Access the full text.
Sign up today, get DeepDyve free for 14 days.
S. Jha, E. Topol (2016)
Adapting to Artificial Intelligence: Radiologists and Pathologists as Information Specialists.JAMA, 316 22
Arti fi cial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success
V. Bolón-Canedo, E. Cansizoglu, Deniz Erdoğmuş, Jayashree Kalpathy-Cramer, O. Fontenla-Romero, Amparo Alonso-Betanzos, M. Chiang (2015)
Dealing with inter-expert variability in retinopathy of prematurity: A machine learning approachComputer methods and programs in biomedicine, 122 1
D. Ting, C. Cheung, Gilbert Lim, G. Tan, N. Quang, A. Gan, Haslina Hamzah, R. García-Franco, Ian Yeo, Shu-Yen Lee, E. Wong, C. Sabanayagam, M. Baskaran, Farah Ibrahim, N. Tan, E. Finkelstein, E. Lamoureux, I. Wong, N. Bressler, S. Sivaprasad, R. Varma, J. Jonas, M. He, Ching-Yu Cheng, G. Cheung, T. Aung, W. Hsu, M. Lee, T. Wong (2017)
Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With DiabetesJAMA, 318
U. Acharya, M. Mookiah, Joel Koh, J. Tan, K. Noronha, S. Bhandary, A. Rao, Yuki Hagiwara, C. Chua, A. Laude (2016)
Novel risk index for the identification of age-related macular degeneration using radon transform and DWT featuresComputers in biology and medicine, 73
T. Aslam, Haider Zaki, S. Mahmood, Zaria Ali, Nur Ahmad, M. Thorell, K. Balaskas (2018)
Use of a Neural Net to Model the Impact of Optical Coherence Tomography Abnormalities on Vision in Age-related Macular Degeneration.American journal of ophthalmology, 185
K. Yasaka, H. Akai, A. Kunimatsu, Shigeru Kiryu, O. Abe (2018)
Deep learning with convolutional neural network in radiologyJapanese Journal of Radiology, 36
E. Gong, J. Pauly, M. Wintermark, G. Zaharchuk (2018)
Deep learning enables reduced gadolinium dose for contrast‐enhanced brain MRIJournal of Magnetic Resonance Imaging, 48
B. Sosale, A. Sosale, H. Murthy, S. Sengupta, Muralidhar Naveenam (2020)
Medios– An offline, smartphone-based artificial intelligence algorithm for the diagnosis of diabetic retinopathyIndian Journal of Ophthalmology, 68
H. Bogunović, A. Montuoro, M. Baratsits, M. Karantonis, S. Waldstein, F. Schlanitz, U. Schmidt-Erfurth (2017)
Machine Learning of the Progression of Intermediate Age-Related Macular Degeneration Based on OCT Imaging.Investigative ophthalmology & visual science, 58 6
T. Schlegl, S. Waldstein, H. Bogunović, Franz Endstraßer, A. Sadeghipour, Ana-Maria Philip, D. Podkowinski, B. Gerendas, G. Langs, U. Schmidt-Erfurth (2017)
Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning.Ophthalmology, 125 4
A. Esteva, Brett Kuprel, R. Novoa, J. Ko, S. Swetter, H. Blau, S. Thrun (2017)
Dermatologist-level classification of skin cancer with deep neural networksNature, 542
F. Jiang, Yong Jiang, Hui Zhi, Yi Dong, Hao Li, Sufeng Ma, Yilong Wang, Q. Dong, Haipeng Shen, Yongjun Wang (2017)
Artificial intelligence in healthcare: past, present and futureStroke and Vascular Neurology, 2
T. Wong, N. Bressler (2016)
Artificial Intelligence With Deep Learning Technology Looks Into Diabetic Retinopathy Screening.JAMA, 316 22
S. Park, Kyunghwa Han (2018)
Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction.Radiology, 286 3
Erping Long, Haotian Lin, Zhenzhen Liu, Xiaohang Wu, Liming Wang, Jiewei Jiang, Yingying An, Zhuoling Lin, Xiao-yan Li, Jingjing Chen, J. Li, Q. Cao, Dongni Wang, Xiyang Liu, Weirong Chen, Yizhi Liu (2017)
An artificial intelligence platform for the multihospital collaborative management of congenital cataractsNature Biomedical Engineering, 1
Hidenori Takahashi, H. Tampo, Y. Arai, Y. Inoue, H. Kawashima (2017)
Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathyPLoS ONE, 12
L. McDonald, S. Ramagopalan, A. Cox, M. Oğuz (2017)
Unintended consequences of machine learning in medicine?F1000Research, 6
L. Brattain, B. Telfer, M. Dhyani, J. Grajo, A. Samir (2018)
Machine learning for medical ultrasound: status, methods, and future opportunitiesAbdominal Radiology, 43
M. Recht, R. Bryan (2017)
Artificial Intelligence: Threat or Boon to Radiologists?Journal of the American College of Radiology : JACR, 14 11
P. Serrano-Aguilar, R. Abreu, L. Antón-Canalís, C. Guerra-Artal, Y. Ramallo-Fariña, F. Gómez-Ulla, J. Nadal (2011)
Development and validation of a computer-aided diagnostic tool to screen for age-related macular degeneration by optical coherence tomographyBritish Journal of Ophthalmology, 96
E. Garcia-Martin, L. Pablo, R. Herrero, J. Ara, Jesús Martín, J. Larrosa, V. Polo, J. García-Feijóo, Javier Fernández (2013)
Neural networks to identify multiple sclerosis with optical coherence tomographyActa Ophthalmologica, 91
V. Patel, E. Shortliffe, M. Stefanelli, Peter Szolovits, M. Berthold, R. Bellazzi, A. Abu-Hanna (2009)
The coming of age of artificial intelligence in medicineArtificial intelligence in medicine, 46 1
H. Bogunović, S. Waldstein, T. Schlegl, G. Langs, A. Sadeghipour, Xuhui Liu, B. Gerendas, A. Osborne, U. Schmidt-Erfurth (2017)
Prediction of Anti-VEGF Treatment Requirements in Neovascular AMD Using a Machine Learning Approach.Investigative ophthalmology & visual science, 58 7
M. Horton, Christopher Brady, J. Cavallerano, M. Abràmoff, G. Barker, M. Chiang, Charlene Crockett, S. Garg, Peter Karth, Yao Liu, Clark Newman, Siddarth Rathi, V. Sheth, Paolo Silva, K. Stebbins, I. Zimmer-Galler (2020)
Practice Guidelines for Ocular Telehealth-Diabetic Retinopathy, Third EditionTelemedicine Journal and e-Health, 26
E. Ataer-Cansizoglu, V. Bolón-Canedo, J. Campbell, A. Bozkurt, Deniz Erdoğmuş, Jayashree Kalpathy-Cramer, Samir Patel, Karyn Jonas, R. Chan, S. Ostmo, M. Chiang (2015)
Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity: Performance of the "i-ROP" System and Image Features Associated With Expert Diagnosis.Translational vision science & technology, 4 6
J. Parreco, Antonio Hidalgo, A. Badilla, Omar Ilyas, R. Rattan (2018)
Predicting central line‐associated bloodstream infections and mortality using supervised machine learningJournal of Critical Care, 45
S. Nemati, A. Holder, Fereshteh Razmi, Matthew Stanley, G. Clifford, T. Buchman (2017)
An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICUCritical Care Medicine, 46
K. Shameer, Kipp Johnson, Benjamin Glicksberg, J. Dudley, P. Sengupta (2018)
Machine learning in cardiovascular medicine: are we there yet?Heart, 104
Beccaria (2018)
Preliminary investigation of human exhaled breath for tuberculosis diagnosis by multidimensional gas chromatography - Time of flight mass spectrometry and machine learningJ Chromatogr B Analyt Technol Biomed Life Sci, 1074-1075
Brattain (2018)
Machine learning for medical ultrasound: status, methods, and future opportunitiesAbdom Radiol (ny), 43
Sajid Iqbal, M. Ghani, T. Saba, A. Rehman (2018)
Brain tumor segmentation in multi‐spectral MRI using convolutional neural networks (CNN)Microscopy Research and Technique, 81
S. Kim, K. Cho, Sejong Oh (2017)
Development of machine learning models for diagnosis of glaucomaPLoS ONE, 12
P. Lakhani, B. Sundaram (2017)
Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks.Radiology, 284 2
M. Smolek, S. Vujosevic, S. Piermarocchi, E. Midena, T. Peto, M. Luca, E. Grisan, A. Ruggeri, S. Klyce (2008)
An Expert System for Diabetic Retinopathy Screening With a Non-Mydriatic, Operator-Free Fundus CameraInvestigative Ophthalmology & Visual Science, 49
D. Bzdok, A. Meyer-Lindenberg (2017)
Machine Learning for Precision Psychiatry: Opportunities and Challenges.Biological psychiatry. Cognitive neuroscience and neuroimaging, 3 3
Cabitza (2017)
Unintended Consequences of Machine Learning in MedicineJAMA, 318
B. Damato, A. Eleuteri, A. Fisher, S. Coupland, A. Taktak (2008)
Artificial neural networks estimating survival probability after treatment of choroidal melanoma.Ophthalmology, 115 9
Sa Xiao, Felicitas Bucher, Yue Wu, A. Rokem, Cecilia Lee, K. Marra, Regis Fallon, Sophia Diaz-Aguilar, E. Aguilar, M. Friedlander, Aaron Lee (2017)
Fully automated, deep learning segmentation of oxygen-induced retinopathy images.JCI insight, 2 24
Idit Solomon, N. Maharshak, Gal Chechik, L. Leibovici, A. Lubetsky, H. Halkin, D. Ezra, N. Ash (2004)
Applying an artificial neural network to warfarin maintenance dose prediction.The Israel Medical Association journal : IMAJ, 6 12
Varun Gulshan, L. Peng, Marc Coram, Martin Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, T. Madams, Jorge Cuadros, R. Kim, R. Raman, Philip Nelson, J. Mega, D. Webster (2016)
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.JAMA, 316 22
R. Evans (2016)
Electronic Health Records: Then, Now, and in the FutureYearbook of Medical Informatics, 25
S. Swaminathan, K. Qirko, Ted Smith, E. Corcoran, N. Wysham, Gaurav Bazaz, G. Kappel, A. Gerber (2017)
A machine learning approach to triaging patients with chronic obstructive pulmonary diseasePLoS ONE, 12
Chengye Li, Lingxian Hou, B. Sharma, Huaizhong Li, Chengshui Chen, Yuping Li, Xuehua Zhao, Hui Huang, Zhennao Cai, Huiling Chen (2018)
Developing a new intelligent system for the diagnosis of tuberculous pleural effusionComputer methods and programs in biomedicine, 153
T. Leng, Rishab Gargeya (2017)
A deep learning approach for automatic identification of referral-warranted diabetic retinopathyInvestigative Ophthalmology & Visual Science, 58
I. Buzaev, V. Plechev, I. Nikolaeva, R. Galimova (2016)
Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakesChronic Diseases and Translational Medicine, 2
Dayong Wang, A. Khosla, Rishab Gargeya, H. Irshad, Andrew Beck (2016)
Deep Learning for Identifying Metastatic Breast CancerArXiv, abs/1606.05718
M. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. Folk, M. Niemeijer (2016)
Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning.Investigative ophthalmology & visual science, 57 13
Hassan Muhammad, Thomas Fuchs, Nicole Cuir, C. Moraes, D. Blumberg, J. Liebmann, R. Ritch, D. Hood (2017)
Hybrid Deep Learning on Single Wide-field Optical Coherence tomography Scans Accurately Classifies Glaucoma SuspectsJournal of Glaucoma, 26
D. Hashimoto, G. Rosman, D. Rus, O. Meireles (2018)
Artificial Intelligence in Surgery: Promises and PerilsAnnals of Surgery, 268
Pegah Khosravi, Ehsan Kazemi, M. Imieliński, O. Elemento, I. Hajirasouliha (2017)
Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology ImagesEBioMedicine, 27
M. Mckee, M. Schalkwyk, D. Stuckler (2019)
The second information revolution: digitalization brings opportunities and concerns for public healthThe European Journal of Public Health, 29
J. Mazzaferri, B. Larrivée, Bertan Cakir, P. Sapieha, S. Costantino (2018)
A machine learning approach for automated assessment of retinal vasculature in the oxygen induced retinopathy modelScientific Reports, 8
Markus Rohm, Volker Tresp, Michael Müller, C. Kern, I. Manakov, M. Weiss, D. Sim, S. Priglinger, P. Keane, K. Kortuem (2018)
Predicting Visual Acuity by Using Machine Learning in Patients Treated for Neovascular Age-Related Macular Degeneration.Ophthalmology, 125 7
F. Morabito, M. Campolo, N. Mammone, M. Versaci, S. Franceschetti, F. Tagliavini, V. Sofia, D. Fatuzzo, A. Gambardella, A. Labate, L. Mumoli, Giovanbattista Tripodi, S. Gasparini, V. Cianci, C. Sueri, E. Ferlazzo, U. Aguglia (2017)
Deep Learning Representation from Electroencephalography of Early-Stage Creutzfeldt-Jakob Disease and Features for Differentiation from Rapidly Progressive DementiaInternational journal of neural systems, 27 2
M. Treder, J. Lauermann, N. Eter (2018)
Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learningGraefe's Archive for Clinical and Experimental Ophthalmology, 256
R. Vivanti, Leo Joskowicz, N. Lev-Cohain, Ariel Ephrat, J. Sosna (2018)
Patient-specific and global convolutional neural networks for robust automatic liver tumor delineation in follow-up CT studiesMedical & Biological Engineering & Computing, 56
Siddarth Rathi, E. Tsui, N. Mehta, Sarwar Zahid, J. Schuman (2017)
The Current State of Teleophthalmology in the United States.Ophthalmology, 124 12
P. Burlina, N. Joshi, M. Pekala, Katia Pacheco, D. Freund, N. Bressler (2017)
Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural NetworksJAMA Ophthalmology, 135
Rishab Gargeya, T. Leng (2017)
Automated Identification of Diabetic Retinopathy Using Deep Learning.Ophthalmology, 124 7
M. Putten, S. Olbrich, M. Arns (2018)
Predicting sex from brain rhythms with deep learningScientific Reports, 8
Christopher Brady, Samantha D'Amico, J. Campbell (2020)
Telemedicine for Retinopathy of Prematurity.Telemedicine journal and e-health : the official journal of the American Telemedicine Association
P. Yi, Ferdinand Hui, D. Ting (2018)
Artificial Intelligence and Radiology: Collaboration Is Key.Journal of the American College of Radiology : JACR, 15 5
Leyuan Fang, David Cunefare, Chong Wang, R. Guymer, Shutao Li, Sina Farsiu (2017)
Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search.Biomedical optics express, 8 5
I. Kaiserman, M. Rosner, J. Pe’er (2005)
Forecasting the prognosis of choroidal melanoma with an artificial neural network.Ophthalmology, 112 9
Thomas Desautels, J. Calvert, J. Hoffman, Melissa Jay, Yaniv Kerem, L. Shieh, David Shimabukuro, Uli Chettipally, M. Feldman, C. Barton, D. Wales, R. Das (2016)
Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning ApproachJMIR Medical Informatics, 4
R. Caruana, Yin Lou, J. Gehrke, Paul Koch, M. Sturm, Noémie Elhadad (2015)
Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day ReadmissionProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Carrillo (2018)
Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depressionJ Affect Disord, 230
Zhixi Li, Yifan He, S. Keel, W. Meng, R. Chang, M. He (2018)
Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs.Ophthalmology, 125 8
Facundo Carrillo, M. Sigman, Diego Slezak, Philip Ashton, Lily Fitzgerald, J. Stroud, D. Nutt, R. Carhart-Harris (2018)
Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression.Journal of affective disorders, 230
F. Venhuizen, B. Ginneken, F. Asten, Mark Grinsven, S. Fauser, C. Hoyng, T. Theelen, C. Sánchez (2017)
Automated Staging of Age-Related Macular Degeneration Using Optical Coherence Tomography.Investigative ophthalmology & visual science, 58 4
M. Beccaria, T. Mellors, Jacky Petion, C. Rees, Mavra Nasir, Hannah Systrom, Jean Sairistil, Marc-Antoine Jean-Juste, V. Rivera, Kerline Lavoile, P. Sévère, J. Pape, Peter Wright, Jane Hill (2018)
Preliminary investigation of human exhaled breath for tuberculosis diagnosis by multidimensional gas chromatography - Time of flight mass spectrometry and machine learning.Journal of chromatography. B, Analytical technologies in the biomedical and life sciences, 1074-1075
Survey of Ophthalmology – Pubmed
Published: Feb 25, 2019
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.