用AI从CT图像精准预测早期肾病，还能解释判断依据。
A Clinically Interpretable Deep CNN Framework for Early Chronic Kidney Disease Prediction Using Grad-CAM-Based Explainable AI
- 基于深度卷积网络+Grad-CAM可解释技术，识别肾部异常。
- 在12,446张CT图像上实现早期肾病100%准确率。
- 适合临床医生辅助诊断，结果透明可信。
慢性肾病（CKD）是全球重大医疗负担，以肾功能渐进性恶化为特征，导致代谢废物清除障碍和体液稳态紊乱。因其对全球发病率和死亡率的显著贡献，开发可靠高效的诊断方法对实现早期发现和及时干预至关重要。本研究提出一种深度卷积神经网络（CNN），用于从CT肾部图像中早期检测CKD，结合合成少数类过采样技术（SMOTE）进行类别平衡，并通过梯度加权类激活映射（Grad-CAM）实现可解释性。模型在包含12,446张CT图像的CT KIDNEY DATASET上训练与评估，涵盖3,709例囊肿、5,077例正常、1,377例结石和2,283例肿瘤病例。所提深度CNN在早期CKD检测中达到100%准确率，展现出解决关键临床诊断难题的强大潜力，有助于提升早期医疗干预能力。
原文摘要 · Abstract (English)
Chronic Kidney Disease (CKD) constitutes a major global medical burden, marked by the gradual deterioration of renal function, which results in the impaired clearance of metabolic waste and disturbances in systemic fluid homeostasis. Owing to its substantial contribution to worldwide morbidity and mortality, the development of reliable and efficient diagnostic approaches is critically important to facilitate early detection and prompt clinical management. This study presents a deep convolutional neural network (CNN) for early CKD detection from CT kidney images, complemented by class balancing using Synthetic Minority Over-sampling Technique (SMOTE) and interpretability via Gradient-weighted Class Activation Mapping (Grad-CAM). The model was trained and evaluated on the CT KIDNEY DATASET, which contains 12,446 CT images, including 3,709 cyst, 5,077 normal, 1,377 stone, and 2,283 tumor cases. The proposed deep CNN achieved a remarkable classification performance, attaining 100% accuracy in the early detection of chronic kidney disease (CKD). This significant advancement demonstrates strong potential for addressing critical clinical diagnostic challenges and enhancing early medical intervention strategies.
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