Imaging Pearls ❯ Trauma ❯ Artificial Intelligence (AI)
|
-- OR -- |
|
- “Artificial intelligence (AI) and its recent increasing healthcare integration has created both new opportunities and challenges in the practice of radiology and medical imaging. Recent advancements in AI technology have allowed for more workplacenefficiency, higher diagnostic accuracy, and overall improvements in patient care. Limitations of AI such as data imbalances, the unclear nature of AI algorithms, and the challenges in detecting certain diseases make it difficult for its widespread adoption. This review article presents cases involving the use of AI models to diagnose intracranial hemorrhage, spinal fractures,and rib fractures, while discussing how certain factors like, type, location, size, presence of artifacts, calcification, and post-surgical changes, affect AI model performance and accuracy. While the use of artificial intelligence has the potential to improve the practice of emergency radiology, it is important to address its limitations to maximize its advantages while ensuring the safety of patients overall.”
Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization.
Fathi M, Eshraghi R, Behzad S, et al..
Emerg Radiol. 2024 Dec;31(6):887-901. - 1. The recent developments of AI technologies create significant potential for increasing efficiency in radiology, improving the diagnostic accuracy of radiologists using various imaging modalities, and most importantly, bettering overall patient care.
2. To encourage the use of AI in radiology, challenges of its use such as the unclear nature of AI algorithms, present data imbalances, and limitations in detecting specific diseases, must be further researched and addressed.
3. This review article highlights the need of understanding factors that affect AI model performance, such as type, location, size, artifacts, calcifications, and post-surgical changes, in order to efficiently and accurately diagnose conditions like intracranial hemorrhage, spinal fractures, and rib fractures in the context of emergency radiology.
Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization.
Fathi M, Eshraghi R, Behzad S, et al..
Emerg Radiol. 2024 Dec;31(6):887-901. - This review article highlights the need of understanding factors that affect AI model performance, such as type, location, size, artifacts, calcifications, and post-surgical changes, in order to efficiently and accurately diagnose conditions like intracranial hemorrhage, spinal fractures, and rib fractures in the context of emergency radiology.
Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization.
Fathi M, Eshraghi R, Behzad S, et al..
Emerg Radiol. 2024 Dec;31(6):887-901. - “Intracranial Hemorrhage (ICH) is a common and serious medical condition that needs quick diagnosis as patients who are left unattended deteriorate rapidly within just a few hours. The gold standard imaging modality for this condition’s diagnosis is a CT scan without contrast. Due to the increase in imaging demands, a radiologist may be overwhelmed with many other emergent imaging ordered, delaying the diagnosis of cerebral hemorrhage on CT scans. Additionally, it is plausible that increases in planned CT scans within late-hour shifts may jeopardize the complete accuracy of the radiologists’ reports.”
Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization.
Fathi M, Eshraghi R, Behzad S, et al..
Emerg Radiol. 2024 Dec;31(6):887-901. - In a study by Kundisch et al., the radiological reports using AIDOC, which is a commercially available software approved by the FDA for the detection of ICH, was compared to the reports of a neuroradiologist. The neuroradiologist was deemed the gold standard in this study. The reported acceptable sensitivity and specificity of AIDOC in identifying ICH was shown to be roughly over 90%. The study also showed that on call shifts accounted for 85% of missed ICHs from reading radiologists. With the increase of emergency head CTs performed at the ED during night shifts, it is sensible to find solutions to lower stress and errors during these times to avoid negative patient outcomes. However, AI models do have limitations: while radiologists can identify ICHs by using their practical knowledge and experience,
Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization.
Fathi M, Eshraghi R, Behzad S, et al..
Emerg Radiol. 2024 Dec;31(6):887-901. - There is great promise and potential for the integration of artificial intelligence into modern clinical practice, especially in emergency situations. In this paper, we showcased various factors that may affect the performance of AI models used in diagnostic imaging of emergency pathology. It should be known that AI should be used as a diagnostic tool rather than as a complete replacement for skillful and experienced radiologists. As AI technologies have been increasingly introduced into the healthcare setting, it is critical for clinicians and radiologists to understand the process of how AI systems are developed and organized. This includes understanding the present strengths, weaknesses, and limitations of such models. By doing so, we can more readily integrate artificial intelligence into the clinical setting and reap the benefits of such technology in our daily practice. Ultimately, radiologists and AI working together has the potential to advance the field of radiology while significantly improving patient care and healthcare outcomes.
Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization.
Fathi M, Eshraghi R, Behzad S, et al..
Emerg Radiol. 2024 Dec;31(6):887-901. - ”Cinematic rendering (CR) is an innovative imaging technique that has emerged as a powerful tool in forensic radiology, offering enhanced visualization capabilities for the analysis of skeletal trauma. This article explores the application of CR in the setting of forensic imaging in skeletal injury and its impact on the field of skeletal trauma analysis. The study discusses the advantages of CR over traditional imaging methods and presents a comprehensive overview of the techniques, materials, and results associated with its application. The results indicate that CR has the potential to revolutionize forensic imaging, providing forensic experts with a highly accurate and detailed depiction of skeletal trauma for improved forensic analysis.”
3D CT Cinematic Rendering: Transforming Forensic Imaging with Enhanced Visualization for Skeletal Trauma Analysis.
Wallner-Essl WM, Pfaff JAR, Grimm J.
Int J Forens Sci Res. 2024; 1(1): 1-5. - “Despite its advantages, CR has limitations. Computational complexity may hinder real-time visualization, and accuracy in representing structures can be imperfect due to factors like noise and limitations in resolution. Interpretation could be subjective, impacting standardization. Accessibility might be limited due to specialized hardware and software requirements. Ongoing research aims to address these challenges, potentially leading to broader adoption of CR in clinical practice.”
3D CT Cinematic Rendering: Transforming Forensic Imaging with Enhanced Visualization for Skeletal Trauma Analysis.
Wallner-Essl WM, Pfaff JAR, Grimm J.
Int J Forens Sci Res. 2024; 1(1): 1-5. - “CT cinematic rendering is a relatively new technique that leverages advanced algorithms and computational power to create highly realistic visualizations from CT scan data. By simulating the interaction of light with tissues, cinematic rendering produces visually immersive images that closely resemble real-world appearances. It captures fine details such as surface textures, shading, and subtle changes in tissue density, resulting in lifelike renderings that aid in the understanding and interpretation of anatomical structures. CT cinematic rendering provides enhanced depth perception, allowing for interactive exploration of the scanned anatomy from multiple angles and perspectives.”
3D CT Cinematic Rendering: Transforming Forensic Imaging with Enhanced Visualization for Skeletal Trauma Analysis.
Wallner-Essl WM, Pfaff JAR, Grimm J.
Int J Forens Sci Res. 2024; 1(1): 1-5.
- One of the major limitations of the AI system highlighted by this study is its susceptibility to false negative and false positive findings. Of the 71 fractures identified by CT, 13 were missed by the AI solution, while the radiologist missed only six fractures [2]. False negatives are of particular concern in the clinical setting, because missed fractures can lead to delayed or inappropriate treatment, increasing the risk of complications and potentially worsening patient outcomes. Moreover, the AI solution also generated 15 false positive findings, which can result in unnecessary further imaging or treatment, increasing patient anxiety and healthcare costs. This result underscore the need for sufficient training data, such as a low prevalence of rare fractures, which is a constant issue for AI applications [3]. As the study suggests, AI should be considered as a complementary tool rather than a replacement tool for human expertise, at least until further fine tuning can address these shortcomings. Future studies could focus on the combination of radiologists and AI tools, which may be a good balance to maximize the accuracy of bone fracture detection.
Artificial intelligence for bone fracture detection: A promising tool but no substitute for human expertise
Daphne Guenouna, Mickael Tordjman
Diagnostic and Interventional Imaging 2024 (in press) - In their study, Pastor et al. compared the diagnostic performance of a deep learning algorithm (Rayvolve®, AZmed) with that of experienced radiologists in detecting bone fractures in adult patients using radiographs [2]. With 94 patients included, this study evaluated both the sensitivity and specificity of the AI solution and human radiologists, using computed tomography (CT) as ground truth. The results demonstrated that while the AI solution performed reasonably well, it was consistently outperformed by the radiologists. The AI solution achieved a sensitivity (i.e., the ability to correctly identify fractures) of 82 % and a specificity (i.e., the ability to correctly rule out fractures in patients without fractures) of 69 % [2]. By comparison, the radiologists achieved a sensitivity of 92 % and a specificity of 88 %, demonstrating that human expertise remains critical in the clinical setting.
Artificial intelligence for bone fracture detection: A promising tool but no substitute for human expertise
Daphne Guenouna, Mickael Tordjman
Diagnostic and Interventional Imaging 2024 (in press) - “ AI tools have varying levels of performance, with some favoring sensitivity and others favoring accuracy, depending on the specific goal to achieve. Future studies should focus on improving the sensitivity and specificity of AI solutions, particularly in detecting fractures in challenging anatomical regions such as the hands, wrists, and feet, which are often missed by both AI and radiologists. As the technology continues to evolve, the role of AI in healthcare will undoubtedly grow. However, the study by Pastor et al. underscores the need for caution in adopting AI without first addressing its limitations. By maintaining a balance between technological innovation and human expertise, we can ensure that AI enhances, rather than diminishes, the quality of patient care. ”
Artificial intelligence for bone fracture detection: A promising tool but no substitute for human expertise
Daphne Guenouna, Mickael Tordjman
Diagnostic and Interventional Imaging 2024 (in press)