Improving rib fracture detection accuracy and reading efficiency with deep learning-based detection software: a clinical evaluation
Autor: | Fuzhou Li, Xiaodong Li, Chunxue Jia, Baotao Lv, Zhenchao Sun, Runze Wu, Beibei Li, Guijin Du, Bin Zhang |
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Rok vydání: | 2021 |
Předmět: |
Adult
Male medicine.medical_specialty Adolescent Rib Fractures media_common.quotation_subject MEDLINE Ribs Sensitivity and Specificity 030218 nuclear medicine & medical imaging Young Adult 03 medical and health sciences Deep Learning 0302 clinical medicine Blunt Software Reading (process) Humans Medicine Radiology Nuclear Medicine and imaging Medical physics Aged Retrospective Studies media_common Aged 80 and over Observer Variation Full Paper business.industry Deep learning Reproducibility of Results 030208 emergency & critical care medicine General Medicine Middle Aged Fracture (geology) Radiographic Image Interpretation Computer-Assisted Female Artificial intelligence Tomography X-Ray Computed business Clinical evaluation |
Zdroj: | Br J Radiol |
ISSN: | 1748-880X 0007-1285 |
Popis: | 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. |
Databáze: | OpenAIRE |
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