DEEP LEARNING–ASSISTED FRACTURE DETECTION ON PLAIN RADIOGRAPHS: DIAGNOSTIC ACCURACY, CLINICAL INTEGRATION, AND FUTURE DIRECTIONS
DOI:
https://doi.org/10.31435/ijitss.3(51).2026.6149Keywords:
Deep Learning, Convolutional Neural Networks, Fractures, Bone, Radiography, Diagnostic Imaging, Artificial Intelligence, Clinical Decision Support SystemsAbstract
Introduction and Purpose. Fractures are a major global health burden, and a non-trivial fraction of fractures presenting to emergency and urgent care settings are initially missed on plain radiographs, causing diagnostic delay and avoidable morbidity. This review appraises the current state of deep learning for fracture detection on conventional radiography, addressing standalone accuracy, the effect of algorithmic assistance on readers, and the conditions of clinical integration.
Brief Description of the State of Knowledge. Across recent meta-analyses, pooled sensitivity and specificity for fracture detection on radiographs cluster in the low-to-mid nineties, with summary AUROC approaching 0.97 in the largest synthesis. The literature is unevenly distributed across the skeleton: the wrist and the hip dominate the evidence base, the pediatric elbow is moderately developed, and other regions remain represented by only a handful of studies. Algorithmic assistance lifts reader sensitivity by several percentage points without a specificity penalty, with the largest gains in clinicians who have less musculoskeletal imaging experience.
Summary. Deep learning is mature for narrow fracture-detection tasks in the appendicular skeleton, but not yet for broad deployment claims. External validation across institutions and vendors, prospective evaluation tied to clinical endpoints, and standards-based integration into PACS and reporting infrastructure are the conditions that would move these tools from accuracy benchmarks toward measurable patient benefit. The clearest near-term value lies in decision support that narrows the diagnostic gap for less experienced readers.
References
Wu, A.-M., Bisignano, C., James, S. L., Abady, G. G., Abedi, A., Abu-Gharbieh, E., et al. (2021). Global, regional, and national burden of bone fractures in 204 countries and territories, 1990–2019: A systematic analysis from the Global Burden of Disease Study 2019. The Lancet Healthy Longevity, 2(9), e580–e92.
Yan, J., Li, F., Zhou, J., Ding, Y., Qin, Q., & Jin, C. (2025). The global burden of fractures and its underlying etiologies: Results from and further analysis of the Global Burden of Disease Study 2021. Archives of Osteoporosis, 20(1), 111.
De Laet, C. E., & Pols, H. A. (2000). Fractures in the elderly: Epidemiology and demography. Baillieres Best Pract Res Clin Endocrinol Metab, 14(2), 171–9.
Cordero, D. M., Miclau, T. A., Paul, A. V., Morshed, S., Miclau, T., 3rd, Martin, C., et al. (2020). The global burden of musculoskeletal injury in low and lower-middle income countries: A systematic literature review. OTA Int, 3(2), e062.
Pinto, A., Berritto, D., Russo, A., Riccitiello, F., Caruso, M., Belfiore, M. P., et al. (2018). Traumatic fractures in adults: Missed diagnosis on plain radiographs in the Emergency Department. Acta Biomed, 89(1-s), 111–23.
Ross, A. B., Lee, K. S., Chang, E. Y., Amini, B., Bussell, J. K., Gorbachova, T., et al. (2019). ACR appropriateness criteria(®) acute hip pain-suspected fracture. J Am Coll Radiol, 16(5s), S18–s25.
Aagesen, A. L., & Melek, M. (2013). Choosing the right diagnostic imaging modality in musculoskeletal diagnosis. Prim Care, 40(4), 849–61, viii.
Expert Panel on Musculoskeletal Imaging. (2020). ACR appropriateness criteria® acute trauma to the knee. J Am Coll Radiol, 17(5S), S12–S25.
National Institute for Health and Care Excellence. (2016). Fractures (non-complex): Assessment and management (NICE guideline [NG38]). NICE. https://www.nice.org.uk/guidance/ng38
Vabo, S., Kjerstad, E., Hunskaar, S., Steen, K., Brudvik, C., & Morken, T. (2023). Acute management of fractures in primary care - a cost minimisation analysis. BMC Health Serv Res, 23(1), 1291.
Pedersen, V., Lampart, A., Bingisser, R., & Nickel, C. H. (2020). Accuracy of plain radiography in detecting fractures in older individuals after low-energy falls: Current evidence. Trauma Surg Acute Care Open, 5(1), e000560.
Chellam, W. B. (2016). Missed subtle fractures on the trauma-meeting digital projector. Injury, 47(3), 674–6.
Bruno, M. A., Walker, E. A., & Abujudeh, H. H. (2015). Understanding and confronting our mistakes: The epidemiology of error in radiology and strategies for error reduction. Radiographics, 35(6), 1668–76.
Busby, L. P., Courtier, J. L., & Glastonbury, C. M. (2018). Bias in radiology: The how and why of misses and misinterpretations. Radiographics, 38(1), 236–47.
Bisset, G. S., 3rd, & Crowe, J. (2014). Diagnostic errors in interpretation of pediatric musculoskeletal radiographs at common injury sites. Pediatr Radiol, 44(5), 552–7.
Taves, J., Skitch, S., & Valani, R. (2018). Determining the clinical significance of errors in pediatric radiograph interpretation between emergency physicians and radiologists. Cjem, 20(3), 420–4.
Ackermann, O., Wetter, A., Chelangattucherry, E., Emmanouilidis, I., & Rülander, C. (2011). [Efficacy of x-ray assessment in emergency surgical departments: An evaluation in a level I trauma center]. Unfallchirurg, 114(1), 41–6.
Weber, M. A. (2025). Easily missed pathologies of the musculoskeletal system in the emergency radiology setting. Rofo, 197(3), 277–87.
Kung, J., Melenevsky, Y., Hochman, M., Didolkar, M., Yablon, C., Eisenberg, R., et al. (2013). On-call musculoskeletal radiographs: Discrepancy rates between radiology residents and musculoskeletal radiologists. AJR American journal of roentgenology, 200, 856–9.
Patel, A. G., Pizzitola, V. J., Johnson, C. D., Zhang, N., & Patel, M. D. (2020). Radiologists make more errors interpreting off-hours body CT studies during overnight assignments as compared with daytime assignments. Radiology, 297(2), 374–9.
Li, W., Stimec, J., Camp, M., Pusic, M., Herman, J., & Boutis, K. (2022). Pediatric musculoskeletal radiographs: Anatomy and fractures prone to diagnostic error among emergency physicians. J Emerg Med, 62(4), 524–33.
Takahashi, S., Sakaguchi, Y., Kouno, N., Takasawa, K., Ishizu, K., Akagi, Y., et al. (2024). Comparison of vision transformers and convolutional neural networks in medical image analysis: A systematic review. Journal of Medical Systems, 48(1), 84.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–44.
Zhang, X., Wang, J., Cheng, X., & Li, J. (2025). A review of deep learning-based medical image segmentation. IET Image Processing, 19(1), e70163.
Chen, Q., Liu, A., Zhang, J., Yang, C., & Zhang, Y. (2026). Foundation models in medical imaging: A review. EngMedicine, 3(2), 100123.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., et al. (2017). A survey on deep learning in medical image analysis. Med Image Anal, 42, 60–88.
Tan, M., & Le, Q. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks.
Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. 1800–7 p.
Sarkar, D., Choudhur, P. G., Dutta, A., Sarkar, S., & Mandal, S. N. (2022). A brief journey of convolutional neural networks from 2012 to 2017. Reason-A Technical Journal (Formerly Reason-A Technical Magazine), 20, 84–96.
Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137–49.
Azad, R., Aghdam, E. K., Rauland, A., Jia, Y., Avval, A. H., Bozorgpour, A., et al. (2024). Medical image segmentation review: The success of U-Net. IEEE Trans Pattern Anal Mach Intell, 46(12), 10076–95.
Jha, D., Riegler, M. A., Johansen, D., Halvorsen, P., & Johansen, H. D. (Eds.). (2020). DoubleU-Net: A deep convolutional neural network for medical image segmentation. 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), 28–30 July 2020.
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., et al. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. 9992–10002 p.
Yang, J., Li, C., Zhang, P., Dai, X., Xiao, B., Yuan, L., et al. (2021). Focal attention for long-range interactions in vision transformers. In Proceedings of the 35th International Conference on Neural Information Processing Systems (Article 2297). Curran Associates Inc.
Aburass, S., Dorgham, O., Al Shaqsi, J., Abu Rumman, M., & Al-Kadi, O. (2025). Vision transformers in medical imaging: A comprehensive review of advancements and applications across multiple diseases. Journal of imaging informatics in medicine, 38.
Ma, J., He, Y., Li, F., Han, L., You, C., & Wang, B. (2024). Segment anything in medical images. Nature Communications, 15(1), 654.
Hosseinzadeh Taher, M. R., Haghighi, F., Gotway, M. B., & Liang, J. (2025). Large-scale benchmarking and boosting transfer learning for medical image analysis. Medical Image Analysis, 102, 103487.
Garcea, F., Serra, A., Lamberti, F., & Morra, L. (2023). Data augmentation for medical imaging: A systematic literature review. Computers in Biology and Medicine, 152, 106391.
Müller, D., Soto-Rey, I., & Kramer, F. (2022). Towards a guideline for evaluation metrics in medical image segmentation. BMC Research Notes, 15.
Aggarwal, R., Sounderajah, V., Martin, G., Ting, D. S. W., Karthikesalingam, A., King, D., et al. (2021). Diagnostic accuracy of deep learning in medical imaging: A systematic review and meta-analysis. npj Digital Medicine, 4(1), 65.
Hansen, V., Jensen, J., Kusk, M. W., Gerke, O., Tromborg, H. B., & Lysdahlgaard, S. (2024). Deep learning performance compared to healthcare experts in detecting wrist fractures from radiographs: A systematic review and meta-analysis. European Journal of Radiology, 174, 111399.
Jung, J., Dai, J., Liu, B., & Wu, Q. (2024). Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis. PLOS Digit Health, 3(1), e0000438.
Nowroozi, A., Salehi, M. A., Shobeiri, P., Agahi, S., Momtazmanesh, S., Kaviani, P., et al. (2024). Artificial intelligence diagnostic accuracy in fracture detection from plain radiographs and comparing it with clinicians: A systematic review and meta-analysis. Clin Radiol, 79(8), 579–88.
Yang, S., Yin, B., Cao, W., Feng, C., Fan, G., & He, S. (2020). Diagnostic accuracy of deep learning in orthopaedic fractures: A systematic review and meta-analysis. Clinical Radiology, 75(9), 713.e17-.e28.
Shelmerdine, S. C., White, R. D., Liu, H., Arthurs, O. J., & Sebire, N. J. (2022). Artificial intelligence for radiological paediatric fracture assessment: A systematic review. Insights into Imaging, 13(1), 94.
Ashworth, E., Allan, E., Pauling, C., Laidlow-Singh, H., Arthurs, O. J., & Shelmerdine, S. C. (2025). Artificial intelligence (AI) in radiological paediatric fracture assessment: An updated systematic review. Eur Radiol, 35(9), 5264–86.
Wang, L., Zhang, C., Wang, Y., Yue, X., Liang, Y., & Sun, N. (2025). Development and validation of a deep learning system for the detection of nondisplaced femoral neck fractures. Bioengineering, 12, 466.
Husarek, J., Fuchss, A. M. C., Ruder, T. D., Mougiakakou, S., Exadaktylos, A., Wahedi, K., et al. (2026). Artificial intelligence for fracture detection on computed tomography: A comprehensive systematic review and meta-analysis of diagnostic test accuracy in non-commercial and commercial solutions. Emergency Radiology, 33(2), 351–75.
Wright, A. A., Hegedus, E. J., Lenchik, L., Kuhn, K. J., Santiago, L., & Smoliga, J. M. (2016). Diagnostic accuracy of various imaging modalities for suspected lower extremity stress fractures: A systematic review with evidence-based recommendations for clinical practice. Am J Sports Med, 44(1), 255–63.
Kulig, L., Malec, J. A., Wiercioch, E., Winiarska, Z., Woźniak, W. M., Zapalska, M., et al. (2026). Applications of artificial intelligence in opportunistic screening and diagnostic imaging of osteoporotic fractures: A systematic review. Journal of Education, Health and Sport, 89, 70382.
Meshaka, R., Pinto Dos Santos, D., Arthurs, O. J., Sebire, N. J., & Shelmerdine, S. C. (2022). Artificial intelligence reporting guidelines: What the pediatric radiologist needs to know. Pediatr Radiol, 52(11), 2101–10.
Anderson, P., Baum, G., Keathley, N., Sicular, S., Venkatesh, S., Sharma, A., et al. (2022). Deep learning assistance closes the accuracy gap in fracture detection across clinician types. Clinical Orthopaedics & Related Research, Publish Ahead of Print.
Oppenheimer, J., Lüken, S., Hamm, B., & Niehues, S. M. (2023). A prospective approach to integration of AI fracture detection software in radiographs into clinical workflow. Life, 13(1), [223 p.].
Herpe, G., Nelken, H., Vendeuvre, T., Guenezan, J., Giraud, C., Mimoz, O., et al. (2024). Effectiveness of an artificial intelligence software for limb radiographic fracture recognition in an emergency department. Journal of Clinical Medicine, 13(18), [5575 p.].
Chan, H. Y., Tang, Y. P., Wong, Z. Y., Koh, S. H., Nickalls, O., Steven, W., et al. (2025). Artificial Intelligence (AI) in a Singaporean Emergency Department: Detecting fractures and reducing recalls. Med J Malaysia, 80(4), 462–5.
Loeffen, D. V., Zijta, F. M., Boymans, T. A., Wildberger, J. E., & Nijssen, E. C. (2025). AI for fracture diagnosis in clinical practice: Four approaches to systematic AI-implementation and their impact on AI-effectiveness. European Journal of Radiology, 187, 112113.
Lo Mastro, A., Grassi, E., Berritto, D., Russo, A., Reginelli, A., Guerra, E., et al. (2025). Artificial intelligence in fracture detection on radiographs: A literature review. Japanese Journal of Radiology, 43(4), 551–85.
Kocak, B., & Cuocolo, R. (2026). Human-AI interaction and collaboration in radiology: From conceptual frameworks to responsible implementation. Diagn Interv Radiol.
Tejani, A., Cook, T., Hussain, M., Schmidt, T., O'Donnell, K., & Arzen, S. (2024). Integrating and adopting AI in the radiology workflow: A primer for standards and integrating the healthcare enterprise (IHE) profiles. Radiology, 311.
Theriault-Lauzier, P., Corbin, D., Tastet, O., Langlais, E. L., Taji, B., Kang, G., et al. (2024). A responsible framework for applying artificial intelligence on medical images and signals at the point of care: The PACS-AI platform. Canadian Journal of Cardiology, 40(10), 1828–40.
Sosna, J., Joskowicz, L., & Saban, M. (2025). Navigating the AI landscape in medical imaging: A critical analysis of technologies, implementation, and implications. Radiology, 315(3), e240982.
Verwey, J., Zwart, B., M, I. J., Visser, J. J., & Sülz, S. (2026). Factors influencing AI acceptance in radiology: A systematic review across the radiology workflow. Eur Radiol, 36(5), 3687–96.
Jungmann, F., Jorg, T., Hahn, F., Pinto Dos Santos, D., Jungmann, S. M., Düber, C., et al. (2021). Attitudes toward artificial intelligence among radiologists, IT specialists, and industry. Acad Radiol, 28(6), 834–40.
Najjar, R. (2023). Redefining radiology: A review of artificial intelligence integration in medical imaging. Diagnostics, 13(17), [2760 p.].
Larrazabal, A. J., Nieto, N., Peterson, V., Milone, D. H., & Ferrante, E. (2020). Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proc Natl Acad Sci U S A, 117(23), 12592–4.
Bhandari, A. (2024). Revolutionizing radiology with artificial intelligence. Cureus, 16(10).
Yan, Y. Y., & Peh, W. C. G. (Eds.). (2025). Technical errors and artifacts causing mistakes in musculoskeletal imaging. Thieme Medical Publishers, Inc.
Nguyen, T., Maarek, R., Hermann, A.-L., Kammoun, A., Marchi, A., Khelifi-Touhami, M. R., et al. (2022). Assessment of an artificial intelligence aid for the detection of appendicular skeletal fractures in children and young adults by senior and junior radiologists. Pediatric Radiology, 52(11), 2215–26.
Nair, A., Ong, W., Lee, A., Leow, N. W., Makmur, A., Ting, Y. H., et al. (2025). Enhancing radiologist productivity with artificial intelligence in magnetic resonance imaging (MRI): A narrative review. Diagnostics, 15(9), 1146.
Kutbi, M. (2024). Artificial intelligence-based applications for bone fracture detection using medical images: A systematic review. Diagnostics, 14(17), 1879.
Masciocchi, C., Conti, L., D’Orazio, F., Conchiglia, A., Lanni, G., & Barile, A. (2012). Errors in musculoskeletal MRI. In Errors in radiology (pp. 209–17). Springer.
Choi, Y. Y., Lee, J. S., & Yang, S.-O. (2017). Musculoskeletal lesions: Nuclear medicine imaging pitfalls. In Pitfalls in musculoskeletal radiology (pp. 951–76). Springer.
Nam, Y., Kim, D. Y., Kyung, S., Seo, J., Song, J. M., Kwon, J., et al. (2025). Multimodal large language models in medical imaging: Current state and future directions. Korean J Radiol, 26(10), 900–23.
Huang, S. C., Jensen, M., Yeung-Levy, S., Lungren, M. P., Poon, H., & Chaudhari, A. S. (2025). A systematic review and implementation guidelines of multimodal foundation models in medical imaging. Res Sq.
Seo, I., Bae, E., Jeon, J.-Y., Yoon, Y.-S., & Cha, J. (2024). The era of foundation models in medical imaging is approaching : a scoping review of the clinical value of large-scale generative AI applications in radiology.
Lee, J. G., Kyung, S., & Kim, N. (2026). Generative AI for developing foundation models in radiology and imaging: Engineering perspectives. Biomed Eng Lett, 16(1), 1–10.
Koutsoubis, N., Waqas, A., Yilmaz, Y., Ramachandran, R. P., Schabath, M. B., & Rasool, G. (2025). Privacy-preserving federated learning and uncertainty quantification in medical imaging. Radiology: Artificial Intelligence, 7(4), e240637.
Sun, M., Yang, Z., Huang, Y., Yu, H., Chen, Y., Qi, S., et al. (2025). Federated learning for large models in medical imaging: A comprehensive review. arXiv preprint arXiv:250820414.
Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., et al. (2020). The future of digital health with federated learning. npj Digital Medicine, 3(1), 119.
Salahuddin, Z., Woodruff, H. C., Chatterjee, A., & Lambin, P. (2022). Transparency of deep neural networks for medical image analysis: A review of interpretability methods. Comput Biol Med, 140, 105111.
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Copyright (c) 2026 Monika Szlachta-Gubernat, Nicol Baran, Marta Bajkowska-Piterak, Adrianna Buż, Przemysław Piterak, Damian Danilczuk, Wiktor Adamiec, Jerzy Buszko, Michał Dyś
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