Abstract

Background

Recently developed convolutional neural network (CNN) models determine bone age more accurately than radiologists.

Objective

The purpose of this study was to determine whether a CNN and radiologists can accurately predict bone age from radiographs using only the index finger rather than the whole hand.

Materials and methods

We used a public anonymized dataset provided by the Radiological Society of North America (RSNA) pediatric bone age challenge. The dataset contains 12,611 hand radiographs for training and 200 radiographs for testing. The index finger was cropped from these images to create a second dataset. Separate CNN models were trained using the whole-hand radiographs and the cropped second-digit dataset using the consensus ground truth provided by the RSNA bone age challenge. Bone age determination using both models was compared with ground truth as provided by the RSNA dataset. Separately, three pediatric radiologists determined bone age from the whole-hand and index-finger radiographs, and the consensus was compared to the ground truth and CNN-model-determined bone ages.

Results

The mean absolute difference between the ground truth and CNN bone age for whole-hand and index-finger was similar (4.7 months vs. 5.1 months, P=0.14), and both values were significantly smaller than that for radiologist bone age determination from the single-finger radiographs (8.0 months, P<0.0001).

Conclusion

CNN-model-determined bone ages from index-finger radiographs are similar to whole-hand bone age interpreted by radiologists in the dataset, as well as a model trained on the whole-hand radiograph. In addition, the index-finger model performed better than the ground truth compared to subspecialty trained pediatric radiologists also using only the index finger to determine bone age. The radiologist interpreting bone age can use the second digit as a reliable starting point in their search pattern.

中文翻译：

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仅使用食指确定骨龄：与人类放射科医生相比，使用卷积神经网络的新颖方法。

摘要

背景

最近开发的卷积神经网络（CNN）模型比放射科医生更准确地确定骨骼年龄。

目的

这项研究的目的是确定CNN和放射科医生是否仅使用食指而不是整个手就能从X光片准确预测骨龄。

材料和方法

我们使用了北美放射学会（RSNA）小儿骨龄挑战赛提供的公共匿名数据集。数据集包含12,611张用于训练的手部X光片和200张用于测试的X光片。从这些图像中裁剪出食指以创建第二个数据集。使用全能放射线照片和使用RSNA骨龄挑战提供的共识地面事实对裁剪后的第二位数据集进行训练的独立CNN模型。将使用这两个模型确定的骨龄与由RSNA数据集提供的地面真实情况进行了比较。另外，三名儿科放射科医生从全手和食指X线片中确定了骨龄，并将共识与地面真相和CNN模型确定的骨龄进行了比较。

结果

整手和食指的地面真相和CNN骨龄之间的平均绝对差值相似（4.7个月vs. 5.1个月，P = 0.14），并且两个值均显着小于放射学专家确定的骨龄。单指射线照相（8.0个月，P <0.0001）。

结论

由食指放射线照片确定的CNN模型骨龄与数据集中放射科医生解释的全手骨龄以及在全手放射线上训练的模型相似。此外，与仅使用食指来确定骨龄的经专科训练的儿科放射线医师相比，食指模型的表现要优于地面实况。解释骨骼年龄的放射科医生可以使用第二个数字作为搜索模式中的可靠起点。