AI-Assisted X-ray Fracture Detection in Residency Training: Evaluation in Pediatric and Adult Trauma Patients
<b>Background</b>: 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. <b>Methods</b>: This study, conducted retrosp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/6/596 |
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| author | Mathias Meetschen Luca Salhöfer Nikolas Beck Lennard Kroll Christoph David Ziegenfuß Benedikt Michael Schaarschmidt Michael Forsting Shamoun Mizan Lale Umutlu René Hosch Felix Nensa Johannes Haubold |
| author_facet | Mathias Meetschen Luca Salhöfer Nikolas Beck Lennard Kroll Christoph David Ziegenfuß Benedikt Michael Schaarschmidt Michael Forsting Shamoun Mizan Lale Umutlu René Hosch Felix Nensa Johannes Haubold |
| author_sort | Mathias Meetschen |
| collection | DOAJ |
| description | <b>Background</b>: 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. <b>Methods</b>: 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. <b>Results</b>: Radiology residents’ sensitivity for fracture detection improved significantly with AI support (58% without AI vs. 77% with AI, <i>p</i> < 0.001), while specificity showed minor improvements (77% without AI vs. 79% with AI, <i>p</i> = 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 (<i>p</i> = 0.0156) and increased resident confidence in the findings (<i>p</i> = 0.0013). <b>Conclusion</b>: 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. |
| first_indexed | 2024-04-24T18:23:48Z |
| format | Article |
| id | doaj.art-ae833b09580d4b6ebc20a51351d288b0 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-04-24T18:23:48Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-ae833b09580d4b6ebc20a51351d288b02024-03-27T13:33:16ZengMDPI AGDiagnostics2075-44182024-03-0114659610.3390/diagnostics14060596AI-Assisted X-ray Fracture Detection in Residency Training: Evaluation in Pediatric and Adult Trauma PatientsMathias Meetschen0Luca Salhöfer1Nikolas Beck2Lennard Kroll3Christoph David Ziegenfuß4Benedikt Michael Schaarschmidt5Michael Forsting6Shamoun Mizan7Lale Umutlu8René Hosch9Felix Nensa10Johannes Haubold11Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, GermanyDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, GermanyDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, GermanyDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, GermanyDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, GermanyDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, GermanyDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, GermanyDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, GermanyDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, GermanyDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, GermanyDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, GermanyDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany<b>Background</b>: 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. <b>Methods</b>: 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. <b>Results</b>: Radiology residents’ sensitivity for fracture detection improved significantly with AI support (58% without AI vs. 77% with AI, <i>p</i> < 0.001), while specificity showed minor improvements (77% without AI vs. 79% with AI, <i>p</i> = 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 (<i>p</i> = 0.0156) and increased resident confidence in the findings (<i>p</i> = 0.0013). <b>Conclusion</b>: 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.https://www.mdpi.com/2075-4418/14/6/596X-raysfracturesboneartificial intelligencediagnostic imagingquality improvement |
| spellingShingle | Mathias Meetschen Luca Salhöfer Nikolas Beck Lennard Kroll Christoph David Ziegenfuß Benedikt Michael Schaarschmidt Michael Forsting Shamoun Mizan Lale Umutlu René Hosch Felix Nensa Johannes Haubold AI-Assisted X-ray Fracture Detection in Residency Training: Evaluation in Pediatric and Adult Trauma Patients Diagnostics X-rays fractures bone artificial intelligence diagnostic imaging quality improvement |
| title | AI-Assisted X-ray Fracture Detection in Residency Training: Evaluation in Pediatric and Adult Trauma Patients |
| title_full | AI-Assisted X-ray Fracture Detection in Residency Training: Evaluation in Pediatric and Adult Trauma Patients |
| title_fullStr | AI-Assisted X-ray Fracture Detection in Residency Training: Evaluation in Pediatric and Adult Trauma Patients |
| title_full_unstemmed | AI-Assisted X-ray Fracture Detection in Residency Training: Evaluation in Pediatric and Adult Trauma Patients |
| title_short | AI-Assisted X-ray Fracture Detection in Residency Training: Evaluation in Pediatric and Adult Trauma Patients |
| title_sort | ai assisted x ray fracture detection in residency training evaluation in pediatric and adult trauma patients |
| topic | X-rays fractures bone artificial intelligence diagnostic imaging quality improvement |
| url | https://www.mdpi.com/2075-4418/14/6/596 |
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