Supplementary Material for: Deep Learning Algorithms for Corneal Amyloid Deposition Quantitation in Familial Amyloidosis

<b><i>Objectives:</i></b> The aim of this study was to train and validate deep learning algorithms to quantitate relative amyloid deposition (RAD; mean amyloid deposited area per stromal area) in corneal sections from patients with familial amyloidosis, Finnish (FAF), and assess its relationship with visual acuity. <b><i>Methods:</i></b> Corneal specimens were obtained from 42 patients undergoing penetrating keratoplasty, stained with Congo red, and digitally scanned. Areas of amyloid deposits and areas of stromal tissue were labeled on a pixel level for training and validation. The algorithms were used to quantify RAD in each cornea, and the association of RAD with visual acuity was assessed. <b><i>Results:</i></b> In the validation of the amyloid area classification, sensitivity was 86%, specificity 92%, and F-score 81. For corneal stromal area classification, sensitivity was 74%, specificity 82%, and F-score 73. There was insufficient evidence to demonstrate correlation (Spearman’s rank correlation, –0.264, <i>p</i> = 0.091) between RAD and visual acuity (logMAR). <b><i>Conclusions:</i></b> Deep learning algorithms can achieve a high sensitivity and specificity in pixel-level classification of amyloid and corneal stromal area. Further modeling and development of algorithms to assess earlier stages of deposition from clinical images is necessary to better assess the correlation between amyloid deposition and visual acuity. The method might be applied to corneal dystrophies as well.

<b><i>研究目的：</i></b> 本研究旨在训练并验证深度学习算法，定量分析芬兰型家族性淀粉样变性（familial amyloidosis, Finnish, FAF）患者角膜标本中的相对淀粉样蛋白沉积量（relative amyloid deposition, RAD，即每基质区域平均淀粉样蛋白沉积面积），并评估其与视力的相关性。<b><i>研究方法：</i></b> 本研究纳入42例行穿透性角膜移植术的患者，获取其角膜标本，经刚果红染色后进行数字化扫描。对淀粉样蛋白沉积区域与角膜基质区域进行像素级标注，用于模型训练与验证。采用上述算法量化每例角膜标本的RAD值，并评估RAD与视力的相关性。<b><i>研究结果：</i></b> 在淀粉样蛋白区域分类任务中，模型灵敏度为86%，特异度为92%，F1分数为81%；在角膜基质区域分类任务中，灵敏度为74%，特异度为82%，F1分数为73%。未发现相对淀粉样蛋白沉积量与logMAR视力存在显著相关性（斯皮尔曼等级相关系数为–0.264，*p* = 0.091）。<b><i>研究结论：</i></b> 深度学习算法可在淀粉样蛋白与角膜基质区域的像素级分类任务中获得较高的灵敏度与特异度。未来需进一步优化算法模型，以从临床影像中更早地检测淀粉样蛋白沉积，从而更准确地评估淀粉样蛋白沉积与视力之间的相关性。本方法亦可应用于角膜营养不良相关研究。