Abstract：
OBJECTIVE: We created an infrastructure for no code machine learning (NML) platform for non-programming physicians to create NML model. We tested the platform by creating an NML model for classifying radiographs for the presence and absence of clavicle fractures.
METHODS: Our IRB-approved retrospective study included 4135 clavicle radiographs from 2039 patients (mean age 52 ± 20 years, F:M 1022:1017) from 13 hospitals. Each patient had two-view clavicle radiographs with axial and anterior-posterior projections. The positive radiographs had either displaced or non-displaced clavicle fractures. We configured the NML platform to automatically retrieve the eligible exams using the series\' unique identification from the hospital virtual network archive via web access to DICOM Objects. The platform trained a model until the validation loss plateaus. Once the testing was complete, the platform provided the receiver operating characteristics curve and confusion matrix for estimating sensitivity, specificity, and accuracy.
RESULTS: The NML platform successfully retrieved 3917 radiographs (3917/4135, 94.7 %) and parsed them for creating a ML classifier with 2151 radiographs in the training, 100 radiographs for validation, and 1666 radiographs in testing datasets (772 radiographs with clavicle fracture, 894 without clavicle fracture). The network identified clavicle fracture with 90 % sensitivity, 87 % specificity, and 88 % accuracy with AUC of 0.95 (confidence interval 0.94-0.96).
CONCLUSIONS: A NML platform can help physicians create and test machine learning models from multicenter imaging datasets such as the one in our study for classifying radiographs based on the presence of clavicle fracture.
METHODS: Our IRB-approved retrospective study included 4135 clavicle radiographs from 2039 patients (mean age 52 ± 20 years, F:M 1022:1017) from 13 hospitals. Each patient had two-view clavicle radiographs with axial and anterior-posterior projections. The positive radiographs had either displaced or non-displaced clavicle fractures. We configured the NML platform to automatically retrieve the eligible exams using the series\' unique identification from the hospital virtual network archive via web access to DICOM Objects. The platform trained a model until the validation loss plateaus. Once the testing was complete, the platform provided the receiver operating characteristics curve and confusion matrix for estimating sensitivity, specificity, and accuracy.
RESULTS: The NML platform successfully retrieved 3917 radiographs (3917/4135, 94.7 %) and parsed them for creating a ML classifier with 2151 radiographs in the training, 100 radiographs for validation, and 1666 radiographs in testing datasets (772 radiographs with clavicle fracture, 894 without clavicle fracture). The network identified clavicle fracture with 90 % sensitivity, 87 % specificity, and 88 % accuracy with AUC of 0.95 (confidence interval 0.94-0.96).
CONCLUSIONS: A NML platform can help physicians create and test machine learning models from multicenter imaging datasets such as the one in our study for classifying radiographs based on the presence of clavicle fracture.
摘要：
目标：我们为非编程医生创建了无代码机器学习（NML）平台的基础架构，以创建NML模型。我们通过创建NML模型来对X射线照片进行分类以确定锁骨骨折的存在与否来测试平台。
方法：我们的IRB批准的回顾性研究包括2039例患者的4135例锁骨X光片（平均年龄52±20岁，F:M1022:1017)来自13家医院。每位患者都有两视锁骨X光片，带有轴向和前后投影。X线片阳性的锁骨骨折移位或非移位。我们将NML平台配置为通过DICOM对象的Web访问，使用医院虚拟网络档案中的系列唯一标识自动检索合格考试。平台训练模型，直到验证损失平台。一旦测试完成,该平台提供了接收机工作特性曲线和混淆矩阵，用于估计灵敏度，特异性，和准确性。
结果：NML平台成功检索了3917张射线照片（3917/4135，94.7％），并对其进行了解析，以便在训练中创建具有2151张射线照片的ML分类器，100张用于验证的射线照片，和测试数据集中的1666张X光照片(772张锁骨骨折的X光照片，894无锁骨骨折）。该网络以90%的灵敏度识别锁骨骨折，87%的特异性，和88%的准确性，AUC为0.95（置信区间0.94-0.96）。
结论：NML平台可以帮助医生从多中心成像数据集创建和测试机器学习模型，例如我们研究中根据锁骨骨折的存在对X射线照片进行分类的模型。
方法：我们的IRB批准的回顾性研究包括2039例患者的4135例锁骨X光片（平均年龄52±20岁，F:M1022:1017)来自13家医院。每位患者都有两视锁骨X光片，带有轴向和前后投影。X线片阳性的锁骨骨折移位或非移位。我们将NML平台配置为通过DICOM对象的Web访问，使用医院虚拟网络档案中的系列唯一标识自动检索合格考试。平台训练模型，直到验证损失平台。一旦测试完成,该平台提供了接收机工作特性曲线和混淆矩阵，用于估计灵敏度，特异性，和准确性。
结果：NML平台成功检索了3917张射线照片（3917/4135，94.7％），并对其进行了解析，以便在训练中创建具有2151张射线照片的ML分类器，100张用于验证的射线照片，和测试数据集中的1666张X光照片(772张锁骨骨折的X光照片，894无锁骨骨折）。该网络以90%的灵敏度识别锁骨骨折，87%的特异性，和88%的准确性，AUC为0.95（置信区间0.94-0.96）。
结论：NML平台可以帮助医生从多中心成像数据集创建和测试机器学习模型，例如我们研究中根据锁骨骨折的存在对X射线照片进行分类的模型。
