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869 results on '"Lee, Cecilia"'

1. OCTCube-M: A 3D multimodal optical coherence tomography foundation model for retinal and systemic diseases with cross-cohort and cross-device validation

Author

Liu, Zixuan, Xu, Hanwen, Woicik, Addie, Shapiro, Linda G., Blazes, Marian, Wu, Yue, Steffen, Verena, Cukras, Catherine, Lee, Cecilia S., Zhang, Miao, Lee, Aaron Y., and Wang, Sheng

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

We present OCTCube-M, a 3D OCT-based multi-modal foundation model for jointly analyzing OCT and en face images. OCTCube-M first developed OCTCube, a 3D foundation model pre-trained on 26,685 3D OCT volumes encompassing 1.62 million 2D OCT images. It then exploits a novel multi-modal contrastive learning framework COEP to integrate other retinal imaging modalities, such as fundus autofluorescence and infrared retinal imaging, into OCTCube, efficiently extending it into multi-modal foundation models. OCTCube achieves best performance on predicting 8 retinal diseases, demonstrating strong generalizability on cross-cohort, cross-device and cross-modality prediction. OCTCube can also predict cross-organ nodule malignancy (CT) and low cardiac ejection fraction as well as systemic diseases, such as diabetes and hypertension, revealing its wide applicability beyond retinal diseases. We further develop OCTCube-IR using COEP with 26,685 OCT and IR image pairs. OCTCube-IR can accurately retrieve between OCT and IR images, allowing joint analysis between 3D and 2D retinal imaging modalities. Finally, we trained a tri-modal foundation model OCTCube-EF from 4 million 2D OCT images and 400K en face retinal images. OCTCube-EF attains the best performance on predicting the growth rate of geographic atrophy (GA) across datasets collected from 6 multi-center global trials conducted in 23 countries. This improvement is statistically equivalent to running a clinical trial with more than double the size of the original study. Our analysis based on another retrospective case study reveals OCTCube-EF's ability to avoid false positive Phase-III results according to its accurate treatment effect estimation on the Phase-II results. In sum, OCTCube-M is a 3D multi-modal foundation model framework that integrates OCT and other retinal imaging modalities revealing substantial diagnostic and prognostic benefits.

Published
2024

2. 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

3. Standardization and clinical applications of retinal imaging biomarkers for cardiovascular disease: a Roadmap from an NHLBI workshop

Author

Chew, Emily Y., Burns, Stephen A., Abraham, Alison G., Bakhoum, Mathieu F., Beckman, Joshua A., Chui, Toco Y. P., Finger, Robert P., Frangi, Alejandro F., Gottesman, Rebecca F., Grant, Maria B., Hanssen, Henner, Lee, Cecilia S., Meyer, Michelle L., Rizzoni, Damiano, Rudnicka, Alicja R., Schuman, Joel S., Seidelmann, Sara B., Tang, W. H. Wilson, Adhikari, Bishow B., Danthi, Narasimhan, Hong, Yuling, Reid, Diane, Shen, Grace L., and Oh, Young S.

Published
2025
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4. Author Correction: Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines

Author

Martindale, Alexander P. L., Llewellyn, Carrie D., de Visser, Richard O., Ng, Benjamin, Ngai, Victoria, Kale, Aditya U., di Ruffano, Lavinia Ferrante, Golub, Robert M., Collins, Gary S., Moher, David, McCradden, Melissa D., Oakden-Rayner, Lauren, Rivera, Samantha Cruz, Calvert, Melanie, Kelly, Christopher J., Lee, Cecilia S., Yau, Christopher, Chan, An-Wen, Keane, Pearse A., Beam, Andrew L., Denniston, Alastair K., and Liu, Xiaoxuan

Published
2024
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6. Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines

Author

Martindale, Alexander P. L., Llewellyn, Carrie D., de Visser, Richard O., Ng, Benjamin, Ngai, Victoria, Kale, Aditya U., di Ruffano, Lavinia Ferrante, Golub, Robert M., Collins, Gary S., Moher, David, McCradden, Melissa D., Oakden-Rayner, Lauren, Rivera, Samantha Cruz, Calvert, Melanie, Kelly, Christopher J., Lee, Cecilia S., Yau, Christopher, Chan, An-Wen, Keane, Pearse A., Beam, Andrew L., Denniston, Alastair K., and Liu, Xiaoxuan

Published
2024
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12. Treatment Patterns of Childhood Glaucoma in the United States: Analysis of IRISⓇ Registry (Intelligent Research in Sight)

Author

Pershing, Suzann, Hyman, Leslie, Haller, Julia A., Lee, Aaron Y., Lee, Cecilia S., Lum, Flora, Miller, Joan W., Lorch, Alice, Fujita, Asahi, Vu, Daniel M., Aziz, Kanza, Chang, Ta Chen, Chung, In Young, Ciociola, Elizabeth C., Friedman, David S., Lokhande, Anagha, Lorch, Alice C., Parikh, Prashit, Rothman, Adam L., Seresirikachorn, Kasem, Elze, Tobias, and Zebardast, Nazlee

Published
2025
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21. Incidence and Risk Factors for Retinal Detachment and Retinal Tear after Cataract Surgery: IRIS® Registry (Intelligent Research in Sight) Analysis

Author

Lee, Aaron Y., Lee, Cecilia S., Van Gelder, Russ, Lorch, Alice, Miller, Joan W., Pershing, Suzann, Goldberg, Jeffrey, Morano, Michael J., Khan, M. Ali, Zhang, Qiang, Halfpenny, Colleen P., Wisner, Douglas M., Sharpe, James, Li, Alexander, Tomaiuolo, Maurizio, Haller, Julia A., Hyman, Leslie, and Ho, Allen C.

Published
2023
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22. Large language models and their impact in ophthalmology

Author

Betzler, Bjorn Kaijun, Chen, Haichao, Cheng, Ching-Yu, Lee, Cecilia S, Ning, Guochen, Song, Su Jeong, Lee, Aaron Y, Kawasaki, Ryo, van Wijngaarden, Peter, Grzybowski, Andrzej, He, Mingguang, Li, Dawei, Ran Ran, An, Ting, Daniel Shu Wei, Teo, Kelvin, Ruamviboonsuk, Paisan, Sivaprasad, Sobha, Chaudhary, Varun, Tadayoni, Ramin, Wang, Xiaofei, Cheung, Carol Y, Zheng, Yingfeng, Wang, Ya Xing, Tham, Yih Chung, and Wong, Tien Yin

Published
2023
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23. Assessing the Clinical Utility of Expanded Macular OCTs Using Machine Learning

Author

Lin, Andrew C, Lee, Cecilia S, Blazes, Marian, Lee, Aaron Y, and Gorin, Michael B

Subjects
Clinical Research, Neurosciences, Biomedical Imaging, Diabetes, Eye Disease and Disorders of Vision, Macular Degeneration, Bioengineering, Aging, Neurodegenerative, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, Eye, Good Health and Well Being, Artificial Intelligence, Diabetic Retinopathy, Glaucoma, Open-Angle, Humans, Machine Learning, Macular Edema, machine learning, primary open-angle glaucoma, optical coherence tomography, age-related macular degeneration, diabetic macular edema, Biomedical Engineering, Opthalmology and Optometry
Abstract

PurposeOptical coherence tomography (OCT) is widely used in the management of retinal pathologies, including age-related macular degeneration (AMD), diabetic macular edema (DME), and primary open-angle glaucoma (POAG). We used machine learning techniques to understand diagnostic performance gains from expanding macular OCT B-scans compared with foveal-only OCT B-scans for these conditions.MethodsElectronic medical records were extracted to obtain 61 B-scans per eye from patients with AMD, diabetic retinopathy, or POAG. We constructed deep neural networks and random forest ensembles and generated area under the receiver operating characteristic (AUROC) and area under the precision recall (AUPR) curves.ResultsAfter extracting 630,000 OCT images, we achieved improved AUROC and AUPR curves when comparing the central image (one B-scan) to all images (61 B-scans). The AUROC and AUPR points of diminishing return for diagnostic accuracy for macular OCT coverage were found to be within 2.75 to 4.00 mm (14-19 B-scans), 4.25 to 4.50 mm (20-21 B-scans), and 4.50 to 6.25 mm (21-28 B-scans) for AMD, DME, and POAG, respectively. All models with >0.25 mm of coverage had statistically significantly improved AUROC/AUPR curves for all diseases (P < 0.05).ConclusionsSystematically expanded macular coverage models demonstrated significant differences in total macular coverage required for improved diagnostic accuracy, with the largest macular area being relevant in POAG followed by DME and then AMD. These findings support our hypothesis that the extent of macular coverage by OCT imaging in the clinical setting, for any of the three major disorders, has a measurable impact on the functionality of artificial intelligence decision support.Translational relevanceWe used machine learning techniques to improve OCT imaging standards for common retinal disease diagnoses.

Published
2021

24. Gene–environment interactions between air pollution and biotransformation enzymes and risk of birth defects

Author

Padula, Amy M, Yang, Wei, Schultz, Kathleen, Lee, Cecilia, Lurmann, Fred, Hammond, S Katharine, and Shaw, Gary M

Subjects
Paediatrics, Reproductive Medicine, Biomedical and Clinical Sciences, Climate-Related Exposures and Conditions, Pediatric, Genetics, Clinical Research, Prevention, 2.2 Factors relating to the physical environment, 2.1 Biological and endogenous factors, Aetiology, Air Pollution, Biotransformation, Case-Control Studies, Female, Gene-Environment Interaction, Humans, Liver-Specific Organic Anion Transporter 1, Mouth Mucosa, Pregnancy, Transposition of Great Vessels, air pollution, cleft lip, cleft palate, congenital anomalies, d&#8208, TGA, gastroschisis, gene, gene&#8211, environment, heart defect, orofacial defect, tetralogy of fallot, d-TGA, gene-environment, Paediatrics and Reproductive Medicine, Public Health and Health Services, Genetics & Heredity, Reproductive medicine
Abstract

Genetic and environmental factors have been observed to influence risks for birth defects, though few studies have investigated gene-environment interactions. Our aim was to examine the interaction terms of gene variants in biotransformation enzyme pathways and air pollution exposures in relation to risk of several structural birth defects. We evaluated the role of ambient air pollutant exposure (nitrogen dioxide [NO2 ], nitrogen oxide, carbon monoxide, particulate matter

Published
2021

25. Evolving consensus for immunomodulatory therapy in non-infectious uveitis during the COVID-19 pandemic

Author

Agrawal, Rupesh, Testi, Ilaria, Lee, Cecilia S, Tsui, Edmund, Blazes, Marian, Thorne, Jennifer E, Okada, Annabelle A, Smith, Justine R, McCluskey, Peter J, Kempen, John H, Tappeiner, Christoph, Agarwal, Manisha, Bodaghi, Bahram, Nguyen, Quan Dong, Gupta, Vishali, De Smet, Marc D, Zierhut, Manfred, and Pavesio, Carlos

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Clinical Research, Good Health and Well Being, COVID-19, Clinical Decision-Making, Consensus, Cross-Sectional Studies, Glucocorticoids, Humans, Immunomodulation, Immunosuppressive Agents, Practice Guidelines as Topic, Risk Assessment, SARS-CoV-2, Surveys and Questionnaires, Uveitis, Choroid, Imaging, Inflammation, Telemedicine, Conjunctiva, Immunology, Macula, Retina, Infection, Iris, Treatment medical, Epidemiology, Cornea, Vitreous, Treatment lasers, Ciliary body, Drugs, COVID-19 IMT Study Group, Opthalmology and Optometry, Public Health and Health Services, Ophthalmology & Optometry, Clinical sciences, Ophthalmology and optometry
Abstract

BackgroundImmunomodulatory therapy (IMT) is often considered for systemic treatment of non-infectious uveitis (NIU). During the evolving coronavirus disease-2019 (COVID-19) pandemic, given the concerns related to IMT and the increased risk of infections, an urgent need for guidance on the management of IMT in patients with uveitis has emerged.MethodsA cross-sectional survey of international uveitis experts was conducted. An expert steering committee identified clinical questions on the use of IMT in patients with NIU during the COVID-19 pandemic. Using an interactive online questionnaire, guided by background experience and knowledge, 139 global uveitis experts generated consensus statements for IMT. In total, 216 statements were developed around when to initiate, continue, decrease and stop systemic and local corticosteroids, conventional immunosuppressive agents and biologics in patients with NIU. Thirty-one additional questions were added, related to general recommendations, including the use of non-steroidal anti-inflammatory drugs (NSAIDs) and hydroxychloroquine.ResultsHighest consensus was achieved for not initiating IMT in patients who have suspected or confirmed COVID-19, and for using local over systemic corticosteroid therapy in patients who are at high-risk and very high-risk for severe or fatal COVID-19. While there was a consensus in starting or initiating NSAIDs for the treatment of scleritis in healthy patients, there was no consensus in starting hydroxychloroquine in any risk groups.ConclusionConsensus guidelines were proposed based on global expert opinion and practical experience to bridge the gap between clinical needs and the absence of medical evidence, to guide the treatment of patients with NIU during the COVID-19 pandemic.

Published
2021

30. Open-Globe Injury Repairs in the American Academy of Ophthalmology IRIS® Registry 2014 – 2018: Incidence, Risk Factors, and Visual Outcomes

Author

Lee, Aaron Y., Lee, Cecilia S., Van Gelder, Russ, Lorch, Alice, Miller, Joan W., Pershing, Suzann, Goldberg, Jeffrey, Tomaiuolo, Maurizio, Woreta, Fasika A., Li, Alexander, Yonekawa, Yoshihiro, Zhang, Qiang (Ed), Sharpe, James E., Zafar, Sidra, Syed, Zeba A., Ramesh, Sathyadeepak, Lorch, Alice C., Hall, Nathan E., Shah, Ankoor S., Justin, Grant A., and Hyman, Leslie

Published
2023
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31. Health-related quality of life and vulnerability among people with myelodysplastic syndromes: a US national study

Author

Abel, Gregory A., Hebert, Donnie, Lee, Cecilia, Rollison, Dana, Gillis, Nancy, Komrokji, Rami, Foran, James M., Liu, Jane Jijun, Al Baghdadi, Tareq, Deeg, Joachim, Gore, Steven, Saber, Wael, Wilson, Steffanie, Otterstatter, Michael, Thompson, Jason, Borchert, Christine, Padron, Eric, DeZern, Amy, Cella, David, and Sekeres, Mikkael A.

Published
2023
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37. Forecasting Future Humphrey Visual Fields Using Deep Learning

Author

Wen, Joanne C., Lee, Cecilia S., Keane, Pearse A., Xiao, Sa, Wu, Yue, Rokem, Ariel, Chen, Philip P., and Lee, Aaron Y.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
Abstract

Purpose: To determine if deep learning networks could be trained to forecast a future 24-2 Humphrey Visual Field (HVF). Participants: All patients who obtained a HVF 24-2 at the University of Washington. Methods: All datapoints from consecutive 24-2 HVFs from 1998 to 2018 were extracted from a University of Washington database. Ten-fold cross validation with a held out test set was used to develop the three main phases of model development: model architecture selection, dataset combination selection, and time-interval model training with transfer learning, to train a deep learning artificial neural network capable of generating a point-wise visual field prediction. Results: More than 1.7 million perimetry points were extracted to the hundredth decibel from 32,443 24-2 HVFs. The best performing model with 20 million trainable parameters, CascadeNet-5, was selected. The overall MAE for the test set was 2.47 dB (95% CI: 2.45 dB to 2.48 dB). The 100 fully trained models were able to successfully predict progressive field loss in glaucomatous eyes up to 5.5 years in the future with a correlation of 0.92 between the MD of predicted and actual future HVF (p < 2.2 x 10 -16 ) and an average difference of 0.41 dB. Conclusions: Using unfiltered real-world datasets, deep learning networks show an impressive ability to not only learn spatio-temporal HVF changes but also to generate predictions for future HVFs up to 5.5 years, given only a single HVF.

Published
2018
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38. Clinical metagenomics for infectious corneal ulcers: Rags to riches?

Author

Ung, Lawson, Bispo, Paulo JM, Doan, Thuy, Van Gelder, Russell N, Gilmore, Michael S, Lietman, Thomas, Margolis, Todd P, Zegans, Michael E, Lee, Cecilia S, and Chodosh, James

Subjects
Biomedical and Clinical Sciences, Ophthalmology and Optometry, Infectious Diseases, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, Good Health and Well Being, Communicable Diseases, Corneal Ulcer, High-Throughput Nucleotide Sequencing, Humans, Metagenomics, Clinical metagenomics, Next generation sequencing, Infectious corneal ulcers, Microbial keratitis, Opthalmology and Optometry, Ophthalmology & Optometry, Ophthalmology and optometry
Abstract

The emergence of clinical metagenomics as an unbiased, hypothesis-free approach to diagnostic testing is set to fundamentally alter the way infectious diseases are detected. Long envisioned as the solution to the limitations of culture-based conventional microbiology, next generation sequencing methods will soon mature, and our attention will inevitably turn to how they can be applied to areas of medicine which need it most urgently. In ophthalmology, the demand for this technology is particularly pressing for the care of infectious corneal ulcers, where current diagnostic tests may fail to identify a causative organism in over half of cases. However, the optimism found in the budding discourse surrounding clinical metagenomics belies the reality that clinicians and scientists will soon be inundated by oppressive volumes of sequencing data, much of which will be foreign and unfamiliar. Therefore, our success in translating clinical metagenomics is likely to hinge on how we make sense of these data, and understanding its implications for the interpretation and implementation of sequencing into routine clinical care. In this consortium-led review, we provide an outline of these data-related issues and how they may be used to inform technical workflows, with the hope that we may edge closer to realizing the potential of clinical metagenomics for this important unmet need.

Published
2020

39. Infectious corneal ulceration: a proposal for neglected tropical disease status

Author

Ung, Lawson, Acharya, Nisha R, Agarwal, Tushar, Alfonso, Eduardo C, Bagga, Bhupesh, Bispo, Paulo JM, Burton, Matthew J, Dart, John KG, Doan, Thuy, Fleiszig, Suzanne MJ, Garg, Prashant, Gilmore, Michael S, Gritz, David C, Hazlett, Linda D, Iovieno, Alfonso, Jhanji, Vishal, Kempen, John H, Lee, Cecilia S, Lietman, Thomas M, Margolis, Todd P, McLeod, Stephen D, Mehta, Jod S, Miller, Darlene, Pearlman, Eric, Prajna, Lalitha, Prajna, N Venkatesh, Seitzman, Gerami D, Shanbhag, Swapna S, Sharma, Namrata, Sharma, Savitri, Srinivasan, Muthiah, Stapleton, Fiona, Tan, Donald TH, Tandon, Radhika, Taylor, Hugh R, Tu, Elmer Y, Tuli, Sonal S, Vajpayee, Rasik B, Van Gelder, Russell N, Watson, Stephanie L, Zegans, Michael E, and Chodosh, James

Subjects
Anti-Bacterial Agents, Bacterial Infections, Corneal Ulcer, Developing Countries, Global Health, Health Promotion, Humans, Leprosy, Neglected Diseases, Onchocerciasis, Ocular, Trachoma, Medical and Health Sciences, Tropical Medicine
Published
2019

41. Machine Learning Prediction of Adenovirus D8 Conjunctivitis Complications from Viral Whole-Genome Sequence

Author

Berdy, Gregg J., Branch, James D., Dixon, El-Roy, El-Harazi, Sherif M., Greiner, Jack V., Herz, Joshua, Lothringer, Larry L., Macaluso, Damien, Moyes, Andrew L., Nardin, George, Perez, Bernard R., Roel, Lawerence E., Reddy, Syamala H.K., Becker, Stephanie, Shmunes, Neil, Smith, Stephen, Tepedino, Michael, Macy, Jonathan, Garg, Prashant, Patil, Nivedita, Bhagat, Yasmin, Krishnaswamy, Malavika, Somshekhar, Nagappa, Acharya, Manisha, Reddy, Shree Kumar, Abraham, Mary, Kini, Shobha, Shanbag, Nita, Biswas, P.N., Agarwal, Virendra, Sahai, Anshu, Devi, P.S. Girija, Lakshmi, Vupputuri Venkata, Rao, Narasimha, Tandon, Radhika, Kapadia, Priti, Mehta, Deepak, Kochar, Anju, dos Santos Forseto, Adriana, Belfort, Rubens, Jr., Cohen, Jacob Moyses, Ghanem, Ramon Coral, De Ventura, Roberta, Pimentel, Sergio Luis Gianotti, Kwitko, Sergio, Nishiwaki Dantas, Maria Cristina, Hofling-Lima, Anna Maria, Nose, Walton, Wariyapola, D., Wijetunge, M., Fonseka, Charith, Banagala, Champa, Salvin, K.A., Kodikara, D.R., Nakamichi, Kenji, Akileswaran, Lakshmi, Meirick, Thomas, Lee, Michele D., Chodosh, James, Rajaiya, Jaya, Stroman, David, Wolf-Yadlin, Alejandro, Jackson, Quinn, Holtz, W. Bradley, Lee, Aaron Y., Lee, Cecilia S., and Van Gelder, Russell N.

Published
2022
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44. Generating retinal flow maps from structural optical coherence tomography with artificial intelligence

Author

Lee, Cecilia S., Tyring, Ariel J., Wu, Yue, Xiao, Sa, Rokem, Ariel S., Deruyter, Nicolaas P., Zhang, Qinqin, Tufail, Adnan, Wang, Ruikang K., and Lee, Aaron Y.

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

Despite significant advances in artificial intelligence (AI) for computer vision, its application in medical imaging has been limited by the burden and limits of expert-generated labels. We used images from optical coherence tomography angiography (OCTA), a relatively new imaging modality that measures perfusion of the retinal vasculature, to train an AI algorithm to generate vasculature maps from standard structural optical coherence tomography (OCT) images of the same retinae, both exceeding the ability and bypassing the need for expert labeling. Deep learning was able to infer perfusion of microvasculature from structural OCT images with similar fidelity to OCTA and significantly better than expert clinicians (P < 0.00001). OCTA suffers from need of specialized hardware, laborious acquisition protocols, and motion artifacts; whereas our model works directly from standard OCT which are ubiquitous and quick to obtain, and allows unlocking of large volumes of previously collected standard OCT data both in existing clinical trials and clinical practice. This finding demonstrates a novel application of AI to medical imaging, whereby subtle regularities between different modalities are used to image the same body part and AI is used to generate detailed and accurate inferences of tissue function from structure imaging., Comment: Under revision at Nature Communications. Submitted on June 5th 2017

Published
2018

45. Artificial intelligence-based strategies to identify patient populations and advance analysis in age-related macular degeneration clinical trials

Author

Yaghy, Antonio, Lee, Aaron Y., Keane, Pearse A., Keenan, Tiarnan D.L., Mendonca, Luisa S.M., Lee, Cecilia S., Cairns, Anne Marie, Carroll, Joseph, Chen, Hao, Clark, Julie, Cukras, Catherine A., de Sisternes, Luis, Domalpally, Amitha, Durbin, Mary K., Goetz, Kerry E., Grassmann, Felix, Haines, Jonathan L., Honda, Naoto, Hu, Zhihong Jewel, Mody, Christopher, Orozco, Luz D., Owsley, Cynthia, Poor, Stephen, Reisman, Charles, Ribeiro, Ramiro, Sadda, Srinivas R., Sivaprasad, Sobha, Staurenghi, Giovanni, Ting, Daniel SW., Tumminia, Santa J., Zalunardo, Luca, and Waheed, Nadia K.

Published
2022
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47. Beyond Pathology– Exploring Salutogenesis as a Novel Approach to Eye Inflammatory Disease Prevention and Management: A Perspective.

Author

Tan Yip Ming, Collin, Rojas-Carabali, William, Cifuentes-González, Carlos, Agrawal, Kajal, Grover, Sandeep, Anand, Akshay, Singh Senjam, Suraj, Singh, Amarjeet, Dubey, Preeti, Sharma, Aman, Blazes, Marian, Lee, Cecilia S., Lee, Aaron Y., Gupta, Vishali, and Agrawal, Rupesh

Subjects
PHYSICIAN-patient relations, PATIENT autonomy, DISEASE management, MEDICAL practice, SALUTOGENESIS
Abstract

The aim of this perspective is to promote the theory of salutogenesis as a novel approach to addressing ophthalmologic inflammatory conditions, illustrating several concepts in which it is based upon and how they can be applied to medical practice. This theory can better contextualize why patients with similar demographics and exposures are not uniform in their clinical presentations. Stressors in daily life can contribute to a state of ill-health and there are various factors that help alleviate their negative impact. These alleviating factors are significantly impaired in people with poor vision, one of the most common presentations of ophthalmologic conditions. Salutogenic principles can guide the treatment of eye conditions to be more respectful of patient autonomy amidst shifting expectations of the doctor-patient relationship. Being able to take ownership of their health and feeling that their cultural beliefs were considered improves compliance and subsequently gives more optimal outcomes. Population-level policy interventions could also utilize salutogenic principles to identify previously overlooked domains that can be addressed. We identified several papers about salutogenesis in an ophthalmological context and acknowledged the relatively few studies on this topic at present and offer directions in which we can explore further in subsequent studies. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Bridging Technology, Design, and Management: An Interdisciplinary Approach to Innovation in the AI Era.

Author

Lee, Cecilia and Carbajal, Carlos

Subjects
SERVICE design, ARTIFICIAL intelligence
Abstract

Not a single domain of technology, design, or management can fully tackle AI's ethical, practical, and strategic dimension. A service design approach is used to explore the intersection of technology, design, and management for a responsible AI implementation that fosters innovation but is also ethically aligned and strategically integrated. [ABSTRACT FROM AUTHOR]

Published
2024
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