Deep learning-based image analysis of eyelid morphology in thyroid-associated ophthalmopathy

Shao, Jing; Huang, Xingru; Gao, Tao; Cao, Jing; Wang, Yaqi; Zhang, Qianni; Lou, Lixia; Ye, Juan

doi:10.21037/qims-22-551

Cited by 14 publications

(13 citation statements)

References 38 publications

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“…The most common AI models used in these studies are convolutional neural networks (CNN). Three of the studies validated algorithms on external datasets ( 18 , 23 , 26 ). 12 of the 13 studies compared algorithm performance against healthcare professionals, whereas one study utilizes electronic medical records (EMR) phenotypes ( 15 ).…”

Section: Resultsmentioning

confidence: 99%

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Application of artificial intelligence in the assessment of thyroid eye disease (TED) - a scoping review

Chng,

Zheng,

Kwee

et al. 2023

Front. Endocrinol.

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BackgroundThere is emerging evidence which suggests the utility of artificial intelligence (AI) in the diagnostic assessment and pre-treatment evaluation of thyroid eye disease (TED). This scoping review aims to (1) identify the extent of the available evidence (2) provide an in-depth analysis of AI research methodology of the studies included in the review (3) Identify knowledge gaps pertaining to research in this area.MethodsThis review was performed according to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA). We quantify the diagnostic accuracy of AI models in the field of TED assessment and appraise the quality of these studies using the modified QUADAS-2 tool.ResultsA total of 13 studies were included in this review. The most common AI models used in these studies are convolutional neural networks (CNN). The majority of the studies compared algorithm performance against healthcare professionals. The overall risk of bias and applicability using the modified Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool led to most of the studies being classified as low risk, although higher deficiency was noted in the risk of bias in flow and timing.ConclusionsWhile the results of the review showed high diagnostic accuracy of the AI models in identifying features of TED relevant to disease assessment, deficiencies in study design causing study bias and compromising study applicability were noted. Moving forward, limitations and challenges inherent to machine learning should be addressed with improved standardized guidance around study design, reporting, and legislative framework.

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Section: Resultsmentioning

confidence: 99%

“…Four studies utilized facial photos to detect signs of TED ( 21 , 23 , 26 ) or assess disease activity ( 27 ). Outcomes assessed were heterogenous amongst studies: diagnosis of TED ( 23 ) presence of severe signs of TED ( 21 ), eyelid morphology in TED ( 26 ) and prediction of CAS score ( 27 ).…”

Section: Resultsmentioning

confidence: 99%

Application of artificial intelligence in the assessment of thyroid eye disease (TED) - a scoping review

Chng,

Zheng,

Kwee

et al. 2023

Front. Endocrinol.

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“…Moreover, Doppler imaging of orbital vessels were also used to evaluate the activity and severity of TAO 17 . In addition, another study proposed a DL-based system that allowed automatic, comprehensive, and objective measurement of eyelid morphology in TAO by using facial images, which might have some potential application prospects in the management of TAO 18 . However, the limitations of these studies include the complex clinical examinations and the relatively small sample size.…”

Section: Discussionmentioning

confidence: 99%

Retinal Photograph-based Deep Learning System for Detection of Thyroid-Associated Ophthalmopathy

Jiang,

Dong,

Luo

et al. 2023

Journal of Craniofacial Surgery

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Background: The diagnosis of thyroid-associated ophthalmopathy (TAO) usually requires a comprehensive examination, including clinical symptoms, radiological examinations, and blood tests. Therefore, cost-effective and noninvasive methods for the detection of TAO are needed. This study aimed to establish a deep learning-based system to detect TAO based on retinal photographs. Materials and methods: The multicenter observational study included retinal photographs taken from TAO patients and normal participants in 2 hospitals in China. Forty-five-degree retinal photographs, centered on the midpoint between the optic disc and the macula, were captured by trained ophthalmologists. The authors first trained a convolutional neural network model to identify TAO using data collected from one hospital. After internal validation, the model was further evaluated in another hospital as an external validation data set. Results: The study included 1182 retinal photographs of 708 participants for model development, and 365 retinal photographs (189 participants) were obtained as the external validation data set. In the internal validation, the area under the receiver operator curve was 0.900 (95% CI: 0.889–0.910) and the accuracy was 0.860 (95% CI: 0.849–0.869). In the external data set, the model reached an area under the curve of 0.747 (95% CI: 0.728–0.763) and achieved an accuracy of 0.709 (95% CI: 0.690–0.724). Conclusions: Deep learning-based systems may be promising for identifying TAO in normal subjects using retinal fundus photographs. It may serve as a cost-effective and noninvasive method to detect TAO in the future.

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“…Lou et al reported an image analysis technique using a deep learning method to compare surgical outcomes before and after blepharoplasty based on facial digital photographs taken separately [17].…”

Section: Discussionmentioning

confidence: 99%

Introduction of Deep Learning-Based Infrared Image Analysis to Marginal Reflex Distance1 Measurement Method to Simultaneously Capture Images and Compute Results: Clinical Validation Study

Song,

Kwon,

Kim

et al. 2023

JCM

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Marginal reflex distance1 (MRD1) is a crucial clinical tool used to evaluate the position of the eyelid margin in relation to the cornea. Traditionally, this assessment has been conducted manually by plastic surgeons, ophthalmologists, or trained technicians. However, with the advancements in artificial intelligence (AI) technology, there is a growing interest in the development of automated systems capable of accurately measuring MRD1. In this context, we introduce novel MRD1 measurement methods based on deep learning algorithms that can simultaneously capture images and compute the results. This prospective observational study involved 154 eyes of 77 patients aged over 18 years who visited Chungnam National University Hospital between 1 January 2023 and 29 July 2023. We collected four different MRD1 datasets from patients using three distinct measurement methods, each tailored to the individual patient. The mean MRD1 values, measured through the manual method using a penlight, the deep learning method, ImageJ analysis from RGB eye images, and ImageJ analysis from IR eye images in 56 eyes of 28 patients, were 2.64 ± 1.04 mm, 2.85 ± 1.07 mm, 2.78 ± 1.08 mm, and 3.07 ± 0.95 mm, respectively. Notably, the strongest agreement was observed between MRD1_deep learning (DL) and MRD1_IR (0.822, p < 0.01). In a Bland–Altman plot, the smallest difference was observed between MRD1_DL and MRD1_IR ImageJ, with a mean difference of 0.0611 and ΔLOA (limits of agreement) of 2.5162, which was the smallest among all of the groups. In conclusion, this novel MRD1 measurement method, based on an IR camera and deep learning, demonstrates statistical significance and can be readily applied in clinical settings.

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Deep learning-based image analysis of eyelid morphology in thyroid-associated ophthalmopathy

Cited by 14 publications

References 38 publications

Application of artificial intelligence in the assessment of thyroid eye disease (TED) - a scoping review

Application of artificial intelligence in the assessment of thyroid eye disease (TED) - a scoping review

Retinal Photograph-based Deep Learning System for Detection of Thyroid-Associated Ophthalmopathy

Introduction of Deep Learning-Based Infrared Image Analysis to Marginal Reflex Distance1 Measurement Method to Simultaneously Capture Images and Compute Results: Clinical Validation Study

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