| 刘宗才,吴锦华,王荣品,刘昌杰,曾宪春.深度学习骨龄评测系统对贵州省儿童及青少年骨龄测评的准确性[J].中国医学影像技术,2019,35(12):1799~1803 |
| 深度学习骨龄评测系统对贵州省儿童及青少年骨龄测评的准确性 |
| Accuracy of deep learning based bone age assessment system of children and adolescents in Guizhou |
| 投稿时间：2019-07-05 修订日期：2019-08-20 |
| DOI：10.13929/j.1003-3289.201907037 |
| 中文关键词: 深度学习 骨龄评测 中华05 RUS-CHN法 临床试验 |
| 英文关键词:deep learning bone age CH05 RUS-CHN method clinical trials |
| 基金项目:贵州省高层次创新型人才培养计划项目（GZSYQCC[2015]001号）。 |
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| 中文摘要: |
| 目的 探讨适用于贵州省儿童及青少年临床应用的深度学习骨龄评测系统。方法 依据RUS-CHN法，由3名经中华05 RUS-CHN法培训的主任、副主任医师双盲评价148例2~17岁儿童及青少年骨龄片，并取3者均值为金标准。深度学习模型（模型组）和对照组医师（医师A、医师B）独立阅片，并分别记录骨龄评测的平均绝对误差（MAE）、绝对误差≤ 1.0岁样本所占比例。结果 与金标准对比，模型组MAE为0.295岁[95% CI（0.238，0.352）]，绝对误差≤ 1.0岁占93.92%（139/148）；对照组医师A MAE为0.438岁[95% CI（0.369，0.508）]；医师B MAE为0.360岁[95% CI（0.295，0.425）]，绝对误差≤ 1.0岁分别占89.19%（132/148；医师A）和89.86%（133/148；医师B）。模型组的MAE显著优于医师A的MAE（t=-3.071，P=0.002），但与医师B的MAE差异无统计学意义（t=-1.563，P=0.120）。结论 采用中华05 RUS-CHN法评测贵州儿童及青少年骨龄，深度学习模型可取得接近甚至优于对照组的骨龄评测结果。 |
| 英文摘要: |
| Objective To explore the clinical applicability of a deep learning based bone age assessment system of children and adolescents in Guizhou. Methods The left hand-wrist radiographs of 148 children and adolescents aged from 2 years to 17 years were assessed independently by three experts who were trained with the CH 05 RUS-CHN method, their mean estimates results were used as the reference standard. The estimates of the deep learning model (model group) and two residents (control group) were evaluated compared with the reference standard, respectively. mean absolute error (MAE) of bone age estimates and the percentage of samples with absolute error (AE) ≤ 1.0 year were calculated. Results MAE of the model group was 0.295[95% CI (0.238, 0.352)] years, with absolute error ≤ 1 years of 93.92% (139/148). Doctor A of the control group recorded MAE was 0.438[95% CI (0.369, 0.508)] years, with 89.19% absolute error ≤ 1.0 years of 89.19% (132/148); doctor B recorded MAE of 0.360[95% CI (0.295, 0.425)] years, with absolute error ≤ 1.0 years of 89.86% (133/148). The MAE of model group was significantly lower than that of doctor A (t=-3.071, P=0.002), but not for the doctor B (t=-1.563, P=0.120). Conclusion When bone age assessed with the CH 05 RUS-CHN method for Guizhou children and adolescents, the deep learning model can estimate bone age with accuracy similar to or even better than that of control group radiologists. |
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