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Volume 14
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10.3390/diagnostics14060596
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Article

AI-Assisted X-ray Fracture Detection in Residency Training: Evaluation in Pediatric and Adult Trauma Patients

by
Mathias Meetschen
1,2,*,
Luca Salhöfer
1,2,
Nikolas Beck
1,
Lennard Kroll
1,
Christoph David Ziegenfuß
1,
Benedikt Michael Schaarschmidt
1,
Michael Forsting
1,
Shamoun Mizan
1,2,
Lale Umutlu
1,
René Hosch
1,2,
Felix Nensa
1,2 and
Johannes Haubold
1,2
1
Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany
2
Institute for Artificial Intelligence in Medicine, University Hospital Essen, 45131 Essen, Germany
*
Author to whom correspondence should be addressed.
Diagnostics 2024, 14(6), 596; https://doi.org/10.3390/diagnostics14060596
Submission received: 28 January 2024 / Revised: 7 March 2024 / Accepted: 10 March 2024 / Published: 11 March 2024
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
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Abstract

Background: This study aimed to evaluate the impact of an AI-assisted fracture detection program on radiology residents’ performance in pediatric and adult trauma patients and assess its implications for residency training. Methods: This study, conducted retrospectively, included 200 radiographs from participants aged 1 to 95 years (mean age: 40.7 ± 24.5 years), encompassing various body regions. Among these, 50% (100/200) displayed at least one fracture, totaling one hundred thirty-five fractures, assessed by four radiology residents with different experience levels. A machine learning algorithm was employed for fracture detection, and the ground truth was established by consensus among two experienced senior radiologists. Fracture detection accuracy, reporting time, and confidence were evaluated with and without AI support. Results: Radiology residents’ sensitivity for fracture detection improved significantly with AI support (58% without AI vs. 77% with AI, p < 0.001), while specificity showed minor improvements (77% without AI vs. 79% with AI, p = 0.0653). AI stand-alone performance achieved a sensitivity of 93% with a specificity of 77%. AI support for fracture detection significantly reduced interpretation time for radiology residents by an average of approximately 2.6 s (p = 0.0156) and increased resident confidence in the findings (p = 0.0013). Conclusion: AI support significantly enhanced fracture detection sensitivity among radiology residents, particularly benefiting less experienced radiologists. It does not compromise specificity and reduces interpretation time, contributing to improved efficiency. This study underscores AI’s potential in radiology, emphasizing its role in training and interpretation improvement.
Keywords: X-rays; fractures; bone; artificial intelligence; diagnostic imaging; quality improvement X-rays; fractures; bone; artificial intelligence; diagnostic imaging; quality improvement

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MDPI and ACS Style

Meetschen, M.; Salhöfer, L.; Beck, N.; Kroll, L.; Ziegenfuß, C.D.; Schaarschmidt, B.M.; Forsting, M.; Mizan, S.; Umutlu, L.; Hosch, R.; et al. AI-Assisted X-ray Fracture Detection in Residency Training: Evaluation in Pediatric and Adult Trauma Patients. Diagnostics 2024, 14, 596. https://doi.org/10.3390/diagnostics14060596

AMA Style

Meetschen M, Salhöfer L, Beck N, Kroll L, Ziegenfuß CD, Schaarschmidt BM, Forsting M, Mizan S, Umutlu L, Hosch R, et al. AI-Assisted X-ray Fracture Detection in Residency Training: Evaluation in Pediatric and Adult Trauma Patients. Diagnostics. 2024; 14(6):596. https://doi.org/10.3390/diagnostics14060596

Chicago/Turabian Style

Meetschen, Mathias, Luca Salhöfer, Nikolas Beck, Lennard Kroll, Christoph David Ziegenfuß, Benedikt Michael Schaarschmidt, Michael Forsting, Shamoun Mizan, Lale Umutlu, René Hosch, and et al. 2024. "AI-Assisted X-ray Fracture Detection in Residency Training: Evaluation in Pediatric and Adult Trauma Patients" Diagnostics 14, no. 6: 596. https://doi.org/10.3390/diagnostics14060596

APA Style

Meetschen, M., Salhöfer, L., Beck, N., Kroll, L., Ziegenfuß, C. D., Schaarschmidt, B. M., Forsting, M., Mizan, S., Umutlu, L., Hosch, R., Nensa, F., & Haubold, J. (2024). AI-Assisted X-ray Fracture Detection in Residency Training: Evaluation in Pediatric and Adult Trauma Patients. Diagnostics, 14(6), 596. https://doi.org/10.3390/diagnostics14060596

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