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811 results on '"Tham, Yih-Chung"'

1. Common and Rare Fundus Diseases Identification Using Vision-Language Foundation Model with Knowledge of Over 400 Diseases

Author

Wang, Meng, Lin, Tian, Lin, Aidi, Yu, Kai, Peng, Yuanyuan, Wang, Lianyu, Chen, Cheng, Zou, Ke, Liang, Huiyu, Chen, Man, Yao, Xue, Zhang, Meiqin, Huang, Binwei, Zheng, Chaoxin, Zhang, Peixin, Chen, Wei, Luo, Yilong, Chen, Yifan, Xia, Honghe, Shi, Tingkun, Zhang, Qi, Guo, Jinming, Chen, Xiaolin, Wang, Jingcheng, Tham, Yih Chung, Liu, Dianbo, Wong, Wendy, Thakur, Sahil, Fenner, Beau, Fang, Danqi, Liu, Siying, Liu, Qingyun, Huang, Yuqiang, Zeng, Hongqiang, Meng, Yanda, Zhou, Yukun, Jiang, Zehua, Qiu, Minghui, Zhang, Changqing, Chen, Xinjian, Wang, Sophia Y, Lee, Cecilia S, Sobrin, Lucia, Cheung, Carol Y, Pang, Chi Pui, Keane, Pearse A, Cheng, Ching-Yu, Chen, Haoyu, and Fu, Huazhu

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

Previous foundation models for retinal images were pre-trained with limited disease categories and knowledge base. Here we introduce RetiZero, a vision-language foundation model that leverages knowledge from over 400 fundus diseases. To RetiZero's pre-training, we compiled 341,896 fundus images paired with text descriptions, sourced from public datasets, ophthalmic literature, and online resources, encompassing a diverse range of diseases across multiple ethnicities and countries. RetiZero exhibits superior performance in several downstream tasks, including zero-shot disease recognition, image-to-image retrieval, and internal- and cross-domain disease identification. In zero-shot scenarios, RetiZero achieves Top5 accuracy scores of 0.8430 for 15 fundus diseases and 0.7561 for 52 fundus diseases. For image retrieval, it achieves Top5 scores of 0.9500 and 0.8860 for the same disease sets, respectively. Clinical evaluations show that RetiZero's Top3 zero-shot performance surpasses the average of 19 ophthalmologists from Singapore, China and the United States. Furthermore, RetiZero significantly enhances clinicians' accuracy in diagnosing fundus disease. These findings underscore the value of integrating the RetiZero foundation model into clinical settings, where a variety of fundus diseases are encountered.

Published
2024

2. EyeFound: A Multimodal Generalist Foundation Model for Ophthalmic Imaging

Author

Shi, Danli, Zhang, Weiyi, Chen, Xiaolan, Liu, Yexin, Yang, Jiancheng, Huang, Siyu, Tham, Yih Chung, Zheng, Yingfeng, and He, Mingguang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Artificial intelligence (AI) is vital in ophthalmology, tackling tasks like diagnosis, classification, and visual question answering (VQA). However, existing AI models in this domain often require extensive annotation and are task-specific, limiting their clinical utility. While recent developments have brought about foundation models for ophthalmology, they are limited by the need to train separate weights for each imaging modality, preventing a comprehensive representation of multi-modal features. This highlights the need for versatile foundation models capable of handling various tasks and modalities in ophthalmology. To address this gap, we present EyeFound, a multimodal foundation model for ophthalmic images. Unlike existing models, EyeFound learns generalizable representations from unlabeled multimodal retinal images, enabling efficient model adaptation across multiple applications. Trained on 2.78 million images from 227 hospitals across 11 ophthalmic modalities, EyeFound facilitates generalist representations and diverse multimodal downstream tasks, even for detecting challenging rare diseases. It outperforms previous work RETFound in diagnosing eye diseases, predicting systemic disease incidents, and zero-shot multimodal VQA. EyeFound provides a generalizable solution to improve model performance and lessen the annotation burden on experts, facilitating widespread clinical AI applications for retinal imaging., Comment: 21 pages, 2 figures, 4 tables

Published
2024

3. A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images

Author

Hou, Qingshan, Cheng, Shuai, Cao, Peng, Yang, Jinzhu, Liu, Xiaoli, Zaiane, Osmar R., and Tham, Yih Chung

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Representation learning offers a conduit to elucidate distinctive features within the latent space and interpret the deep models. However, the randomness of lesion distribution and the complexity of low-quality factors in medical images pose great challenges for models to extract key lesion features. Disease diagnosis methods guided by contrastive learning (CL) have shown significant advantages in lesion feature representation. Nevertheless, the effectiveness of CL is highly dependent on the quality of the positive and negative sample pairs. In this work, we propose a clinical-oriented multi-level CL framework that aims to enhance the model's capacity to extract lesion features and discriminate between lesion and low-quality factors, thereby enabling more accurate disease diagnosis from low-quality medical images. Specifically, we first construct multi-level positive and negative pairs to enhance the model's comprehensive recognition capability of lesion features by integrating information from different levels and qualities of medical images. Moreover, to improve the quality of the learned lesion embeddings, we introduce a dynamic hard sample mining method based on self-paced learning. The proposed CL framework is validated on two public medical image datasets, EyeQ and Chest X-ray, demonstrating superior performance compared to other state-of-the-art disease diagnostic methods.

Published
2024

4. A Peer-to-Peer Live-Streaming Intervention for Children During COVID-19 Homeschooling to Promote Physical Activity and Reduce Anxiety and Eye Strain: Cluster Randomized Controlled Trial

Author

Zheng, Yingfeng, Wang, Wei, Zhong, Yuxin, Wu, Fengchun, Zhu, Zhuoting, Tham, Yih-Chung, Lamoureux, Ecosse, Xiao, Liang, Zhu, Erta, Liu, Haoning, Jin, Ling, Liang, Linyi, Luo, Lixia, He, Mingguang, Morgan, Ian, Congdon, Nathan, and Liu, Yizhi

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundThe COVID-19 pandemic has led to worldwide school closures, with millions of children confined to online learning at home. As a result, children may be susceptible to anxiety and digital eye strain, highlighting a need for population interventions. ObjectiveThe objective of our study was to investigate whether a digital behavior change intervention aimed at promoting physical activity could reduce children’s anxiety and digital eye strain while undergoing prolonged homeschooling during the COVID-19 pandemic. MethodsIn this cluster randomized controlled trial, homeschooled grade 7 students at 12 middle schools in southern China were recruited through local schools and randomly assigned by the school to receive (1:1 allocation): (1) health education information promoting exercise and ocular relaxation, and access to a digital behavior change intervention, with live streaming and peer sharing of promoted activities (intervention), or (2) health education information only (control). The primary outcome was change in self-reported anxiety score. Secondary outcomes included change in self-reported eye strain and sleep quality. ResultsOn March 16, 2020, 1009 children were evaluated, and 954 (94.5%) eligible children of consenting families were included in the intention-to-treat analysis. Children in the intervention (n=485, 6 schools) and control (n=469, 6 schools) groups were aged 13.5 (SD 0.5) years, and 52.3% (n=499) were male. The assigned interventions were completed by 896 children (intervention: n=467, 96.3%; control: n=429, 91.5%). The 2-week change in square-root–transformed self-reported anxiety scores was greater in the intervention (–0.23, 95% CI –0.27 to –0.20) vs control group (0.12, 95% CI 0.09-0.16; unadjusted difference –0.36, 95% CI –0.63 to –0.08; P=.02). There was a significant reduction in square-root–transformed eye strain in the intervention group (–0.08, 95% CI –0.10 to 0.06) compared to controls (0.07, 95% CI 0.05-0.09; difference –0.15, 95% CI –0.26 to –0.03; P=.02). Change in sleep quality was similar between the two groups. ConclusionsThis digital behavior change intervention reduced children’s anxiety and eye strain during COVID-19–associated online schooling. Trial RegistrationClinicalTrials.gov NCT04309097; http://clinicaltrials.gov/ct2/show/NCT04309097

Published
2021
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5. Telehealth Demand Trends During the COVID-19 Pandemic in the Top 50 Most Affected Countries: Infodemiological Evaluation

Author

Wong, Mark Yu Zheng, Gunasekeran, Dinesh Visva, Nusinovici, Simon, Sabanayagam, Charumathi, Yeo, Khung Keong, Cheng, Ching-Yu, and Tham, Yih-Chung

Subjects
Public aspects of medicine, RA1-1270
Abstract

BackgroundThe COVID-19 pandemic has led to urgent calls for the adoption of telehealth solutions. However, public interest and demand for telehealth during the pandemic remain unknown. ObjectiveWe used an infodemiological approach to estimate the worldwide demand for telehealth services during COVID-19, focusing on the 50 most affected countries and comparing the demand for such services with the level of information and communications technology (ICT) infrastructure available. MethodsWe used Google Trends, the Baidu Index (China), and Yandex Keyword Statistics (Russia) to extract data on worldwide and individual countries’ telehealth-related internet searches from January 1 to July 7, 2020, presented as relative search volumes (RSV; range 0-100). Daily COVID-19 cases and deaths were retrieved from the World Health Organization. Individual countries’ ICT infrastructure profiles were retrieved from the World Economic Forum Report. ResultsAcross the 50 countries, the mean RSV was 18.5 (SD 23.2), and the mean ICT index was 62.1 (SD 15.0). An overall spike in worldwide telehealth-related RSVs was observed from March 11, 2020 (RSV peaked to 76.0), which then tailed off in June-July 2020 (mean RSV for the period was 25.8), but remained higher than pre-March RSVs (mean 7.29). By country, 42 (84%) manifested increased RSVs over the evaluation period, with the highest observed in Canada (RSV=100) and the United States (RSV=96). When evaluating associations between RSV and the ICT index, both the United States and Canada demonstrated high RSVs and ICT scores (≥70.3). In contrast, European countries had relatively lower RSVs (range 3.4-19.5) despite high ICT index scores (mean 70.3). Several Latin American (Brazil, Chile, Colombia) and South Asian (India, Bangladesh, Pakistan) countries demonstrated relatively higher RSVs (range 13.8-73.3) but low ICT index scores (mean 44.6), indicating that the telehealth demand outstrips the current ICT infrastructure. ConclusionsThere is generally increased interest and demand for telehealth services across the 50 countries most affected by COVID-19, highlighting the need to scale up telehealth capabilities, during and beyond the pandemic.

Published
2021
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7. HRDC challenge: a public benchmark for hypertension and hypertensive retinopathy classification from fundus images

Author

Qian, Bo, Wang, Xiangning, Guan, Zhouyu, Yang, Dawei, Ran, Anran, Li, Tingyao, Wang, Zheyuan, Wen, Yang, Shu, Xinming, Xie, Jinyang, Liu, Shichang, Xing, Guanyu, Silva-Rodríguez, Julio, Kobbi, Riadh, Li, Ping, Chen, Tingli, Bi, Lei, Kim, Jinman, Jia, Weiping, Li, Huating, Qin, Jing, Zhang, Ping, Cheng, Ching-Yu, Heng, Pheng-Ann, Wong, Tien Yin, Cheung, Carol Y., Tham, Yih-Chung, Thalmann, Nadia Magnenat, and Sheng, Bin

Published
2024
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9. VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence

Author

Qiu, Jianing, Wu, Jian, Wei, Hao, Shi, Peilun, Zhang, Minqing, Sun, Yunyun, Li, Lin, Liu, Hanruo, Liu, Hongyi, Hou, Simeng, Zhao, Yuyang, Shi, Xuehui, Xian, Junfang, Qu, Xiaoxia, Zhu, Sirui, Pan, Lijie, Chen, Xiaoniao, Zhang, Xiaojia, Jiang, Shuai, Wang, Kebing, Yang, Chenlong, Chen, Mingqiang, Fan, Sujie, Hu, Jianhua, Lv, Aiguo, Miao, Hui, Guo, Li, Zhang, Shujun, Pei, Cheng, Fan, Xiaojuan, Lei, Jianqin, Wei, Ting, Duan, Junguo, Liu, Chun, Xia, Xiaobo, Xiong, Siqi, Li, Junhong, Lo, Benny, Tham, Yih Chung, Wong, Tien Yin, Wang, Ningli, and Yuan, Wu

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

We present VisionFM, a foundation model pre-trained with 3.4 million ophthalmic images from 560,457 individuals, covering a broad range of ophthalmic diseases, modalities, imaging devices, and demography. After pre-training, VisionFM provides a foundation to foster multiple ophthalmic artificial intelligence (AI) applications, such as disease screening and diagnosis, disease prognosis, subclassification of disease phenotype, and systemic biomarker and disease prediction, with each application enhanced with expert-level intelligence and accuracy. The generalist intelligence of VisionFM outperformed ophthalmologists with basic and intermediate levels in jointly diagnosing 12 common ophthalmic diseases. Evaluated on a new large-scale ophthalmic disease diagnosis benchmark database, as well as a new large-scale segmentation and detection benchmark database, VisionFM outperformed strong baseline deep neural networks. The ophthalmic image representations learned by VisionFM exhibited noteworthy explainability, and demonstrated strong generalizability to new ophthalmic modalities, disease spectrum, and imaging devices. As a foundation model, VisionFM has a large capacity to learn from diverse ophthalmic imaging data and disparate datasets. To be commensurate with this capacity, in addition to the real data used for pre-training, we also generated and leveraged synthetic ophthalmic imaging data. Experimental results revealed that synthetic data that passed visual Turing tests, can also enhance the representation learning capability of VisionFM, leading to substantial performance gains on downstream ophthalmic AI tasks. Beyond the ophthalmic AI applications developed, validated, and demonstrated in this work, substantial further applications can be achieved in an efficient and cost-effective manner using VisionFM as the foundation.

Published
2023

10. Genetic insights into resting heart rate and its role in cardiovascular disease.

Author

van de Vegte, Yordi, Eppinga, Ruben, van der Ende, M, Hagemeijer, Yanick, Mahendran, Yuvaraj, Salfati, Elias, Smith, Albert, Tan, Vanessa, Arking, Dan, Ntalla, Ioanna, Appel, Emil, Schurmann, Claudia, Brody, Jennifer, Rueedi, Rico, Polasek, Ozren, Sveinbjornsson, Gardar, Lecoeur, Cecile, Ladenvall, Claes, Zhao, Jing, Isaacs, Aaron, Wang, Lihua, Luan, Jianan, Hwang, Shih-Jen, Mononen, Nina, Auro, Kirsi, Jackson, Anne, Bielak, Lawrence, Zeng, Linyao, Shah, Nabi, Nethander, Maria, Campbell, Archie, Rankinen, Tuomo, Pechlivanis, Sonali, Qi, Lu, Zhao, Wei, Rizzi, Federica, Tanaka, Toshiko, Robino, Antonietta, Cocca, Massimiliano, Lange, Leslie, Müller-Nurasyid, Martina, Roselli, Carolina, Zhang, Weihua, Kleber, Marcus, Guo, Xiuqing, Lin, Henry, Pavani, Francesca, Galesloot, Tessel, Noordam, Raymond, Milaneschi, Yuri, Schraut, Katharina, den Hoed, Marcel, Degenhardt, Frauke, Trompet, Stella, van den Berg, Marten, Pistis, Giorgio, Tham, Yih-Chung, Weiss, Stefan, Sim, Xueling, Li, Hengtong, van der Most, Peter, Nolte, Ilja, Lyytikäinen, Leo-Pekka, Said, M, Witte, Daniel, Iribarren, Carlos, Launer, Lenore, Ring, Susan, de Vries, Paul, Sever, Peter, Linneberg, Allan, Bottinger, Erwin, Padmanabhan, Sandosh, Psaty, Bruce, Sotoodehnia, Nona, Kolcic, Ivana, Arnar, David, Gudbjartsson, Daniel, Holm, Hilma, Balkau, Beverley, Silva, Claudia, Newton-Cheh, Christopher, Nikus, Kjell, Salo, Perttu, Mohlke, Karen, Peyser, Patricia, Schunkert, Heribert, Lorentzon, Mattias, Lahti, Jari, Rao, Dabeeru, Cornelis, Marilyn, Faul, Jessica, Smith, Jennifer, Stolarz-Skrzypek, Katarzyna, Bandinelli, Stefania, Concas, Maria, Sinagra, Gianfranco, Meitinger, Thomas, Waldenberger, Melanie, and Sinner, Moritz

Subjects
Humans, Cardiovascular Diseases, Risk Factors, Heart Rate, Genetic Predisposition to Disease, Atrial Fibrillation, Mendelian Randomization Analysis, Genome-Wide Association Study, Polymorphism, Single Nucleotide
Abstract

Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development.

Published
2023

11. A deep learning system for predicting time to progression of diabetic retinopathy

Author

Dai, Ling, Sheng, Bin, Chen, Tingli, Wu, Qiang, Liu, Ruhan, Cai, Chun, Wu, Liang, Yang, Dawei, Hamzah, Haslina, Liu, Yuexing, Wang, Xiangning, Guan, Zhouyu, Yu, Shujie, Li, Tingyao, Tang, Ziqi, Ran, Anran, Che, Haoxuan, Chen, Hao, Zheng, Yingfeng, Shu, Jia, Huang, Shan, Wu, Chan, Lin, Shiqun, Liu, Dan, Li, Jiajia, Wang, Zheyuan, Meng, Ziyao, Shen, Jie, Hou, Xuhong, Deng, Chenxin, Ruan, Lei, Lu, Feng, Chee, Miaoli, Quek, Ten Cheer, Srinivasan, Ramyaa, Raman, Rajiv, Sun, Xiaodong, Wang, Ya Xing, Wu, Jiarui, Jin, Hai, Dai, Rongping, Shen, Dinggang, Yang, Xiaokang, Guo, Minyi, Zhang, Cuntai, Cheung, Carol Y., Tan, Gavin Siew Wei, Tham, Yih-Chung, Cheng, Ching-Yu, Li, Huating, Wong, Tien Yin, and Jia, Weiping

Published
2024
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13. Localizing Anatomical Landmarks in Ocular Images using Zoom-In Attentive Networks

Author

Lei, Xiaofeng, Li, Shaohua, Xu, Xinxing, Fu, Huazhu, Liu, Yong, Tham, Yih-Chung, Feng, Yangqin, Tan, Mingrui, Xu, Yanyu, Goh, Jocelyn Hui Lin, Goh, Rick Siow Mong, and Cheng, Ching-Yu

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

Localizing anatomical landmarks are important tasks in medical image analysis. However, the landmarks to be localized often lack prominent visual features. Their locations are elusive and easily confused with the background, and thus precise localization highly depends on the context formed by their surrounding areas. In addition, the required precision is usually higher than segmentation and object detection tasks. Therefore, localization has its unique challenges different from segmentation or detection. In this paper, we propose a zoom-in attentive network (ZIAN) for anatomical landmark localization in ocular images. First, a coarse-to-fine, or "zoom-in" strategy is utilized to learn the contextualized features in different scales. Then, an attentive fusion module is adopted to aggregate multi-scale features, which consists of 1) a co-attention network with a multiple regions-of-interest (ROIs) scheme that learns complementary features from the multiple ROIs, 2) an attention-based fusion module which integrates the multi-ROIs features and non-ROI features. We evaluated ZIAN on two open challenge tasks, i.e., the fovea localization in fundus images and scleral spur localization in AS-OCT images. Experiments show that ZIAN achieves promising performances and outperforms state-of-the-art localization methods. The source code and trained models of ZIAN are available at https://github.com/leixiaofeng-astar/OMIA9-ZIAN.

Published
2022

16. Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank

Author

Tseng, Rachel Marjorie Wei Wen, Rim, Tyler Hyungtaek, Shantsila, Eduard, Yi, Joseph K., Park, Sungha, Kim, Sung Soo, Lee, Chan Joo, Thakur, Sahil, Nusinovici, Simon, Peng, Qingsheng, Kim, Hyeonmin, Lee, Geunyoung, Yu, Marco, Tham, Yih-Chung, Bakhai, Ameet, Leeson, Paul, Lip, Gregory Y.H., Wong, Tien Yin, and Cheng, Ching-Yu

Published
2023
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17. Contribution of common and rare variants to Asian neovascular age-related macular degeneration subtypes

Author

Fan, Qiao, Li, Hengtong, Wang, Xiaomeng, Tham, Yih-Chung, Teo, Kelvin Yi Chong, Yasuda, Masayuki, Lim, Weng Khong, Kwan, Yuet Ping, Teo, Jing Xian, Chen, Ching-Jou, Chen, Li Jia, Ahn, Jeeyun, Davila, Sonia, Miyake, Masahiro, Tan, Patrick, Park, Kyu Hyung, Pang, Chi Pui, Khor, Chiea Chuan, Wong, Tien Yin, Yanagi, Yasuo, Cheung, Chui Ming Gemmy, and Cheng, Ching-Yu

Published
2023
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18. Artificial intelligence for diabetes care: current and future prospects

Author

Sheng, Bin, Pushpanathan, Krithi, Guan, Zhouyu, Lim, Quan Hziung, Lim, Zhi Wei, Yew, Samantha Min Er, Goh, Jocelyn Hui Lin, Bee, Yong Mong, Sabanayagam, Charumathi, Sevdalis, Nick, Lim, Cynthia Ciwei, Lim, Chwee Teck, Shaw, Jonathan, Jia, Weiping, Ekinci, Elif Ilhan, Simó, Rafael, Lim, Lee-Ling, Li, Huating, and Tham, Yih-Chung

Published
2024
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21. Is Kidney Function Associated with Age-Related Macular Degeneration?: Findings from the Asian Eye Epidemiology Consortium

Author

Xue, Can Can, Sim, Ralene, Chee, Miao Li, Yu, Marco, Wang, Ya Xing, Rim, Tyler Hyungtaek, Hyung, Park Kyu, Woong, Kang Se, Song, Su Jeong, Nangia, Vinay, Panda-Jonas, Songhomitra, Wang, Ning Li, Hao, Jie, Zhang, Qing, Cao, Kai, Sasaki, Mariko, Harada, Sei, Toru, Takebayashi, Ryo, Kawasaki, Raman, Rajiv, Surya, Janani, Khan, Rehana, Bikbov, Mukharram, Wong, Ian Y., Cheung, Chui Ming Gemmy, Jonas, Jost B., Cheng, Ching-Yu, and Tham, Yih-Chung

Published
2024
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25. Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

Author

Phelps, Nowell H, Singleton, Rosie K, Zhou, Bin, Heap, Rachel A, Mishra, Anu, Bennett, James E, Paciorek, Christopher J, Lhoste, Victor PF, Carrillo-Larco, Rodrigo M, Stevens, Gretchen A, Rodriguez-Martinez, Andrea, Bixby, Honor, Bentham, James, Di Cesare, Mariachiara, Danaei, Goodarz, Rayner, Archie W, Barradas-Pires, Ana, Cowan, Melanie J, Savin, Stefan, Riley, Leanne M, Aguilar-Salinas, Carlos A, Baker, Jennifer L, Barkat, Amina, Bhutta, Zulfiqar A, Branca, Francesco, Caixeta, Roberta B, Cuschieri, Sarah, Farzadfar, Farshad, Ganapathy, Shubash, Ikeda, Nayu, Iotova, Violeta, Kengne, Andre P, Khang, Young-Ho, Laxmaiah, Avula, Lin, Hsien-Ho, Ma, Jun, Mbanya, Jean Claude N, Miranda, J Jaime, Pradeepa, Rajendra, Rodríguez-Artalejo, Fernando, Sorić, Maroje, Turley, Maria, Wang, Limin, Webster-Kerr, Karen, Aarestrup, Julie, Abarca-Gómez, Leandra, Abbasi-Kangevari, Mohsen, Abdeen, Ziad A, Abdrakhmanova, Shynar, Abdul Ghaffar, Suhaila, Abdul Rahim, Hanan F, Abdurrahmonova, Zulfiya, Abu-Rmeileh, Niveen M, Abubakar Garba, Jamila, Acosta-Cazares, Benjamin, Adam, Ishag, Adamczyk, Marzena, Adams, Robert J, Adu-Afarwuah, Seth, Aekplakorn, Wichai, Afsana, Kaosar, Afzal, Shoaib, Agbor, Valirie N, Agdeppa, Imelda A, Aghazadeh-Attari, Javad, Ågren, Åsa, Aguenaou, Hassan, Agyemang, Charles, Ahmad, Mohamad Hasnan, Ahmad, Noor Ani, Ahmadi, Ali, Ahmadi, Naser, Ahmadi, Nastaran, Ahmed, Imran, Ahmed, Soheir H, Ahrens, Wolfgang, Aitmurzaeva, Gulmira, Ajlouni, Kamel, Al-Hazzaa, Hazzaa M, Al-Hinai, Halima, Al-Lahou, Badreya, Al-Lawati, Jawad A, Al-Raddadi, Rajaa, Al Asfoor, Deena, Al Hourani, Huda M, Al Qaoud, Nawal M, Alarouj, Monira, AlBuhairan, Fadia, AlDhukair, Shahla, Aldwairji, Maryam A, Alexius, Sylvia, Ali, Mohamed M, Alieva, Anna V, Alkandari, Abdullah, Alkerwi, Ala'a, Alkhatib, Buthaina M, Allin, Kristine, Alomary, Shaker A, Alomirah, Husam F, Alshangiti, Arwa M, Alvarez-Pedrerol, Mar, Aly, Eman, Amarapurkar, Deepak N, Amiano Etxezarreta, Pilar, Amoah, John, Amougou, Norbert, Amouyel, Philippe, Andersen, Lars Bo, Anderssen, Sigmund A, Androutsos, Odysseas, Ängquist, Lars, Anjana, Ranjit Mohan, Ansari-Moghaddam, Alireza, Anufrieva, Elena, Aounallah-Skhiri, Hajer, Araújo, Joana, Ariansen, Inger, Aris, Tahir, Arku, Raphael E, Arlappa, Nimmathota, Aryal, Krishna K, Assefa, Nega, Aspelund, Thor, Assah, Felix K, Assembekov, Batyrbek, Assunção, Maria Cecília F, Aung, May Soe, Aurélio de Valois, Correia Júnior Marco, Auvinen, Juha, Avdičová, Mária, Avi, Shina, Azad, Kishwar, Azevedo, Ana, Azimi-Nezhad, Mohsen, Azizi, Fereidoun, Babu, Bontha V, Bacopoulou, Flora, Bæksgaard Jørgensen, Maja, Baharudin, Azli, Bahijri, Suhad, Bajramovic, Izet, Bakacs, Marta, Balakrishna, Nagalla, Balanova, Yulia, Bamoshmoosh, Mohamed, Banach, Maciej, Banegas, José R, Baran, Joanna, Baran, Rafał, Barbagallo, Carlo M, Barbosa Filho, Valter, Barceló, Alberto, Baretić, Maja, Barnoya, Joaquin, Barrera, Lena, Barreto, Marta, Barros, Aluisio JD, Barros, Mauro Virgílio Gomes, Bartosiewicz, Anna, Basit, Abdul, Bastos, Joao Luiz, Bata, Iqbal, Batieha, Anwar M, Batista, Aline P, Batista, Rosangela L, Battakova, Zhamilya, Baur, Louise A, Bayauli, Pascal M, Beaglehole, Robert, Bel-Serrat, Silvia, Belavendra, Antonisamy, Ben Romdhane, Habiba, Benedek, Theodora, Benedics, Judith, Benet, Mikhail, Benitez Rolandi, Gilda Estela, Benzeval, Michaela, Bere, Elling, Berger, Nicolas, Bergh, Ingunn Holden, Berhane, Yemane, Berkinbayev, Salim, Bernabe-Ortiz, Antonio, Bernotiene, Gailute, Berrios Carrasola, Ximena, Bettiol, Heloísa, Beutel, Manfred E, Beybey, Augustin F, Bezerra, Jorge, Bhagyalaxmi, Aroor, Bharadwaj, Sumit, Bhargava, Santosh K, Bi, Hongsheng, Bi, Yufang, Bia, Daniel, Biasch, Katia, Bika Lele, Elysée Claude, Bikbov, Mukharram M, Bista, Bihungum, Bjelica, Dusko J, Bjerregaard, Anne A, Bjerregaard, Peter, Bjertness, Espen, Bjertness, Marius B, Björkelund, Cecilia, Bloch, Katia V, Blokstra, Anneke, Blychfeld Magnazu, Moran, Bo, Simona, Bobak, Martin, Boddy, 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Parsaeian, Mahboubeh, Pascanu, Ionela M, Pasquet, Patrick, Patel, Nikhil D, Pattussi, Marcos, Pavlyshyn, Halyna, Pechlaner, Raimund, Pećin, Ivan, Pednekar, Mangesh S, Pedro, João M, Peer, Nasheeta, Peixoto, Sergio Viana, Peltonen, Markku, Pereira, Alexandre C, Peres, Marco A, Perez-Londoño, Agustín, Pérez, Cynthia M, Peterkova, Valentina, Peters, Annette, Petkeviciene, Janina, Petrauskiene, Ausra, Petrovna Kovtun, Olga, Pettenuzzo, Emanuela, Peykari, Niloofar, Pfeiffer, Norbert, Phall, Modou Cheyassin, Pham, Son Thai, Phiri, Felix P, Pichardo, Rafael N, Pierannunzio, Daniela, Pierre-Marie, Preux, Pigeot, Iris, Pikhart, Hynek, Pilav, Aida, Piler, Pavel, Pilotto, Lorenza, Pistelli, Francesco, Pitakaka, Freda, Piwonska, Aleksandra, Pizarro, Andreia N, Plans-Rubió, Pedro, Platonova, Alina G, Poh, Bee Koon, Pohlabeln, Hermann, Polka, Nadija S, Pop, Raluca M, Popkin, Barry M, Popovic, Stevo R, Porta, Miquel, Posch, Georg, Poudyal, Anil, Poulimeneas, Dimitrios, Pouraram, Hamed, Pourfarzi, Farhad, Pourshams, Akram, Poustchi, Hossein, Price, Alison J, Price, Jacqueline F, Prista, Antonio, Providencia, Rui, Puder, Jardena J, Pudule, Iveta, Puhakka, Solie, Puiu, Maria, Punab, Margus, Qadir, Muhammed S, Qasrawi, Radwan F, Qiao, Qing, Qorbani, Mostafa, Quintana, Hedley K, Quiroga-Padilla, Pedro J, Quoc Bao, Tran, Rach, Stefan, Radic, Ivana, Radisauskas, Ricardas, Rahimikazerooni, Salar, Rahman, Mahfuzar, Rahman, Mahmudur, Raitakari, Olli, Raj, Manu, Rajabov, Tamerlan, Rakhmatulloev, Sherali, Rakovac, Ivo, Ramachandra Rao, Sudha, Ramachandran, Ambady, Ramadan, Otim PC, Ramires, Virgílio V, Ramirez-Zea, Manuel, Ramke, Jacqueline, Ramos, Elisabete, Ramos, Rafel, Rampal, Lekhraj, Rampal, Sanjay, Ramsay, Sheena E, Rangelova, Lalka S, Rarra, Vayia, Rascon-Pacheco, Ramon A, Rashidi, Mohammad-Mahdi, Rech, Cassiano Ricardo, Redon, Josep, Reganit, Paul Ferdinand M, Regecová, Valéria, Renner, Jane DP, Repasy, Judit A, Reuter, Cézane P, Revilla, Luis, Reynolds, Andrew, Rezaei, Negar, Rezaianzadeh, Abbas, Rho, Yeunsook, Ribas-Barba, Lourdes, Ribeiro, Robespierre, Riboli, Elio, Rigo, Fernando, Rigotti, Attilio, Rinaldo, Natascia, Rinke de Wit, Tobias F, Risérus, Ulf, Rito, Ana I, Ritti-Dias, Raphael M, Rivera, Juan A, Roa, Reina G, Robinson, Louise, Roccaldo, Romana, Rodrigues, Daniela, Rodriguez-Perez, María del Cristo, Rodríguez-Villamizar, Laura A, Rodríguez, Andrea Y, Roggenbuck, Ulla, Rohloff, Peter, Rohner, Fabian, Rojas-Martinez, Rosalba, Rojroongwasinkul, Nipa, Romaguera, Dora, Romeo, Elisabetta L, Rosario, Rafaela V, Rosengren, Annika, Rouse, Ian, Rouzier, Vanessa, Roy, Joel GR, Ruano, Maira H, Rubinstein, Adolfo, Rühli, Frank J, Ruidavets, Jean-Bernard, Ruiz-Betancourt, Blanca Sandra, Ruiz-Castell, Maria, Ruiz Moreno, Emma, Rusakova, Iuliia A, Rusek, Wojciech, Russell Jonsson, Kenisha, Russo, Paola, Rust, Petra, Rutkowski, Marcin, Saamel, Marge, Saar, Crizian G, Sabanayagam, Charumathi, Sabbaghi, Hamideh, Sacchini, Elena, Sachdev, Harshpal S, Sadjadi, Alireza, Safarpour, Ali Reza, Safi, Sare, Safiri, Saeid, Saghi, Mohammad Hossien, Saidi, Olfa, Saieva, Calogero, Sakata, Satoko, Saki, Nader, Šalaj, Sanja, Salanave, Benoit, Salazar Martinez, Eduardo, Salhanova, Akkumis, Salmerón, Diego, Salomaa, Veikko, Salonen, Jukka T, Salvetti, Massimo, Samoutian, Margarita, Sánchez-Abanto, Jose, Sánchez Rodríguez, Inés, Sandjaja, Sans, Susana, Santa-Marina, Loreto, Santacruz, Ethel, Santos, Diana A, Santos, Ina S, Santos, Lèlita C, Santos, Maria Paula, Santos, Osvaldo, Santos, Rute, Santos, Tamara R, Saramies, Jouko L, Sardinha, Luis B, Sarrafzadegan, Nizal, Sathish, Thirunavukkarasu, Saum, Kai-Uwe, Savva, Savvas, Savy, Mathilde, Sawada, Norie, Sbaraini, Mariana, Scazufca, Marcia, Schaan, Beatriz D, Schaffrath Rosario, Angelika, Schargrodsky, Herman, Schienkiewitz, Anja, Schindler, Karin, Schipf, Sabine, Schmidt, Börge, Schmidt, Carsten O, Schmidt, Ida Maria, Schneider, Andrea, Schnohr, Peter, Schöttker, Ben, Schramm, Sara, Schramm, Stine, Schröder, Helmut, Schultsz, Constance, Schultz, Gry, Schulze, Matthias B, Schutte, Aletta E, Sebert, Sylvain, Sedaghattalab, Moslem, Selamat, Rusidah, Sember, Vedrana, Sen, Abhijit, Senbanjo, Idowu O, Sepanlou, Sadaf G, Sequera, Guillermo, Serra-Majem, Luis, Servais, Jennifer, Ševčíková, Ľudmila, Sewpaul, Ronel, Shalnova, Svetlana, Shamah-Levy, Teresa, Shamshirgaran, Seyed Morteza, Shanthirani, Coimbatore Subramaniam, Sharafkhah, Maryam, Sharma, Sanjib K, Sharman, Almaz, Shaw, Jonathan E, Shayanrad, Amaneh, Shayesteh, Ali Akbar, Shengelia, Lela, Shi, Zumin, Shibuya, Kenji, Shimizu-Furusawa, Hana, Shimony, Tal, Shiri, Rahman, Shrestha, Namuna, Si-Ramlee, Khairil, Siani, Alfonso, Siantar, Rosalynn, Sibai, Abla M, Sidossis, Labros S, Silitrari, Natalia, Silva, Antonio M, Silva, Caroline Ramos de Moura, Silva, Diego Augusto Santos, Silva, Kelly Samara, Sim, Xueling, Simon, Mary, Simons, Judith, Simons, Leon A, Sjöberg, Agneta, Sjöström, Michael, Skoblina, Elena V, Skoblina, Natalia A, Slazhnyova, Tatyana, Slowikowska-Hilczer, Jolanta, Slusarczyk, Przemysław, Smeeth, Liam, So, Hung-Kwan, Soares, Fernanda Cunha, Sobek, Grzegorz, Sobngwi, Eugène, Sodemann, Morten, Söderberg, Stefan, Soekatri, Moesijanti YE, Soemantri, Agustinus, Sofat, Reecha, Solfrizzi, Vincenzo, Solovieva, Yuliya V, Somi, Mohammad Hossein, Sonestedt, Emily, Song, Yi, Soofi, Sajid, Sørensen, Thorkild IA, Sørgjerd, Elin P, Sossa Jérome, Charles, Soto-Rojas, Victoria E, Soumaré, Aïcha, Sousa-Poza, Alfonso, Sovic, Slavica, Sparboe-Nilsen, Bente, Sparrenberger, Karen, Spencer, Phoebe R, Spinelli, Angela, Spiroski, Igor, Staessen, Jan A, Stamm, Hanspeter, Stang, Andreas, Starc, Gregor, Staub, Kaspar, Stavreski, Bill, Steene-Johannessen, Jostein, Stehle, Peter, Stein, Aryeh D, Steinsbekk, Silje, Stergiou, George S, Stessman, Jochanan, Stevanović, Ranko, Stieber, Jutta, Stöckl, Doris, Stokwiszewski, Jakub, Stoyanova, Ekaterina, Stratton, Gareth, Stronks, Karien, Strufaldi, Maria Wany, Sturua, Lela, Suárez-Medina, Ramón, Suarez-Ortegón, Milton F, Suebsamran, Phalakorn, Sugiyama, Mindy, Suka, Machi, Sulo, Gerhard, Sun, Chien-An, Sun, Liang, Sund, Malin, Sundström, Johan, Sung, Yn-Tz, Sunyer, Jordi, Suriyawongpaisal, Paibul, Sweis, Nabil William G, Swinburn, Boyd A, Sy, Rody G, Sylva, René Charles, Szponar, Lucjan, Tabone, Lorraine, Tai, E Shyong, Takuro, Furusawa, Tambalis, Konstantinos D, Tammesoo, Mari-Liis, Tamosiunas, Abdonas, Tan, Eng Joo, Tang, Xun, Tanrygulyyeva, Maya, Tanser, Frank, Tao, Yong, Tarawneh, Mohammed Rasoul, Tarp, Jakob, Tarqui-Mamani, Carolina B, Taxová Braunerová, Radka, Taylor, Anne, Taylor, Julie, Tchibindat, Félicité, Te Velde, Saskia, Tebar, William R, Tell, Grethe S, Tello, Tania, Tessema, Masresha, Tham, Yih Chung, Thankappan, KR, Theobald, Holger, Theodoridis, Xenophon, Thomas, Nihal, Thorand, Barbara, Thrift, Amanda G, Tichá, Ľubica, Timmermans, Erik J, Tjandrarini, Dwi Hapsari, Tjonneland, Anne, Tolonen, Hanna K, Tolstrup, Janne S, Tomaszewski, Maciej, Topbas, Murat, Topór-Mądry, Roman, Torheim, Liv Elin, Tornaritis, Michael J, Torrent, Maties, Torres-Collado, Laura, Toselli, Stefania, Touloumi, Giota, Traissac, Pierre, Tran, Thi Tuyet-Hanh, Tremblay, Mark S, Triantafyllou, Areti, Trichopoulos, Dimitrios, Trichopoulou, Antonia, Trinh, Oanh TH, Trivedi, Atul, Tshepo, Lechaba, Tsigga, Maria, Tsintavis, Panagiotis, Tsugane, Shoichiro, Tuitele, John, Tuliakova, Azaliia M, Tulloch-Reid, Marshall K, Tullu, Fikru, Tuomainen, Tomi-Pekka, Tuomilehto, Jaakko, Twig, Gilad, Tynelius, Per, Tzala, Evangelia, Tzotzas, Themistoklis, Tzourio, Christophe, Udoji, Nwannedimma, Ueda, Peter, Ugel, Eunice, Ukoli, Flora AM, Ulmer, Hanno, Unal, Belgin, Usupova, Zhamyila, Uusitalo, Hannu MT, Uysal, Nalan, Vaitkeviciute, Justina, Valdivia, Gonzalo, Vale, Susana, Valvi, Damaskini, van Dam, Rob M, van den Born, Bert-Jan, Van der Heyden, Johan, van der Schouw, Yvonne T, Van Herck, Koen, Van Lippevelde, Wendy, Van Minh, Hoang, Van Schoor, Natasja M, van Valkengoed, Irene GM, Vanderschueren, Dirk, Vanuzzo, Diego, Varbo, Anette, Varela-Moreiras, Gregorio, Vargas, Luz Nayibe, Varona-Pérez, Patricia, Vasan, Senthil K, Vasques, Daniel G, Vatasescu, Radu, Vega, Tomas, Veidebaum, Toomas, Velasquez-Melendez, Gustavo, Velika, Biruta, Verloigne, Maïté, Veronesi, Giovanni, Verschuren, WM Monique, Victora, Cesar G, Viegi, Giovanni, Viet, Lucie, Vik, Frøydis N, Vilar, Monica, Villalpando, Salvador, Vioque, Jesus, Viriyautsahakul, Napaphan, Virtanen, Jyrki K, Visser, Marjolein, Visvikis-Siest, Sophie, Viswanathan, Bharathi, Vladulescu, Mihaela, Vlasoff, Tiina, Vocanec, Dorja, Vollenweider, Peter, Völzke, Henry, Vourli, Georgia, Voutilainen, Ari, Vrijheid, Martine, Vrijkotte, Tanja GM, Vuletić, Silvije, Wade, Alisha N, Waldhör, Thomas, Walton, Janette, Wambiya, Elvis OA, Wan Bebakar, Wan Mohamad, Wan Mohamud, Wan Nazaimoon, Wanderley Júnior, Rildo de Souza, Wang, Chongjian, Wang, Huijun, Wang, Ming-Dong, Wang, Ningli, Wang, Qian, Wang, Xiangjun, Wang, Ya Xing, Wang, Ying-Wei, Wannamethee, S Goya, Wareham, Nicholas, Wartha, Olivia, Weber, Adelheid, Wedderkopp, Niels, Weghuber, Daniel, Wei, Wenbin, Weres, Aneta, Werner, Bo, Westbury, Leo D, Whincup, Peter H, Wichstrøm, Lars, Wickramasinghe, Kremlin, Widhalm, Kurt, Widyahening, Indah S, Więcek, Andrzej, Wild, Philipp S, Wilks, Rainford J, Willeit, Johann, Willeit, Peter, Williams, Julianne, Wilsgaard, Tom, Wirth, James P, Wojtyniak, Bogdan, Woldeyohannes, Meseret, Wolf, Kathrin, Wong-McClure, Roy A, Wong, Andrew, Wong, Emily B, Wong, Jyh Eiin, Wong, Tien Yin, Woo, Jean, Woodward, Mark, Wu, Frederick C, Wu, Hon-Yen, Wu, Jianfeng, Wu, Li Juan, Wu, Shouling, Wyszyńska, Justyna, Xu, Haiquan, Xu, Liang, Yaacob, Nor Azwany, Yamborisut, Uruwan, Yan, Li, Yan, Weili, Yang, Ling, Yang, Xiaoguang, Yang, Yang, Yardim, Nazan, Yasuharu, Tabara, Yépez García, Martha, Yiallouros, Panayiotis K, Yngve, Agneta, Yoosefi, Moein, Yoshihara, Akihiro, Yotov, Yoto, You, Qi Sheng, You, San-Lin, Younger-Coleman, Novie O, Yu, Yu-Ling, Yu, Yunjiang, Yusof, Safiah Md, Yusoff, Ahmad Faudzi, Zaccagni, Luciana, Zafiropulos, Vassilis, Zainuddin, Ahmad A, Zakavi, Seyed Rasoul, Zamani, Farhad, Zambon, Sabina, Zampelas, Antonis, Zamrazilová, Hana, Zapata, Maria Elisa, Zargar, Abdul Hamid, Zaw, Ko Ko, Zayed, Ayman A, Zdrojewski, Tomasz, Żegleń, Magdalena, Zejglicova, Kristyna, Zeljkovic Vrkic, Tajana, Zeng, Yi, Zentai, Andrea, Zhang, Bing, Zhang, Luxia, Zhang, Zhen-Yu, Zhao, Dong, Zhao, Ming-Hui, Zhao, Wenhua, Zhecheva, Yanitsa V, Zhen, Shiqi, Zheng, Wei, Zheng, Yingfeng, Zholdin, Bekbolat, Zhou, Maigeng, Zhu, Dan, Zimmet, Paul, Zins, Marie, Zitt, Emanuel, Zocalo, Yanina, Zoghlami, Nada, Zuñiga Cisneros, Julio, Zuziak, Monika, and Ezzati, Majid

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2024
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26. Large language models for diabetes care: Potentials and prospects

Author

Sheng, Bin, Guan, Zhouyu, Lim, Lee-Ling, Jiang, Zehua, Mathioudakis, Nestoras, Li, Jiajia, Liu, Ruhan, Bao, Yuqian, Bee, Yong Mong, Wang, Ya-Xing, Zheng, Yingfeng, Tan, Gavin Siew Wei, Ji, Hongwei, Car, Josip, Wang, Haibo, Klonoff, David C., Li, Huating, Tham, Yih-Chung, Wong, Tien Yin, and Jia, Weiping

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2024
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27. DRAC 2022: A public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images

Author

Qian, Bo, Chen, Hao, Wang, Xiangning, Guan, Zhouyu, Li, Tingyao, Jin, Yixiao, Wu, Yilan, Wen, Yang, Che, Haoxuan, Kwon, Gitaek, Kim, Jaeyoung, Choi, Sungjin, Shin, Seoyoung, Krause, Felix, Unterdechler, Markus, Hou, Junlin, Feng, Rui, Li, Yihao, El Habib Daho, Mostafa, Yang, Dawei, Wu, Qiang, Zhang, Ping, Yang, Xiaokang, Cai, Yiyu, Tan, Gavin Siew Wei, Cheung, Carol Y., Jia, Weiping, Li, Huating, Tham, Yih Chung, Wong, Tien Yin, and Sheng, Bin

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2024
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29. Category-Independent Visual Explanation for Medical Deep Network Understanding

Author

Qian, Yiming, Li, Liangzhi, Fu, Huazhu, Wang, Meng, Peng, Qingsheng, Tham, Yih Chung, Cheng, Chingyu, Liu, Yong, Goh, Rick Siow Mong, Xu, Xinxing, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Greenspan, Hayit, editor, Madabhushi, Anant, editor, Mousavi, Parvin, editor, Salcudean, Septimiu, editor, Duncan, James, editor, Syeda-Mahmood, Tanveer, editor, and Taylor, Russell, editor

Published
2023
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31. Rationale and protocol paper for the Asia Pacific Network for inherited eye diseases

Author

Wong, Wendy M., Tham, Yih Chung, Simunovic, Matthew P., Chen, Fred Kuanfu, Luu, Chi D., Chen, Haoyu, Jin, Zi-Bing, Shen, Ren-Juan, Li, Shiying, Sui, Ruifang, Zhao, Chen, Yang, Liping, Bhende, Muna, Raman, Rajiv, Sen, Parveen, Ghosh, Arkasubhra, Poornachandra, B., Sasongko, Muhammad Bayu, Arianti, Alia, Chia, Valen, Mangunsong, Cosmos O., Manurung, Florence, Fujinami, Kaoru, Ikeda, Hanako, Woo, Se Joon, Kim, Sang Jin, Mohd Khialdin, Safinaz, Othman, Othmaliza, Bastion, Mae-Lynn Catherine, Kamalden, Ain Tengku, Lott, Pooi Wah Penny, Fong, Kenneth, Shunmugam, Manoharan, Lim, Amelia, Thapa, Raba, Pradhan, Eli, Rajkarnikar, Sthapit Purnima, Adhikari, Srijana, Ibañez, B.Manuel Benjamin IV, Koh, Adrian, Chan, Choi Mun M., Fenner, Beau J., Tan, Tien-En, Laude, Augustinus, Ngo, Wei Kiong, Holder, Graham E., Su, Xinyi, Chen, Ta-Ching, Wang, Nan-Kai, Kang, Eugene Yu-Chuan, Huang, Chu-Hsuan, Surawatsatien, Nuntachai, Pisuchpen, Phattrawan, Sujirakul, Tharikarn, Kumaramanickavel, Govindasamy, Singh, Mandeep, Leroy, Bart, Michaelides, Michel, Cheng, Ching-Yu, Chen, Li Jia, and Chan, Hwei Wuen

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2024
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40. X-chromosome and kidney function: evidence from a multi-trait genetic analysis of 908,697 individuals reveals sex-specific and sex-differential findings in genes regulated by androgen response elements

Author

Scholz, Markus, Horn, Katrin, Pott, Janne, Wuttke, Matthias, Kühnapfel, Andreas, Nasr, M. Kamal, Kirsten, Holger, Li, Yong, Hoppmann, Anselm, Gorski, Mathias, Ghasemi, Sahar, Li, Man, Tin, Adrienne, Chai, Jin-Fang, Cocca, Massimiliano, Wang, Judy, Nutile, Teresa, Akiyama, Masato, Åsvold, Bjørn Olav, Bansal, Nisha, Biggs, Mary L., Boutin, Thibaud, Brenner, Hermann, Brumpton, Ben, Burkhardt, Ralph, Cai, Jianwen, Campbell, Archie, Campbell, Harry, Chalmers, John, Chasman, Daniel I., Chee, Miao Ling, Chee, Miao Li, Chen, Xu, Cheng, Ching-Yu, Cifkova, Renata, Daviglus, Martha, Delgado, Graciela, Dittrich, Katalin, Edwards, Todd L., Endlich, Karlhans, Michael Gaziano, J., Giri, Ayush, Giulianini, Franco, Gordon, Scott D., Gudbjartsson, Daniel F., Hallan, Stein, Hamet, Pavel, Hartman, Catharina A., Hayward, Caroline, Heid, Iris M., Hellwege, Jacklyn N., Holleczek, Bernd, Holm, Hilma, Hutri-Kähönen, Nina, Hveem, Kristian, Isermann, Berend, Jonas, Jost B., Joshi, Peter K., Kamatani, Yoichiro, Kanai, Masahiro, Kastarinen, Mika, Khor, Chiea Chuen, Kiess, Wieland, Kleber, Marcus E., Körner, Antje, Kovacs, Peter, Krajcoviechova, Alena, Kramer, Holly, Krämer, Bernhard K., Kuokkanen, Mikko, Kähönen, Mika, Lange, Leslie A., Lash, James P., Lehtimäki, Terho, Li, Hengtong, Lin, Bridget M., Liu, Jianjun, Loeffler, Markus, Lyytikäinen, Leo-Pekka, Magnusson, Patrik K. E., Martin, Nicholas G., Matsuda, Koichi, Milaneschi, Yuri, Mishra, Pashupati P., Mononen, Nina, Montgomery, Grant W., Mook-Kanamori, Dennis O., Mychaleckyj, Josyf C., März, Winfried, Nauck, Matthias, Nikus, Kjell, Nolte, Ilja M., Noordam, Raymond, Okada, Yukinori, Olafsson, Isleifur, Oldehinkel, Albertine J., Penninx, Brenda W. J. H., Perola, Markus, Pirastu, Nicola, Polasek, Ozren, Porteous, David J., Poulain, Tanja, Psaty, Bruce M., Rabelink, Ton J., Raffield, Laura M., Raitakari, Olli T., Rasheed, Humaira, Reilly, Dermot F., Rice, Kenneth M., Richmond, Anne, Ridker, Paul M., Rotter, Jerome I., Rudan, Igor, Sabanayagam, Charumathi, Salomaa, Veikko, Schneiderman, Neil, Schöttker, Ben, Sims, Mario, Snieder, Harold, Stark, Klaus J., Stefansson, Kari, Stocker, Hannah, Stumvoll, Michael, Sulem, Patrick, Sveinbjornsson, Gardar, Svensson, Per O., Tai, E-Shyong, Taylor, Kent D., Tayo, Bamidele O., Teren, Andrej, Tham, Yih-Chung, Thiery, Joachim, Thio, Chris H. L., Thomas, Laurent F., Tremblay, Johanne, Tönjes, Anke, van der Most, Peter J., Vitart, Veronique, Völker, Uwe, Wang, Ya Xing, Wang, Chaolong, Wei, Wen Bin, Whitfield, John B., Wild, Sarah H., Wilson, James F., Winkler, Thomas W., Wong, Tien-Yin, Woodward, Mark, Sim, Xueling, Chu, Audrey Y., Feitosa, Mary F., Thorsteinsdottir, Unnur, Hung, Adriana M., Teumer, Alexander, Franceschini, Nora, Parsa, Afshin, Köttgen, Anna, Schlosser, Pascal, and Pattaro, Cristian

Published
2024
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47. Comparing generative and retrieval-based chatbots in answering patient questions regarding age-related macular degeneration and diabetic retinopathy

Author

Cheong, Kai Xiong, primary, Zhang, Chenxi, additional, Tan, Tien-En, additional, Fenner, Beau J, additional, Wong, Wendy Meihua, additional, Teo, Kelvin YC, additional, Wang, Ya Xing, additional, Sivaprasad, Sobha, additional, Keane, Pearse A, additional, Lee, Cecilia Sungmin, additional, Lee, Aaron Y, additional, Cheung, Chui Ming Gemmy, additional, Wong, Tien Yin, additional, Cheong, Yun-Gyung, additional, Song, Su Jeong, additional, and Tham, Yih Chung, additional

Published
2024
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