article · International Journal of Intelligent Systems
Detecting pelvis fractures swiftly and accurately is critical for managing traumatic injuries. Reviewing these X-ray scans manually demands substantial time and expertise, which presents a challenge in healthcare settings facing shortages of trained radiology personnel. To address this issue, a deep learning diagnostic system has been developed to classify pelvis fractures using an explainable artificial intelligence framework. The model was trained and evaluated on an imaging dataset comprising 876 X-ray scans, which included 472 examples of fractured pelvises and 404 normal cases. Across testing, the system demonstrated consistent diagnostic performance, achieving 98.5 percent accuracy, 98.5 percent sensitivity, 98.5 percent specificity, and 98.5 percent precision. By incorporating explainability into automated image analysis, the approach aims to assist clinicians in reaching reliable, error-free diagnostic decisions during medical evaluations.
Pelvis fractures require fast and reliable diagnosis to guide treatment decisions. In medical centres with a shortage of trained radiologists, reviewing complex trauma radiographs can delay care. Using explainable artificial intelligence to assist medical staff could reduce diagnostic errors, shorten assessment times, and make clinical decision-making more trustworthy in emergency and trauma settings.
This software tool could serve as a clinical decision-support system for hospital emergency departments and radiology units facing specialist shortages. Because the model has been developed and evaluated on a retrospective dataset of 876 images, the technology remains at an applied research stage. Transitioning to real-world clinical use would require prospective clinical validation across diverse hospital imaging systems and integration into picture archiving and communication workflows.
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Pelvis fracture detection is vital for diagnosing patients and making treatment decisions for traumatic pelvis injuries. Computer‐aided diagnostic approaches have recently become popular for assisting doctors in disease diagnosis, making their conclusions more trustworthy and error‐free. Inspecting X‐ray images with fractures needs a lot of time from experienced physicians. However, there is a lack of inexperienced radiologists in many hospitals to deal with these images. Therefore, this study presents an accurate computer‐aided‐diagnosing system based on deep learning for detecting pelvis fractures. In this research, we construct an explainable artificial intelligence (XAI) framework for pelvis fracture classification. We used a dataset containing 876 X‐ray images (472 pelvis fractures and 404 normal images) to train the model. The obtained results are 98.5%, 98.5%, 98.5%, and 98.5% for accuracy, sensitivity, specificity, and precision.
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DOI: 10.1155/2023/3281998
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