Introduction to Machine Learning for Ophthalmologists

Consejo, Alejandra; Melcer, Tomasz; Rozema, J.

doi:10.1080/08820538.2018.1551496

Cited by 28 publications

(42 citation statements)

References 104 publications

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“…13 It was chosen over other classifiers because it is generally able to handle overfitting, it has a more balanced boundary between two given categories, and its hypothesis is a discriminator function producing 1 or 0, unlike other classifiers that do not offer an absolute prediction but rather a given probability of belonging to a certain group. 15 In 5-fold cross-validation, the original dataset, consisting of 40 eyes (i.e., 20 keratoconus and 20 control), is randomly partitioned into five equally sized subsamples of 32 eyes per subsample (i.e., 16 keratoconus and 16 control). Of the five subsamples, a single subsample is retained as the validation data for testing the model (equivalent to 20% of the original dataset), and the remaining four subsamples are used as training data (equivalent to 80% of the original dataset) to define the line that would separate both groups (i.e., KC vs. control eyes).…”

Section: Discussionmentioning

confidence: 99%

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Keratoconus Detection Based on a Single Scheimpflug Image

Consejo

Solarski

Karnowski

et al. 2020

Trans. Vis. Sci. Tech.

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To introduce a new approach for keratoconus detection based on corneal microstructure observed in vivo derived from a single Scheimpflug image. Methods: Scheimpflug single-image snapshots from 25 control subjects and 25 keratoconus eyes were analyzed; from each group, five subjects were randomly selected to provide out-of-sample data. Each corneal image was segmented, after which the stromal pixel intensities were statistically modeled with a Weibull distribution. Distribution estimated parameters α and β, characterizing corneal microstructure, were used in combination with a macrostructure parameter, central corneal thickness (CCT), for the detection of keratoconus. In addition, receiver operating characteristic curves were used to determine the sensitivity and specificity of each parameter for keratoconus detection. Results: The combination of CCT (sensitivity = 88%; specificity = 84%) with microscopic parameters extracted from statistical modeling of light intensity distribution, α (sensitivity = 76%; specificity = 76%) and β (sensitivity = 96%; specificity = 88%), from a single Scheimpflug image was found to be a successful tool to differentiate between keratoconus and control eyes with no misclassifications (sensitivity = 100%; specificity = 100%) with coefficients of variation up to 2.5%. Conclusions: The combination of microscopic and macroscopic corneal parameters extracted from a static Scheimpflug image is a promising, non-invasive tool to differentiate corneal diseases without the need to perform measurements based on induced deformation of the corneal structure. Translational Relevance: The proposed methodology has the potential to support clinicians in the detection of keratoconus, without compromising patient comfort.

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Section: Discussionmentioning

confidence: 99%

“…The advantage of this method over repeated random subsampling is that all observations are used for both training and validation. 15 In addition to the method of 5-fold cross-validation, an out-of-sample dataset (consisting of 10 eyes: five KC and five control) was used to benchmark the model.…”

Section: Discussionmentioning

confidence: 99%

Keratoconus Detection Based on a Single Scheimpflug Image

Consejo

Solarski

Karnowski

et al. 2020

Trans. Vis. Sci. Tech.

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“…It has also been used for robotassisted repair of epiretinal membranes [95], retinal vessel segmentation [96][97][98][99], and systemic disease prediction from fundus images [100]. For a detailed review, see [4][5][6][7][8][9][10][11].…”

Section: Non-pediatric Applicationsmentioning

confidence: 99%

“…To date, most AI applications have focused on adult ophthalmic diseases, as discussed by several reviews [4][5][6][7][8][9][10][11]. Comparatively little progress has been made in applying AI and ML techniques to pedi-atric ophthalmology, despite the pressing need.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for pediatric ophthalmology

Reid

Eaton

2019

Current Opinion in Ophthalmology

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Purpose of reviewDespite the impressive results of recent artificial intelligence (AI) applications to general ophthalmology, comparatively less progress has been made toward solving problems in pediatric ophthalmology using similar techniques. This article discusses the unique needs of pediatric ophthalmology patients and how AI techniques can address these challenges, surveys recent applications of AI to pediatric ophthalmology, and discusses future directions in the field. Recent findingsThe most significant advances involve the automated detection of retinopathy of prematurity (ROP), yielding results that rival experts. Machine learning (ML) has also been successfully applied to the classification of pediatric cataracts, prediction of post-operative complications following cataract surgery, detection of strabismus and refractive error, prediction of future high myopia, and diagnosis of reading disability via eye tracking. In addition, ML techniques have been used for the study of visual development, vessel segmentation in pediatric fundus images, and ophthalmic image synthesis. SummaryAI applications could significantly benefit clinical care for pediatric ophthalmology patients by optimizing disease detection and grading, broadening access to care, furthering scientific discovery, and improving clinical efficiency. These methods need to match or surpass physician performance in clinical trials before deployment with patients. Due to widespread use of closed-access data sets and software implementations, it is difficult to directly compare the performance of these approaches, and reproducibility is poor. Open-access data sets and software implementations could alleviate these issues, and encourage further AI applications to pediatric ophthalmology.

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“…Diagnostic and imaging techniques generate such an incredible amount of data. Machine Learning techniques emerged as an objective tool to assist practitioners to diagnose certain conditions and make clinical decisions [1].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Medicine: A Primer

Sadiku¹,

Musa²,

Omotoso³

2019

IJTSRD

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Machine learning is the field that focuses on how computers learn from data. Today, machine learning is playing an integral role in the medical industry. This is due to its ability to process huge datasets beyond the scope of human capability, and then convert the data analyzed into clinical insights that aid physicians in providing care. Machine learning is a powerful, relatively easy to implement tool with numerous possibilities to enhance medical practice. The applications of machine learning in medicine are advancing medicine into a new realm. Therefore, educating the next generation of medical professionals with machine learning is essential. This paper provides a brief introduction to applying machine learning in medicine.

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Introduction to Machine Learning for Ophthalmologists

Cited by 28 publications

References 104 publications

Keratoconus Detection Based on a Single Scheimpflug Image

Keratoconus Detection Based on a Single Scheimpflug Image

Artificial intelligence for pediatric ophthalmology

Machine Learning in Medicine: A Primer

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