Change patterns in the corneal sub-basal nerve and corneal aberrations in patients with dry eye disease: An artificial intelligence analysis

Jing, Dalan; Liu, Yushi; Chou, Yilin; Jiang, Xiaodan; Ren, Xiaotong; Yang, Luling; Su, Jie; Li, Xuemin

doi:10.1016/j.exer.2021.108851

Cited by 13 publications

(35 citation statements)

References 34 publications

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“…Patients with eye dryness have greater corneal aberration, which is usually due to the instability of the tear film [ 36 ]. Based on our previous studies [ 37 ], DED patients had increased corneal intrinsic aberrations except for the aberration caused by tear film. Pentacam was used to measure corneal intrinsic aberrations.…”

Section: Discussionmentioning

confidence: 99%

Change Patterns in Corneal Intrinsic Aberrations and Nerve Density after Cataract Surgery in Patients with Dry Eye Disease

Jing

Jiang

Ren

et al. 2022

JCM

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This study aimed to evaluate the change patterns in corneal intrinsic aberrations and nerve density after cataract surgery in dry eye disease. The preoperative, 1- and 3-month postoperative dry eye-related parameters were obtained by the Oculus keratograph and the ocular surface disease index questionnaire. The corneal intrinsic aberrations were measured using the Pentacam HR system. In vivo confocal microscopy was performed to observe the vortical and peripheral corneal nerves. An artificial intelligence technique run by the deep learning model generated the corneal nerve parameters. Corneal aberrations on the anterior and total corneal surfaces were significantly increased at 1 month compared with the baseline (p < 0.05) but gradually returned to the baseline by 3 months (p > 0.05). However, the change in posterior corneal aberration lasted up to 3 months (p < 0.05). There was a significant decrease in the corneal vortical nerve maximum length and average density after the operation (p < 0.05), and this damage lasted approximately 3 months. The corneal vortical nerve maximum length and average density were negatively correlated with the anterior corneal surface aberrations before and 1 month after the operation (correlation coefficients, CC = −0.26, −0.25, −0.28; all p < 0.05). Corneal vortex provided a unique site to observe long-term corneal nerve injury related to eye dryness. The continuous damage to the corneal vortical nerve may be due to the continuous dry eye state.

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Section: Discussionmentioning

confidence: 99%

Change Patterns in Corneal Intrinsic Aberrations and Nerve Density after Cataract Surgery in Patients with Dry Eye Disease

Jing

Jiang

Ren

et al. 2022

JCM

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“…Su et al (2020) proposed training a deep CNN model to detect superficial punctate keratitis (SPK) automatically, and this AI method can be used to reliably grade the severity of SPK to improve the efficiency (97% accuracy) of dry eye diagnosis. Through AI analysis, Jing et al (2022) have found a significant correlation between corneal nerve morphological changes in patients with dry eyes and intrinsic corneal aberrations, particularly higher-order aberrations. Zheng et al (2022) established a blink analysis model using AI to generate a blink profile, which provides a new method for evaluating incomplete blinking and diagnosing dry eye.…”

Section: Figurementioning

confidence: 99%

Artificial intelligence-assisted diagnosis of ocular surface diseases

Zhang

Wang²,

Zhang³

et al. 2023

Front. Cell Dev. Biol.

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With the rapid development of computer technology, the application of artificial intelligence (AI) in ophthalmology research has gained prominence in modern medicine. Artificial intelligence-related research in ophthalmology previously focused on the screening and diagnosis of fundus diseases, particularly diabetic retinopathy, age-related macular degeneration, and glaucoma. Since fundus images are relatively fixed, their standards are easy to unify. Artificial intelligence research related to ocular surface diseases has also increased. The main issue with research on ocular surface diseases is that the images involved are complex, with many modalities. Therefore, this review aims to summarize current artificial intelligence research and technologies used to diagnose ocular surface diseases such as pterygium, keratoconus, infectious keratitis, and dry eye to identify mature artificial intelligence models that are suitable for research of ocular surface diseases and potential algorithms that may be used in the future.

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“…This model attained an AUC of 0.96 and mAP of 94%, with a substantially higher speed (32 images per second) than that of clinical investigators, suggesting that it can allow for the rapid and accurate assessment of changes in the corneal nerves in DED [ 106 ]. Subsequently, they analyzed the morphologic features of corneal sub-basal nerves in IVCM images using a DL model based on CNNs [ 107 ]. In this study, DED was associated with reduced density and the maximum length of corneal nerves measured using an AI algorithm [ 107 ].…”

Section: Application Of Ai In Diagnosis and Treatment Of Dedmentioning

confidence: 99%

“…Subsequently, they analyzed the morphologic features of corneal sub-basal nerves in IVCM images using a DL model based on CNNs [ 107 ]. In this study, DED was associated with reduced density and the maximum length of corneal nerves measured using an AI algorithm [ 107 ]. The average corneal nerve density evaluated using a DL model had a negative correlation with corneal intrinsic aberrations, particularly higher-order aberrations [ 107 ].…”

Section: Application Of Ai In Diagnosis and Treatment Of Dedmentioning

confidence: 99%

“…In this study, DED was associated with reduced density and the maximum length of corneal nerves measured using an AI algorithm [ 107 ]. The average corneal nerve density evaluated using a DL model had a negative correlation with corneal intrinsic aberrations, particularly higher-order aberrations [ 107 ]. Xu et al [ 108 ] introduced deep transfer learning models that comprised pre-trained networks and an adaptation layer for automated detection of activated dendritic cells and inflammatory cells using IVCM images.…”

Section: Application Of Ai In Diagnosis and Treatment Of Dedmentioning

confidence: 99%

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Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease

et al. 2022

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Dry eye disease (DED) is one of the most common diseases worldwide that can lead to a significant impairment of quality of life. The diagnosis and treatment of the disease are often challenging because of the lack of correlation between the signs and symptoms, limited reliability of diagnostic tests, and absence of established consensus on the diagnostic criteria. The advancement of machine learning, particularly deep learning technology, has enabled the application of artificial intelligence (AI) in various anterior segment disorders, including DED. Currently, many studies have reported promising results of AI-based algorithms for the accurate diagnosis of DED and precise and reliable assessment of data obtained by imaging devices for DED. Thus, the integration of AI into clinical approaches for DED can enhance diagnostic and therapeutic performance. In this review, in addition to a brief summary of the application of AI in anterior segment diseases, we will provide an overview of studies regarding the application of AI in DED and discuss the recent advances in the integration of AI into the clinical approach for DED.

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Change patterns in the corneal sub-basal nerve and corneal aberrations in patients with dry eye disease: An artificial intelligence analysis

Cited by 13 publications

References 34 publications

Change Patterns in Corneal Intrinsic Aberrations and Nerve Density after Cataract Surgery in Patients with Dry Eye Disease

Change Patterns in Corneal Intrinsic Aberrations and Nerve Density after Cataract Surgery in Patients with Dry Eye Disease

Artificial intelligence-assisted diagnosis of ocular surface diseases

Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease

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