Determination of Tear Lipid Film Thickness Based on a Reflected Placido Disk Tear Film Analyzer

Fu, Pin-I; Fang, Po-Chiung; Ho, Ren-Wen; Chao, Tsi-Chian; Cho, Wan-Hua; Lai, Hung-Yin; Hsiao, Yu‐Ting; Kuo, Ming‐Tse

doi:10.3390/diagnostics10060353

Cited by 5 publications

(10 citation statements)

References 20 publications

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“…Comparison of two different image analysis methods using a generalized linear model showed that there was a high correlation between the two techniques [36]. The authors concluded that the simple technique was sufficient for evaluation of tear film lipid layer thickness.…”

Section: Interferometry and Slit-lamp Imagesmentioning

confidence: 98%

“…It is difficult to compare the studies due to different applications and evaluation metrics. Despite promising results, most of the studies [60,59,55,52,34,35,50,36,49,61,48] did not evaluate their systems on external data. The systems should be tested on independent data before they can be considered for clinical application.…”

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], and the suggested models should be tested on a larger number of subjects.…”

Section: Interferometry and Slit-lamp Imagesmentioning

confidence: 99%

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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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Section: Interferometry and Slit-lamp Imagesmentioning

confidence: 98%

Section: Interferometry and Slit-lamp Imagesmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence in Dry Eye Disease

Storaas¹,

Strümke

Riegler

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…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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