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首页> 外文期刊>Ocular oncology and pathology. >Deep Learning Algorithms for Corneal Amyloid Deposition Quantitation in Familial Amyloidosis
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Deep Learning Algorithms for Corneal Amyloid Deposition Quantitation in Familial Amyloidosis

机译：家族淀粉样蛋白区角膜淀粉样蛋白沉积定量的深层学习算法

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Objectives: 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. Methods: 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. Results: 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, p = 0.091) between RAD and visual acuity (logMAR). Conclusions: 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.
机译：目的：本研究的目的是培训和验证来自家族淀粉样蛋白病，芬兰语（FAF）的患者的角膜切片中的相对淀粉样沉积（rad;平均淀粉样沉积面积），芬兰语（FAF），评估其与患者的关系视力。方法：从42例患者中获得角膜标本，用刚果红色染色，并用刚果红色染色，并数字扫描。在像素水平上标记淀粉样沉积物和基质组织区域的区域以进​​行训练和验证。该算法用于量化每个角膜中的Rad，评估RAD与视力的关联。结果：在淀粉样蛋白面积分类的验证中，敏感性为86％，特异性92％和F分81.对于角膜基质区域分类，敏感性为74％，特异性82％和F分数73。没有足够的评分73.证据证明相关性（Spearman的等级相关性，-0.264，p = 0.091）（logmar）。结论：深度学习算法可以在淀粉样蛋白和角膜基质区域的像素水平分类中实现高灵敏度和特异性。算法的进一步建模和开发评估临床图像的早期沉积阶段的阶段是必要的，以便更好地评估淀粉样蛋白沉积和视力之间的相关性。该方法也可以应用于角膜营养不良。

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