Predicting Intraocular Collamer Lens Vault in Myopic Patients With Shallow Anterior Chamber Using FedEYE Platform and ChatGPT

doi:10.1167/tvst.14.10.20

CSpace

	Predicting Intraocular Collamer Lens Vault in Myopic Patients With Shallow Anterior Chamber Using FedEYE Platform and ChatGPT
	Su, Zhanyu 1; Wang, Zhaoxiang 2; Wang, Shanshan 2; Ni, Meng 2; Hu, Jinwei 2; Wang, Ruiwen 2; Yang, Jie 2; Bai, Yunxia 2; Xu, Yonghong 2; Di, Shuli 2; Xu, Yaqian 2; Wang, Zheng 1; Chen, Zhuoyue 3; Ortega-Usobiaga, Julio 4; Wang, Ming X.5; Jiang, Xinlong 6; Chen, Yiqiang 6; Dai, Weiwei 1,7; Chen, Jiansu 1; Li, Kangjun 1,2,3,8
	2025-10-01
发表期刊	TRANSLATIONAL VISION SCIENCE & TECHNOLOGY
ISSN	2164-2591
卷号	14 期号:10 页码:11
摘要	Purpose: This study evaluated the effectiveness of the code-free platforms FedEYE and ChatGPT in predicting vault in patients with shallow anterior chamber depth (ACD) undergoing implantable collamer lens (ICL) surgery. Methods: This retrospective study included 160 eyes with shallow ACD (<2.8 mm) and developed two code-free artificial intelligence (AI) models. An integrated neural network (INN) was developed using the FedEYE platform with ResNet and gradient-boosting trees, combined with optical coherence tomography (OCT) images and examination data, to determine how different data combinations achieve the best model performance and the correlation between parameters and vault. ChatGPT prompted with memory function and also used to validate its capability to predict vault based on these preoperative multimodal data. Results: Based on the test set results, the INN model, using the combined OCT image and examination data, achieved favorable performance with accuracy of 0.875, sensitivity of 0.857, specificity of 0.889, weighted F1 score of 0.86, and area under the curve (AUC) of 0.873. Anterior chamber angle and spherical equivalent positively correlated with vault, and age and white-to-white diameter negatively correlated. Following memory prompting, ChatGPT improved itself and reached accuracy of 0.813, recall of 0.571, weighted F1 score of 0.73, and AUC of 0.865. Conclusions: Code-free tools are particularly notable for their straightforward development and deployment. These two code-free AI models demonstrated encouraging performance in predicting vault in shallow ACD patients. Translational Relevance: Using different AI models to predict vault after ICL implantation could be of clinical significance for improving surgical safety and efficacy in shallow ACD patients.
关键词	myopia ICL shallow ACD AI ChatGPT
DOI	10.1167/tvst.14.10.20
收录类别	SCI
语种	英语
WOS研究方向	Ophthalmology
WOS类目	Ophthalmology
WOS记录号	WOS:001632788200001
出版者	ASSOC RESEARCH VISION OPHTHALMOLOGY INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42980
专题	中国科学院计算技术研究所
通讯作者	Li, Kangjun
作者单位	1.Cent South Univ, Aier Acad Ophthalmol, Changsha, Hunan, Peoples R China 2.Northwest Univ, Xian Aier Eye Hosp, Xian, Shaanxi, Peoples R China 3.Northwest Univ, Sch Med, Xian, Shaanxi, Peoples R China 4.Aier Eye Hosp Grp, Dept Cataract & Refract Surg, Clin Baviera, Bilbao, Spain 5.Aier Eye Hosp Grp, Wang Vis Inst, Nashville, TN USA 6.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 7.Changsha Aier Eye Hosp, Inst Digital Ophthalmol & Visual Sci, Changsha, Hunan, Peoples R China 8.Northwest Univ, Xian Peoples Hosp, Xian Hosp 4, 6 Zhangba East Rd, Xian 710000, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Su, Zhanyu,Wang, Zhaoxiang,Wang, Shanshan,et al. Predicting Intraocular Collamer Lens Vault in Myopic Patients With Shallow Anterior Chamber Using FedEYE Platform and ChatGPT[J]. TRANSLATIONAL VISION SCIENCE & TECHNOLOGY,2025,14(10):11.
APA	Su, Zhanyu.,Wang, Zhaoxiang.,Wang, Shanshan.,Ni, Meng.,Hu, Jinwei.,...&Li, Kangjun.(2025).Predicting Intraocular Collamer Lens Vault in Myopic Patients With Shallow Anterior Chamber Using FedEYE Platform and ChatGPT.TRANSLATIONAL VISION SCIENCE & TECHNOLOGY,14(10),11.
MLA	Su, Zhanyu,et al."Predicting Intraocular Collamer Lens Vault in Myopic Patients With Shallow Anterior Chamber Using FedEYE Platform and ChatGPT".TRANSLATIONAL VISION SCIENCE & TECHNOLOGY 14.10(2025):11.