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Mai J, Lachinov D, Reiter GS, Riedl S, Grechenig C, Bogunovic H, Schmidt-Erfurth U. Deep Learning-Based Prediction of Individual Geographic Atrophy Progression from a Single Baseline OCT. OPHTHALMOLOGY SCIENCE 2024; 4:100466. [PMID: 38591046 PMCID: PMC11000109 DOI: 10.1016/j.xops.2024.100466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 11/08/2023] [Accepted: 01/09/2024] [Indexed: 04/10/2024]
Abstract
Objective To identify the individual progression of geographic atrophy (GA) lesions from baseline OCT images of patients in routine clinical care. Design Clinical evaluation of a deep learning-based algorithm. Subjects One hundred eighty-four eyes of 100 consecutively enrolled patients. Methods OCT and fundus autofluorescence (FAF) images (both Spectralis, Heidelberg Engineering) of patients with GA secondary to age-related macular degeneration in routine clinical care were used for model validation. Fundus autofluorescence images were annotated manually by delineating the GA area by certified readers of the Vienna Reading Center. The annotated FAF images were anatomically registered in an automated manner to the corresponding OCT scans, resulting in 2-dimensional en face OCT annotations, which were taken as a reference for the model performance. A deep learning-based method for modeling the GA lesion growth over time from a single baseline OCT was evaluated. In addition, the ability of the algorithm to identify fast progressors for the top 10%, 15%, and 20% of GA growth rates was analyzed. Main Outcome Measures Dice similarity coefficient (DSC) and mean absolute error (MAE) between manual and predicted GA growth. Results The deep learning-based tool was able to reliably identify disease activity in GA using a standard OCT image taken at a single baseline time point. The mean DSC for the total GA region increased for the first 2 years of prediction (0.80-0.82). With increasing time intervals beyond 3 years, the DSC decreased slightly to a mean of 0.70. The MAE was low over the first year and with advancing time slowly increased, with mean values ranging from 0.25 mm to 0.69 mm for the total GA region prediction. The model achieved an area under the curve of 0.81, 0.79, and 0.77 for the identification of the top 10%, 15%, and 20% growth rates, respectively. Conclusions The proposed algorithm is capable of fully automated GA lesion growth prediction from a single baseline OCT in a time-continuous fashion in the form of en face maps. The results are a promising step toward clinical decision support tools for therapeutic dosing and guidance of patient management because the first treatment for GA has recently become available. 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)
- Julia Mai
- OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Dmitrii Lachinov
- OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Gregor S. Reiter
- OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Sophie Riedl
- OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Christoph Grechenig
- OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Hrvoje Bogunovic
- OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Ursula Schmidt-Erfurth
- OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
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Crincoli E, Sacconi R, Querques G. Reshaping the use of Artificial Intelligence in Ophthalmology: Sometimes you Need to go Backwards. Retina 2023; 43:1429-1432. [PMID: 37343295 DOI: 10.1097/iae.0000000000003878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/23/2023]
Affiliation(s)
- Emanuele Crincoli
- Department of Ophthalmology, University Vita-Salute, IRCCS San Raffaele Scientific Institute, Milan, Italy
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Shen J, Chen Z, Peng Y, Zhang S, Xu C, Zhu W, Liu H, Chen X. Morphological prognosis prediction of choroid neovascularization from longitudinal SD-OCT images. Med Phys 2023; 50:4839-4853. [PMID: 36789971 DOI: 10.1002/mp.16294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 01/09/2023] [Accepted: 01/31/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND Choroid neovascularization (CNV) has no obvious symptoms in the early stage, but with its gradual expansion, leakage, rupture, and bleeding, it can cause vision loss and central scotoma. In some severe cases, it will lead to permanent visual impairment. PURPOSE Accurate prediction of disease progression can greatly help ophthalmologists to formulate appropriate treatment plans and prevent further deterioration of the disease. Therefore, we aim to predict the growth trend of CNV to help the attending physician judge the effectiveness of treatment. METHODS In this paper, we develop a CNN-based method for CNV growth prediction. To achieve this, we first design a registration network to rigidly register the spectral domain optical coherence tomography (SD-OCT) B-scans of each subject at different time points to eliminate retinal displacements of longitudinal data. Then, considering the correlation of longitudinal data, we propose a co-segmentation network with a correlation attention guidance (CAG) module to cooperatively segment CNV lesions of a group of follow-up images and use them as input for growth prediction. Finally, based on the above registration and segmentation networks, an encoder-recurrent-decoder framework is developed for CNV growth prediction, in which an attention-based gated recurrent unit (AGRU) is embedded as the recurrent neural network to recurrently learn robust representations. RESULTS The registration network rigidly registers the follow-up images of patients to the reference images with a root mean square error (RMSE) of 6.754 pixels. And compared with other state-of-the-art segmentation methods, the proposed segmentation network achieves high performance with the Dice similarity coefficients (Dsc) of 85.27%. Based on the above experiments, the proposed growth prediction network can play a role in predicting the future CNV morphology, and the predicted CNV has a Dsc of 83.69% with the ground truth, which is significantly consistent with the actual follow-up visit. CONCLUSION The proposed registration and segmentation networks provide the possibility for growth prediction. In addition, accurately predicting the growth of CNV enables us to know the efficacy of the drug against individuals in advance, creating opportunities for formulating appropriate treatment plans.
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Affiliation(s)
- Jiayan Shen
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu Province, China
| | - Zhongyue Chen
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu Province, China
| | - Yuanyuan Peng
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu Province, China
| | - Siqi Zhang
- Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai, China
| | - Chenan Xu
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu Province, China
| | - Weifang Zhu
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu Province, China
| | - Haiyun Liu
- Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai, China
| | - Xinjian Chen
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu Province, China
- State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou, China
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Muntean GA, Marginean A, Groza A, Damian I, Roman SA, Hapca MC, Muntean MV, Nicoară SD. The Predictive Capabilities of Artificial Intelligence-Based OCT Analysis for Age-Related Macular Degeneration Progression-A Systematic Review. Diagnostics (Basel) 2023; 13:2464. [PMID: 37510207 PMCID: PMC10378064 DOI: 10.3390/diagnostics13142464] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 06/16/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
The era of artificial intelligence (AI) has revolutionized our daily lives and AI has become a powerful force that is gradually transforming the field of medicine. Ophthalmology sits at the forefront of this transformation thanks to the effortless acquisition of an abundance of imaging modalities. There has been tremendous work in the field of AI for retinal diseases, with age-related macular degeneration being at the top of the most studied conditions. The purpose of the current systematic review was to identify and evaluate, in terms of strengths and limitations, the articles that apply AI to optical coherence tomography (OCT) images in order to predict the future evolution of age-related macular degeneration (AMD) during its natural history and after treatment in terms of OCT morphological structure and visual function. After a thorough search through seven databases up to 1 January 2022 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 1800 records were identified. After screening, 48 articles were selected for full-text retrieval and 19 articles were finally included. From these 19 articles, 4 articles concentrated on predicting the anti-VEGF requirement in neovascular AMD (nAMD), 4 articles focused on predicting anti-VEGF efficacy in nAMD patients, 3 articles predicted the conversion from early or intermediate AMD (iAMD) to nAMD, 1 article predicted the conversion from iAMD to geographic atrophy (GA), 1 article predicted the conversion from iAMD to both nAMD and GA, 3 articles predicted the future growth of GA and 3 articles predicted the future outcome for visual acuity (VA) after anti-VEGF treatment in nAMD patients. Since using AI methods to predict future changes in AMD is only in its initial phase, a systematic review provides the opportunity of setting the context of previous work in this area and can present a starting point for future research.
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Affiliation(s)
- George Adrian Muntean
- Department of Ophthalmology, "Iuliu Hatieganu" University of Medicine and Pharmacy, Emergency County Hospital, 400347 Cluj-Napoca, Romania
| | - Anca Marginean
- Department of Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Adrian Groza
- Department of Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Ioana Damian
- Department of Ophthalmology, "Iuliu Hatieganu" University of Medicine and Pharmacy, Emergency County Hospital, 400347 Cluj-Napoca, Romania
| | - Sara Alexia Roman
- Faculty of Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Mădălina Claudia Hapca
- Department of Ophthalmology, "Iuliu Hatieganu" University of Medicine and Pharmacy, Emergency County Hospital, 400347 Cluj-Napoca, Romania
| | - Maximilian Vlad Muntean
- Plastic Surgery Department, "Prof. Dr. I. Chiricuta" Institute of Oncology, 400015 Cluj-Napoca, Romania
| | - Simona Delia Nicoară
- Department of Ophthalmology, "Iuliu Hatieganu" University of Medicine and Pharmacy, Emergency County Hospital, 400347 Cluj-Napoca, Romania
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Zhang Y, Huang K, Li M, Yuan S, Chen Q. Learn Single-horizon Disease Evolution for Predictive Generation of Post-therapeutic Neovascular Age-related Macular Degeneration. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 230:107364. [PMID: 36716636 DOI: 10.1016/j.cmpb.2023.107364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 01/16/2023] [Accepted: 01/20/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Most of the existing disease prediction methods in the field of medical image processing fall into two classes, namely image-to-category predictions and image-to-parameter predictions.Few works have focused on image-to-image predictions. Different from multi-horizon predictions in other fields, ophthalmologists prefer to show more confidence in single-horizon predictions due to the low tolerance of predictive risk. METHODS We propose a single-horizon disease evolution network (SHENet) to predictively generate post-therapeutic SD-OCT images by inputting pre-therapeutic SD-OCT images with neovascular age-related macular degeneration (nAMD). In SHENet, a feature encoder converts the input SD-OCT images to deep features, then a graph evolution module predicts the process of disease evolution in high-dimensional latent space and outputs the predicted deep features, and lastly, feature decoder recovers the predicted deep features to SD-OCT images. We further propose an evolution reinforcement module to ensure the effectiveness of disease evolution learning and obtain realistic SD-OCT images by adversarial training. RESULTS SHENet is validated on 383 SD-OCT cubes of 22 nAMD patients based on three well-designed schemes (P-0, P-1 and P-M) based on the quantitative and qualitative evaluations. Three metrics (PSNR, SSIM, 1-LPIPS) are used here for quantitative evaluations. Compared with other generative methods, the generative SD-OCT images of SHENet have the highest image quality (P-0: 23.659, P-1: 23.875, P-M: 24.198) by PSNR. Besides, SHENet achieves the best structure protection (P-0: 0.326, P-1: 0.337, P-M: 0.349) by SSIM and content prediction (P-0: 0.609, P-1: 0.626, P-M: 0.642) by 1-LPIPS. Qualitative evaluations also demonstrate that SHENet has a better visual effect than other methods. CONCLUSIONS SHENet can generate post-therapeutic SD-OCT images with both high prediction performance and good image quality, which has great potential to help ophthalmologists forecast the therapeutic effect of nAMD.
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Affiliation(s)
- Yuhan Zhang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
| | - Kun Huang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
| | - Mingchao Li
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
| | - Songtao Yuan
- Department of Ophthalmology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, 210094, China.
| | - Qiang Chen
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
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Anegondi N, Gao SS, Steffen V, Spaide RF, Sadda SR, Holz FG, Rabe C, Honigberg L, Newton EM, Cluceru J, Kawczynski MG, Bengtsson T, Ferrara D, Yang Q. Deep Learning to Predict Geographic Atrophy Area and Growth Rate from Multimodal Imaging. Ophthalmol Retina 2023; 7:243-252. [PMID: 36038116 DOI: 10.1016/j.oret.2022.08.018] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 08/04/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022]
Abstract
OBJECTIVE To develop deep learning models for annualized geographic atrophy (GA) growth rate prediction using fundus autofluorescence (FAF) images and spectral-domain OCT volumes from baseline visits, which can be used for prognostic covariate adjustment to increase power of clinical trials. DESIGN This retrospective analysis estimated GA growth rate as the slope of a linear fit on all available measurements of lesion area over a 2-year period. Three multitask deep learning models-FAF-only, OCT-only, and multimodal (FAF and OCT)-were developed to predict concurrent GA area and annualized growth rate. PARTICIPANTS Patients were from prospective and observational lampalizumab clinical trials. METHODS The 3 models were trained on the development data set, tested on the holdout set, and further evaluated on the independent test sets. Baseline FAF images and OCT volumes from study eyes of patients with bilateral GA (NCT02247479; NCT02247531; and NCT02479386) were split into development (1279 patients/eyes) and holdout (443 patients/eyes) sets. Baseline FAF images from study eyes of NCT01229215 (106 patients/eyes) and NCT02399072 (169 patients/eyes) were used as independent test sets. MAIN OUTCOME MEASURES Model performance was evaluated using squared Pearson correlation coefficient (r2) between observed and predicted lesion areas/growth rates. Confidence intervals were calculated by bootstrap resampling (B = 10 000). RESULTS On the holdout data set, r2 (95% confidence interval) of the FAF-only, OCT-only, and multimodal models for GA lesion area prediction was 0.96 (0.95-0.97), 0.91 (0.87-0.95), and 0.94 (0.92-0.96), respectively, and for GA growth rate prediction was 0.48 (0.41-0.55), 0.36 (0.29-0.43), and 0.47 (0.40-0.54), respectively. On the 2 independent test sets, r2 of the FAF-only model for GA lesion area was 0.98 (0.97-0.99) and 0.95 (0.93-0.96), and for GA growth rate was 0.65 (0.52-0.75) and 0.47 (0.34-0.60). CONCLUSIONS We show the feasibility of using baseline FAF images and OCT volumes to predict individual GA area and growth rates using a multitask deep learning approach. The deep learning-based growth rate predictions could be used for covariate adjustment to increase power of clinical trials. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found after the references.
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Affiliation(s)
- Neha Anegondi
- Clinical Imaging Group, Genentech, Inc., South San Francisco, California; Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California
| | - Simon S Gao
- Clinical Imaging Group, Genentech, Inc., South San Francisco, California; Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California
| | - Verena Steffen
- Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California; Biostatistics, Genentech, Inc., South San Francisco, California
| | - Richard F Spaide
- Vitreous Retina Macula Consultants of New York, New York, New York
| | - SriniVas R Sadda
- Doheny Eye Institute, Los Angeles, California; Department of Ophthalmology, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California
| | - Frank G Holz
- Department of Ophthalmology and GRADE Reading Center, University of Bonn, Bonn, Germany
| | - Christina Rabe
- Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California; Biostatistics, Genentech, Inc., South San Francisco, California
| | - Lee Honigberg
- Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California; Biomarker Development, Genentech, Inc., South San Francisco, California
| | - Elizabeth M Newton
- Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California
| | - Julia Cluceru
- Clinical Imaging Group, Genentech, Inc., South San Francisco, California; Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California
| | - Michael G Kawczynski
- Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California; Data Science Imaging, Genentech, Inc., South San Francisco, California
| | - Thomas Bengtsson
- Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California; Data Science Imaging, Genentech, Inc., South San Francisco, California
| | - Daniela Ferrara
- Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California
| | - Qi Yang
- Roche Ophthalmology Personalized Healthcare, Genentech, Inc., South San Francisco, California; Data Science Imaging, Genentech, Inc., South San Francisco, California.
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Li Y, Wu Y, Huang M, Zhang Y, Bai Z. Automatic prostate and peri-prostatic fat segmentation based on pyramid mechanism fusion network for T2-weighted MRI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 223:106918. [PMID: 35779461 DOI: 10.1016/j.cmpb.2022.106918] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 05/10/2022] [Accepted: 05/25/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Automatic and accurate segmentation of prostate and peri-prostatic fat in male pelvic MRI images is a critical step in the diagnosis and prognosis of prostate cancer. The boundary of prostate tissue is not clear, which makes the task of automatic segmentation very challenging. The main issues, especially for the peri-prostatic fat, which is being offered for the first time, are hazy boundaries and a large form variation. METHODS We propose a pyramid mechanism fusion network (PMF-Net) to learn global features and more comprehensive context information. In the proposed PMF-Net, we devised two pyramid techniques in particular. A pyramid mechanism module made of dilated convolutions of varying rates is inserted before each down sample of the fundamental network architecture encoder. The module is intended to address the issue of information loss during the feature coding process, particularly in the case of segmentation object boundary information. In the transition stage from encoder to decoder, pyramid fusion module is designed to extract global features. The features of the decoder not only integrate the features of the previous stage after up sampling and the output features of pyramid mechanism, but also include the features of skipping connection transmission under the same scale of the encoder. RESULTS The segmentation results of prostate and peri-prostatic fat on numerous diverse male pelvic MRI datasets show that our proposed PMF-Net has higher performance than existing methods. The average surface distance (ASD) and Dice similarity coefficient (DSC) of prostate segmentation results reached 10.06 and 90.21%, respectively. The ASD and DSC of the peri-prostatic fat segmentation results reached 50.96 and 82.41%. CONCLUSIONS The results of our segmentation are substantially connected and consistent with those of expert manual segmentation. Furthermore, peri-prostatic fat segmentation is a new issue, and good automatic segmentation has substantial therapeutic implications.
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Affiliation(s)
- Yuchun Li
- State Key Laboratory of Marine Resource Utilization in South China Sea, College of Information Science and Technology, Hainan University, Haikou 570288, China
| | - Yuanyuan Wu
- State Key Laboratory of Marine Resource Utilization in South China Sea, College of Information Science and Technology, Hainan University, Haikou 570288, China
| | - Mengxing Huang
- State Key Laboratory of Marine Resource Utilization in South China Sea, College of Information Science and Technology, Hainan University, Haikou 570288, China.
| | - Yu Zhang
- School of Computer science and Technology, Hainan University, Haikou 570288, China
| | - Zhiming Bai
- Haikou Municipal People's Hospital and Central South University Xiangya Medical College Affiliated Hospital, Haikou 570288, China
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Gigon A, Mosinska A, Montesel A, Derradji Y, Apostolopoulos S, Ciller C, De Zanet S, Mantel I. Personalized Atrophy Risk Mapping in Age-Related Macular Degeneration. Transl Vis Sci Technol 2021; 10:18. [PMID: 34767623 PMCID: PMC8590159 DOI: 10.1167/tvst.10.13.18] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Purpose To develop and validate an automatic retinal pigment epithelial and outer retinal atrophy (RORA) progression prediction model for nonexudative age-related macular degeneration (AMD) cases in optical coherence tomography (OCT) scans. Methods Longitudinal OCT data from 129 eyes/119 patients with RORA was collected and separated into training and testing groups. RORA was automatically segmented in all scans and additionally manually annotated in the test scans. OCT-based features such as layers thicknesses, mean reflectivity, and a drusen height map served as an input to the deep neural network. Based on the baseline OCT scan or the previous visit OCT, en face RORA predictions were calculated for future patient visits. The performance was quantified over time with the means of Dice scores and square root area errors. Results The average Dice score for segmentations at baseline was 0.85. When predicting progression from baseline OCTs, the Dice scores ranged from 0.73 to 0.80 for total RORA area and from 0.46 to 0.72 for RORA growth region. The square root area error ranged from 0.13 mm to 0.33 mm. By providing continuous time output, the model enabled creation of a patient-specific atrophy risk map. Conclusions We developed a machine learning method for RORA progression prediction, which provides continuous-time output. It was used to compute atrophy risk maps, which indicate time-to-RORA-conversion, a novel and clinically relevant way of representing disease progression. Translational Relevance Application of recent advances in artificial intelligence to predict patient-specific progression of atrophic AMD.
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Affiliation(s)
- Anthony Gigon
- Department of Ophthalmology, University of Lausanne, Jules-Gonin Eye Hospital, Fondation Asile des Aveugles, Lausanne, Switzerland
| | | | - Andrea Montesel
- Department of Ophthalmology, University of Lausanne, Jules-Gonin Eye Hospital, Fondation Asile des Aveugles, Lausanne, Switzerland
| | - Yasmine Derradji
- Department of Ophthalmology, University of Lausanne, Jules-Gonin Eye Hospital, Fondation Asile des Aveugles, Lausanne, Switzerland
| | | | | | | | - Irmela Mantel
- Department of Ophthalmology, University of Lausanne, Jules-Gonin Eye Hospital, Fondation Asile des Aveugles, Lausanne, Switzerland
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