, Inc. (D.B.), Oakland, California, USA
| | - William V Good
- Smith Kettlewell Eye Research Institute (M.M., W.V.G.), San Francisco, California, USA.
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Maitra P, Shah PK, Campbell PJ, Rishi P. The scope of artificial intelligence in retinopathy of prematurity (ROP) management. Indian J Ophthalmol 2024; 72:931-934. [PMID: 38454859 DOI: 10.4103/ijo.ijo_2544_23] [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: 09/18/2023] [Accepted: 01/04/2024] [Indexed: 03/09/2024] Open
Abstract
Artificial Intelligence (AI) is a revolutionary technology that has the potential to develop into a widely implemented system that could reduce the dependence on qualified professionals/experts for screening the large at-risk population, especially in the Indian scenario. Deep learning involves learning without being explicitly told what to focus on and utilizes several layers of artificial neural networks (ANNs) to create a robust algorithm that is capable of high-complexity tasks. Convolutional neural networks (CNNs) are a subset of ANNs that are particularly useful for image processing as well as cognitive tasks. Training of these algorithms involves inputting raw human-labeled data, which are then processed through the algorithm's multiple layers and allow CNN to develop their own learning of image features. AI systems must be validated using different population datasets since the performance of the AI system would vary according to the population. Indian datasets have been used in AI-based risk model that could predict whether an infant would develop treatment-requiring retinopathy of prematurity (ROP). AI also served as an epidemiological tool by objectively showing that a higher ROP severity was in Neonatal intensive care units (NICUs) that did not have the resources to monitor and titrate oxygen. There are rising concerns about the medicolegal aspect of AI implementation as well as discussion on the possibilities of catastrophic life-threatening diseases like retinoblastoma and lipemia retinalis being missed by AI. Computer-based systems have the advantage over humans in not being susceptible to biases or fatigue. This is especially relevant in a country like India with an increased rate of ROP and a preexisting strained doctor-to-preterm child ratio. Many AI algorithms can perform in a way comparable to or exceeding human experts, and this opens possibilities for future large-scale prospective studies.
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Affiliation(s)
- Puja Maitra
- Department of Vitreoretina Services, Aravind Eye Hospital, Chennai, Tamil Nadu, India
| | - Parag K Shah
- Department of Pediatric Retina and Ocular Oncology, Aravind Eye Hospital, Coimbatore, Tamil Nadu, India
| | - Peter J Campbell
- Department of Ophthalmology, Oregon Health and Science University, Portland, Oregon, United States
| | - Pukhraj Rishi
- Ocular Oncology and Vitreoretinal Surgery, Truhlsen Eye Institute, University of Nebraska Medical Centre, Omaha, NE, USA
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5
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Marra KV, Chen JS, Robles-Holmes HK, Miller J, Wei G, Aguilar E, Ideguchi Y, Ly KB, Prenner S, Erdogmus D, Ferrara N, Campbell JP, Friedlander M, Nudleman E. Development of a Semi-automated Computer-based Tool for the Quantification of Vascular Tortuosity in the Murine Retina. OPHTHALMOLOGY SCIENCE 2024; 4:100439. [PMID: 38361912 PMCID: PMC10867761 DOI: 10.1016/j.xops.2023.100439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 10/10/2023] [Accepted: 11/27/2023] [Indexed: 02/17/2024]
Abstract
Purpose The murine oxygen-induced retinopathy (OIR) model is one of the most widely used animal models of ischemic retinopathy, mimicking hallmark pathophysiology of initial vaso-obliteration (VO) resulting in ischemia that drives neovascularization (NV). In addition to NV and VO, human ischemic retinopathies, including retinopathy of prematurity (ROP), are characterized by increased vascular tortuosity. Vascular tortuosity is an indicator of disease severity, need to treat, and treatment response in ROP. Current literature investigating novel therapeutics in the OIR model often report their effects on NV and VO, and measurements of vascular tortuosity are less commonly performed. No standardized quantification of vascular tortuosity exists to date despite this metric's relevance to human disease. This proof-of-concept study aimed to apply a previously published semi-automated computer-based image analysis approach (iROP-Assist) to develop a new tool to quantify vascular tortuosity in mouse models. Design Experimental study. Subjects C57BL/6J mice subjected to the OIR model. Methods In a pilot study, vasculature was manually segmented on flat-mount images of OIR and normoxic (NOX) mice retinas and segmentations were analyzed with iROP-Assist to quantify vascular tortuosity metrics. In a large cohort of age-matched (postnatal day 12 [P12], P17, P25) NOX and OIR mice retinas, NV, VO, and vascular tortuosity were quantified and compared. In a third experiment, vascular tortuosity in OIR mice retinas was quantified on P17 following intravitreal injection with anti-VEGF (aflibercept) or Immunoglobulin G isotype control on P12. Main Outcome Measures Vascular tortuosity. Results Cumulative tortuosity index was the best metric produced by iROP-Assist for discriminating between OIR mice and NOX controls. Increased vascular tortuosity correlated with disease activity in OIR. Treatment of OIR mice with aflibercept rescued vascular tortuosity. Conclusions Vascular tortuosity is a quantifiable feature of the OIR model that correlates with disease severity and may be quickly and accurately quantified using the iROP-Assist algorithm. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Kyle V. Marra
- Department of Molecular Medicine, The Scripps Research Institute, San Diego, California
- School of Medicine, University of California San Diego, San Diego, California
| | - Jimmy S. Chen
- Department of Ophthalmology, Shiley Eye Institute, University of California San Diego, San Diego, California
| | - Hailey K. Robles-Holmes
- Department of Ophthalmology, Shiley Eye Institute, University of California San Diego, San Diego, California
| | - Joseph Miller
- Department of Ophthalmology, Shiley Eye Institute, University of California San Diego, San Diego, California
| | - Guoqin Wei
- Department of Molecular Medicine, The Scripps Research Institute, San Diego, California
| | - Edith Aguilar
- Department of Molecular Medicine, The Scripps Research Institute, San Diego, California
| | - Yoichiro Ideguchi
- Department of Molecular Medicine, The Scripps Research Institute, San Diego, California
| | - Kristine B. Ly
- College of Optometry, Pacific University, Forest Grove, Oregon
| | - Sofia Prenner
- Department of Ophthalmology, Shiley Eye Institute, University of California San Diego, San Diego, California
| | - Deniz Erdogmus
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts
| | - Napoleone Ferrara
- Department of Ophthalmology, Shiley Eye Institute, University of California San Diego, San Diego, California
| | - J. Peter Campbell
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Martin Friedlander
- Department of Molecular Medicine, The Scripps Research Institute, San Diego, California
| | - Eric Nudleman
- Department of Ophthalmology, Shiley Eye Institute, University of California San Diego, San Diego, California
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6
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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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Chen JS, Marra KV, Robles-Holmes HK, Ly KB, Miller J, Wei G, Aguilar E, Bucher F, Ideguchi Y, Coyner AS, Ferrara N, Campbell JP, Friedlander M, Nudleman E. Applications of Deep Learning: Automated Assessment of Vascular Tortuosity in Mouse Models of Oxygen-Induced Retinopathy. OPHTHALMOLOGY SCIENCE 2024; 4:100338. [PMID: 37869029 PMCID: PMC10585474 DOI: 10.1016/j.xops.2023.100338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 05/01/2023] [Accepted: 05/19/2023] [Indexed: 10/24/2023]
Abstract
Objective To develop a generative adversarial network (GAN) to segment major blood vessels from retinal flat-mount images from oxygen-induced retinopathy (OIR) and demonstrate the utility of these GAN-generated vessel segmentations in quantifying vascular tortuosity. Design Development and validation of GAN. Subjects Three datasets containing 1084, 50, and 20 flat-mount mice retina images with various stains used and ages at sacrifice acquired from previously published manuscripts. Methods Four graders manually segmented major blood vessels from flat-mount images of retinas from OIR mice. Pix2Pix, a high-resolution GAN, was trained on 984 pairs of raw flat-mount images and manual vessel segmentations and then tested on 100 and 50 image pairs from a held-out and external test set, respectively. GAN-generated and manual vessel segmentations were then used as an input into a previously published algorithm (iROP-Assist) to generate a vascular cumulative tortuosity index (CTI) for 20 image pairs containing mouse eyes treated with aflibercept versus control. Main Outcome Measures Mean dice coefficients were used to compare segmentation accuracy between the GAN-generated and manually annotated segmentation maps. For the image pairs treated with aflibercept versus control, mean CTIs were also calculated for both GAN-generated and manual vessel maps. Statistical significance was evaluated using Wilcoxon signed-rank tests (P ≤ 0.05 threshold for significance). Results The dice coefficient for the GAN-generated versus manual vessel segmentations was 0.75 ± 0.27 and 0.77 ± 0.17 for the held-out test set and external test set, respectively. The mean CTI generated from the GAN-generated and manual vessel segmentations was 1.12 ± 0.07 versus 1.03 ± 0.02 (P = 0.003) and 1.06 ± 0.04 versus 1.01 ± 0.01 (P < 0.001), respectively, for eyes treated with aflibercept versus control, demonstrating that vascular tortuosity was rescued by aflibercept when quantified by GAN-generated and manual vessel segmentations. Conclusions GANs can be used to accurately generate vessel map segmentations from flat-mount images. These vessel maps may be used to evaluate novel metrics of vascular tortuosity in OIR, such as CTI, and have the potential to accelerate research in treatments for ischemic retinopathies. Financial Disclosures The author(s) have no proprietary or commercial interest in any materials discussed in this article.
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Affiliation(s)
- Jimmy S. Chen
- Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, San Diego, California
| | - Kyle V. Marra
- Molecular Medicine, the Scripps Research Institute, San Diego, California
- School of Medicine, University of California San Diego, San Diego, California
| | - Hailey K. Robles-Holmes
- Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, San Diego, California
| | - Kristine B. Ly
- College of Optometry, Pacific University, Forest Grove, Oregon
| | - Joseph Miller
- Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, San Diego, California
| | - Guoqin Wei
- Molecular Medicine, the Scripps Research Institute, San Diego, California
| | - Edith Aguilar
- Molecular Medicine, the Scripps Research Institute, San Diego, California
| | - Felicitas Bucher
- Eye Center, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Yoichi Ideguchi
- Molecular Medicine, the Scripps Research Institute, San Diego, California
| | - Aaron S. Coyner
- Casey Eye Institute, Department of Ophthalmology, Oregon Health & Science University, Portland, Oregon
| | - Napoleone Ferrara
- Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, San Diego, California
| | - J. Peter Campbell
- Casey Eye Institute, Department of Ophthalmology, Oregon Health & Science University, Portland, Oregon
| | - Martin Friedlander
- Molecular Medicine, the Scripps Research Institute, San Diego, California
| | - Eric Nudleman
- Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, San Diego, California
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Hoyek S, Cruz NFSD, Patel NA, Al-Khersan H, Fan KC, Berrocal AM. Identification of novel biomarkers for retinopathy of prematurity in preterm infants by use of innovative technologies and artificial intelligence. Prog Retin Eye Res 2023; 97:101208. [PMID: 37611892 DOI: 10.1016/j.preteyeres.2023.101208] [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: 06/19/2023] [Revised: 08/16/2023] [Accepted: 08/18/2023] [Indexed: 08/25/2023]
Abstract
Retinopathy of prematurity (ROP) is a leading cause of preventable vision loss in preterm infants. While appropriate screening is crucial for early identification and treatment of ROP, current screening guidelines remain limited by inter-examiner variability in screening modalities, absence of local protocol for ROP screening in some settings, a paucity of resources and an increased survival of younger and smaller infants. This review summarizes the advancements and challenges of current innovative technologies, artificial intelligence (AI), and predictive biomarkers for the diagnosis and management of ROP. We provide a contemporary overview of AI-based models for detection of ROP, its severity, progression, and response to treatment. To address the transition from experimental settings to real-world clinical practice, challenges to the clinical implementation of AI for ROP are reviewed and potential solutions are proposed. The use of optical coherence tomography (OCT) and OCT angiography (OCTA) technology is also explored, providing evaluation of subclinical ROP characteristics that are often imperceptible on fundus examination. Furthermore, we explore several potential biomarkers to reduce the need for invasive procedures, to enhance diagnostic accuracy and treatment efficacy. Finally, we emphasize the need of a symbiotic integration of biologic and imaging biomarkers and AI in ROP screening, where the robustness of biomarkers in early disease detection is complemented by the predictive precision of AI algorithms.
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Affiliation(s)
- Sandra Hoyek
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Natasha F S da Cruz
- Bascom Palmer Eye Institute, University of Miami Leonard M. Miller School of Medicine, Miami, FL, USA
| | - Nimesh A Patel
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Hasenin Al-Khersan
- Bascom Palmer Eye Institute, University of Miami Leonard M. Miller School of Medicine, Miami, FL, USA
| | - Kenneth C Fan
- Bascom Palmer Eye Institute, University of Miami Leonard M. Miller School of Medicine, Miami, FL, USA
| | - Audina M Berrocal
- Bascom Palmer Eye Institute, University of Miami Leonard M. Miller School of Medicine, Miami, FL, USA.
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Hou N, Shi J, Ding X, Nie C, Wang C, Wan J. ROP-GAN: an image synthesis method for retinopathy of prematurity based on generative adversarial network. Phys Med Biol 2023; 68:205016. [PMID: 37619572 DOI: 10.1088/1361-6560/acf3c9] [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: 04/24/2023] [Accepted: 08/24/2023] [Indexed: 08/26/2023]
Abstract
Objective. Training data with annotations are scarce in the intelligent diagnosis of retinopathy of prematurity (ROP), and existing typical data augmentation methods cannot generate data with a high degree of diversity. In order to increase the sample size and the generalization ability of the classification model, we propose a method called ROP-GAN for image synthesis of ROP based on a generative adversarial network.Approach. To generate a binary vascular network from color fundus images, we first design an image segmentation model based on U2-Net that can extract multi-scale features without reducing the resolution of the feature map. The vascular network is then fed into an adversarial autoencoder for reconstruction, which increases the diversity of the vascular network diagram. Then, we design an ROP image synthesis algorithm based on a generative adversarial network, in which paired color fundus images and binarized vascular networks are input into the image generation model to train the generator and discriminator, and attention mechanism modules are added to the generator to improve its detail synthesis ability.Main results. Qualitative and quantitative evaluation indicators are applied to evaluate the proposed method, and experiments demonstrate that the proposed method is superior to the existing ROP image synthesis methods, as it can synthesize realistic ROP fundus images.Significance. Our method effectively alleviates the problem of data imbalance in ROP intelligent diagnosis, contributes to the implementation of ROP staging tasks, and lays the foundation for further research. In addition to classification tasks, our synthesized images can facilitate tasks that require large amounts of medical data, such as detecting lesions and segmenting medical images.
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Affiliation(s)
- Ning Hou
- School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, People's Republic of China
| | - Jianhua Shi
- School of Mechanical and Electrical Engineering, Shanxi Datong University, Shanxi 037009, People's Republic of China
| | - Xiaoxuan Ding
- School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, People's Republic of China
| | - Chuan Nie
- Department of Neonatology, Guangdong Women and Children Hospital, Guangzhou 511442, People's Republic of China
| | - Cuicui Wang
- Graduate School, Guangzhou Medical University, Guangzhou 511495, People's Republic of China
| | - Jiafu Wan
- School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, People's Republic of China
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Nakayama LF, Mitchell WG, Ribeiro LZ, Dychiao RG, Phanphruk W, Celi LA, Kalua K, Santiago APD, Regatieri CVS, Moraes NSB. Fairness and generalisability in deep learning of retinopathy of prematurity screening algorithms: a literature review. BMJ Open Ophthalmol 2023; 8:e001216. [PMID: 37558406 PMCID: PMC10414056 DOI: 10.1136/bmjophth-2022-001216] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 07/04/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Retinopathy of prematurity (ROP) is a vasoproliferative disease responsible for more than 30 000 blind children worldwide. Its diagnosis and treatment are challenging due to the lack of specialists, divergent diagnostic concordance and variation in classification standards. While artificial intelligence (AI) can address the shortage of professionals and provide more cost-effective management, its development needs fairness, generalisability and bias controls prior to deployment to avoid producing harmful unpredictable results. This review aims to compare AI and ROP study's characteristics, fairness and generalisability efforts. METHODS Our review yielded 220 articles, of which 18 were included after full-text assessment. The articles were classified into ROP severity grading, plus detection, detecting treatment requiring, ROP prediction and detection of retinal zones. RESULTS All the article's authors and included patients are from middle-income and high-income countries, with no low-income countries, South America, Australia and Africa Continents representation.Code is available in two articles and in one on request, while data are not available in any article. 88.9% of the studies use the same retinal camera. In two articles, patients' sex was described, but none applied a bias control in their models. CONCLUSION The reviewed articles included 180 228 images and reported good metrics, but fairness, generalisability and bias control remained limited. Reproducibility is also a critical limitation, with few articles sharing codes and none sharing data. Fair and generalisable ROP and AI studies are needed that include diverse datasets, data and code sharing, collaborative research, and bias control to avoid unpredictable and harmful deployments.
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Affiliation(s)
- Luis Filipe Nakayama
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Ophthalmology, Sao Paulo Federal University, Sao Paulo, Brazil
| | - William Greig Mitchell
- Department of Ophthalmology, The Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Lucas Zago Ribeiro
- Department of Ophthalmology, Sao Paulo Federal University, Sao Paulo, Brazil
| | - Robyn Gayle Dychiao
- University of the Philippines Manila College of Medicine, Manila, Philippines
| | | | - Leo Anthony Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biostatistics, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
| | - Khumbo Kalua
- Department of Ophthalmology, Blantyre Institute for Community Ophthalmology, BICO, Blantyre, Malawi
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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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Raja Sankari VM, Snekhalatha U, Chandrasekaran A, Baskaran P. Automated diagnosis of Retinopathy of prematurity from retinal images of preterm infants using hybrid deep learning techniques. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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14
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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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GabROP: Gabor Wavelets-Based CAD for Retinopathy of Prematurity Diagnosis via Convolutional Neural Networks. Diagnostics (Basel) 2023; 13:diagnostics13020171. [PMID: 36672981 PMCID: PMC9857608 DOI: 10.3390/diagnostics13020171] [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: 10/13/2022] [Revised: 12/12/2022] [Accepted: 12/19/2022] [Indexed: 01/05/2023] Open
Abstract
One of the most serious and dangerous ocular problems in premature infants is retinopathy of prematurity (ROP), a proliferative vascular disease. Ophthalmologists can use automatic computer-assisted diagnostic (CAD) tools to help them make a safe, accurate, and low-cost diagnosis of ROP. All previous CAD tools for ROP diagnosis use the original fundus images. Unfortunately, learning the discriminative representation from ROP-related fundus images is difficult. Textural analysis techniques, such as Gabor wavelets (GW), can demonstrate significant texture information that can help artificial intelligence (AI) based models to improve diagnostic accuracy. In this paper, an effective and automated CAD tool, namely GabROP, based on GW and multiple deep learning (DL) models is proposed. Initially, GabROP analyzes fundus images using GW and generates several sets of GW images. Next, these sets of images are used to train three convolutional neural networks (CNNs) models independently. Additionally, the actual fundus pictures are used to build these networks. Using the discrete wavelet transform (DWT), texture features retrieved from every CNN trained with various sets of GW images are combined to create a textural-spectral-temporal demonstration. Afterward, for each CNN, these features are concatenated with spatial deep features obtained from the original fundus images. Finally, the previous concatenated features of all three CNN are incorporated using the discrete cosine transform (DCT) to lessen the size of features caused by the fusion process. The outcomes of GabROP show that it is accurate and efficient for ophthalmologists. Additionally, the effectiveness of GabROP is compared to recently developed ROP diagnostic techniques. Due to GabROP's superior performance compared to competing tools, ophthalmologists may be able to identify ROP more reliably and precisely, which could result in a reduction in diagnostic effort and examination time.
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Nisha KL, Ganapathy S, Puthumangalathu Savithri S, Idaguri M, Mohanachandran P, Vinekar A, Chandra P, Kulkarni S, Dogra M. A Novel Method to Improve Inter-Clinician Variation in the Diagnosis of Retinopathy of Prematurity Using Machine Learning. Curr Eye Res 2023; 48:60-69. [PMID: 36322485 DOI: 10.1080/02713683.2022.2139847] [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: 11/06/2022]
Abstract
PURPOSE Inter-clinician variation could cause uncertainty in disease management. This is reported to be high in Retinopathy of Prematurity (ROP), a potentially blinding retinal disease affecting premature infants. Machine learning has the potential to quantify the differences in decision-making between ROP specialists and trainees and may improve the accuracy of diagnosis. METHODS An anonymized survey of ROP images was administered to the expert(s) and the trainee(s) using a study-designed user interface. The results were analyzed for repeatability as well as to identify the level of agreement in the classification. "Ground truth" was prepared for each individual and a unique classifier was built for each individual using the same. The classifier allowed the identification of the most important features used by each individual. RESULTS Correlation and disagreement between the expert and the trainees were visualized using the Dipstick™ diagram. Intra-clinician repeatability and reclassification statistics were assessed for all. The repeatability was 88.4% and 86.2% for two trainees and 92.1% for the expert, respectively. Commonly used features differed for the expert and the trainees and accounted for the variability. CONCLUSION This novel, automated algorithm quantifies the differences using machine learning techniques. This will help audit the training process by objectively measuring differences between experts and trainees. TRANSLATIONAL RELEVANCE Training for image-based ROP diagnosis can be more objectively performed using this novel, machine learning-based automated image analyzer and classifier.
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Affiliation(s)
- K L Nisha
- National Institute of Technology Calicut, Calicut, India
| | | | | | | | | | | | - Parijat Chandra
- Dr R. P. Centre for Ophthalmic Sciences, AIIMS, New Delhi, India
| | | | - Mangat Dogra
- Advanced Eye Centre, Postgraduate Institute of Medical Education and Research, Chandigarh, India
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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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Ferro Desideri L, Rutigliani C, Corazza P, Nastasi A, Roda M, Nicolo M, Traverso CE, Vagge A. The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases. JOURNAL OF OPTOMETRY 2022; 15 Suppl 1:S50-S57. [PMID: 36216736 PMCID: PMC9732476 DOI: 10.1016/j.optom.2022.08.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/14/2022] [Accepted: 08/16/2022] [Indexed: 06/16/2023]
Abstract
In recent years, the role of artificial intelligence (AI) and deep learning (DL) models is attracting increasing global interest in the field of ophthalmology. DL models are considered the current state-of-art among the AI technologies. In fact, DL systems have the capability to recognize, quantify and describe pathological clinical features. Their role is currently being investigated for the early diagnosis and management of several retinal diseases and glaucoma. The application of DL models to fundus photographs, visual fields and optical coherence tomography (OCT) imaging has provided promising results in the early detection of diabetic retinopathy (DR), wet age-related macular degeneration (w-AMD), retinopathy of prematurity (ROP) and glaucoma. In this review we analyze the current evidence of AI applied to these ocular diseases, as well as discuss the possible future developments and potential clinical implications, without neglecting the present limitations and challenges in order to adopt AI and DL models as powerful tools in the everyday routine clinical practice.
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Affiliation(s)
- Lorenzo Ferro Desideri
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy.
| | | | - Paolo Corazza
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy
| | | | - Matilde Roda
- Ophthalmology Unit, Department of Experimental, Diagnostic and Specialty Medicine (DIMES), Alma Mater Studiorum University of Bologna and S.Orsola-Malpighi Teaching Hospital, Bologna, Italy
| | - Massimo Nicolo
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy
| | - Carlo Enrico Traverso
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy
| | - Aldo Vagge
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy
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Sabri K, Ells AL, Lee EY, Dutta S, Vinekar A. Retinopathy of Prematurity: A Global Perspective and Recent Developments. Pediatrics 2022; 150:188757. [PMID: 35948728 DOI: 10.1542/peds.2021-053924] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/26/2022] [Indexed: 11/24/2022] Open
Abstract
Retinopathy of prematurity (ROP) is a significant cause of potentially preventable blindness in preterm infants worldwide. It is a disease caused by abnormal retinal vascularization that, if not detected and treated in a timely manner, can lead to retinal detachment and severe long term vision impairment. Neonatologists and pediatricians have an important role in the prevention, detection, and management of ROP. Geographic differences in the epidemiology of ROP have been seen globally over the last several decades because of regional differences in neonatal care. Our understanding of the pathophysiology, risk factors, prevention, screening, diagnosis, and treatment of ROP have also evolved over the years. New technological advances are now allowing for the incorporation of telemedicine and artificial intelligence in the management of ROP. In this comprehensive update, we provide a comprehensive review of pathophysiology, classification, diagnosis, global screening, and treatment of ROP. Key historical milestones as well as touching upon the very recent updates to the ROP classification system and technological advances in the field of artificial intelligence and ROP will also be discussed.
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Affiliation(s)
- Kourosh Sabri
- Department of Ophthalmology, McMaster University, Ontario, Canada
| | - Anna L Ells
- Calgary Retina Consultants, University of Calgary, Calgary, Alberta, Canada
| | - Elizabeth Y Lee
- Department of Ophthalmology, McMaster University, Ontario, Canada
| | - Sourabh Dutta
- Department of Pediatrics, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Anand Vinekar
- Department of Pediatric Retina, Narayana Nethralaya Eye Institute, Bangalore, India
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20
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Bai A, Carty C, Dai S. Performance of deep-learning artificial intelligence algorithms in detecting retinopathy of prematurity: A systematic review. SAUDI JOURNAL OF OPHTHALMOLOGY : OFFICIAL JOURNAL OF THE SAUDI OPHTHALMOLOGICAL SOCIETY 2022; 36:296-307. [PMID: 36276252 DOI: 10.4103/sjopt.sjopt_219_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/09/2021] [Accepted: 11/12/2021] [Indexed: 11/04/2022]
Abstract
PURPOSE Artificial intelligence (AI) offers considerable promise for retinopathy of prematurity (ROP) screening and diagnosis. The development of deep-learning algorithms to detect the presence of disease may contribute to sufficient screening, early detection, and timely treatment for this preventable blinding disease. This review aimed to systematically examine the literature in AI algorithms in detecting ROP. Specifically, we focused on the performance of deep-learning algorithms through sensitivity, specificity, and area under the receiver operating curve (AUROC) for both the detection and grade of ROP. METHODS We searched Medline OVID, PubMed, Web of Science, and Embase for studies published from January 1, 2012, to September 20, 2021. Studies evaluating the diagnostic performance of deep-learning models based on retinal fundus images with expert ophthalmologists' judgment as reference standard were included. Studies which did not investigate the presence or absence of disease were excluded. Risk of bias was assessed using the QUADAS-2 tool. RESULTS Twelve studies out of the 175 studies identified were included. Five studies measured the performance of detecting the presence of ROP and seven studies determined the presence of plus disease. The average AUROC out of 11 studies was 0.98. The average sensitivity and specificity for detecting ROP was 95.72% and 98.15%, respectively, and for detecting plus disease was 91.13% and 95.92%, respectively. CONCLUSION The diagnostic performance of deep-learning algorithms in published studies was high. Few studies presented externally validated results or compared performance to expert human graders. Large scale prospective validation alongside robust study design could improve future studies.
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Affiliation(s)
- Amelia Bai
- Department of Ophthalmology, Queensland Children's Hospital, Brisbane, Australia.,Centre for Children's Health Research, Brisbane, Australia.,School of Medical Science, Griffith University, Gold Coast, Australia
| | - Christopher Carty
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland, Griffith University Gold Coast, Australia.,Department of Orthopaedics, Children's Health Queensland Hospital and Health Service, Queensland Children's Hospital, Brisbane, Australia
| | - Shuan Dai
- Department of Ophthalmology, Queensland Children's Hospital, Brisbane, Australia.,School of Medical Science, Griffith University, Gold Coast, Australia.,University of Queensland, Australia
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Li P, Liu J. Early Diagnosis and Quantitative Analysis of Stages in Retinopathy of Prematurity Based on Deep Convolutional Neural Networks. Transl Vis Sci Technol 2022; 11:17. [PMID: 35579887 PMCID: PMC9123509 DOI: 10.1167/tvst.11.5.17] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Purpose Retinopathy of prematurity (ROP) is a leading cause of childhood blindness. An accurate and timely diagnosis of the early stages of ROP allows ophthalmologists to recommend appropriate treatment while blindness is still preventable. The purpose of this study was to develop an automatic deep convolutional neural network-based system that provided a diagnosis of stage I to III ROP with feature parameters. Methods We developed three data sets containing 18,827 retinal images of preterm infants. These retinal images were obtained from the ophthalmology department of Jiaxing Maternal and Child Health Hospital in China. After segmenting images, we calculated the region of interest (ROI). We trained our system based on segmented ROI images from the training data set, tested the performance of the classifier on the test data set, and evaluated the widths of the demarcation lines or ridges extracted by the system, as well as the ratios of vascular proliferation within the ROI on a comparison data set. Results The trained network achieved a sensitivity of 90.21% with 97.67% specificity for the diagnosis of stage I ROP, 92.75% sensitivity with 98.74% specificity for stage II ROP, and 91.84% sensitivity with 99.29% sensitivity for stage III ROP. When the system diagnosed normal images, the sensitivity and specificity reached 95.93% and 96.41%, respectively. The widths (in pixels) of the demarcation lines or ridges for normal, stage I, stage II, and stage III were 15.22 ± 1.06, 26.35 ± 1.36, and 30.75 ± 1.55. The ratios of the vascular proliferation within the ROI were 1.40 ± 0.29, 1.54 ± 0.26, and 1.81 ± 0.33. All parameters were statistically different among the groups. When physicians integrated quantitative parameters of the extracted features with their clinic diagnosis, the κ score was significantly improved. Conclusions Our system achieved a high accuracy of diagnosis for stage I to III ROP. It used the quantitative analysis of the extracted features to assist physicians in providing classification decisions. Translational Relevance The high performance of the system suggests potential applications in ancillary diagnosis of the early stages of ROP.
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Affiliation(s)
- Peng Li
- School 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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22
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Attallah O. DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity. Diagnostics (Basel) 2021; 11:2034. [PMID: 34829380 PMCID: PMC8620568 DOI: 10.3390/diagnostics11112034] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 09/24/2021] [Accepted: 11/01/2021] [Indexed: 12/12/2022] Open
Abstract
Retinopathy of Prematurity (ROP) affects preterm neonates and could cause blindness. Deep Learning (DL) can assist ophthalmologists in the diagnosis of ROP. This paper proposes an automated and reliable diagnostic tool based on DL techniques called DIAROP to support the ophthalmologic diagnosis of ROP. It extracts significant features by first obtaining spatial features from the four Convolution Neural Networks (CNNs) DL techniques using transfer learning and then applying Fast Walsh Hadamard Transform (FWHT) to integrate these features. Moreover, DIAROP explores the best-integrated features extracted from the CNNs that influence its diagnostic capability. The results of DIAROP indicate that DIAROP achieved an accuracy of 93.2% and an area under receiving operating characteristic curve (AUC) of 0.98. Furthermore, DIAROP performance is compared with recent ROP diagnostic tools. Its promising performance shows that DIAROP may assist the ophthalmologic diagnosis of ROP.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 1029, Egypt
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Accuracy of Deep Learning Algorithms for the Diagnosis of Retinopathy of Prematurity by Fundus Images: A Systematic Review and Meta-Analysis. J Ophthalmol 2021; 2021:8883946. [PMID: 34394982 PMCID: PMC8363465 DOI: 10.1155/2021/8883946] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 06/30/2021] [Accepted: 07/27/2021] [Indexed: 12/14/2022] Open
Abstract
Background Retinopathy of prematurity (ROP) occurs in preterm infants and may contribute to blindness. Deep learning (DL) models have been used for ophthalmologic diagnoses. We performed a systematic review and meta-analysis of published evidence to summarize and evaluate the diagnostic accuracy of DL algorithms for ROP by fundus images. Methods We searched PubMed, EMBASE, Web of Science, and Institute of Electrical and Electronics Engineers Xplore Digital Library on June 13, 2021, for studies using a DL algorithm to distinguish individuals with ROP of different grades, which provided accuracy measurements. The pooled sensitivity and specificity values and the area under the curve (AUC) of summary receiver operating characteristics curves (SROC) summarized overall test performance. The performances in validation and test datasets were assessed together and separately. Subgroup analyses were conducted between the definition and grades of ROP. Threshold and nonthreshold effects were tested to assess biases and evaluate accuracy factors associated with DL models. Results Nine studies with fifteen classifiers were included in our meta-analysis. A total of 521,586 objects were applied to DL models. For combined validation and test datasets in each study, the pooled sensitivity and specificity were 0.953 (95% confidence intervals (CI): 0.946-0.959) and 0.975 (0.973-0.977), respectively, and the AUC was 0.984 (0.978-0.989). For the validation dataset and test dataset, the AUC was 0.977 (0.968-0.986) and 0.987 (0.982-0.992), respectively. In the subgroup analysis of ROP vs. normal and differentiation of two ROP grades, the AUC was 0.990 (0.944-0.994) and 0.982 (0.964-0.999), respectively. Conclusions Our study shows that DL models can play an essential role in detecting and grading ROP with high sensitivity, specificity, and repeatability. The application of a DL-based automated system may improve ROP screening and diagnosis in the future.
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Abstract
PURPOSE OF REVIEW In this article, we review the current state of artificial intelligence applications in retinopathy of prematurity (ROP) and provide insight on challenges as well as strategies for bringing these algorithms to the bedside. RECENT FINDINGS In the past few years, there has been a dramatic shift from machine learning approaches based on feature extraction to 'deep' convolutional neural networks for artificial intelligence applications. Several artificial intelligence for ROP approaches have demonstrated adequate proof-of-concept performance in research studies. The next steps are to determine whether these algorithms are robust to variable clinical and technical parameters in practice. Integration of artificial intelligence into ROP screening and treatment is limited by generalizability of the algorithms to maintain performance on unseen data and integration of artificial intelligence technology into new or existing clinical workflows. SUMMARY Real-world implementation of artificial intelligence for ROP diagnosis will require massive efforts targeted at developing standards for data acquisition, true external validation, and demonstration of feasibility. We must now focus on ethical, technical, clinical, regulatory, and financial considerations to bring this technology to the infant bedside to realize the promise offered by this technology to reduce preventable blindness from ROP.
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