Research progress and application of artificial intelligence in thyroid associated ophthalmopathy

Diao, Jiale; Chen, Xinxin; Shen, Ya; Li, Jian; Chen, Yuqing; He, Lin-feng; Chen, Sainan; Mou, Pei; Ma, Xin; Wei, Rui-Li

doi:10.3389/fcell.2023.1124775

Cited by 7 publications

(5 citation statements)

References 64 publications

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“…Despite AI’s transformative potential in TAO management ( 55 ), challenges remain. The complexity of TAO as an ocular disease, the high cost and scarcity of data for model training, and the difficulty in generalizing DL models across diverse patient populations present significant barriers.…”

Section: Discussionmentioning

confidence: 99%

Advances in artificial intelligence in thyroid-associated ophthalmopathy

Yi,

Niu,

Zhang

et al. 2024

Front. Endocrinol.

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Thyroid-associated ophthalmopathy (TAO), also referred to as Graves’ ophthalmopathy, is a medical condition wherein ocular complications arise due to autoimmune thyroid illness. The diagnosis of TAO, reliant on imaging, typical ocular symptoms, and abnormalities in thyroid function or thyroid-associated antibodies, is generally graded and staged. In recent years, Artificial intelligence(AI), particularly deep learning(DL) technology, has gained widespread use in the diagnosis and treatment of ophthalmic diseases. This paper presents a discussion on specific studies involving AI, specifically DL, in the context of TAO, highlighting their applications in TAO diagnosis, staging, grading, and treatment decisions. Additionally, it addresses certain limitations in AI research on TAO and potential future directions for the field.

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Section: Discussionmentioning

confidence: 99%

Advances in artificial intelligence in thyroid-associated ophthalmopathy

Yi,

Niu,

Zhang

et al. 2024

Front. Endocrinol.

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“…The higher the quality of the DR image, the higher the accuracy of the AI model. However, image quality is affected by many factors, such as the advanced level of equipment, operator proficiency, and degree of patient cooperation [83] . Therefore, high-quality images should be selected in AI research to eliminate the influence of image quality on research results.…”

Section: Limitations and Challengesmentioning

confidence: 99%

Research progress in artificial intelligence assisted diabetic retinopathy diagnosis

Liu,

Fei,

Chen

et al. 2023

Int J Ophthalmol

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Diabetic retinopathy (DR) is one of the most common retinal vascular diseases and one of the main causes of blindness worldwide. Early detection and treatment can effectively delay vision decline and even blindness in patients with DR. In recent years, artificial intelligence (AI) models constructed by machine learning and deep learning (DL) algorithms have been widely used in ophthalmology research, especially in diagnosing and treating ophthalmic diseases, particularly DR. Regarding DR, AI has mainly been used in its diagnosis, grading, and lesion recognition and segmentation, and good research and application results have been achieved. This study summarizes the research progress in AI models based on machine learning and DL algorithms for DR diagnosis and discusses some limitations and challenges in AI research.

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“…However, TAO diagnosis based on computed tomography (CT) or magnetic resonance imaging often requires considerable time in practice because clinical decisions must be made after observing changes in various TAO-related structures [4]. Consequently, neural network (NN)-based TAO diagnostic systems have been widely considered [5].…”

Section: Introductionmentioning

confidence: 99%