Yang Jihyun, Lee Young Rae, Hyun Young Youl, Kim Hyun Jung, Shin Tae Young, Lee Kyu-Beck
Division of Nephrology and Hypertension, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea.
Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea.
J Pers Med. 2025 Aug 20;15(8):392. doi: 10.3390/jpm15080392.
The Mayo imaging classification (MIC) for polycystic kidney disease (PKD) is a crucial basis for clinical treatment decisions; however, the volumetric assessment for its evaluation remains tedious and inaccurate. While the ellipsoid method for measuring the total kidney volume (TKV) in patients with PKD provides a practical TKV estimation using computed tomography (CT), its inconsistency and inaccuracy are limitations, highlighting the need for improved, accessible techniques in real-world clinics. We compared manual ellipsoid and artificial intelligence (AI)-based kidney volumetry methods using a convolutional neural network-based segmentation model (3D Dynamic U-Net) for measuring the TKV by assessing 32 patients with PKD in a single tertiary hospital. The median age and average TKV were 56 years and 1200.24 mL, respectively. Most of the patients were allocated to Mayo Clinic classifications 1B and 1C using the ellipsoid method, similar to the AI volumetry classification. AI volumetry outperformed the ellipsoid method with highly correlated scores (AI vs. nephrology professor ICC: r = 0.991, 95% confidence interval (CI) = 0.9780-0.9948, < 0.01; AI vs. trained clinician ICC: r = 0.983, 95% CI = 0.9608-0.9907, < 0.01). The Bland-Altman plot also showed that the mean differences between professor and AI volumetry were statistically insignificant (mean difference 159.5 mL, 95% CI = 11.8368-330.7817, = 0.07). AI-based kidney volumetry demonstrates strong agreement with expert manual measurements and offers a reliable, labor-efficient alternative for TKV assessment in clinical practice. It is helpful and essential for managing PKD and optimizing therapeutic outcomes.
多囊肾病（PKD）的梅奥成像分类（MIC）是临床治疗决策的关键依据；然而，用于其评估的体积测量仍然繁琐且不准确。虽然用于测量PKD患者总肾体积（TKV）的椭球体方法通过计算机断层扫描（CT）提供了一种实用的TKV估计，但它的不一致性和不准确性是局限性，这突出了在实际临床中需要改进的、易于使用的技术。我们在一家三级医院对32例PKD患者进行评估，比较了使用基于卷积神经网络的分割模型（3D动态U-Net）的手动椭球体和基于人工智能（AI）的肾脏体积测量方法来测量TKV。患者的中位年龄和平均TKV分别为56岁和1200.24 mL。使用椭球体方法，大多数患者被归类为梅奥诊所分类1B和1C，这与AI体积测量分类相似。AI体积测量在高度相关的评分方面优于椭球体方法（AI与肾病学教授的组内相关系数：r = 0.991，95%置信区间（CI）= 0.9780 - 0.9948，P < 0.01；AI与训练有素的临床医生的组内相关系数：r = 0.983，95% CI = 0.9608 - 0.9907，P < 0.01）。布兰德-奥特曼图还显示，教授和AI体积测量之间的平均差异在统计学上不显著（平均差异159.5 mL，95% CI = 11.8368 - 330.7817，P = 0.07）。基于AI的肾脏体积测量与专家手动测量显示出高度一致性，并为临床实践中的TKV评估提供了一种可靠、省力的替代方法。它对PKD的管理和优化治疗结果有帮助且至关重要。
