Skeletal Fracture Detection with Deep Learning: A Comprehensive Review

Zhihao Su; Afzan Adam; Mohammad Faidzul Nasrudin; Masri Ayob; Gauthamen Punganan

doi:10.3390/diagnostics13203245

journal article Open Access Oct 18, 2023

Skeletal Fracture Detection with Deep Learning: A Comprehensive Review

Zhihao Su

Afzan Adam

Mohammad Faidzul Nasrudin

Masri Ayob

Gauthamen Punganan

Diagnostics Vol. 13 No. 20 pp. 3245 · MDPI AG

View at Publisher Save 10.3390/diagnostics13203245

Abstract

Deep learning models have shown great promise in diagnosing skeletal fractures from X-ray images. However, challenges remain that hinder progress in this field. Firstly, a lack of clear definitions for recognition, classification, detection, and localization tasks hampers the consistent development and comparison of methodologies. The existing reviews often lack technical depth or have limited scope. Additionally, the absence of explainable facilities undermines the clinical application and expert confidence in results. To address these issues, this comprehensive review analyzes and evaluates 40 out of 337 recent papers identified in prestigious databases, including WOS, Scopus, and EI. The objectives of this review are threefold. Firstly, precise definitions are established for the bone fracture recognition, classification, detection, and localization tasks within deep learning. Secondly, each study is summarized based on key aspects such as the bones involved, research objectives, dataset sizes, methods employed, results obtained, and concluding remarks. This process distills the diverse approaches into a generalized processing framework or workflow. Moreover, this review identifies the crucial areas for future research in deep learning models for bone fracture diagnosis. These include enhancing the network interpretability, integrating multimodal clinical information, providing therapeutic schedule recommendations, and developing advanced visualization methods for clinical application. By addressing these challenges, deep learning models can be made more intelligent and specialized in this domain. In conclusion, this review fills the gap in precise task definitions within deep learning for bone fracture diagnosis and provides a comprehensive analysis of the recent research. The findings serve as a foundation for future advancements, enabling improved interpretability, multimodal integration, clinical decision support, and advanced visualization techniques.

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Details

Published: Oct 18, 2023
Vol/Issue: 13(20)
Pages: 3245
License: View

Authors

Z

Zhihao Su

Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia

A

Afzan Adam

Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia

M

Mohammad Faidzul Nasrudin

Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia

M

Masri Ayob

Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia

G

Gauthamen Punganan

Department of Orthopedics and Traumatology, Hospital Raja Permaisuri Bainun, Ipoh 30450, Perak, Malaysia

Funding

Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia Award: FRGS/1/2022/ICT02/UKM/02/5

Malaysian Ministry of Higher Education (MoHE) Award: FRGS/1/2022/ICT02/UKM/02/5

Cite This Article

Zhihao Su, Afzan Adam, Mohammad Faidzul Nasrudin, et al. (2023). Skeletal Fracture Detection with Deep Learning: A Comprehensive Review. Diagnostics, 13(20), 3245. https://doi.org/10.3390/diagnostics13203245

Skeletal Fracture Detection with Deep Learning: A Comprehensive Review

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