PDF(Pubmed)
Abstract：
UNASSIGNED: The Mayo imaging classification model (MICM) requires a prestep qualitative assessment to determine whether a patient is in class 1 (typical) or class 2 (atypical), where patients assigned to class 2 are excluded from the MICM application.
UNASSIGNED: We developed a deep learning-based method to automatically classify class 1 and 2 from magnetic resonance (MR) images and provide classification confidence utilizing abdominal T 2 -weighted MR images from 486 subjects, where transfer learning was applied. In addition, the explainable artificial intelligence (XAI) method was illustrated to enhance the explainability of the automated classification results. For performance evaluations, confusion matrices were generated, and receiver operating characteristic curves were drawn to measure the area under the curve.
UNASSIGNED: The proposed method showed excellent performance for the classification of class 1 (97.7%) and 2 (100%), where the combined test accuracy was 98.01%. The precision and recall for predicting class 1 were 1.00 and 0.98, respectively, with F 1 -score of 0.99; whereas those for predicting class 2 were 0.87 and 1.00, respectively, with F 1 -score of 0.93. The weighted averages of precision and recall were 0.98 and 0.98, respectively, showing the classification confidence scores whereas the XAI method well-highlighted contributing regions for the classification.
UNASSIGNED: The proposed automated method can classify class 1 and 2 cases as accurately as the level of a human expert. This method may be a useful tool to facilitate clinical trials investigating different types of kidney morphology and for clinical management of patients with autosomal dominant polycystic kidney disease (ADPKD).
UNASSIGNED: We developed a deep learning-based method to automatically classify class 1 and 2 from magnetic resonance (MR) images and provide classification confidence utilizing abdominal T 2 -weighted MR images from 486 subjects, where transfer learning was applied. In addition, the explainable artificial intelligence (XAI) method was illustrated to enhance the explainability of the automated classification results. For performance evaluations, confusion matrices were generated, and receiver operating characteristic curves were drawn to measure the area under the curve.
UNASSIGNED: The proposed method showed excellent performance for the classification of class 1 (97.7%) and 2 (100%), where the combined test accuracy was 98.01%. The precision and recall for predicting class 1 were 1.00 and 0.98, respectively, with F 1 -score of 0.99; whereas those for predicting class 2 were 0.87 and 1.00, respectively, with F 1 -score of 0.93. The weighted averages of precision and recall were 0.98 and 0.98, respectively, showing the classification confidence scores whereas the XAI method well-highlighted contributing regions for the classification.
UNASSIGNED: The proposed automated method can classify class 1 and 2 cases as accurately as the level of a human expert. This method may be a useful tool to facilitate clinical trials investigating different types of kidney morphology and for clinical management of patients with autosomal dominant polycystic kidney disease (ADPKD).
摘要：
■Mayo成像分类模型（MICM）需要进行步骤前定性评估，以确定患者是在1类（典型）还是2类（非典型），其中被分配到2级的患者被排除在MICM申请之外。
■我们开发了一种基于深度学习的方法，可以从磁共振（MR）图像中自动分类1类和2类，并利用来自486名受试者的腹部T2加权MR图像提供分类置信度。应用迁移学习的地方。此外,说明了可解释的人工智能（XAI）方法可以增强自动分类结果的可解释性。对于绩效评估，产生混淆矩阵，绘制受试者工作特性曲线，测量曲线下面积。
■所提出的方法对第1类（97.7%）和第2类（100%）的分类表现出优异的性能，其中组合测试准确度为98.01%。预测1类的准确率和召回率分别为1.00和0.98，F1评分为0.99；而预测2级的分别为0.87和1.00，F1-得分为0.93。准确率和召回率的加权平均值分别为0.98和0.98，显示分类置信度得分，而XAI方法突出显示了分类的贡献区域。
■所提出的自动化方法可以将1类和2类病例分类得与人类专家的水平一样准确。此方法可能是促进研究不同类型肾脏形态的临床试验以及常染色体显性多囊肾病（ADPKD）患者的临床治疗的有用工具。
■我们开发了一种基于深度学习的方法，可以从磁共振（MR）图像中自动分类1类和2类，并利用来自486名受试者的腹部T2加权MR图像提供分类置信度。应用迁移学习的地方。此外,说明了可解释的人工智能（XAI）方法可以增强自动分类结果的可解释性。对于绩效评估，产生混淆矩阵，绘制受试者工作特性曲线，测量曲线下面积。
■所提出的方法对第1类（97.7%）和第2类（100%）的分类表现出优异的性能，其中组合测试准确度为98.01%。预测1类的准确率和召回率分别为1.00和0.98，F1评分为0.99；而预测2级的分别为0.87和1.00，F1-得分为0.93。准确率和召回率的加权平均值分别为0.98和0.98，显示分类置信度得分，而XAI方法突出显示了分类的贡献区域。
■所提出的自动化方法可以将1类和2类病例分类得与人类专家的水平一样准确。此方法可能是促进研究不同类型肾脏形态的临床试验以及常染色体显性多囊肾病（ADPKD）患者的临床治疗的有用工具。
