CT-based artificial intelligence prediction model for ocular motility score of thyroid eye disease.

IF 3 3区医学 Q2 ENDOCRINOLOGY & METABOLISM

Endocrine Pub Date : 2024-12-01 Epub Date: 2024-07-24 DOI:10.1007/s12020-024-03906-0

Zijia Liu, Kexin Tan, Haiyang Zhang, Jing Sun, Yinwei Li, Sijie Fang, Jipeng Li, Xuefei Song, Huifang Zhou, Guangtao Zhai

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Abstract

Purpose: Thyroid eye disease (TED) is the most common orbital disease in adults. Ocular motility restriction is the primary complaint of patients, while its evaluation is quite difficult. The present study aimed to introduce an artificial intelligence (AI) model based on orbital computed tomography (CT) images for ocular motility score.

Methods: A total of 410 sets of CT images and clinical data were obtained from the hospital. To build a triple classification predictive model for ocular motility score, multiple deep learning models were employed to extract features of images and clinical data. Subgroup analyses based on pertinent clinical features were performed to test the efficacy of models.

Results: The ResNet-34 network outperformed Alex-Net and VGG16-Net in prediction of ocular motility score, with the optimal accuracy (ACC) of 0.907, 0.870, and 0.890, respectively. Subgroup analyses indicated no significant difference in ACC between active or inactive phase, functional visual field diplopia or peripheral visual field diplopia (p > 0.05). However, in the gender subgroup, the prediction model performed more accurately in female patients than males (p = 0.02).

Conclusion: In conclusion, the AI model based on CT images and clinical data successfully realized automatic scoring of ocular motility in TED patients. This approach potentially enhanced the efficiency and accuracy of ocular motility evaluation, thus facilitating clinical application.

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基于 CT 的甲状腺眼病眼球运动评分人工智能预测模型

目的：甲状腺眼病（TED）是成年人最常见的眼眶疾病。眼球运动受限是患者的主要主诉，但其评估却相当困难。本研究旨在引入一种基于眼眶计算机断层扫描（CT）图像的人工智能（AI）模型，用于眼球运动评分：方法：从医院获得 410 组 CT 图像和临床数据。为了建立眼球运动评分的三重分类预测模型，我们采用了多种深度学习模型来提取图像和临床数据的特征。根据相关临床特征进行分组分析，以检验模型的有效性：结果：在预测眼球运动得分方面，ResNet-34 网络的最佳准确率（ACC）分别为 0.907、0.870 和 0.890，优于 Alex-Net 和 VGG16-Net。亚组分析表明，活动期或非活动期、功能性视野复视或周边视野复视之间的 ACC 无显著差异（P > 0.05）。然而，在性别亚组中，女性患者的预测模型比男性更准确（P = 0.02）：总之，基于 CT 图像和临床数据的人工智能模型成功实现了对 TED 患者眼球运动的自动评分。这种方法有望提高眼球运动评估的效率和准确性，从而促进临床应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Endocrine ENDOCRINOLOGY & METABOLISM-

CiteScore

6.50

自引率

5.40%

发文量

295

审稿时长

1.5 months

期刊介绍： Well-established as a major journal in today’s rapidly advancing experimental and clinical research areas, Endocrine publishes original articles devoted to basic (including molecular, cellular and physiological studies), translational and clinical research in all the different fields of endocrinology and metabolism. Articles will be accepted based on peer-reviews, priority, and editorial decision. Invited reviews, mini-reviews and viewpoints on relevant pathophysiological and clinical topics, as well as Editorials on articles appearing in the Journal, are published. Unsolicited Editorials will be evaluated by the editorial team. Outcomes of scientific meetings, as well as guidelines and position statements, may be submitted. The Journal also considers special feature articles in the field of endocrine genetics and epigenetics, as well as articles devoted to novel methods and techniques in endocrinology. Endocrine covers controversial, clinical endocrine issues. Meta-analyses on endocrine and metabolic topics are also accepted. Descriptions of single clinical cases and/or small patients studies are not published unless of exceptional interest. However, reports of novel imaging studies and endocrine side effects in single patients may be considered. Research letters and letters to the editor related or unrelated to recently published articles can be submitted. Endocrine covers leading topics in endocrinology such as neuroendocrinology, pituitary and hypothalamic peptides, thyroid physiological and clinical aspects, bone and mineral metabolism and osteoporosis, obesity, lipid and energy metabolism and food intake control, insulin, Type 1 and Type 2 diabetes, hormones of male and female reproduction, adrenal diseases pediatric and geriatric endocrinology, endocrine hypertension and endocrine oncology.