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Updated: Jun 22, 2026

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy
Published on: July 2, 2021
Added value of artificial intelligence for the detection of pelvic and hip fractures
Anthony Jaillat1, Catherine Cyteval1, Marie-Pierre Baron Sarrabere1
1Osteoarticular Medical Imaging Section, Department of Medical Imaging, Lapeyronie University Hospital, Montpellier, France.
Purpose:
To assess the added value of artificial intelligence (AI) for radiologists and emergency physicians in the radiographic detection of pelvic fractures.
Materials & Methods:
In this retrospective study, one junior radiologist reviewed 940 X-rays of patients admitted to emergency for a fall with suspicion of pelvic fracture between March 2020 and June 2021. The radiologist analyzed the X-rays alone and then using an AI system (BoneView). In a random sample of 100 exams, the same procedure was repeated alongside five other readers (three radiologists and two emergency physicians with 3-30 years of experience). The reference diagnosis was based on the patient's full set of medical imaging exams and medical records in the months following emergency admission.
Results:
A total of 633 confirmed pelvic fractures (64.8% from hip and 35.2% from pelvic ring) in 940 patients and 68 pelvic fractures (60% from hip and 40% from pelvic ring) in the 100-patient sample were included. In the whole dataset, the junior radiologist achieved a significant sensitivity improvement with AI assistance (Se-PELVIC = 77.25% to 83.73%; p < 0.001, Se-HIP 93.24 to 96.49%; p < 0.001 and Se-PELVIC RING 54.60% to 64.50%; p < 0.001). However, there was a significant decrease in specificity with AI assistance (Spe-PELVIC = 95.24% to 93.25%; p = 0.005 and Spe-HIP = 98.30% to 96.90%; p = 0.005). In the 100-patient sample, the two emergency physicians obtained an improvement in fracture detection sensitivity across the pelvic area + 14.70% (p = 0.0011) and + 10.29% (p < 0.007) respectively without a significant decrease in specificity. For hip fractures, E1's sensitivity increased from 59.46% to 70.27% (p = 0.04), and E2's sensitivity increased from 78.38% to 86.49% (p = 0.08). For pelvic ring fractures, E1's sensitivity increased from 12.90% to 32.26% (p = 0.012), and E2's sensitivity increased from 19.35% to 32.26% (p = 0.043).
Conclusion:
AI improved the diagnostic performance for emergency physicians and radiologists with limited experience in pelvic fracture screening.
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