Using Artificial Intelligence for an Efficient Prediction of Outcomes of Deep Anterior Lamellar Keratoplasty (DALK) in Advanced Keratoconus.

IF 2.6 3区医学 Q2 OPHTHALMOLOGY

Translational Vision Science & Technology Pub Date : 2025-05-01 DOI:10.1167/tvst.14.5.30

Gairik Kundu, Sharon D'Souza, Durgalaxmi Modak, Srihari Balaraj, Rohit Shetty, Rudy M M A Nuijts, Raghav Narasimhan, Abhijit Sinha Roy

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引用次数: 0

Abstract

Purpose: To identify and analyze clinical risk factors and imaging parameters influencing the outcomes of deep anterior lamellar keratoplasty (DALK) for advanced keratoconus (KC) using an artificial intelligence (AI) model.

Methods: This study included 250 DALK eyes with a 5-year follow-up for advanced KC. The DALK eyes were classified as having "favorable" or "unfavorable" outcomes based on graft clarity, scarring at the graft-host interface involving the visual axis which was not pre-existing, early suture loosening less than 3 months after the surgery, corneal vascularization reaching up to or into the graft-host junction at any follow up period, persistent corneal edema greater than 3 months after surgery, and change in visual acuity. Clinical risk factors were determined through a detailed clinical evaluation and questionnaire assessment and included the presence of systemic allergy, ocular allergy, or eye rubbing. Immunoglobulin E (IgE) and vitamin D and B12 levels were obtained from blood investigations. A total of 37 tomographic parameters were exported from an OCULUS Pentacam HR. An AI model was then built to assess these risk factors and imaging parameters. The area under the curve (AUC) and other metrics were evaluated.

Results: The AI model classified 92.2% and 89.4% cases as favorable or unfavorable, respectively, based on clinical risk factors and imaging parameters. Systemic allergy, IgE, eye rubbing, and vitamin D had the highest information gains followed by posterior corneal data from the Pentacam HR. The AI model achieved an AUC of 0.957 with sensitivity of 98% and specificity of 85.6%.

Conclusions: Our findings demonstrate the importance of preoperative risk stratification, which can affect surgical outcomes of DALK using AI.

Translational relevance: Better identification and control of these factors would enable better management and outcomes of DALK for advanced KC.

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应用人工智能有效预测晚期圆锥角膜深前板层角膜移植术（DALK）的预后。

目的：利用人工智能（AI）模型，识别和分析影响晚期圆锥角膜（KC）深前板层角膜移植术（DALK）疗效的临床危险因素和影像学参数。方法:这项研究包括250只DALK眼，对晚期KC进行了5年的随访。DALK眼根据移植物清晰度、移植物-宿主界面不存在的涉及视觉轴的瘢痕、术后不到3个月的早期缝线松动、角膜血管化在任何随访期间达到或进入移植物-宿主连接处、术后3个月以上持续角膜水肿，将其分为“有利”或“不利”结果。以及视力的变化。临床危险因素通过详细的临床评估和问卷评估确定，包括全身性过敏、眼部过敏或揉眼。免疫球蛋白E （IgE）、维生素D和B12水平通过血液检查。总共37层析参数从OCULUS Pentacam HR导出。然后建立一个人工智能模型来评估这些风险因素和成像参数。评估曲线下面积（AUC）及其他指标。结果：AI模型根据临床危险因素和影像学参数，分别对92.2%和89.4%的病例进行了有利和不利的分类。系统性过敏、IgE、揉眼和维生素D获得的信息最多，其次是Pentacam HR的后角膜数据。AI模型的AUC为0.957，灵敏度为98%，特异性为85.6%。结论：我们的研究结果表明术前风险分层的重要性，它可以影响人工智能治疗DALK的手术结果。翻译相关性：更好地识别和控制这些因素将使晚期KC的DALK管理和结果更好。

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

Translational Vision Science & Technology Engineering-Biomedical Engineering

CiteScore

5.70

自引率

3.30%

发文量

346

审稿时长

25 weeks

期刊介绍： Translational Vision Science & Technology (TVST), an official journal of the Association for Research in Vision and Ophthalmology (ARVO), an international organization whose purpose is to advance research worldwide into understanding the visual system and preventing, treating and curing its disorders, is an online, open access, peer-reviewed journal emphasizing multidisciplinary research that bridges the gap between basic research and clinical care. A highly qualified and diverse group of Associate Editors and Editorial Board Members is led by Editor-in-Chief Marco Zarbin, MD, PhD, FARVO. The journal covers a broad spectrum of work, including but not limited to: Applications of stem cell technology for regenerative medicine, Development of new animal models of human diseases, Tissue bioengineering, Chemical engineering to improve virus-based gene delivery, Nanotechnology for drug delivery, Design and synthesis of artificial extracellular matrices, Development of a true microsurgical operating environment, Refining data analysis algorithms to improve in vivo imaging technology, Results of Phase 1 clinical trials, Reverse translational ("bedside to bench") research. TVST seeks manuscripts from scientists and clinicians with diverse backgrounds ranging from basic chemistry to ophthalmic surgery that will advance or change the way we understand and/or treat vision-threatening diseases. TVST encourages the use of color, multimedia, hyperlinks, program code and other digital enhancements.