Introduction to Machine Learning for Ophthalmologists

Anterior eye surface changes following miniscleral contact lens wear

(2018) Alejandra Consejo et al.   Contact Lens & Anterior Eye

Screening for medication errors using an outlier detection system

(2017) Gordon D Schiff et al.   JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION

Automated Identification of Diabetic Retinopathy Using Deep Learning

(2017) Rishab Gargeya et al.   OPHTHALMOLOGY

Corneo-scleral limbal changes following short-term soft contact lens wear

(2017) Alejandra Consejo et al.   Contact Lens & Anterior Eye

Evaluation of Diabetic Retinal Screening and Factors for Ophthalmology Referral in a Telemedicine Network

(2017) Pooja D. Jani et al.   JAMA Ophthalmology

How Abnormal Is the Noncorneal Biometry of Keratoconic Eyes?

(2016) Jos J. Rozema et al.   CORNEA

Evaluation of a Machine-Learning Classifier for Keratoconus Detection Based on Scheimpflug Tomography

(2016) Irene Ruiz Hidalgo et al.   CORNEA

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

(2016) Varun Gulshan et al.   JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION

Accuracy of machine learning classifiers using bilateral data from a Scheimpflug camera for identifying eyes with preclinical signs of keratoconus

(2016) Illés Kovács et al.   JOURNAL OF CATARACT AND REFRACTIVE SURGERY

Detection of Glaucoma Using Image Processing Techniques: A Critique

(2016) B. Naveen Kumar et al.   Seminars in Ophthalmology

Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields

(2016) Siamak Yousefi et al.   Translational Vision Science & Technology

Medical image classification based on artificial intelligence approaches: A practical study on normal and abnormal confocal corneal images

(2015) M.S. Sharif et al.   APPLIED SOFT COMPUTING

Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images

(2015) Albert K. Feeny et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach

(2015) Paolo Fraccaro et al.   BMC Ophthalmology

Wavelet Representation of the Corneal Pulse for Detecting Ocular Dicrotism

(2015) Tomasz Melcer et al.   PLoS One

Automated detection of circinate exudates in retina digital images using empirical mode decomposition and the entropy and uniformity of the intrinsic mode functions

(2014) Salim Lahmiri et al.   Biomedical Engineering-Biomedizinische Technik

A new approach based on Machine Learning for predicting corneal curvature (K1) and astigmatism in patients with keratoconus after intracorneal ring implantation

(2014) M.A. Valdés-Mas et al.   COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

Corneal Enantiomorphism in Normal and Keratoconic Eyes

(2014) Alain Saad et al.   JOURNAL OF REFRACTIVE SURGERY

Patterns of functional vision loss in glaucoma determined with archetypal analysis

(2014) T. Elze et al.   Journal of the Royal Society Interface

Empirical mode decomposition and neural network for the classification of electroretinographic data

(2014) Abdollah Bagheri et al.   MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING

Decision support system for age-related macular degeneration using discrete wavelet transform

(2014) Muthu Rama Krishnan Mookiah et al.   MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING

A Systematic Comparison of Supervised Classifiers

(2014) Diego Raphael Amancio et al.   PLoS One

Using Natural Language Processing and Machine Learning to Identify Gout Flares From Electronic Clinical Notes

(2014) Chengyi Zheng et al.   ARTHRITIS CARE & RESEARCH

DREAM: Diabetic Retinopathy Analysis Using Machine Learning

(2014) Sohini Roychowdhury et al.   IEEE Journal of Biomedical and Health Informatics

Detection of Subclinical Keratoconus Using an Automated Decision Tree Classification

(2013) David Smadja et al.   AMERICAN JOURNAL OF OPHTHALMOLOGY

Data science and prediction

(2013) Vasant Dhar   COMMUNICATIONS OF THE ACM

Computer-aided diagnosis of diabetic retinopathy: A review

(2013) Muthu Rama Krishnan Mookiah et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Age-Related Changes in Corneal Pulsation

(2013) Monika E. Danielewska et al.   OPTOMETRY AND VISION SCIENCE

Artificial Neural Network Techniques to Improve the Ability of Optical Coherence Tomography to Detect Optic Neuritis

(2013) Elena Garcia-Martin et al.   Seminars in Ophthalmology

Statistical Comparison of Classifiers Applied to the Interferential Tear Film Lipid Layer Automatic Classification

(2012) B. Remeseiro et al.   Computational and Mathematical Methods in Medicine

Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

(2012) Geoffrey Hinton et al.   IEEE SIGNAL PROCESSING MAGAZINE

Detection of meibomian glands and classification of meibography images

(2012) Yang Wei Koh   JOURNAL OF BIOMEDICAL OPTICS

A multiple-instance learning framework for diabetic retinopathy screening

(2012) Gwénolé Quellec et al.   MEDICAL IMAGE ANALYSIS

Use of a Support Vector Machine for Keratoconus and Subclinical Keratoconus Detection by Topographic and Tomographic Data

(2012) Maria Clara Arbelaez et al.   OPHTHALMOLOGY

Evaluation of machine learning classifiers in keratoconus detection from orbscan II examinations

(2011) Murilo Barreto Souza et al.   Clinics

Small-sample precision of ROC-related estimates

(2010) B. Hanczar et al.   BIOINFORMATICS

Iris Recognition: On the Segmentation of Degraded Images Acquired in the Visible Wavelength

(2009) Hugo Proença   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

The UBIRIS.v2: A Database of Visible Wavelength Iris Images Captured On-the-Move and At-a-Distance

(2009) Hugo Proenca et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Machine learning approach to automatic exudate detection in retinal images from diabetic patients

(2009) Akara Sopharak et al.   JOURNAL OF MODERN OPTICS

Computer-based detection of diabetes retinopathy stages using digital fundus images

(2009) U R Acharya et al.   PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE

Kernel methods in machine learning

(2008) Thomas Hofmann et al.   ANNALS OF STATISTICS

AUC: a misleading measure of the performance of predictive distribution models

(2007) Jorge M. Lobo et al.   GLOBAL ECOLOGY AND BIOGEOGRAPHY

Identification of different stages of diabetic retinopathy using retinal optical images

(2007) Wong Li Yun et al.   INFORMATION SCIENCES