Imaging Pearls ❯ Trauma ❯ Artificial Intelligence (AI)
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- 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)