The British journal of radiology · 2020
Improving rib fracture detection accuracy and reading efficiency with deep learning-based detection software: a clinical evaluation.
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Zhang Bin, Jia Chunxue, Wu Runze, Lv Baotao, Li Beibei, Li Fuzhou, Du Guijin, Sun Zhenchao, Li Xiaodong
Аннотация
OBJECTIVES: To investigate the impact of deep learning (DL) on radiologists' detection accuracy and reading efficiency of rib fractures on CT.
METHODS: Blunt chest trauma patients (n = 198) undergoing thin-slice CT were enrolled. Images were read by two radiologists (R1, R2) in three sessions: S1, unassisted reading; S2, assisted by DL as the concurrent reader; S3, DL as the second reader. The fractures detected by the readers and total reading time were documented. The reference standard for rib fractures was established by an expert panel. The sensitivity and false-positives per scan were calculated and compared among S1, S2, and S3.
RESULTS: The reference standard identified 865 fractures on 713 ribs (102 patients) The sensitivity of S1, S2, and S3 was 82.8, 88.9, and 88.7% for R1, and 83.9, 88.7, and 88.8% for R2, respectively. The sensitivity of S2 and S3 was significantly higher compared to S1 for both readers (all p < 0.05). The sensitivity between S2 and S3 did not differ significantly (both p > 0.9). The false-positive per scan had no difference between sessions for R1 (p = 0.24) but was lower for S2 and S3 than S1 for R2 (both p < 0.05). Reading time decreased by 36% (R1) and 34% (R2) in S2 compared to S1.
CONCLUSIONS: Using DL as a concurrent reader can improve the detection accuracy and reading efficiency for rib fracture.
ADVANCES IN KNOWLEDGE: DL can be integrated into the radiology workflow to improve the accuracy and reading efficiency of CT rib fracture detection.
METHODS: Blunt chest trauma patients (n = 198) undergoing thin-slice CT were enrolled. Images were read by two radiologists (R1, R2) in three sessions: S1, unassisted reading; S2, assisted by DL as the concurrent reader; S3, DL as the second reader. The fractures detected by the readers and total reading time were documented. The reference standard for rib fractures was established by an expert panel. The sensitivity and false-positives per scan were calculated and compared among S1, S2, and S3.
RESULTS: The reference standard identified 865 fractures on 713 ribs (102 patients) The sensitivity of S1, S2, and S3 was 82.8, 88.9, and 88.7% for R1, and 83.9, 88.7, and 88.8% for R2, respectively. The sensitivity of S2 and S3 was significantly higher compared to S1 for both readers (all p < 0.05). The sensitivity between S2 and S3 did not differ significantly (both p > 0.9). The false-positive per scan had no difference between sessions for R1 (p = 0.24) but was lower for S2 and S3 than S1 for R2 (both p < 0.05). Reading time decreased by 36% (R1) and 34% (R2) in S2 compared to S1.
CONCLUSIONS: Using DL as a concurrent reader can improve the detection accuracy and reading efficiency for rib fracture.
ADVANCES IN KNOWLEDGE: DL can be integrated into the radiology workflow to improve the accuracy and reading efficiency of CT rib fracture detection.