Identification of fractures on pediatric foot radiographs: do localization cues improve diagnostic accuracy and reduce interpretation time?

SKELETAL RADIOLOGY(2024)

引用 0|浏览3
暂无评分
摘要
Objective To investigate the diagnostic accuracy and time in the detection of fractures on pediatric foot radiographs marked without and with localization cues. Method One-hundred randomly selected foot radiographic examinations that were performed on children (<18 years old) after injury and with at least 4 weeks of follow-up were included. Blinded to history and diagnosis, 4 readers (one each: medical student, pediatrician, pediatric orthopedic surgeon, and pediatric musculoskeletal radiologist) retrospectively and independently reviewed each examination twice (without and with cue, at least 1 month apart, and after randomization). Each reader recorded the presence or absence of a fracture, fracture location, diagnostic confidence, and the total (interpretation) time spent on each study. Diagnostic accuracy, reader confidence, and interpretation time were compared between examinations without and with cues. Results Our study included 59 examinations without and 41 with fractures (21 phalangeal, 18 metatarsal, and 2 tarsal fractures). Localization cues improved inter-reader agreement ( κ =0.36 to 0.64), overall sensitivity (68 to 72%), specificity (66 to 73%), and diagnostic accuracy (67 to 73%); thus, overcalled and missed rates also improved from 34 to 27% and 32 to 28%, respectively. Reader confidence improved with cue (49 to 61%, p <0.01) with higher incremental improvement with younger children (30% for 1–6 years; 14% for 7–11 years; and 10% for 12–17 years). Interpretation time decreased by 40% per examination (40±22 s without to 24±13 s with cues, p <0.001). Conclusion Localization cues improved diagnostic accuracy and reader confidence, reducing interpretation time in the detection of pediatric foot fractures.
更多
查看译文
关键词
Children,Diagnostic accuracy,Fracture detection,Foot,Interpretation time,Localization cue,Marker,Radiographs
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要