Kundu Gairik, D'Souza Sharon, Modak Durgalaxmi, Balaraj Srihari, Shetty Rohit, Nuijts Rudy M M A, Narasimhan Raghav, Roy Abhijit Sinha
Department of Cornea and Refractive Surgery, Narayana Nethralaya, Bangalore, India.
Department of Ophthalmology, Maastricht University Medical Center, Maastricht, The Netherlands.
Transl Vis Sci Technol. 2025 May 1;14(5):30. doi: 10.1167/tvst.14.5.30.
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.
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.
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%.
Our findings demonstrate the importance of preoperative risk stratification, which can affect surgical outcomes of DALK using AI.
Better identification and control of these factors would enable better management and outcomes of DALK for advanced KC.
使用人工智能（AI）模型识别和分析影响晚期圆锥角膜（KC）行深前板层角膜移植术（DALK）预后的临床危险因素和影像学参数。
本研究纳入了250例行DALK治疗晚期KC且随访5年的患眼。根据植片透明度、累及视轴的植片-宿主界面瘢痕（非术前存在）、术后3个月内早期缝线松动、随访期间任何时间角膜血管化达植片-宿主交界或进入植片-宿主交界、术后3个月以上持续性角膜水肿以及视力变化，将DALK患眼分为“预后良好”或“预后不良”。通过详细的临床评估和问卷评估确定临床危险因素，包括全身过敏、眼部过敏或揉眼情况。从血液检查中获取免疫球蛋白E（IgE）、维生素D和维生素B12水平。从OCULUS Pentacam HR导出总共37个断层扫描参数。然后建立一个AI模型来评估这些危险因素和影像学参数。评估曲线下面积（AUC）和其他指标。
基于临床危险因素和影像学参数，AI模型分别将92.2%和89.4%的病例分类为预后良好或预后不良。全身过敏、IgE、揉眼和维生素D的信息增益最高，其次是Pentacam HR的后角膜数据。AI模型的AUC为0.957，敏感性为98%，特异性为85.6%。
我们的研究结果证明了术前风险分层的重要性，其可影响使用AI的DALK手术预后。
更好地识别和控制这些因素将有助于改善晚期KC的DALK管理和预后。
