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真实世界中基于人工智能骨龄测量系统TW3-Carpal和TW3-RUS的临床应用差异

Clinical application differences between TW3-Carpal and TW3-RUS based artificial intelligence-assisted bone age assessment system:a real-world pilot study
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摘要 目的利用真实世界的数据探讨基于人工智能(AI)骨龄测量系统的Tanner-Whitehouse(TW)3评分系统中尺骨、桡骨、短骨(RUS)和腕骨(Carpal)骨龄临床应用中的差异。方法回顾性收集2021年7月至9月于首都儿科研究所附属儿童医院行左侧手腕部X线正位片检查的262例儿童的影像资料。采用AI骨龄辅助软件分别采用TW3-RUS和TW3-Carpal标准对入组影像资料进行骨龄评估,得到AI骨龄。由两名高年资儿科影像医师依据TW3-RUS和TW3-Carpal标准进行评估,所得骨龄结果的平均值作为金标准。根据金标准获得的骨龄以3岁为间隔进行分层,骨龄1~3、4~6、7~9、10~12、13~15和16~18岁的例数分别为10、35、70、118、27、2例。使用组内相关系数(ICC)评估AI骨龄与金标准骨龄结果的一致性,采用Pearson相关法分析AI骨龄与金标准骨龄的相关性。采用配对t检验比较不同骨龄段内AI测定的TW3-RUS与TW3-Carpal骨龄的差异。结果整体样本中AI测定的TW3-RUS和TW3-Carpal骨龄分别为(8.9±3.1)和(8.7±3.0)岁,金标准TW3-RUS和TW3-Carpal骨龄分别为(8.7±2.9)和(8.8±2.8)岁。AI测定的TW3-RUS和TW3-Carpal骨龄结果均与金标准具有高度一致性(ICC分别为0.983、0.976)和高度正相关(r分别为0.985、0.978,P均<0.001)。7~9、10~12和13~15岁骨龄段内,TW3-RUS与TW3-Carpal骨龄间差异有统计学意义(t=-3.36、-1.77、1.84,P=0.001、0.046、0.040)。4~6和7~9岁骨龄段内,TW3-RUS、TW3-Carpal骨龄与金标准一致性良好(ICC均>0.75),且TW3-Carpal骨龄一致性(ICC分别为0.940、0.927)稍优于TW3-RUS(ICC分别为0.929、0.882)。10~12和13~15岁骨龄段内,TW3-RUS骨龄与金标准的一致性(ICC分别为0.962、0.963)明显优于TW3-Carpal骨龄(ICC分别为0.744、0.605)。结论AI骨龄测量结果具有良好的可靠性与准确性。利用骨龄AI辅助评估系统时,4~9岁儿童采用TW3-Carpal标准更准确,而10~15岁儿童则建议采用TW3-RUS标准。 Objective To investigate the differences between Tanner-Whitehouse(TW)3-Carpal and TW3-RUS(radius,ulna and short bone)-based artificial intelligence(AI)-assisted bone age assessment system using real world data.Methods The image data of 262 children who received X-ray examination of left wrist in the Affiliated Children′s Hospital,Capital Institute of Pediatrics from July to September 2021 were retrospectively collected.The AI bone age assistant methods based on TW3-RUS and TW3-Carpal criteria were used to obtain the bone age results,respectively.Two senior pediatric radiologists evaluated the bone age on the basis of TW3-RUS and TW3-Carpal criteria,and the averaged values of two reviewers was calculated and taken as the gold standard reference.The cases were stratified into six age groups at 3-year intervals according to the gold standard reference,including 1-3(n=10),4-6(n=35),7-9(n=70),10-12(n=118),13-15(n=27)and 16-18(n=2)years old groups.Intraclass correlation coefficient(ICC)was used to evaluate the consistency between AI results and the gold standard bone age results.Pearson correlation method was used to measure the reliability between AI results and the gold standard results.The difference of bone age results between using TW3-RUS and TW3-Carpal criteria in different age groups was compared using paired t-test.Results As for the whole sample,the results based on TW3-RUS criteria were 8.9±3.1 years old for AI assessment and 8.7±2.9 years old for the golden standard reference,with the ICC of 0.983;and the results based on TW3-Carpal criteria were 8.7±3.0 years old for AI and 8.8±2.8 years old for the golden standard reference,with the ICC of 0.976.Positive correlation were found in both TW3-RUS(r=0.985,P<0.001)and TW3-Carpal criteria groups(r=0.978,P<0.001).There were significant differences between TW3-RUS and TW3-Carpal at age groups of 7-9(t=-3.36,P=0.001),10-12(t=-1.77,P=0.046),and 13-15 years old(t=1.84,P=0.040).The bone age assessment using TW3-RUS and TW3-Carpal criteria were both in good agreement with the gold standard reference in age group of 4-6 years old(ICC=0.929 and 0.940),as well as in age group of 7-9 years old(ICC=0.882 and 0.927,respectively),with the results using TW3-Carpal criteria were slightly higher.As for the age groups of 10-12 and 13-15 years old,the method using TW3-RUS criteria showed excellent agreement with the gold standard reference(ICC=0.962 and 0.963,respectively),which were better than the performance of method using TW3-Carpal criteria(ICC=0.744 and 0.605,respectively).Conclusions AI-assisted bone age system based TW3-Carpal and TW3-RUS criteria both show good reliability and accuracy in the bone age measurements.The AI method based TW3-Carpal criteria shows better performance in age group of 4-9 years old,while the method based on TW3-RUS criteria may be better for children of age 10-15 years old.
作者 杨洋 王驰 欧凌 白凤森 袁新宇 Yang Yang;Wang Chi;Ou Ling;Bai Fengsen;Yuan Xinyu(Department of Radiology,the Affiliated Children′s Hospital,Capital Institute of Pediatrics,Beijing 100020,China;GCP Office,the Affiliated Children′s Hospital,Capital Institute of Pediatrics,Beijing 100020,China)
出处 《中华放射学杂志》 CAS CSCD 北大核心 2023年第4期359-363,共5页 Chinese Journal of Radiology
关键词 儿童 年龄测定 骨骼 人工智能 Tanner-Whitehouse法 Child Age determination by skeleton Artificial intelligence Tanner-Whitehouse method
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