XEns-CKD：一种基于可解释集成的慢性肾脏病分期检测方法
XEns-CKD: An Explainable Ensemble-Based Approach for Chronic Kidney Disease Stage Detection
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中文总结 AI 辅助
本文提出XEns-CKD，一种基于集成视觉Transformer的可解释模型，结合LIME等可解释AI技术，利用超声图像将CKD分为5个分期及正常状态，准确率达86.36%，较现有方法提升4%，可识别CKD进展影响的肾脏区域。
中文摘要 AI 辅助
慢性肾脏病（CKD）是一种“沉默性”疾病，其进展可能不会显著影响患者的日常活动，人体肾功能可分为正常状态或CKD的五个分期。早期检测CKD分期可帮助患者了解自身肾功能状态，并遵循医嘱延缓CKD进展。本文提出了XEns-CKD，一种基于集成视觉Transformer（ViT）的新型方案，用于利用超声图像进行CKD分期分类。研究人员在一个私有超声图像数据集上采用不同训练参数训练了三个ViT，使用宏灵敏度、宏特异度、宏精确率、宏F1值、宏Youden指数、马修斯相关系数（MCC）和宏平衡准确率评估每个ViT的性能。集成模型的总体分类准确率达到86.36%。本研究还强调识别和解释CKD进展影响的肾脏区域，采用了包括LIME、LRP、Attention-Min和Attention-Max在内的可解释人工智能技术，以提高模型透明度和临床可信度。结合Attention-Min与Attention-Max结果的注意力图，可有效识别和解释CKD从一个分期进展至另一分期时受影响的肾脏区域，还能突出显示这些区域中CKD进展的影响。与现有方法相比，所提方法对五个CKD分期及正常肾脏状态的分类准确率提升了4%。
英文摘要
Chronic kidney disease (CKD) is a silent disease. Its progression may not significantly hamper a person's daily routine. Human kidney function can be classified as normal or as one of the five stages of CKD. Early detection of the CKD stage can help patients understand the functional status of their kidneys and follow medical advice to slow CKD progression. In this paper, we propose XEns-CKD, a novel ensemble vision transformer-based scheme for CKD stage classification using ultrasound images. Three ViTs were trained on a private ultrasound image dataset using different training parameters. The performance of each ViT was evaluated using macro sensitivity, macro specificity, macro precision, macro F1-score, macro Youden index, the Matthews correlation coefficient (MCC), and macro balanced accuracy. The ensemble model achieved an overall classification accuracy of 86.36%. This work also emphasizes identifying and interpreting kidney regions affected by CKD progression. Explainable artificial intelligence techniques, including LIME, LRP, Attention-Min, and Attention-Max, were used to improve model transparency and clinical trust. An attention map combining the Attention-Min and Attention-Max results effectively identified and interpreted kidney regions affected during CKD progression from one stage to another. The attention map also highlighted the effects of CKD progression in these regions. Compared with existing methods, the proposed method classified the five CKD stages and normal kidney status with a 4% improvement in accuracy.
