A deep learning approach for the quantification of lower tear meniscus height

Deng, Xiao-Dong; Tian, Lei; Liu, Ziyu; Zhou, Yongjin; Jin, Ying

doi:10.1016/j.bspc.2021.102655

Cited by 12 publications

(14 citation statements)

References 19 publications

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“…However, since images were obtained from a custom-built system, clinical translatability may be difficult. Another group trained a CNN on topography images to quantify the LTMH, which achieved a sensitivity and F1 score of 90% [53 ▪ ]. Sub-basal corneal nerve fibers have also been quantified on confocal images [54].…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence and corneal diseases

Kang¹,

Ballouz²,

Woodward

2022

Current Opinion in Ophthalmology

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Purpose of reviewArtificial intelligence has advanced rapidly in recent years and has provided powerful tools to aid with the diagnosis, management, and treatment of ophthalmic diseases. This article aims to review the most current clinical artificial intelligence applications in anterior segment diseases, with an emphasis on microbial keratitis, keratoconus, dry eye syndrome, and Fuchs endothelial dystrophy.Recent findingsMost current artificial intelligence approaches have focused on developing deep learning algorithms based on various imaging modalities. Algorithms have been developed to detect and differentiate microbial keratitis classes and quantify microbial keratitis features. Artificial intelligence may aid with early detection and staging of keratoconus. Many advances have been made to detect, segment, and quantify features of dry eye syndrome and Fuchs. There is significant variability in the reporting of methodology, patient population, and outcome metrics.SummaryArtificial intelligence shows great promise in detecting, diagnosing, grading, and measuring diseases. There is a need for standardization of reporting to improve the transparency, validity, and comparability of algorithms.

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Section: Methodsmentioning

confidence: 99%

Artificial intelligence and corneal diseases

Kang¹,

Ballouz²,

Woodward

2022

Current Opinion in Ophthalmology

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“…The systems should be tested on independent data before they can be considered for clinical application. Moreover, some studies were small [61,36] or pilots [48,29], and the suggested models should be tested on a larger number of subjects.…”

Section: Interferometry and Slit-lamp Imagesmentioning

confidence: 99%

“…The machine learning system was found to be more accurate than four experienced ophthalmologists. The tear meniscus height can also be estimated from keratography images using a CNN [29]. The automatic machine learning system achieved an accuracy of 82.5% and was found to be more effective and consistent than a well-trained clinician working with limited time.…”

Section: Interferometry and Slit-lamp Imagesmentioning

confidence: 99%

Artificial Intelligence in Dry Eye Disease

Storaas¹,

Strümke

Riegler

et al. 2021

Preprint

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Dry eye disease (DED) has a prevalence of between 5 and 50\%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term `AI' is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.

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“…Interferometry is a useful tool that gives a snapshot of the status of the tear film lipid layer, which can be used to aid diagnosis of DED. Machine learning systems have been applied to interferometry and slit-lamp images for lipid layer classification based on morphological properties [60,59,55,54,52,34,35], estimation of the lipid layer thickness [50,36], diagnosis of DED [49,47], determination of ocular redness [61] and estimation of tear meniscus height [48,29].…”

Section: Interferometry and Slit-lamp Imagesmentioning

confidence: 99%

“…The systems should be tested on independent data before they can be considered for clinical application. Moreover, some studies were small [61,36] or pilots [48,29],…”

Section: Interferometry and Slit-lamp Imagesmentioning

confidence: 99%

Artificial Intelligence in Dry Eye Disease

Storås¹,

Strümke²,

Riegler³

et al. 2021

Preprint

View full text Add to dashboard Cite

Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term 'AI' is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.

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A deep learning approach for the quantification of lower tear meniscus height

Cited by 12 publications

References 19 publications

Artificial intelligence and corneal diseases

Artificial intelligence and corneal diseases

Artificial Intelligence in Dry Eye Disease

Artificial Intelligence in Dry Eye Disease

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