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1. Unpaired Optical Coherence Tomography Angiography Image Super-Resolution via Frequency-Aware Inverse-Consistency GAN

Author

Zhang, Weiwen, Yang, Dawei, Che, Haoxuan, Ran, An Ran, Cheung, Carol Y., and Chen, Hao

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

For optical coherence tomography angiography (OCTA) images, a limited scanning rate leads to a trade-off between field-of-view (FOV) and imaging resolution. Although larger FOV images may reveal more parafoveal vascular lesions, their application is greatly hampered due to lower resolution. To increase the resolution, previous works only achieved satisfactory performance by using paired data for training, but real-world applications are limited by the challenge of collecting large-scale paired images. Thus, an unpaired approach is highly demanded. Generative Adversarial Network (GAN) has been commonly used in the unpaired setting, but it may struggle to accurately preserve fine-grained capillary details, which are critical biomarkers for OCTA. In this paper, our approach aspires to preserve these details by leveraging the frequency information, which represents details as high-frequencies ($\textbf{hf}$) and coarse-grained backgrounds as low-frequencies ($\textbf{lf}$). In general, we propose a GAN-based unpaired super-resolution method for OCTA images and exceptionally emphasize $\textbf{hf}$ fine capillaries through a dual-path generator. To facilitate a precise spectrum of the reconstructed image, we also propose a frequency-aware adversarial loss for the discriminator and introduce a frequency-aware focal consistency loss for end-to-end optimization. Experiments show that our method outperforms other state-of-the-art unpaired methods both quantitatively and visually., Comment: 10 pages, 9 figures

Published
2023

2. Reference-based OCT Angiogram Super-resolution with Learnable Texture Generation

Author

Ruan, Yuyan, Yang, Dawei, Tang, Ziqi, Ran, An Ran, Cheung, Carol Y., and Chen, Hao

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, 68T07, I.2, I.4
Abstract

Optical coherence tomography angiography (OCTA) is a new imaging modality to visualize retinal microvasculature and has been readily adopted in clinics. High-resolution OCT angiograms are important to qualitatively and quantitatively identify potential biomarkers for different retinal diseases accurately. However, one significant problem of OCTA is the inevitable decrease in resolution when increasing the field-of-view given a fixed acquisition time. To address this issue, we propose a novel reference-based super-resolution (RefSR) framework to preserve the resolution of the OCT angiograms while increasing the scanning area. Specifically, textures from the normal RefSR pipeline are used to train a learnable texture generator (LTG), which is designed to generate textures according to the input. The key difference between the proposed method and traditional RefSR models is that the textures used during inference are generated by the LTG instead of being searched from a single reference image. Since the LTG is optimized throughout the whole training process, the available texture space is significantly enlarged and no longer limited to a single reference image, but extends to all textures contained in the training samples. Moreover, our proposed LTGNet does not require a reference image at the inference phase, thereby becoming invulnerable to the selection of the reference image. Both experimental and visual results show that LTGNet has superior performance and robustness over state-of-the-art methods, indicating good reliability and promise in real-life deployment. The source code will be made available upon acceptance., Comment: 12 pages, 11 figures

Published
2023

4. List of contributors

Author

Ahn, Joseph C., primary, Anand, Shankara, additional, Arnaout, O., additional, Bandyopadhyay, Anuja, additional, Bartholomew, Erin, additional, Bates, David W., additional, Bender, Sarah M.L., additional, Bhullar, Puneet K., additional, Bhutani, Tina, additional, Boaro, A., additional, Boyer, Edward W., additional, Brown, Ethan D.L., additional, Carreiro, Stephanie, additional, Chahla, Jorge, additional, Chandran, Viji Pulikkel, additional, Cheng, Ching-Yu, additional, Cheung, Carol Y., additional, Choudhary, Anirudh, additional, Choudhury, Avishek, additional, Chung, Mimi, additional, Ciurtin, Coziana, additional, Comfere, Nneka I., additional, Corriveau-Lecavalier, Nick, additional, Côté, Mélina, additional, del Alamo, Diego, additional, Dias, Roger D., additional, Dönnes, Pierre, additional, Duong, Dat, additional, Ebnali, Mahdi, additional, El Sherbini, Adham, additional, ElZarrad, M. Khair, additional, Fakhouri, Tala H., additional, Fakhoury, Marc, additional, Fein, Joshua A., additional, Fischer, Uwe M., additional, Glicksberg, Benjamin S., additional, Goldstein, Cathy, additional, Gonem, Sherif, additional, Green, Darren V.S., additional, Gupta, Raghav, additional, Hakimi, Marwa, additional, Halamka, John D., additional, Han, Christina S., additional, Hannah-Shmouni, Fady, additional, Harrer, Stefan, additional, Hazime, Ali Amer, additional, Howard, Michael A., additional, Hui, Herbert Y.H., additional, Ildardashty, Alexander, additional, J.F. Shaikh, Hashim, additional, Jeliazkov, Jeliazko R., additional, Jones, David T., additional, Juhn, Young J., additional, Jury, Elizabeth C., additional, Kandaswamy, Swaminathan, additional, Kang, Yanna, additional, Kann, Benjamin H., additional, Kaplin, Scott, additional, Karpiak, Joel, additional, Kassir, Elias, additional, Kaur, Harsimran, additional, Khan, Sohil, additional, Khatib, Reem, additional, Kherabi, Yousra, additional, Knake, Lindsey A., additional, Krittanawong, Chayakrit, additional, Kuhn, Veronica C., additional, Kunze, Kyle, additional, Lamarche, Benoît, additional, Laplante, Simon, additional, Lehmann, Lisa Soleymani, additional, Li, Dawei, additional, Liao, Wilson, additional, Likitlersuang, Jirapat, additional, Liu, Qi, additional, Madani, Amin, additional, Malik, Momin M., additional, Mann, Matthias, additional, Masood, Sameer, additional, Mathur, Piyush, additional, McManus, Sean, additional, Menard, Jeffrey, additional, Mivalt, Filip, additional, Murphree, Dennis, additional, Myers, Thomas G., additional, Narayan, Sanjiv M., additional, Natarajan, Vivek, additional, Pandav, Krunal, additional, Parwani, Anil V., additional, Pedraza Bermeo, Adriana Marcela, additional, Peiffer-Smadja, Nathan, additional, Peng, Junjie, additional, Peng, Lily, additional, Peters, Margot S., additional, Petrick, Nicholas, additional, Polce, Evan, additional, Poojari, Pooja Gopal, additional, Price, W. Nicholson, additional, Raghavan, Lavanya, additional, Rajan, Asha K., additional, Ran, An Ran, additional, Rangu, Sowmith, additional, Rashid, Muhammed, additional, Reddy, Charitha D., additional, Rider, Nicholas L., additional, Rigatti, Marc, additional, Rivers, Michael, additional, Robinson, George, additional, Rogers, Albert J., additional, Rogerson, Colin M., additional, Ryu, Euijung, additional, Sahiner, Berkman, additional, Sarnaik, Kunaal, additional, Shafi, Saba, additional, Shah, Vijay H., additional, Shapovalov, Maxim V., additional, Sharma, Samin K, additional, Shekhar, Skand, additional, Shin, Harold, additional, Shouval, Roni, additional, Smith, Kenneth, additional, Sokumbi, Olayemi, additional, Solomon, Benjamin D., additional, Somani, Sulaiman S., additional, Sternke, Matt C., additional, Strauss, Maximillian T., additional, Syrowatka, Ania, additional, Tang, W. H. Wilson, additional, Teven, Chad M., additional, Tewari, Ashutosh Kumar, additional, Thunga, Girish, additional, Urena, Estefania, additional, Verma, Ashish, additional, Vietas, Jay, additional, Waikel, Rebekah L., additional, Wi, Chung-Il, additional, Winter, Meredith C., additional, Wong, Melissa S., additional, Wong, Tien Yin, additional, Wu, Chao-Ping, additional, Yeroushalmi, Samuel, additional, Zenati, Marco A., additional, Zeng, Wen-Feng, additional, and Zheng, Yingfeng, additional

Published
2024
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7. Finding New Diagnostic Information for Detecting Glaucoma using Neural Networks

Author

Noury, Erfan, Mannil, Suria S., Chang, Robert T., Ran, An Ran, Cheung, Carol Y., Thapa, Suman S., Rao, Harsha L., Dasari, Srilakshmi, Riyazuddin, Mohammed, Chang, Dolly, Nagaraj, Sriharsha, Tham, Clement C., and Zadeh, Reza

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

We describe a new approach to automated Glaucoma detection in 3D Spectral Domain Optical Coherence Tomography (OCT) optic nerve scans. First, we gathered a unique and diverse multi-ethnic dataset of OCT scans consisting of glaucoma and non-glaucomatous cases obtained from four tertiary care eye hospitals located in four different countries. Using this longitudinal data, we achieved state-of-the-art results for automatically detecting Glaucoma from a single raw OCT using a 3D Deep Learning system. These results are close to human doctors in a variety of settings across heterogeneous datasets and scanning environments. To verify correctness and interpretability of the automated categorization, we used saliency maps to find areas of focus for the model. Matching human doctor behavior, the model predictions indeed correlated with the conventional diagnostic parameters in the OCT printouts, such as the retinal nerve fiber layer. We further used our model to find new areas in the 3D data that are presently not being identified as a diagnostic parameter to detect glaucoma by human doctors. Namely, we found that the Lamina Cribrosa (LC) region can be a valuable source of helpful diagnostic information previously unavailable to doctors during routine clinical care because it lacks a quantitative printout. Our model provides such volumetric quantification of this region. We found that even when a majority of the RNFL is removed, the LC region can distinguish glaucoma. This is clinically relevant in high myopes, when the RNFL is already reduced, and thus the LC region may help differentiate glaucoma in this confounding situation. We further generalize this approach to create a new algorithm called DiagFind that provides a recipe for finding new diagnostic information in medical imagery that may have been previously unusable by doctors., Comment: 28 pages, 12 figures, 15 tables, title changed, new authors added

Published
2019

8. High Myopia Normative Database of Peripapillary Retinal Nerve Fiber Layer Thickness to Detect Myopic Glaucoma in a Chinese Population

Author

Zhang, Xiulan, Liu, Yizhi, Lv, Lin, Friedman, David S., Jonas, Jost B., Aung, Tin, Chen, Shida, Wang, Wei, Lin, Fengbin, Song, Yunhe, Wang, Peiyuan, Li, Fei, Gao, Kai, Liu, Bingqian, Liu, Yuhong, Chen, Meiling, Bressler, Neil M., Park, Ki Ho, Lam, Dennis S.C., He, Mingguang, Ohno-Matsui, Kyoko, Weinreb, Robert N., Cheng, Ching-Yu, Healey, Paul, Zangwill, Linda M., Chen, Xiang, Tang, Guangxian, Jin, Ling, Chong, Rachel S., Ran, An Ran, Wang, Zhenyu, Jiang, Jingwen, Kong, Kangjie, Sun, Jian, Wang, Deming, Tham, Clement C., Sun, Xiaodong, and Cheung, Carol Y.

Published
2023
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9. Unifying Structure Analysis and Surrogate-driven Function Regression for Glaucoma OCT Image Screening

Author

Wang, Xi, Chen, Hao, Luo, Luyang, Ran, An-ran, Chan, Poemen P., Tham, Clement C., Cheung, Carol Y., and Heng, Pheng-Ann

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Optical Coherence Tomography (OCT) imaging plays an important role in glaucoma diagnosis in clinical practice. Early detection and timely treatment can prevent glaucoma patients from permanent vision loss. However, only a dearth of automated methods has been developed based on OCT images for glaucoma study. In this paper, we present a novel framework to effectively classify glaucoma OCT images from normal ones. A semi-supervised learning strategy with smoothness assumption is applied for surrogate assignment of missing function regression labels. Besides, the proposed multi-task learning network is capable of exploring the structure and function relationship from the OCT image and visual field measurement simultaneously, which contributes to classification performance boosting. Essentially, we are the first to unify the structure analysis and function regression for glaucoma screening. It is also worth noting that we build the largest glaucoma OCT image dataset involving 4877 volumes to develop and evaluate the proposed method. Extensive experiments demonstrate that our framework outperforms the baseline methods and two glaucoma experts by a large margin, achieving 93.2%, 93.2% and 97.8% on accuracy, F1 score and AUC, respectively., Comment: 9 pages, 2 figures, MICCAI2019

Published
2019

12. A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study

Author

Cheung, Carol Y, Ran, An Ran, Wang, Shujun, Chan, Victor T T, Sham, Kaiser, Hilal, Saima, Venketasubramanian, Narayanaswamy, Cheng, Ching-Yu, Sabanayagam, Charumathi, Tham, Yih Chung, Schmetterer, Leopold, McKay, Gareth J, Williams, Michael A, Wong, Adrian, Au, Lisa W C, Lu, Zhihui, Yam, Jason C, Tham, Clement C, Chen, John J, Dumitrascu, Oana M, Heng, Pheng-Ann, Kwok, Timothy C Y, Mok, Vincent C T, Milea, Dan, Chen, Christopher Li-Hsian, and Wong, Tien Yin

Published
2022
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16. Performance of Artificial Intelligence in Detecting Diabetic Macular Edema From Fundus Photography and Optical Coherence Tomography Images: A Systematic Review and Meta-analysis.

Author

Lam, Ching, Wong, Yiu Lun, Tang, Ziqi, Hu, Xiaoyan, Nguyen, Truong X., Yang, Dawei, Zhang, Shuyi, Ding, Jennifer, Szeto, Simon K.H., Ran, An Ran, and Cheung, Carol Y.

Subjects
OPTICAL coherence tomography, MACULAR edema, ARTIFICIAL intelligence, RECEIVER operating characteristic curves, PEOPLE with diabetes
Abstract

BACKGROUND: Diabetic macular edema (DME) is the leading cause of vision loss in people with diabetes. Application of artificial intelligence (AI) in interpreting fundus photography (FP) and optical coherence tomography (OCT) images allows prompt detection and intervention. PURPOSE: To evaluate the performance of AI in detecting DME from FP or OCT images and identify potential factors affecting model performances. DATA SOURCES: We searched seven electronic libraries up to 12 February 2023. STUDY SELECTION: We included studies using AI to detect DME from FP or OCT images. DATA EXTRACTION: We extracted study characteristics and performance parameters. DATA SYNTHESIS: Fifty-three studies were included in the meta-analysis. FP-based algorithms of 25 studies yielded pooled area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity of 0.964, 92.6%, and 91.1%, respectively. OCT-based algorithms of 28 studies yielded pooled AUROC, sensitivity, and specificity of 0.985, 95.9%, and 97.9%, respectively. Potential factors improving model performance included deep learning techniques, larger size, and more diversity in training data sets. Models demonstrated better performance when validated internally than externally, and those trained with multiple data sets showed better results upon external validation. LIMITATIONS: Analyses were limited by unstandardized algorithm outcomes and insufficient data in patient demographics, OCT volumetric scans, and external validation. CONCLUSIONS: This meta-analysis demonstrates satisfactory performance of AI in detecting DME from FP or OCT images. External validation is warranted for future studies to evaluate model generalizability. Further investigations may estimate optimal sample size, effect of class balance, patient demographics, and additional benefits of OCT volumetric scans. [ABSTRACT FROM AUTHOR]

Published
2024
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17. High Myopia Normative Database of Peripapillary Retinal Nerve Fiber Layer Thickness to Detect Myopic Glaucoma in a Chinese Population

Author

Song, Yunhe, primary, Li, Fei, additional, Chong, Rachel S., additional, Wang, Wei, additional, Ran, An Ran, additional, Lin, Fengbin, additional, Wang, Peiyuan, additional, Wang, Zhenyu, additional, Jiang, Jingwen, additional, Kong, Kangjie, additional, Jin, Ling, additional, Chen, Meiling, additional, Sun, Jian, additional, Wang, Deming, additional, Tham, Clement C., additional, Lam, Dennis S.C., additional, Zangwill, Linda M., additional, Weinreb, Robert N., additional, Aung, Tin, additional, Jonas, Jost B., additional, Ohno-Matsui, Kyoko, additional, Cheng, Ching-Yu, additional, Bressler, Neil M., additional, Sun, Xiaodong, additional, Cheung, Carol Y., additional, Chen, Shida, additional, Zhang, Xiulan, additional, Liu, Yizhi, additional, Lv, Lin, additional, Friedman, David S., additional, Song, Yunhe, additional, Gao, Kai, additional, Liu, Bingqian, additional, Liu, Yuhong, additional, Park, Ki Ho, additional, He, Mingguang, additional, Healey, Paul, additional, Chen, Xiang, additional, and Tang, Guangxian, additional

Published
2023
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22. Developing a privacy-preserving deep learning model for glaucoma detection: a multicentre study with federated learning

Author

Ran, An Ran, Wang, Xi, Chan, Poemen P, Wong, Mandy O M, Yuen, Hunter, Lam, Nai Man, Chan, Noel C Y, Yip, Wilson W K, Young, Alvin L, Yung, Hon-Wah, Chang, Robert T, Mannil, Suria S, Tham, Yih-Chung, Cheng, Ching-Yu, Wong, Tien Yin, Pang, Chi Pui, Heng, Pheng-Ann, Tham, Clement C, and Cheung, Carol Y

Abstract

BackgroundDeep learning (DL) is promising to detect glaucoma. However, patients’ privacy and data security are major concerns when pooling all data for model development. We developed a privacy-preserving DL model using the federated learning (FL) paradigm to detect glaucoma from optical coherence tomography (OCT) images.MethodsThis is a multicentre study. The FL paradigm consisted of a ‘central server’ and seven eye centres in Hong Kong, the USA and Singapore. Each centre first trained a model locally with its own OCT optic disc volumetric dataset and then uploaded its model parameters to the central server. The central server used FedProx algorithm to aggregate all centres’ model parameters. Subsequently, the aggregated parameters are redistributed to each centre for its local model optimisation. We experimented with three three-dimensional (3D) networks to evaluate the stabilities of the FL paradigm. Lastly, we tested the FL model on two prospectively collected unseen datasets.ResultsWe used 9326 volumetric OCT scans from 2785 subjects. The FL model performed consistently well with different networks in 7 centres (accuracies 78.3%–98.5%, 75.9%–97.0%, and 78.3%–97.5%, respectively) and stably in the 2 unseen datasets (accuracies 84.8%-87.7%, 81.3%-84.8%, and 86.0%–87.8%, respectively). The FL model achieved non-inferior performance in classifying glaucoma compared with the traditional model and significantly outperformed the individual models.ConclusionThe 3D FL model could leverage all the datasets and achieve generalisable performance, without data exchange across centres. This study demonstrated an OCT-based FL paradigm for glaucoma identification with ensured patient privacy and data security, charting another course toward the real-world transition of artificial intelligence in ophthalmology.

Published
2024
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26. Clinically relevant factors associated with a binary outcome of diabetic macular ischaemia: an OCTA study.

Author

Da Wei Yang, Zi Qi Tang, Fang Yao Tang, Szeto, Simon K. H., Chan, Jason, Fanny Yip, Wong, Cherie Y. K., Ran, An Ran, Lai, Timothy Y. Y., and Cheung, Carol Y.

Abstract

Aims We investigated the demographic, ocular, diabetes-related and systemic factors associated with a binary outcome of diabetic macular ischaemia (DMI) as assessed by optical coherence tomography angiography (OCTA) evaluation of non-perfusion at the level of the superficial capillary plexus (SCP) and deep capillary plexus (DCP) in a cohort of patients with diabetes mellitus (DM). Materials and methods 617 patients with DM were recruited from July 2015 to December 2020 at the Chinese University of Hong Kong Eye Centre. Image quality assessment (gradable or ungradable for assessing DMI) and DMI evaluation (presence or absence of DMI) were assessed at the level of the SCP and DCP by OCTA. Results 1107 eyes from 593 subjects were included in the final analysis. 560 (50.59%) eyes had DMI at the level of SCP, and 647 (58.45%) eyes had DMI at the level of DCP. Among eyes without diabetic retinopathy (DR), DMI was observed in 19.40% and 24.13% of eyes at SCP and DCP, respectively. In the multivariable logistic regression models, older age, poorer visual acuity, thinner ganglion cell-inner plexiform layer thickness, worsened DR severity, higher haemoglobin A1c level, lower estimated glomerular filtration rate and higher low-density lipoprotein cholesterol level were associated with SCP-DMI. In addition to the aforementioned factors, presence of diabetic macular oedema and shorter axial length were associated with DCP-DMI. Conclusion We reported a series of associated factors of SCP-DMI and DCP-DMI. The binary outcome of DMI might promote a simplified OCTA-based DMI evaluation before subsequent quantitative analysis for assessing DMI extent and fulfil the urge for an updating diabetic retinal disease staging to be implemented with OCTA. [ABSTRACT FROM AUTHOR]

Published
2023
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29. Three-Dimensional Multi-Task Deep Learning Model to Detect Glaucomatous Optic Neuropathy and Myopic Features From Optical Coherence Tomography Scans: A Retrospective Multi-Centre Study

Author

Ran, An Ran, primary, Wang, Xi, additional, Chan, Poemen P., additional, Chan, Noel C., additional, Yip, Wilson, additional, Young, Alvin L., additional, Wong, Mandy O. M., additional, Yung, Hon-Wah, additional, Chang, Robert T., additional, Mannil, Suria S., additional, Tham, Yih Chung, additional, Cheng, Ching-Yu, additional, Chen, Hao, additional, Li, Fei, additional, Zhang, Xiulan, additional, Heng, Pheng-Ann, additional, Tham, Clement C., additional, and Cheung, Carol Y., additional

Published
2022
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30. Deep Learning for Glaucoma Detection and Identification of Novel Diagnostic Areas in Diverse Real-World Datasets

Author

Noury, Erfan, primary, Mannil, Suria S., additional, Chang, Robert T., additional, Ran, An Ran, additional, Cheung, Carol Y., additional, Thapa, Suman S., additional, Rao, Harsha L., additional, Dasari, Srilakshmi, additional, Riyazuddin, Mohammed, additional, Chang, Dolly, additional, Nagaraj, Sriharsha, additional, Tham, Clement C., additional, and Zadeh, Reza, additional

Published
2022
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32. At the Junction of Organic Solar Cells: Charge Generation and Recombination at Donor/Acceptor Interfaces

Author

Ran, Niva Ran

Subjects
Energy, Physics, Materials Science, Charge transfer state, Device Physics, Donor/acceptor interfaces, Energetic offsets, Organic photovoltaics, Organic semiconductors
Abstract

Heterojunction, what’s your function?In organic electronic devices composed of donor and acceptor semiconductors, the donor/acceptor interface is most typically the site with all the action: i.e. charge-carrier generation and recombination. Developing an understanding of the optimal geometry and energetics at this interface is necessary to optimize the active material and device architecture for their desired application. In this dissertation, we explore the role of morphology and energetics at the donor/acceptor interfaces on photovoltaic performance, but the results can be applied to any device with donor/acceptor heterojunctions.We begin our investigation by characterizing emission from small-molecule blends. We find a correlation between the emergence of phase separation and crystallinity, electroluminescence from the donor singlet-state, and good photovoltaic performance. Next, upon demonstrating control over the molecular orientation, we then uncover the genuine effects of molecular geometry at the donor/acceptor interface on charge generation and recombination: (i) Face-on devices have a higher open-circuit voltage, due to greater charge transfer state energy and radiative efficiency. (ii) Edge-on devices are more efficient at charge generation, which is attributed to a smaller electronic coupling and a lower activation energy for charge generation.From the perspective of energetics, we focus on a polymer-fullerene blend system with small energetic offsets. This system has very low potential losses: it achieves a high open circuit voltage relative to the energy of the absorbed photons. We characterize the energetic landscape in this blend and conclude that the blend has very high energetic order, and that potential losses associated with charge transfer have been minimized. Unfortunately, the blend is also characterized by exceptionally fast bimolecular recombination, most likely resulting from a highly-mixed blend morphology and charge-trapping effects. Nonetheless, these results are promising as they suggest that given an optimized morphology, organic solar cells (and other organic electronic devices) have more potential than we had previously believed.

Published
2016

33. A UWB CMOS transceiver.

Author

Behzad Razavi, Turgut Aytur, Christopher Lam 0002, Fei-Ran Yang, Kuang-Yu (Jason) Li, Ran-Hong (Ran) Yan, Han-Chang Kang, Cheng-Chung Hsu, and Chao-Cheng Lee

Published
2005
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35. Detection of Diabetic Retinopathy from Ultra-Widefield Scanning Laser Ophthalmoscope Images: A Multicenter Deep Learning Analysis

Author

Tang, Fangyao, primary, Luenam, Phoomraphee, additional, Ran, An Ran, additional, Quadeer, Ahmed Abdul, additional, Raman, Rajiv, additional, Sen, Piyali, additional, Khan, Rehana, additional, Giridhar, Anantharaman, additional, Haridas, Swathy, additional, Iglicki, Matias, additional, Zur, Dinah, additional, Loewenstein, Anat, additional, Negri, Hermino P., additional, Szeto, Simon, additional, Lam, Bryce Ka Yau, additional, Tham, Clement C., additional, Sivaprasad, Sobha, additional, Mckay, Matthew, additional, and Cheung, Carol Y., additional

Published
2021
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38. A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis

Author

Tang, Fangyao, primary, Wang, Xi, additional, Ran, An-ran, additional, Chan, Carmen K.M., additional, Ho, Mary, additional, Yip, Wilson, additional, Young, Alvin L., additional, Lok, Jerry, additional, Szeto, Simon, additional, Chan, Jason, additional, Yip, Fanny, additional, Wong, Raymond, additional, Tang, Ziqi, additional, Yang, Dawei, additional, Ng, Danny S., additional, Chen, Li Jia, additional, Brelén, Marten, additional, Chu, Victor, additional, Li, Kenneth, additional, Lai, Tracy H.T., additional, Tan, Gavin S., additional, Ting, Daniel S.W., additional, Huang, Haifan, additional, Chen, Haoyu, additional, Ma, Jacey Hongjie, additional, Tang, Shibo, additional, Leng, Theodore, additional, Kakavand, Schahrouz, additional, Mannil, Suria S., additional, Chang, Robert T., additional, Liew, Gerald, additional, Gopinath, Bamini, additional, Lai, Timothy Y.Y., additional, Pang, Chi Pui, additional, Scanlon, Peter H., additional, Wong, Tien Yin, additional, Tham, Clement C., additional, Chen, Hao, additional, Heng, Pheng-Ann, additional, and Cheung, Carol Y., additional

Published
2021
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40. Detecting Glaucoma Using 3D Convolutional Neural Network of Raw SD-OCT Optic Nerve Scans

Author

Noury, Erfan, Mannil, Suria S., Chang, Robert T., Ran, An Ran, Cheung, Carol Y., Thapa, Suman S., Rao, Harsha L., Dasari, Srilakshmi, Riyazuddin, Mohammed, Nagaraj, Sriharsha, and Zadeh, Reza

Subjects
glaucoma, genetic structures, retinal nerve fiber layer thickness (RNFL), sense organs, chronic progressive optic neuropathy, Spectral Domain Optical Coherence Tomography, eye diseases
Abstract

Background. Glaucoma is a chronic progressive optic neuropathy with characteristic visual field defects and corresponding structural changes, including nerve fiber layer thinning and optic nerve neuroretinal rim loss. These changes are traditionally monitored by SD-OCT (Spectral Domain Optical Coherence Tomography), which contains a large amount of 3D voxel information in a 6mm × 6mm × 2mm cube of data. However, only a fixed 3.4 mm diameter circle (2D slice) centered over the optic nerve is currently extracted using automated segmentation of the retinal nerve fiber layer thickness (RNFL). This RNFL thickness is reported relative to a normative database to help detect thinning and neuroretinal rim loss, which does not use the additional information in the optic nerve head cube. Clinicians rarely scroll through the entire cube. Therefore we propose developing and validating a three-dimensional (3D) deep learning system using the entire unprocessed OCT optic nerve volumes to distinguish true glaucoma from normals in order to discover any additional imaging biomarkers within the cube through saliency mapping. The algorithm has been validated against 4 additional distinct datasets from different countries using multimodal test results to define glaucoma rather than just the OCT alone. We hypothesize that the output from this 3D model, alongside a map of the regions where the model attends to make a prediction, can help identify novel diagnostic information in the cube. Methods. 2076 OCT (Cirrus SD-OCT, Carl Zeiss Meditec, Dublin, CA) 6 mm cubes centered over the optic nerve, 200 × 200 × 1024 volumes of 879 eyes (390 healthy and 489 glaucoma) from 487 patients, age 18-84 years, were exported from the Glaucoma Clinic Imaging Database at the Byers Eye Institute, Stanford University, from March 2010 to December 2017. This included bilateral eyes of 391 patients and unilateral eyes of 97 patients with a right eye to left eye ratio of 1.05:1. A 3D deep neural network was trained and tested on this unique OCT optic nerve head dataset from Stanford. 570 randomly selected optic nerve head cube scans of eyes with a diagnosis of glaucoma (True Glaucoma) and 342 scans of eyes with a normal diagnosis (True Normal) were used for training. A total of 81 scans of eyes with True Glaucoma and 32 scans of eyes with True Normal annotations were included in the primary validation set. 58 scans of eyes with True Glaucoma annotation and 50 scans of eyes with a True Normal annotation were included in the test set. A total of 3620 scans (all obtained using the Cirrus SD-OCT device) from 1458 eyes obtained from 4 different institutions, from United States (943 scans), Hong Kong (1625 scans), India (672 scans), and Nepal (380 scans) were used for external evaluation. True Glaucoma for the training data was defined as glaucomatous disc changes along with defects on SD-OCT RNFL and/or GCIPL (thickness and/or deviation) maps with corresponding visual field defects as well as intraocular pressure lowering treatment upon chart review. The range of glaucoma patients included mild to severe without excluding high myopes. True normal was defined as cases with non-glaucomatous optic disc with no structural defects on OCT RNFL/GCIPL deviation or sector maps and normal visual fields upon chart review. Results. The 3D deep learning system achieved an area under the receiver operation characteristics curve (AUROC) of 0.8883 in the primary Stanford test set identifying true normal from true glaucoma. The system obtained AUROCs of 0.8571, 0.7695, 0.8706, and 0.7965 on OCT cubes from United States, Hong Kong, India, and Nepal, respectively. We also analyzed the performance of the model separately for each myopia severity level as defined by spherical equivalent and the model was able to achieve F1 scores of 0.9673, 0.9491, and 0.8528 on severe, moderate, and mild myopia cases, respectively. Saliency map visualizations highlighted a significant association between the optic nerve lamina cribrosa region in the glaucoma group. Conclusions. A 3D convolutional neural network using SD-OCT optic nerve head cubes can distinguish true glaucoma from normal with good accuracy and this generalized to multiple diverse external SD-OCT datasets. Highlighted areas from saliency mapping revealed new areas within the deep lamina cribrosa. This deserves further investigation, as there is potential to monitor laminar changes even after RNFL has thinned.

Published
2019
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46. A Multi-Task Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multi-Centre Analysis

Author

Tang, Fangyao, primary, Wang, Xi, additional, Ran, An-Ran, additional, Chan, Carmen K. M., additional, Ho, Mary, additional, Yip, Wilson, additional, Young, Alvin L., additional, Lok, Jerry, additional, Szeto, Simon, additional, Chan, Jason, additional, Yip, Fanny, additional, Wong, Raymond, additional, Tang, Ziqi, additional, Yang, Dawei, additional, Ng, Danny S., additional, Chen, Li Jia, additional, Brelén, Marten, additional, Chu, Victor, additional, Li, Kenneth, additional, Lai, Tracy H.T., additional, Tan, Gavin Siew Wei, additional, Ting, Daniel S. W., additional, Huang, Haifang, additional, Chen, Haoyu, additional, Ma, Jacey Hongjie, additional, Tang, Shibo, additional, Leng, Theodore, additional, Kakavand, Schahrouz, additional, Mannil, Suria S., additional, Chang, Robert T., additional, Liew, Gerald, additional, Gopinath, Bamini, additional, Lai, Timothy Y. Y., additional, Pang, Chi Pui, additional, Scanlon, Peter H., additional, Wong, Tien Yin, additional, Tham, Clement C., additional, Chen, Hao, additional, Heng, Pheng-Ann, additional, and Cheung, Carol Y., additional

Published
2020
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50. The association of TGFB1 genetic polymorphisms with high myopia: a systematic review and meta-analysis

Author

Meng, Bo, LI, Shi-Ming, Yang, Yu, Yang, Zhi-Rong, Sun, Feng, Kang, Meng-Tian, Sun, Yun-Yun, Ran, An-Ran, Wang, Jia-Nan, Yan, Ran, BaI, Ya-Wen, Wang, Ning-Li, and Zhan, Si-Yan

Subjects
Original Article
Abstract

Objective: The TGFB1 gene is among the most studied genes in high myopia due to its role in scleral remodeling. But reported findings of association on TGFB1 and high myopia are inconsistent. This present study is to evaluate the association of TGFB1 polymorphisms and high myopia. Methods: A comprehensive literature search was conducted on studies published up to April 5, 2015. Summary odds ratios (ORs) and 95% confidence intervals were analyzed. Heterogeneity across studies was evaluated by Cochran Q statistic test and the I2 index. Sensitivity analyses were conducted by the approach of one-study remove to assess the influence of single study on the combined effect. Results: Eight studies were included in this study for meta-analysis. Rs1982073 was associated with high myopia in dominant model (OR=1.64; 95% CI=1.04~2.58; P

Published
2015

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