AI in Fracture Detection

Trauma care hinges upon quick and precise fracture detection to alleviate patient pain and prevent grim complications. This is where artificial intelligence (AI) has become a transformative tool that improves the diagnostic process and enables a seamless clinical workflow.

Numerous studies highlight AI's ability to detect minute fractures that may go unnoticed. For instance, a study presented at RSNA 2023 found that AI was more effective than radiologists at detecting commonly missed fractures, suggesting that a radiologist associated with AI approach could improve detection¹. National standards are also beginning to acknowledge AI's role in diagnostic workflows. AI has shown utility in urgent care settings for fracture detection, as evidenced by the UK's National Institute for Health and Care Excellence (NICE). NICE endorsed Rayvolve®, which encompasses four verticals: AZtrauma, AZchest, AZmeasure, and AZboneage, for its ability to reduce diagnostic errors (nice.org.uk)².

AI-led fracture detection is just a starting point, marking a new threshold for improving diagnostic accuracy and accelerating the patients' healthcare journey. This article examines how AI fracture detection works and its advantages for clinical settings.

Using AI for Fracture Detection in Medical Imaging

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AZtrauma, fully certified MDaaS software by AZmed, uses sophisticated algorithms to analyze and detect bone fractures, dislocations, and joint effusions in medical images. Utilizing advanced deep learning and computer vision technologies, AZtrauma significantly enhances the productivity and precision of fracture diagnosis for radiologists.

As an aid to diagnosis, AZtrauma offers several advantages over traditional diagnostics:

Advanced Machine Learning Techniques

‍High-quality support is provided by Convolutional Neural Networks (CNNs), visual transformers, and other advanced deep learning models, which power the software. AZmed’s Rayvolve® AI Suite is trained on one of the largest datasets of X-ray images (>15 million) with meticulous annotations by board-certified expert radiologists, enabling the AI to identify complex fracture patterns accurately. This exhaustive training transforms AZtrauma into a powerful resource. The following is an outline of how the model was developed:

• Creation of Dataset: The first step is to create a massive dataset of X-ray images containing fractures and otherwise.

• Annotation: Expert radiologists label fractures in images which serves as a reliable basis for training.

• Model Training: Dependent on the annotation data, the algorithms learn to differentiate fracture patterns via iterations between the two.

• Validation: Checking the accuracy and reliability of the model using new or previously untested images.

Advantages of AI Fracture Detection

AZtrauma utilizes algorithms to analyze medical images, providing improved detection of subtle fractures and lower false positives and false negatives, while also ensuring consistent readings across cases. It is this speed and accuracy that allows for the faster prioritization of critical cases, minimizing wait times and leading to on-time treatment. AZtrauma automates routine tasks, relieving radiologists of mundane workloads, allowing them to concentrate on more complex cases, reducing burnout, and improving radiology department throughput.

At RSNA 2023, Dr. Sean Raj, Chief Innovation Officer of SimonMed, presented a study validating the performance of Rayvolve®³. In this study, examination data were extracted from 2022 (without AI) and 2023 (with AI), comprising 159,601 and 170,703 examinations, respectively. The results indicated improved detection rates, with fracture prevalence of 10.4% without AI and 11.8% with AI. The mean report turnaround time (TAT) for fracture-positive cases was significantly reduced from 48 hours in the absence of AI to 8.3 hours with AI, a mean reduction of 39 hours.

Implementation in Healthcare Settings

AZmed’s approach is to provide in-depth training to healthcare professionals on basic AI principles, direct experience with AZtrauma software, and training on how to read AI-assistance diagnostic reports and how to use these insights in clinical decision-making. With different needs in infrastructure and security, AZmed offers three deployment models: On-premise & VPN, VPN, and on-premise model. Each model is designed to secure patient data and prevent data leaks, providing and managing secure network systems and controlled data storage solutions. This flexibility allows healthcare providers to implement the option that best fits their existing infrastructure and security policies.

Integration with Existing Medical Imaging Systems

AZtrauma seamlessly incorporates into hospital workflows, connecting directly into medical imaging systems for AI-powered fracture detection:

Real-time Fracture Identification Process

When a new X-ray image is taken:

1. The AI system receives the image
2. The trained model analyzes the image for fracture patterns
3. Results are generated within seconds, highlighting potential fractures
4. Radiologists review AI-assisted findings on second capture for final interpretation

Conclusion

Using deep learning algorithms that are trained on vast datasets, AZtrauma analyzes X-rays in seconds to detect bone fractures which can sometimes be difficult to interpret. The benefits of this technology are obvious, with better diagnostic accuracy, faster results, and more potential cost-saving for health systems.

Despite the promise of AI potential in fracture detection, overcoming issues of data quality, algorithmic bias, and integration into existing healthcare systems is critical. AZmed is determined to tackle these challenges to bring AZtrauma retaining its high performance and reliability during clinical usages. With ongoing research it is expected that timely and future technology will further optimize the agent of AI for fracture diagnosis, broaden its applicability and further supplement patient care.

‍To learn more about the AZtrauma solution, schedule a demo with one of our AI experts or visit us on booth #4567 at the RSNA 2024 conference.

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¹Erik L. Ridley. AI algorithm helps to spot overlooked fractures. RSNA. 2023

²AZmed. NHS to Use AI Technology for Faster, More Accurate Fracture Detection. AZmed website. 2024

³ Will Morton. AI cuts time for radiologists reporting fractures on x-rays. RSNA. 2023

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US - Medical device Class II according to the 510K clearance. Rayvolve is a computer-assisted detection and diagnosis (CAD) software device to assist radiologists and emergency physicians in detecting fractures during the review of radiographs of the musculoskeletal system. Rayvolve is indicated for adult and pediatric population (≥ 2 years). EU - Medical Device Class IIa in Europe (CE 2797) in compliance with the Medical Device Regulation (2017/745). Rayvolve is a computer-aided diagnosis tool, intended to help radiologists and emergency physicians to detect and localize abnormalities on standard X-rays. Caution: The data mentioned are sourced from internal documents, internal studies and literature reviews. This material with associated pictures is non-contractual. It is for distribution to Health Care Professionals only and should not be relied upon by any other persons. Testimonial reflects the opinion of Health Care Professionals, not the opinion of AZmed. Carefully read the instructions for use before use. Please refer to our Privacy policy on our website. AZboneage is an uncertified feature currently under development. For more information, please contact contact@azmed.co. AZmed 10 rue d’Uzès, 75002 Paris - www.azmed.co - RCS Laval B 841 673 601© 2024 AZmed – All rights reserved. MM-24-23