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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

5. 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. 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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8. 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

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

10. 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. 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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17. 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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23. Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future Directions

Author

Yang, Dawei, Ran, An Ran, Nguyen, Truong X., Lin, Timothy P.H., Chen, Hao, Lai, Timothy Y.Y., Tham, Clement C., Cheung, Carol Y., Yang, Dawei, Ran, An Ran, Nguyen, Truong X., Lin, Timothy P.H., Chen, Hao, Lai, Timothy Y.Y., Tham, Clement C., and Cheung, Carol Y.

Abstract

Optical coherence tomography angiography (OCT-A) provides depth-resolved visualization of the retinal microvasculature without intravenous dye injection. It facilitates investigations of various retinal vascular diseases and glaucoma by assessment of qualitative and quantitative microvascular changes in the different retinal layers and radial peripapillary layer non-invasively, individually, and efficiently. Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has been applied in OCT-A image analysis in recent years and achieved good performance for different tasks, such as image quality control, segmentation, and classification. DL technologies have further facilitated the potential implementation of OCT-A in eye clinics in an automated and efficient manner and enhanced its clinical values for detecting and evaluating various vascular retinopathies. Nevertheless, the deployment of this combination in real-world clinics is still in the “proof-of-concept” stage due to several limitations, such as small training sample size, lack of standardized data preprocessing, insufficient testing in external datasets, and absence of standardized results interpretation. In this review, we introduce the existing applications of DL in OCT-A, summarize the potential challenges of the clinical deployment, and discuss future research directions. © 2023 by the authors.

Published
2023

24. Deep learning-based image quality assessment for optical coherence tomography macular scans: a multicentre study

Author

Tang, Ziqi, Wang, Xi, Ran, An Ran, Yang, Dawei, Ling, Anni, Yam, Jason C, Zhang, Xiujuan, Szeto, Simon K H, Chan, Jason, Wong, Cherie Y K, Hui, Vivian W K, Chan, Carmen K M, Wong, Tien Yin, Cheng, Ching-Yu, Sabanayagam, Charumathi, Tham, Yih Chung, Liew, Gerald, Anantharaman, Giridhar, Raman, Rajiv, Cai, Yu, Che, Haoxuan, Luo, Luyang, Liu, Quande, Wong, Yiu Lun, Ngai, Amanda K Y, Yuen, Vincent L, Kei, Nelson, Lai, Timothy Y Y, Chen, Hao, Tham, Clement C, Heng, Pheng-Ann, and Cheung, Carol Y

Abstract

AimsTo develop and externally test deep learning (DL) models for assessing the image quality of three-dimensional (3D) macular scans from Cirrus and Spectralis optical coherence tomography devices.MethodsWe retrospectively collected two data sets including 2277 Cirrus 3D scans and 1557 Spectralis 3D scans, respectively, for training (70%), fine-tuning (10%) and internal validation (20%) from electronic medical and research records at The Chinese University of Hong Kong Eye Centre and the Hong Kong Eye Hospital. Scans with various eye diseases (eg, diabetic macular oedema, age-related macular degeneration, polypoidal choroidal vasculopathy and pathological myopia), and scans of normal eyes from adults and children were included. Two graders labelled each 3D scan as gradable or ungradable, according to standardised criteria. We used a 3D version of the residual network (ResNet)-18 for Cirrus 3D scans and a multiple-instance learning pipline with ResNet-18 for Spectralis 3D scans. Two deep learning (DL) models were further tested via three unseen Cirrus data sets from Singapore and five unseen Spectralis data sets from India, Australia and Hong Kong, respectively.ResultsIn the internal validation, the models achieved the area under curves (AUCs) of 0.930 (0.885–0.976) and 0.906 (0.863–0.948) for assessing the Cirrus 3D scans and Spectralis 3D scans, respectively. In the external testing, the models showed robust performance with AUCs ranging from 0.832 (0.730–0.934) to 0.930 (0.906–0.953) and 0.891 (0.836–0.945) to 0.962 (0.918–1.000), respectively.ConclusionsOur models could be used for filtering out ungradable 3D scans and further incorporated with a disease-detection DL model, allowing a fully automated eye disease detection workflow.

Published
2024
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25. 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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31. 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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32. 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, Wang, Xi, Chan, Poemen P., Chan, Noel C., Yip, Wilson, Young, Alvin L., Wong, Mandy O.M., Yung, Hon-Wah, Chang, Robert T., Mannil, Suria S., Tham, Yih Chung, Cheng, Ching-Yu, Chen, Hao, Li, Fei, Zhang, Xiulan, Heng, Pheng-Ann, Tham, Clement C., Cheung, Carol Y., Ran, An Ran, Wang, Xi, Chan, Poemen P., Chan, Noel C., Yip, Wilson, Young, Alvin L., Wong, Mandy O.M., Yung, Hon-Wah, Chang, Robert T., Mannil, Suria S., Tham, Yih Chung, Cheng, Ching-Yu, Chen, Hao, Li, Fei, Zhang, Xiulan, Heng, Pheng-Ann, Tham, Clement C., and Cheung, Carol Y.

Abstract

Purpose: We aim to develop a multi-task three-dimensional (3D) deep learning (DL) model to detect glaucomatous optic neuropathy (GON) and myopic features (MF) simultaneously from spectral-domain optical coherence tomography (SDOCT) volumetric scans. Methods: Each volumetric scan was labelled as GON according to the criteria of retinal nerve fibre layer (RNFL) thinning, with a structural defect that correlated in position with the visual field defect (i.e., reference standard). MF were graded by the SDOCT en face images, defined as presence of peripapillary atrophy (PPA), optic disc tilting, or fundus tessellation. The multi-task DL model was developed by ResNet with output of Yes/No GON and Yes/No MF. SDOCT scans were collected in a tertiary eye hospital (Hong Kong SAR, China) for training (80%), tuning (10%), and internal validation (10%). External testing was performed on five independent datasets from eye centres in Hong Kong, the United States, and Singapore, respectively. For GON detection, we compared the model to the average RNFL thickness measurement generated from the SDOCT device. To investigate whether MF can affect the model’s performance on GON detection, we conducted subgroup analyses in groups stratified by Yes/No MF. The area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy were reported. Results: A total of 8,151 SDOCT volumetric scans from 3,609 eyes were collected. For detecting GON, in the internal validation, the proposed 3D model had significantly higher AUROC (0.949 vs. 0.913, p < 0.001) than average RNFL thickness in discriminating GON from normal. In the external testing, the two approaches had comparable performance. In the subgroup analysis, the multi-task DL model performed significantly better in the group of “no MF” (0.883 vs. 0.965, p-value < 0.001) in one external testing dataset, but no significant difference in internal validation and other external testing datasets. The multi-task DL

Published
2022

33. 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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35. 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

37. 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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40. 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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41. Detection of Diabetic Retinopathy from Ultrawide Field Scanning Laser Ophthalmoscope Images: A Multi-Center Deep Learning Analysis

Author

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

Abstract

Purpose: To develop a deep learning (DL) system that can detect referable diabetic retinopathy (RDR) and vision-threatening diabetic retinopathy (VTDR) from images obtained on ultra-widefield scanning laser ophthalmoscope (UWF-SLO). Design: Observational, cross-sectional study. Participants: A total of 9392 UWF-SLO images of 1903 eyes from 1022 subjects with diabetes from Hong Kong, the United Kingdom, India, and Argentina. Methods: All images were labeled according to the presence or absence of RDR and the presence or absence of VTDR. Labeling was performed by retina specialists from fundus examination, according to the International Clinical Diabetic Retinopathy Disease Severity Scale. Three convolutional neural networks (ResNet50) were trained with a transfer-learning procedure for assessing gradability and identifying VTDR and RDR. External validation was performed on 4 datasets spanning different geographical regions. Main Outcome Measures: Area under the receiver operating characteristic curve (AUROC); area under the precision-recall curve (AUPRC); sensitivity, specificity, and accuracy of the DL system in gradability assessment; and detection of RDR and VTDR. Results: For gradability assessment, the system achieved an AUROC of 0.923 (95% confidence interval [CI], 0.892–0.947), sensitivity of 86.5% (95% CI, 77.6–92.8), and specificity of 82.1% (95% CI, 77.3–86.2) for the primary validation dataset, and >0.82 AUROCs, >79.6% sensitivity, and >70.4% specificity for the geographical external validation datasets. For detecting RDR and VTDR, the AUROCs were 0.981 (95% CI, 0.977–0.984) and 0.966 (95% CI, 0.961–0.971), with sensitivities of 94.9% (95% CI, 92.3–97.9) and 87.2% (95% CI, 81.5–91.6), specificities of 95.1% (95% CI, 90.6–97.9) and 95.8% (95% CI, 93.3–97.6), and positive predictive values (PPVs) of 98.0% (95% CI, 96.1–99.0) and 91.1% (95% CI, 86.3–94.3) for the primary validation dataset, respectively. The AUROCs and accuracies for detecting both RDR and VT

Published
2021

42. Towards multi-center glaucoma OCT image screening with semi-supervised joint structure and function multi-task learning

Author

Wang, Xi, Chen, Hao, Ran, An-Ran, Luo, Luyang, Chan, Poemen P., Tham, Clement C., Chang, Robert T., Mannil, Suria S., Cheung, Carol Y., Heng, Pheng-Ann, Wang, Xi, Chen, Hao, Ran, An-Ran, Luo, Luyang, Chan, Poemen P., Tham, Clement C., Chang, Robert T., Mannil, Suria S., Cheung, Carol Y., and Heng, Pheng-Ann

Abstract

Glaucoma is the leading cause of irreversible blindness in the world. Structure and function assessments play an important role in diagnosing glaucoma. Nowadays, Optical Coherence Tomography (OCT) imaging gains increasing popularity in measuring the structural change of eyes. However, few automated methods have been developed based on OCT images to screen glaucoma. In this paper, we are the first to unify the structure analysis and function regression to distinguish glaucoma patients from normal controls effectively. Specifically, our method works in two steps: a semi-supervised learning strategy with smoothness assumption is first applied for the surrogate assignment of missing function regression labels. Subsequently, the proposed multi-task learning network is capable of exploring the structure and function relationship between the OCT image and visual field measurement simultaneously, which contributes to classification performance improvement. It is also worth noting that the proposed method is assessed by two large-scale multi-center datasets. In other words, we first build the largest glaucoma OCT image dataset (i.e., HK dataset) involving 975,400 B-scans from 4,877 volumes to develop and evaluate the proposed method, then the model without further fine-tuning is directly applied on another independent dataset (i.e., Stanford dataset) containing 246,200 B-scans from 1,231 volumes. Extensive experiments are conducted to assess the contribution of each component within our framework. The proposed method outperforms the baseline methods and two glaucoma experts by a large margin, achieving volume-level Area Under ROC Curve (AUC) of 0.977 on HK dataset and 0.933 on Stanford dataset, respectively. The experimental results indicate the great potential of the proposed approach for the automated diagnosis system. © 2020 Elsevier B.V.

Published
2020

49. 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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