Digital Preoperative Planning for High Tibial Osteotomy using 2D Medical Imaging

Awang, Norazimah; Ahmad, Faudzi; Rahaman, Rosnita A.; Sulaiman, Riza; Shapi’i, Azrulhizam; Rashid, Abdul

doi:10.14569/ijacsa.2021.0121086

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…Foremost, we should try to add the interpretability of deep learning algorithms to provide doctors with sound and logical principles or the mathematics behind the algorithm's judgment [4,91]. Additionally, we should try to process multi-modal clinical information including X-ray images, CT images, patient process data in hospitals, and some baseline clinical information (age, sex, body mass index, glucocorticoid use, and secondary osteoporosis) for personalized treatment and therapeutic schedule recommendation [92][93][94][95][96][97].…”

Section: Solutions Following the Three Issuesmentioning

confidence: 99%

Skeletal Fracture Detection with Deep Learning: A Comprehensive Review

Su,

Adam,

Nasrudin

et al. 2023

Diagnostics

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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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