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Li Y, Jie C, Wang J, Zhang W, Wang J, Deng Y, Liu Z, Hou X, Bi X. Global research trends and future directions in diabetic macular edema research: A bibliometric and visualized analysis. Medicine (Baltimore) 2024; 103:e38596. [PMID: 38905408 PMCID: PMC11191902 DOI: 10.1097/md.0000000000038596] [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: 04/06/2024] [Accepted: 05/24/2024] [Indexed: 06/23/2024] Open
Abstract
BACKGROUND Diabetic Macular Edema (DME) significantly impairs vision in diabetics, with varied patient responses to current treatments like anti-vascular endothelial growth factor (VEGF) therapy underscoring the necessity for continued research into more effective strategies. This study aims to evaluate global research trends and identify emerging frontiers in DME to guide future research and clinical management. METHODS A qualitative and quantitative analysis of publications related to diabetic macular edema retrieved from the Web of Science Core Collection (WoSCC) between its inception and September 4, 2023, was conducted. Microsoft Excel, CiteSpace, VOSviewer, Bibliometrix Package, and Tableau were used for the bibliometric analysis and visualization. This encompasses an examination of the overall distribution of annual output, major countries, regions, institutions, authors, core journals, co-cited references, and keyword analyses. RESULTS Overall, 5624 publications were analyzed, indicating an increasing trend in DME research. The United States was identified as the leading country in DME research, with the highest h-index of 135 and 91,841 citations. Francesco Bandello emerged as the most prolific author with 97 publications. Neil M. Bressler has the highest h-index and highest total citation count of 46 and 9692, respectively. The journals "Retina - the Journal of Retinal and Vitreous Diseases" and "Ophthalmology" were highlighted as the most prominent in this field. "Retina" leads with 354 publications, a citation count of 11,872, and an h-index of 59. Meanwhile, "Ophthalmology" stands out with the highest overall citation count of 31,558 and the highest h-index of 90. The primary research focal points in diabetic macular edema included "prevalence and risk factors," "pathological mechanisms," "imaging modalities," "treatment strategies," and "clinical trials." Emerging research areas encompassed "deep learning and artificial intelligence," "novel treatment modalities," and "biomarkers." CONCLUSION Our bibliometric analysis delineates the leading role of the United States in DME research. We identified current research hotspots, including epidemiological studies, pathophysiological mechanisms, imaging advancements, and treatment innovations. Emerging trends, such as the integration of artificial intelligence and novel therapeutic approaches, highlight future directions. These insights underscore the importance of collaborative and interdisciplinary approaches in advancing DME research and clinical management.
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Affiliation(s)
- Yuanyuan Li
- Eye Hospital China Academy of Chinese Medical Sciences, Beijing, China
| | - Chuanhong Jie
- Eye Hospital China Academy of Chinese Medical Sciences, Beijing, China
| | - Jianwei Wang
- Eye Hospital China Academy of Chinese Medical Sciences, Beijing, China
| | - Weiqiong Zhang
- Eye Hospital China Academy of Chinese Medical Sciences, Beijing, China
| | - Jingying Wang
- Eye Hospital China Academy of Chinese Medical Sciences, Beijing, China
| | - Yu Deng
- Eye Hospital China Academy of Chinese Medical Sciences, Beijing, China
| | - Ziqiang Liu
- Eye Hospital China Academy of Chinese Medical Sciences, Beijing, China
| | - Xiaoyu Hou
- Eye Hospital China Academy of Chinese Medical Sciences, Beijing, China
| | - Xuqi Bi
- Eye Hospital China Academy of Chinese Medical Sciences, Beijing, China
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Yao J, Lim J, Lim GYS, Ong JCL, Ke Y, Tan TF, Tan TE, Vujosevic S, Ting DSW. Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema. EYE AND VISION (LONDON, ENGLAND) 2024; 11:23. [PMID: 38880890 PMCID: PMC11181581 DOI: 10.1186/s40662-024-00389-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 05/09/2024] [Indexed: 06/18/2024]
Abstract
BACKGROUND Diabetic retinopathy (DR) and diabetic macular edema (DME) are major causes of visual impairment that challenge global vision health. New strategies are needed to tackle these growing global health problems, and the integration of artificial intelligence (AI) into ophthalmology has the potential to revolutionize DR and DME management to meet these challenges. MAIN TEXT This review discusses the latest AI-driven methodologies in the context of DR and DME in terms of disease identification, patient-specific disease profiling, and short-term and long-term management. This includes current screening and diagnostic systems and their real-world implementation, lesion detection and analysis, disease progression prediction, and treatment response models. It also highlights the technical advancements that have been made in these areas. Despite these advancements, there are obstacles to the widespread adoption of these technologies in clinical settings, including regulatory and privacy concerns, the need for extensive validation, and integration with existing healthcare systems. We also explore the disparity between the potential of AI models and their actual effectiveness in real-world applications. CONCLUSION AI has the potential to revolutionize the management of DR and DME, offering more efficient and precise tools for healthcare professionals. However, overcoming challenges in deployment, regulatory compliance, and patient privacy is essential for these technologies to realize their full potential. Future research should aim to bridge the gap between technological innovation and clinical application, ensuring AI tools integrate seamlessly into healthcare workflows to enhance patient outcomes.
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Affiliation(s)
- Jie Yao
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Joshua Lim
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
| | - Gilbert Yong San Lim
- Duke-NUS Medical School, Singapore, Singapore
- SingHealth AI Health Program, Singapore, Singapore
| | - Jasmine Chiat Ling Ong
- Duke-NUS Medical School, Singapore, Singapore
- Division of Pharmacy, Singapore General Hospital, Singapore, Singapore
| | - Yuhe Ke
- Department of Anesthesiology and Perioperative Science, Singapore General Hospital, Singapore, Singapore
| | - Ting Fang Tan
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
| | - Tien-En Tan
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Stela Vujosevic
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
- Eye Clinic, IRCCS MultiMedica, Milan, Italy
| | - Daniel Shu Wei Ting
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore.
- Duke-NUS Medical School, Singapore, Singapore.
- SingHealth AI Health Program, Singapore, Singapore.
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Zhang L, Huang Y, Chen J, Xu X, Xu F, Yao J. Multimodal deep transfer learning to predict retinal vein occlusion macular edema recurrence after anti-VEGF therapy. Heliyon 2024; 10:e29334. [PMID: 38655307 PMCID: PMC11036002 DOI: 10.1016/j.heliyon.2024.e29334] [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: 04/05/2023] [Revised: 03/28/2024] [Accepted: 04/05/2024] [Indexed: 04/26/2024] Open
Abstract
Purpose To develop a multimodal deep transfer learning (DTL) fusion model using optical coherence tomography angiography (OCTA) images to predict the recurrence of retinal vein occlusion (RVO) and macular edema (ME) after three consecutive anti-VEGF therapies. Methods This retrospective cross-sectional study consisted of 2800 B-scan OCTA macular images collected from 140 patients with RVO-ME. The central macular thickness (CMT) > 250 μm was used as a criterion for recurrence in the three-month follow-up after three injections of anti-VEGF therapy. The qualified OCTA image preprocessing and the lesion area segmentation were performed by senior ophthalmologists. We developed and validated the clinical, DTL, and multimodal fusion models based on clinical and extracted OCTA imaging features. The performance of the models and experts predictions were evaluated using several performance metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results The DTL models exhibited higher prediction efficacy than the clinical models and experts' predictions. Among the DTL models, the Vgg19 performed better than that of the other models, with an AUC of 0.968 (95 % CI, 0.943-0.994), accuracy of 0.913, sensitivity of 0.922, and specificity of 0.902 in the validation cohort. Moreover, the fusion Vgg19 model showed the highest prediction efficacy among all the models, with an AUC of 0.972 (95 % CI, 0.946-0.997), accuracy of 0.935, sensitivity of 0.935, and specificity of 0.934 in the validation cohort. Conclusions Multimodal fusion DTL models showed robust performance in predicting RVO-ME recurrence and may be applied to assist clinicians in determining patients' follow-up time after anti-VEGF therapy.
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Affiliation(s)
- Laihe Zhang
- The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Ying Huang
- The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Jiaqin Chen
- The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Xiangzhong Xu
- The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Fan Xu
- The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Jin Yao
- The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
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Ma X, Ji Z, Chen Q, Ge L, Wang X, Chen C, Fan W. Controllable editing via diffusion inversion on ultra-widefield fluorescein angiography for the comprehensive analysis of diabetic retinopathy. BIOMEDICAL OPTICS EXPRESS 2024; 15:1831-1846. [PMID: 38495723 PMCID: PMC10942674 DOI: 10.1364/boe.517819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 02/16/2024] [Accepted: 02/16/2024] [Indexed: 03/19/2024]
Abstract
By incorporating multiple indicators that facilitate clinical decision making and effective management of diabetic retinopathy (DR), a comprehensive understanding of the progression of the disease can be achieved. However, the diversity of DR complications poses challenges to the automatic analysis of various information within images. This study aims to establish a deep learning system designed to examine various metrics linked to DR in ultra-widefield fluorescein angiography (UWFA) images. We have developed a unified model based on image generation that transforms input images into corresponding disease-free versions. By incorporating an image-level supervised training process, the model significantly reduces the need for extensive manual involvement in clinical applications. Furthermore, compared to other comparative methods, the quality of our generated images is significantly superior.
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Affiliation(s)
- Xiao Ma
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 XiaoLinwei, Nanjing, Jiangsu 210094, China
| | - Zexuan Ji
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 XiaoLinwei, Nanjing, Jiangsu 210094, China
| | - Qiang Chen
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 XiaoLinwei, Nanjing, Jiangsu 210094, China
| | - Lexin Ge
- Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, Jiangsu 210029, China
| | - Xiaoling Wang
- Eye Center, Renmin Hospital of Wuhan University, No. 9 ZhangZhiDong Street, Wuchang District, Wuhan, Hubei 430060, China
| | - Changzheng Chen
- Eye Center, Renmin Hospital of Wuhan University, No. 9 ZhangZhiDong Street, Wuchang District, Wuhan, Hubei 430060, China
| | - Wen Fan
- Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, Jiangsu 210029, China
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Sarici K, Yordi S, Martin A, Lunasco L, Mugnaini C, Chu K, Moini H, Vitti R, Srivastava SK, Ehlers JP. Longitudinal Quantitative Ultrawide-field Fluorescein Angiography Dynamics in the RUBY Diabetic Macular Edema Study. Ophthalmol Retina 2023; 7:543-552. [PMID: 36736895 DOI: 10.1016/j.oret.2023.01.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 12/17/2022] [Accepted: 01/23/2023] [Indexed: 02/04/2023]
Abstract
OBJECTIVE The purpose of this study was to evaluate the longitudinal change in quantitative ultrawide-field angiographic (UWFA) parameters and correlate them with functional outcomes and spectral domain-OCT metrics. DESIGN This study is a post hoc analysis of the phase II RUBY study: a prospective, randomized trial of patients with diabetic macular edema (DME) treated with either intravitreal aflibercept injection (IAI) or combined IAI/nesvacumab (antiangiopoietin 2 mAb). SUBJECTS Subjects with DME that underwent UWFA across all treatment groups (n = 44). METHODS A machine learning-enabled feature extraction system generated panretinal quantitative UWFA metrics, including leakage, ischemia, and microaneurysm (MA) burden. Zonal assessments were performed corresponding to the macula, midperiphery, and far periphery. MAIN OUTCOME MEASURES Changes in ischemic area and index (proportion of nonperfusion in analyzable retina), leakage area and index (proportion of leakage in analyzable retina), and MA count at baseline, week 12, week 24, and week 36 were analyzed. Spectral-domain-OCT quantitative metrics, such as central subfield thickness, ellipsoid zone (EZ) integrity parameters, intraretinal fluid (IRF) volume, and subretinal fluid (SRF) volume were extracted via a machine learning-enhanced OCT feature extraction platform and analyzed. Additionally, the effect of these changes on best-corrected visual acuity (BCVA) was evaluated. RESULTS Mean panretinal leakage index, zonal leakage area, and panretinal MA count improved significantly between baseline and week 36. Panretinal ischemic index decreased between baseline and week 36, with some aspects showing significant improvement. Mean BCVA significantly improved from baseline to week 36. There was a significant inverse correlation between change in BCVA and change in macular leakage area. A direct correlation was observed between both baseline macular leakage area and panretinal leakage index with IRF volume, SRF volume, and EZ disruption on OCT. CONCLUSIONS Assessment of UWFA parameters demonstrates a significant improvement in panretinal leakage index, leakage area, and MA burden in eyes treated with IAI with or without nesvacumab. A numeric reduction in panretinal ischemic index and area was noted. The analysis also shows the critical association of leakage with visual and OCT features. This highlights the potential role of UWFA in disease burden assessment, with leakage parameters serving as a primary end point. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found after the references.
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Affiliation(s)
- Kubra Sarici
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cleveland Clinic, Cleveland, Ohio; Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
| | - Sari Yordi
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cleveland Clinic, Cleveland, Ohio; Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
| | - Alison Martin
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cleveland Clinic, Cleveland, Ohio; Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
| | - Leina Lunasco
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cleveland Clinic, Cleveland, Ohio; Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
| | - Christopher Mugnaini
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cleveland Clinic, Cleveland, Ohio; Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
| | - Karen Chu
- Regeneron Pharmaceuticals, Inc., Tarrytown, New York
| | - Hadi Moini
- Regeneron Pharmaceuticals, Inc., Tarrytown, New York
| | - Robert Vitti
- Regeneron Pharmaceuticals, Inc., Tarrytown, New York
| | - Sunil K Srivastava
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cleveland Clinic, Cleveland, Ohio; Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
| | - Justis P Ehlers
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cleveland Clinic, Cleveland, Ohio; Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio.
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Wang N, Zhang Y, Wang W, Ye Z, Chen H, Hu G, Ouyang D. How can machine learning and multiscale modeling benefit ocular drug development? Adv Drug Deliv Rev 2023; 196:114772. [PMID: 36906232 DOI: 10.1016/j.addr.2023.114772] [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: 12/16/2022] [Revised: 02/06/2023] [Accepted: 03/05/2023] [Indexed: 03/12/2023]
Abstract
The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.
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Affiliation(s)
- Nannan Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Yunsen Zhang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Wei Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Zhuyifan Ye
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Hongyu Chen
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China; Faculty of Science and Technology (FST), University of Macau, Macau, China
| | - Guanghui Hu
- Faculty of Science and Technology (FST), University of Macau, Macau, China
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China; Department of Public Health and Medicinal Administration, Faculty of Health Sciences (FHS), University of Macau, Macau, China.
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Deep learning based diagnostic quality assessment of choroidal OCT features with expert-evaluated explainability. Sci Rep 2023; 13:1570. [PMID: 36709332 PMCID: PMC9884235 DOI: 10.1038/s41598-023-28512-4] [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: 10/11/2022] [Accepted: 01/19/2023] [Indexed: 01/30/2023] Open
Abstract
Various vision-threatening eye diseases including age-related macular degeneration (AMD) and central serous chorioretinopathy (CSCR) are caused due to the dysfunctions manifested in the highly vascular choroid layer of the posterior segment of the eye. In the current clinical practice, screening choroidal structural changes is widely based on optical coherence tomography (OCT) images. Accordingly, to assist clinicians, several automated choroidal biomarker detection methods using OCT images are developed. However, the performance of these algorithms is largely constrained by the quality of the OCT scan. Consequently, determining the quality of choroidal features in OCT scans is significant in building standardized quantification tools and hence constitutes our main objective. This study includes a dataset of 1593 good and 2581 bad quality Spectralis OCT images graded by an expert. Noting the efficacy of deep-learning (DL) in medical image analysis, we propose to train three state-of-the-art DL models: ResNet18, EfficientNet-B0 and EfficientNet-B3 to detect the quality of OCT images. The choice of these models was inspired by their ability to preserve the salient features across all the layers without information loss. To evaluate the attention of DL models on the choroid, we introduced color transparency maps (CTMs) based on GradCAM explanations. Further, we proposed two subjective grading scores: overall choroid coverage (OCC) and choroid coverage in the visible region(CCVR) based on CTMs to objectively correlate visual explanations vis-à-vis DL model attentions. We observed that the average accuracy and F-scores for the three DL models are greater than 96%. Further, the OCC and CCVR scores achieved for the three DL models under consideration substantiate that they mostly focus on the choroid layer in making the decision. In particular, of the three DL models, EfficientNet-B3 is in close agreement with the clinician's inference. The proposed DL-based framework demonstrated high detection accuracy as well as attention on the choroid layer, where EfficientNet-B3 reported superior performance. Our work assumes significance in bench-marking the automated choroid biomarker detection tools and facilitating high-throughput screening. Further, the methods proposed in this work can be adopted for evaluating the attention of DL-based approaches developed for other region-specific quality assessment tasks.
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Carrera-Escalé L, Benali A, Rathert AC, Martín-Pinardel R, Bernal-Morales C, Alé-Chilet A, Barraso M, Marín-Martinez S, Feu-Basilio S, Rosinés-Fonoll J, Hernandez T, Vilá I, Castro-Dominguez R, Oliva C, Vinagre I, Ortega E, Gimenez M, Vellido A, Romero E, Zarranz-Ventura J. Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis. OPHTHALMOLOGY SCIENCE 2022; 3:100259. [PMID: 36578904 PMCID: PMC9791596 DOI: 10.1016/j.xops.2022.100259] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 10/25/2022] [Accepted: 11/14/2022] [Indexed: 11/23/2022]
Abstract
Purpose To evaluate the diagnostic accuracy of machine learning (ML) techniques applied to radiomic features extracted from OCT and OCT angiography (OCTA) images for diabetes mellitus (DM), diabetic retinopathy (DR), and referable DR (R-DR) diagnosis. Design Cross-sectional analysis of a retinal image dataset from a previous prospective OCTA study (ClinicalTrials.govNCT03422965). Participants Patients with type 1 DM and controls included in the progenitor study. Methods Radiomic features were extracted from fundus retinographies, OCT, and OCTA images in each study eye. Logistic regression, linear discriminant analysis, support vector classifier (SVC)-linear, SVC-radial basis function, and random forest models were created to evaluate their diagnostic accuracy for DM, DR, and R-DR diagnosis in all image types. Main Outcome Measures Area under the receiver operating characteristic curve (AUC) mean and standard deviation for each ML model and each individual and combined image types. Results A dataset of 726 eyes (439 individuals) were included. For DM diagnosis, the greatest AUC was observed for OCT (0.82, 0.03). For DR detection, the greatest AUC was observed for OCTA (0.77, 0.03), especially in the 3 × 3 mm superficial capillary plexus OCTA scan (0.76, 0.04). For R-DR diagnosis, the greatest AUC was observed for OCTA (0.87, 0.12) and the deep capillary plexus OCTA scan (0.86, 0.08). The addition of clinical variables (age, sex, etc.) improved most models AUC for DM, DR and R-DR diagnosis. The performance of the models was similar in unilateral and bilateral eyes image datasets. Conclusions Radiomics extracted from OCT and OCTA images allow identification of patients with DM, DR, and R-DR using standard ML classifiers. OCT was the best test for DM diagnosis, OCTA for DR and R-DR diagnosis and the addition of clinical variables improved most models. This pioneer study demonstrates that radiomics-based ML techniques applied to OCT and OCTA images may be an option for DR screening in patients with type 1 DM. Financial Disclosures Proprietary or commercial disclosure may be found after the references.
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Key Words
- AI, artificial intelligence
- AUC, area under the curve
- Artificial intelligence
- DCP, deep capillary plexus
- DM, diabetes mellitus
- DR, diabetic retinopathy
- Diabetic retinopathy
- FR, fundus retinographies
- LDA, linear discriminant analysis
- LR, logistic regression
- ML, machine learning
- Machine learning
- OCT angiography
- OCTA, OCT angiography
- R-DR, referable DR
- RF, random forest
- Radiomics
- SCP, superficial capillary plexus
- SVC, support vector classifier
- rbf, radial basis function
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Affiliation(s)
- Laura Carrera-Escalé
- Intelligent Data Science and Artificial Intelligence (IDEAI) Research Center,Department of Computer Science, Facultat d’Informàtica de Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Anass Benali
- Intelligent Data Science and Artificial Intelligence (IDEAI) Research Center,Department of Computer Science, Facultat d’Informàtica de Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Ann-Christin Rathert
- Intelligent Data Science and Artificial Intelligence (IDEAI) Research Center,Department of Computer Science, Facultat d’Informàtica de Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Ruben Martín-Pinardel
- Intelligent Data Science and Artificial Intelligence (IDEAI) Research Center,Department of Computer Science, Facultat d’Informàtica de Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain,August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain
| | | | - Anibal Alé-Chilet
- Institut Clínic d´Oftalmología (ICOF), Hospital Clínic de Barcelona, Barcelona, Spain
| | - Marina Barraso
- Institut Clínic d´Oftalmología (ICOF), Hospital Clínic de Barcelona, Barcelona, Spain
| | - Sara Marín-Martinez
- Institut Clínic d´Oftalmología (ICOF), Hospital Clínic de Barcelona, Barcelona, Spain
| | - Silvia Feu-Basilio
- Institut Clínic d´Oftalmología (ICOF), Hospital Clínic de Barcelona, Barcelona, Spain
| | - Josep Rosinés-Fonoll
- Institut Clínic d´Oftalmología (ICOF), Hospital Clínic de Barcelona, Barcelona, Spain
| | - Teresa Hernandez
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain,Institut Clínic d´Oftalmología (ICOF), Hospital Clínic de Barcelona, Barcelona, Spain
| | - Irene Vilá
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain,Institut Clínic d´Oftalmología (ICOF), Hospital Clínic de Barcelona, Barcelona, Spain
| | | | - Cristian Oliva
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain,Institut Clínic d´Oftalmología (ICOF), Hospital Clínic de Barcelona, Barcelona, Spain
| | - Irene Vinagre
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain,Diabetes Unit, Hospital Clínic de Barcelona, Spain,Institut Clínic de Malalties Digestives i Metaboliques (ICMDM), Hospital Clínic de Barcelona, Spain
| | - Emilio Ortega
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain,Diabetes Unit, Hospital Clínic de Barcelona, Spain,Institut Clínic de Malalties Digestives i Metaboliques (ICMDM), Hospital Clínic de Barcelona, Spain
| | - Marga Gimenez
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain,Diabetes Unit, Hospital Clínic de Barcelona, Spain,Institut Clínic de Malalties Digestives i Metaboliques (ICMDM), Hospital Clínic de Barcelona, Spain
| | - Alfredo Vellido
- Intelligent Data Science and Artificial Intelligence (IDEAI) Research Center,Department of Computer Science, Facultat d’Informàtica de Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Enrique Romero
- Intelligent Data Science and Artificial Intelligence (IDEAI) Research Center,Department of Computer Science, Facultat d’Informàtica de Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Javier Zarranz-Ventura
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain,Institut Clínic d´Oftalmología (ICOF), Hospital Clínic de Barcelona, Barcelona, Spain,Diabetes Unit, Hospital Clínic de Barcelona, Spain,School of Medicine, Universitat de Barcelona, Spain,Correspondence: Javier Zarranz-Ventura, MD, PhD, C/ Sabino Arana 1, Barcelona 08028, Spain.
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Chaikijurajai T, Ehlers JP, Tang WHW. Retinal Microvasculature: A Potential Window Into Heart Failure Prevention. JACC. HEART FAILURE 2022; 10:785-791. [PMID: 36328644 DOI: 10.1016/j.jchf.2022.07.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/06/2022] [Accepted: 07/11/2022] [Indexed: 06/16/2023]
Abstract
Endothelial dysfunction and microvascular disease have been shown to play an important role in the development and progression of heart failure (HF). Retinal imaging provides a unique opportunity to noninvasively assess vascular structure and function, vessel features, and microcirculation within the retina. Accumulating evidence suggests that retinal vessel caliber, microvascular features, and vascular characteristics extracted from various imaging modalities are associated with alterations in left ventricular structure and function in stage B HF, as well as incident development of symptomatic HF in the general population. Moreover, dynamic retinal vessel analysis has been shown to differentiate HF patients based on their phenotypes. Given the increasing availability of rapid image acquisition devices (eg, nonmydriatic widefield systems and smartphone-based retinal cameras) and the integration of artificial intelligence-based interrogation/assessment techniques, retinal imaging is a promising noninvasive tool, in conjunction with cardiac imaging and biomarkers, to prevent HF and risk stratify those at risk of developing HF. This review focuses on the current evidence on retinal microvasculature changes, and potential clinical relevance and promising utility of retinal imaging in HF.
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Affiliation(s)
- Thanat Chaikijurajai
- Kaufman Center for Heart Failure Treatment and Recovery, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA; Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | | | - W H Wilson Tang
- Kaufman Center for Heart Failure Treatment and Recovery, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA.
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Sevgi DD, Srivastava SK, Wykoff C, Scott AW, Hach J, O'Connell M, Whitney J, Vasanji A, Reese JL, Ehlers JP. Deep learning-enabled ultra-widefield retinal vessel segmentation with an automated quality-optimized angiographic phase selection tool. Eye (Lond) 2022; 36:1783-1788. [PMID: 34373610 PMCID: PMC9391395 DOI: 10.1038/s41433-021-01661-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 05/22/2021] [Accepted: 06/21/2021] [Indexed: 11/09/2022] Open
Abstract
OBJECTIVES To demonstrate the feasibility of a deep learning-based vascular segmentation tool for UWFA and evaluate its ability to automatically identify quality-optimized phase-specific images. METHODS Cumulative retinal vessel areas (RVA) were extracted from all available UWFA frames. Cubic splines were fitted for serial vascular assessment throughout the angiographic phases of eyes with diabetic retinopathy (DR), sickle cell retinopathy (SCR), or normal retinal vasculature. The image with maximum RVA was selected as the optimum early phase. A late phase frame was selected at a minimum of 4 min that most closely mirrored the RVA from the selected early image. Trained image analysts evaluated the selected pairs. RESULTS A total of 13,980 UWFA sequences from 462 sessions were used to evaluate the performance and 1578 UWFA sequences from 66 sessions were used to create cubic splines. Maximum RVA was detected at a mean of 41 ± 15, 47 ± 27, 38 ± 8 s for DR, SCR, and normals respectively. In 85.2% of the sessions, appropriate images for both phases were successfully identified. The individual success rate was 90.7% for early and 94.6% for late frames. CONCLUSIONS Retinal vascular characteristics are highly phased and field-of-view sensitive. Vascular parameters extracted by deep learning algorithms can be used for quality assessment of angiographic images and quality optimized phase selection. Clinical applications of a deep learning-based vascular segmentation and phase selection system might significantly improve the speed, consistency, and objectivity of UWFA evaluation.
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Affiliation(s)
- Duriye Damla Sevgi
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Sunil K Srivastava
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Charles Wykoff
- Retina Consultants of America, Houston, Texas; Blanton Eye Institute, Houston Methodist Hospital & Weill Cornell Medical College, Houston, TX, USA
| | - Adrienne W Scott
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jenna Hach
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Margaret O'Connell
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jon Whitney
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA
| | | | - Jamie L Reese
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Justis P Ehlers
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA.
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11
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Dong V, Sevgi DD, Kar SS, Srivastava SK, Ehlers JP, Madabhushi A. Evaluating the utility of deep learning for predicting therapeutic response in diabetic eye disease. FRONTIERS IN OPHTHALMOLOGY 2022; 2:852107. [PMID: 36744216 PMCID: PMC9894083 DOI: 10.3389/fopht.2022.852107] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 07/14/2022] [Indexed: 11/13/2022]
Abstract
Purpose Deep learning (DL) is a technique explored within ophthalmology that requires large datasets to distinguish feature representations with high diagnostic performance. There is a need for developing DL approaches to predict therapeutic response, but completed clinical trial datasets are limited in size. Predicting treatment response is more complex than disease diagnosis, where hallmarks of treatment response are subtle. This study seeks to understand the utility of DL for clinical problems in ophthalmology such as predicting treatment response and where large sample sizes for model training are not available. Materials and Methods Four DL architectures were trained using cross-validated transfer learning to classify ultra-widefield angiograms (UWFA) and fluid-compartmentalized optical coherence tomography (OCT) images from a completed clinical trial (PERMEATE) dataset (n=29) as tolerating or requiring extended interval Anti-VEGF dosing. UWFA images (n=217) from the Anti-VEGF study were divided into five increasingly larger subsets to evaluate the influence of dataset size on performance. Class activation maps (CAMs) were generated to identify regions of model attention. Results The best performing DL model had a mean AUC of 0.507 ± 0.042 on UWFA images, and highest observed AUC of 0.503 for fluid-compartmentalized OCT images. DL had a best performing AUC of 0.634 when dataset size was incrementally increased. Resulting CAMs show inconsistent regions of interest. Conclusions This study demonstrated the limitations of DL for predicting therapeutic response when large datasets were not available for model training. Our findings suggest the need for hand-crafted approaches for complex and data scarce prediction problems in ophthalmology.
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Affiliation(s)
- Vincent Dong
- The Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH, United States
| | - Duriye Damla Sevgi
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cleveland Clinic, Cleveland, OH, United States
| | - Sudeshna Sil Kar
- The Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH, United States
| | - Sunil K. Srivastava
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cleveland Clinic, Cleveland, OH, United States
| | - Justis P. Ehlers
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cleveland Clinic, Cleveland, OH, United States
| | - Anant Madabhushi
- Wallace H Coulter Department of Biomedical Engineering, Emory University and Georgia Institute for Technology, Atlanta, GA, United States
- Atlanta Veterans Administration Medical Center, Atlanta, GA, United States
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12
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Kar SS, Abraham J, Wykoff CC, Sevgi DD, Lunasco L, Brown DM, Srivastava SK, Madabhushi A, Ehlers JP. Computational Imaging Biomarker Correlation with Intraocular Cytokine Expression in Diabetic Macular Edema: Radiomics Insights from the IMAGINE Study. OPHTHALMOLOGY SCIENCE 2022; 2:100123. [PMID: 36249694 PMCID: PMC9560558 DOI: 10.1016/j.xops.2022.100123] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 01/22/2022] [Accepted: 01/27/2022] [Indexed: 11/03/2022]
Abstract
Purpose Various pathways and cytokines are implicated in pathogenesis of diabetic macular edema (DME). Computational imaging biomarkers (CIBs) of vessel tortuosity from ultra-widefield fluorescein angiography (UWFA) and texture patterns from OCT images have been associated with anti-vascular endothelial growth factor (VEGF) therapy treatment response in DME. This analysis was a radiogenomic assessment of the association between underlying cytokines, UWFA, and OCT-based DME CIBs. Design Biclustering analysis based on UWFA and OCT CIBs to identify a common imaging phenotype across patients with subsequent assessment of underlying cytokine signatures and treatment response attributes. Participants The IMAGINE DME study was a post hoc study of cytokine expressions that included 24 eyes with sufficient baseline aqueous humor samples and an in-depth assessment of the imaging studies obtained during the phase I/II DmeAntiVEgf study (DAVE) that measured different cytokine expressions. Methods A total of 151 graph or morphologic features quantifying leakage shape, size, density, interobject distance, and architecture of leakage spots and 5 vessel tortuosity features were extracted from the baseline UWFA scans, and 494 texture-based radiomics features were extracted from each of the fluid and retinal tissue compartments of OCT images. Biclustering enables simultaneous clustering of patients and features and was used to aggregate patients in terms of their commonality of phenotypes (based on similar imaging attributes) and to identify commonality in terms of cytokine expression and treatment response to anti-VEGF therapy. Main Outcome Measures Identification of eyes with similar imaging phenotypes to evaluate commonalities of patterns and underlying cytokine expression. Results Strong correlations between VEGF and 7 UWFA leakage morphologic features (Pearson correlation coefficient [PCC], 0.45-0.51; P < 0.05), 1 vascular tortuosity-based UWFA feature (PCC, 0.45; P = 0.00016), and 2 OCT-derived intraretinal fluid texture features (PCC, 0.58-0.63; P < 0.05) were identified. Strong correlation between intraretinal fluid features and other cytokines (PCC, 0.41-0.59; P < 0.05) were also observed. Conclusions This study identified groups of eyes with similar imaging phenotypes as defined by UWFA and OCT CIBs that demonstrated similar treatment response patterns and cytokine expression, including a strong association between VEGF with UWFA-derived leakage morphologic and vessel tortuosity features.
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Key Words
- ANGPTL4, angiopoietin-like 4
- CIB, computational imaging biomarker
- Cytokine
- DAVE, DmeAntiVEgf study
- DME, diabetic macular edema
- DR, diabetic retinopathy
- Diabetic macular edema
- IRF, intraretinal fluid
- MCP-1, monocyte chemoattractant protein 1
- OCT
- PCC, Pearson correlation coefficient
- Radiomics
- UWFA, ultra-widefield fluorescein angiography
- Ultra-widefield fluorescein angiography
- VEGF, vascular endothelial growth factor
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Affiliation(s)
- Sudeshna Sil Kar
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Joseph Abraham
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
| | - Charles C. Wykoff
- Retina Consultants of Texas, Retina Consultants of America, Houston, Texas
- Blanton Eye Institute, Houston Methodist Hospital, Houston, Texas
| | - Duriye Damla Sevgi
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
| | - Leina Lunasco
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
| | - David M. Brown
- Retina Consultants of Texas, Retina Consultants of America, Houston, Texas
- Blanton Eye Institute, Houston Methodist Hospital, Houston, Texas
| | - Sunil K. Srivastava
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
- Vitreoretinal Service, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio
| | - Justis P. Ehlers
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
- Vitreoretinal Service, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
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Xiao H, Guo Y, Zhou Q, Chen Q, Du Q, Chen S, Fu S, Lin J, Li D, Song X, Peng S, Huang Y, Shen J, Kuang M. Prediction of microvascular invasion in hepatocellular carcinoma with expert-inspiration and skeleton sharing deep learning. Liver Int 2022; 42:1423-1431. [PMID: 35319151 DOI: 10.1111/liv.15254] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 02/28/2022] [Accepted: 03/13/2022] [Indexed: 01/15/2023]
Abstract
BACKGROUND AND AIMS Radiological prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC) is essential but few models were clinically implemented because of limited interpretability and generalizability. METHODS Based on 2096 patients in three independent HCC cohorts, we established and validated an MVI predicting model. First, we used data from the primary cohort to train a 3D-ResNet network for MVI prediction and then optimised the model with "expert-inspired training" for model construction. Second, we implemented the model to the other two cohorts using three implementation strategies, the original model implementation, data sharing model implementation and skeleton sharing model implementation, the latter two of which used part of the cohorts' data for fine-tuning. The areas under the receiver operating characteristic curve (AUCs) were calculated to compare the performances of different models. RESULTS For the MVI predicting model, the AUC of the expert-inspired model was 0.83 (95% CI: 0.77-0.88) compared to 0.54 (95% CI: 0.46-0.62) of model before expert-inspiring. Taking this model as an original model, AUC on the second cohort was 0.76 (95% CI: 0.67-0.84). The AUC was improved to 0.83 (95% CI: 0.77-0.90) with the data-sharing model, and further improved to 0.85 (95% CI: 0.79-0.92) with the skeleton sharing model. The trend that the skeleton sharing model had an advantage in performance was similar in the third cohort. CONCLUSIONS We established an expert-inspired model with better predictive performance and interpretability than the traditional constructed model. Skeleton sharing process is superior to data sharing and direct model implementation in model implementation.
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Affiliation(s)
- Han Xiao
- Department of Medical Ultrasonics, Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yuchen Guo
- Institute for Brain and Cognitive Sciences, BRNist, Tsinghua University, Beijing, China
| | - Qian Zhou
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Qiaofeng Chen
- Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | | | - Shuling Chen
- Department of Medical Ultrasonics, Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Shunjun Fu
- General Surgery Center, Department of Hepatobiliary Surgery II, Guangdong Provincial Research Center for Artificial Organ and Tissue Engineering, Guangzhou Clinical Research and Transformation Center for Artificial Liver, Institute of Regenerative Medicine, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, China
| | - Jie Lin
- Department of Liver and Pancreatobiliary Surgery, Shunde Hospital of Southern Medical University, Shunde, Guangdong, China
| | | | - Xinming Song
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Sui Peng
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.,Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yuhua Huang
- Department of Pathology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in Southern China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Jingxian Shen
- Department of Medical Imaging, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in Southern China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Ming Kuang
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
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Kalra G, Kar SS, Sevgi DD, Madabhushi A, Srivastava SK, Ehlers JP. Quantitative Imaging Biomarkers in Age-Related Macular Degeneration and Diabetic Eye Disease: A Step Closer to Precision Medicine. J Pers Med 2021; 11:1161. [PMID: 34834513 PMCID: PMC8622761 DOI: 10.3390/jpm11111161] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 10/31/2021] [Accepted: 11/04/2021] [Indexed: 01/21/2023] Open
Abstract
The management of retinal diseases relies heavily on digital imaging data, including optical coherence tomography (OCT) and fluorescein angiography (FA). Targeted feature extraction and the objective quantification of features provide important opportunities in biomarker discovery, disease burden assessment, and predicting treatment response. Additional important advantages include increased objectivity in interpretation, longitudinal tracking, and ability to incorporate computational models to create automated diagnostic and clinical decision support systems. Advances in computational technology, including deep learning and radiomics, open new doors for developing an imaging phenotype that may provide in-depth personalized disease characterization and enhance opportunities in precision medicine. In this review, we summarize current quantitative and radiomic imaging biomarkers described in the literature for age-related macular degeneration and diabetic eye disease using imaging modalities such as OCT, FA, and OCT angiography (OCTA). Various approaches used to identify and extract these biomarkers that utilize artificial intelligence and deep learning are also summarized in this review. These quantifiable biomarkers and automated approaches have unleashed new frontiers of personalized medicine where treatments are tailored, based on patient-specific longitudinally trackable biomarkers, and response monitoring can be achieved with a high degree of accuracy.
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Affiliation(s)
- Gagan Kalra
- Cole Eye Institute, Cleveland Clinic, Cleveland, OH 44195, USA; (G.K.); (D.D.S.); (S.K.S.)
- Tony and Leona Campane Center for Excellence in Image-Guided Surgery & Advanced, Cleveland Clinic, Cleveland, OH 44195, USA;
| | - Sudeshna Sil Kar
- Tony and Leona Campane Center for Excellence in Image-Guided Surgery & Advanced, Cleveland Clinic, Cleveland, OH 44195, USA;
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA;
| | - Duriye Damla Sevgi
- Cole Eye Institute, Cleveland Clinic, Cleveland, OH 44195, USA; (G.K.); (D.D.S.); (S.K.S.)
- Tony and Leona Campane Center for Excellence in Image-Guided Surgery & Advanced, Cleveland Clinic, Cleveland, OH 44195, USA;
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA;
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH 44106, USA
| | - Sunil K. Srivastava
- Cole Eye Institute, Cleveland Clinic, Cleveland, OH 44195, USA; (G.K.); (D.D.S.); (S.K.S.)
- Tony and Leona Campane Center for Excellence in Image-Guided Surgery & Advanced, Cleveland Clinic, Cleveland, OH 44195, USA;
| | - Justis P. Ehlers
- Cole Eye Institute, Cleveland Clinic, Cleveland, OH 44195, USA; (G.K.); (D.D.S.); (S.K.S.)
- Tony and Leona Campane Center for Excellence in Image-Guided Surgery & Advanced, Cleveland Clinic, Cleveland, OH 44195, USA;
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Sil Kar S, Sevgi DD, Dong V, Srivastava SK, Madabhushi A, Ehlers JP. Multi-Compartment Spatially-Derived Radiomics From Optical Coherence Tomography Predict Anti-VEGF Treatment Durability in Macular Edema Secondary to Retinal Vascular Disease: Preliminary Findings. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2021; 9:1000113. [PMID: 34350068 PMCID: PMC8328398 DOI: 10.1109/jtehm.2021.3096378] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 06/06/2021] [Accepted: 07/05/2021] [Indexed: 12/30/2022]
Abstract
OBJECTIVE Diabetic macular edema (DME) and retinal vein occlusion (RVO) are the leading causes of visual impairments across the world. Vascular endothelial growth factor (VEGF) stimulates breakdown of blood-retinal barrier that causes accumulation of fluid within macula. Anti-VEGF therapy is the first-line treatment for both the diseases; however, the degree of response varies for individual patients. The main objective of this work was to identify the (i) texture-based radiomics features within individual fluid and retinal tissue compartments of baseline spectral-domain optical coherence tomography (SD-OCT) images and (ii) the specific spatial compartments that contribute most pertinent features for predicting therapeutic response. METHODS A total of 962 texture-based radiomics features were extracted from each of the fluid and retinal tissue compartments of OCT images, obtained from the PERMEATE study. Top-performing features selected from the consensus of different feature selection methods were evaluated in conjunction with four different machine learning classifiers: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Random Forest (RF), and Support Vector Machine (SVM) in a cross-validated approach to distinguish eyes tolerating extended interval dosing (non-rebounders) and those requiring more frequent dosing (rebounders). RESULTS Combination of fluid and retinal tissue features yielded a cross-validated area under receiver operating characteristic curve (AUC) of 0.78±0.08 in distinguishing rebounders from non-rebounders. CONCLUSIONS This study revealed that the texture-based radiomics features pertaining to IRF subcompartment were most discriminating between rebounders and non-rebounders to anti-VEGF therapy. Clinical Impact: With further validation, OCT-based imaging biomarkers could be used for treatment management of DME patients.
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Affiliation(s)
- Sudeshna Sil Kar
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOH44106USA
| | - Duriye Damla Sevgi
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advancing Imaging ResearchCleveland Clinic Cole Eye InstituteClevelandOH44106USA
| | - Vincent Dong
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOH44106USA
| | - Sunil K. Srivastava
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advancing Imaging ResearchCleveland Clinic Cole Eye InstituteClevelandOH44106USA
| | - Anant Madabhushi
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOH44106USA
| | - Justis P. Ehlers
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advancing Imaging ResearchCleveland Clinic Cole Eye InstituteClevelandOH44106USA
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