Hsu Jia-Lien, Singaravelan Anandakumar, Lai Chih-Yun, Li Zhi-Lin, Lin Chia-Nan, Wu Wen-Shuo, Kao Tze-Wah, Chu Pei-Lun
Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City 24205, Taiwan.
Graduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei City 24205, Taiwan.
Bioengineering (Basel). 2024 Sep 26;11(10):963. doi: 10.3390/bioengineering11100963.
Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary renal disease leading to end-stage renal disease. Total kidney volume (TKV) measurement has been considered as a surrogate in the evaluation of disease severity and prognostic predictor of ADPKD. However, the traditional manual measurement of TKV by medical professionals is labor-intensive, time-consuming, and human error prone.
In this investigation, we conducted TKV measurements utilizing magnetic resonance imaging (MRI) data. The dataset consisted of 30 patients with ADPKD and 10 healthy individuals. To calculate TKV, we trained models using both coronal- and axial-section MRI images. The process involved extracting images in Digital Imaging and Communications in Medicine (DICOM) format, followed by augmentation and labeling. We employed a U-net model for image segmentation, generating mask images of the target areas. Subsequent post-processing steps and TKV estimation were performed based on the outputs obtained from these mask images.
The average TKV, as assessed by medical professionals from the testing dataset, was 1501.84 ± 965.85 mL with axial-section images and 1740.31 ± 1172.21 mL with coronal-section images, respectively ( = 0.73). Utilizing the deep learning model, the mean TKV derived from axial- and coronal-section images was 1536.33 ± 958.68 mL and 1636.25 ± 964.67 mL, respectively ( = 0.85). The discrepancy in mean TKV between medical professionals and the deep learning model was 44.23 ± 58.69 mL with axial-section images ( = 0.8) and 329.12 ± 352.56 mL with coronal-section images ( = 0.9), respectively. The average variability in TKV measurement was 21.6% with the coronal-section model and 3.95% with the axial-section model. The axial-section model demonstrated a mean Dice Similarity Coefficient (DSC) of 0.89 ± 0.27 and an average patient-wise Jaccard coefficient of 0.86 ± 0.27, while the mean DSC and Jaccard coefficient of the coronal-section model were 0.82 ± 0.29 and 0.77 ± 0.31, respectively.
The integration of deep learning into image processing and interpretation is becoming increasingly prevalent in clinical practice. In our pilot study, we conducted a comparative analysis of the performance of a deep learning model alongside corresponding axial- and coronal-section models, a comparison that has been less explored in prior research. Our findings suggest that our deep learning model for TKV measurement performs comparably to medical professionals. However, we observed that varying image orientations could introduce measurement bias. Specifically, our AI model exhibited superior performance with axial-section images compared to coronal-section images.
常染色体显性遗传性多囊肾病（ADPKD）是导致终末期肾病的最常见遗传性肾病。总肾体积（TKV）测量被视为评估ADPKD疾病严重程度和预后预测指标的替代方法。然而，医学专业人员传统的手动测量TKV既费力、耗时，又容易出现人为误差。
在本研究中，我们利用磁共振成像（MRI）数据进行TKV测量。数据集包括30例ADPKD患者和10名健康个体。为了计算TKV，我们使用冠状面和横断面MRI图像训练模型。该过程包括提取医学数字成像和通信（DICOM）格式的图像，随后进行增强和标记。我们采用U-net模型进行图像分割，生成目标区域的掩码图像。基于从这些掩码图像获得的输出进行后续后处理步骤和TKV估计。
测试数据集中医学专业人员评估的平均TKV，横断面图像为1501.84±965.85 mL，冠状面图像为1740.31±1172.21 mL（P = 0.73）。利用深度学习模型，从横断面和冠状面图像得出的平均TKV分别为1536.33±958.68 mL和1636.25±964.67 mL（P = 0.85）。医学专业人员与深度学习模型之间平均TKV的差异，横断面图像为44.23±58.69 mL（P = 0.8），冠状面图像为329.12±352.56 mL（P = 0.9）。TKV测量的平均变异性，冠状面模型为21.6%，横断面模型为3.95%。横断面模型的平均骰子相似系数（DSC）为0.89±0.27，平均患者层面的杰卡德系数为0.86±0.27，而冠状面模型的平均DSC和杰卡德系数分别为0.82±0.29和0.77±0.31。
深度学习在图像处理和解读中的整合在临床实践中越来越普遍。在我们的初步研究中，我们对深度学习模型与相应的横断面和冠状面模型的性能进行了对比分析，这一对比在先前研究中较少涉及。我们的研究结果表明，我们用于TKV测量的深度学习模型与医学专业人员的表现相当。然而，我们观察到不同的图像方向可能会引入测量偏差。具体而言，我们的人工智能模型在横断面图像上的表现优于冠状面图像。
