Figure
Compilation of published articles focusing on automated fracture detection in the hand or wrist using ML models
Source publication
Artificial intelligence (AI) is currently utilized across numerous medical disciplines. Nevertheless, despite its promising advancements, AI’s integration in hand surgery remains in its early stages and has not yet been widely implemented, necessitating continued research to validate its efficacy and ensure its safety. Therefore, this review aims t...
Contexts in source publication
Context 1
... primary search was conducted on August 6, 2024, utilizing the following keywords in articles' titles and abstracts: "Artificial Intelligence" OR "Computer-Aid" OR "Machine learning" OR "ChatGPT" along with specific terms related to hand surgery such as "hand surgery" OR "wrist surgery" OR "plastic surgery" OR "wrist" OR "finger" OR "Peripheral Nerve Surgery" OR "scaphoid" OR "carpal bone" OR "thumb". A variety of Table 1. Inclusion and exclusion criteria of the systematic review Inclusion criteria Exclusion criteria Articles on the integration of AI in hand and wrist surgery Articles published between 2014-2024 Letters to the editor Systematic reviews Articles not related to hand or wrist surgery Languages other than English Articles related to prosthetic hand or arm AI: Artificial intelligence. ...Context 2
... compilation of published articles focusing on AI-driven fracture detection in the hand or wrist (including the distal radius and ulna, carpal bones, and fingers) is presented in Table 2. [29] Distal radius or distal ulna 542 WFD-C, deep-learning-based object detection model 0.864 -- [31] Humerus or wrist 10,558 [44] Scaphoid 356 VGG16, VGG19, RN50, RN101, RN152, DN121, DN169, DN201, Inv, ENB0 RN101: 0.950 DN201: 0.910 RN101: 0.889 DN201: 0.944 RN101: 0.889 DN201: 0.861 AI as an adjuvant in ultrasound fracture detection While X-rays remain the gold standard for diagnosing distal radius fractures, the use of ultrasound (US) in emergency departments (ED) has gained popularity due to its accessibility, minimal training requirements, and capacity to assess surrounding soft tissues. ...Citations
... However, interpreting X-ray images can be challenging due to variations in image quality, subtle fracture patterns, and inter-observer variability among clinicians. Conventional diagnostic methods and standalone artificial intelligence (AI) systems have shown promise in fracture detection but often lack the robustness and reproducibility required for widespread clinical deployment [6][7][8][9][10][11][12]. These limitations highlight the need for advanced approaches that integrate complementary methodologies to enhance diagnostic performance. ...
Objective
The aim of this study was to develop a hybrid diagnostic framework integrating radiomic and deep features for accurate and reproducible detection and classification of wrist fractures using X-ray images.
Materials and Methods
A total of 3,537 X-ray images, including 1,871 fracture and 1,666 non-fracture cases, were collected from three healthcare centers. Radiomic features were extracted using the PyRadiomics library, and deep features were derived from the bottleneck layer of an autoencoder. Both feature modalities underwent reliability assessment via Intraclass Correlation Coefficient (ICC) and cosine similarity. Feature selection methods, including ANOVA, Mutual Information (MI), Principal Component Analysis (PCA), and Recursive Feature Elimination (RFE), were applied to optimize the feature set. Classifiers such as XGBoost, CatBoost, Random Forest, and a Voting Classifier were used to evaluate diagnostic performance. The dataset was divided into training (70%) and testing (30%) sets, and metrics such as accuracy, sensitivity, and AUC-ROC were used for evaluation.
Results
The combined radiomic and deep feature approach consistently outperformed standalone methods. The Voting Classifier paired with MI achieved the highest performance, with a test accuracy of 95%, sensitivity of 94%, and AUC-ROC of 96%. The end-to-end model achieved competitive results with an accuracy of 93% and AUC-ROC of 94%. SHAP analysis and t-SNE visualizations confirmed the interpretability and robustness of the selected features.
Conclusions
This hybrid framework demonstrates the potential for integrating radiomic and deep features to enhance diagnostic performance for wrist and forearm fractures, providing a reliable and interpretable solution suitable for clinical applications.
