首页|Department of Orthopedics Reports Findings in Artificial Intelligence (Developme nt of a diagnostic support system for distal humerus fracture using artificial i ntelligence)
Department of Orthopedics Reports Findings in Artificial Intelligence (Developme nt of a diagnostic support system for distal humerus fracture using artificial i ntelligence)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting from Nagpur, India, by NewsRx journalists, research stated, "AI has shown promise in automating and im proving various tasks, including medical image analysis. Distal humerus fracture s are a critical clinical concern that requires early diagnosis and treatment to avoid complications." The news correspondents obtained a quote from the research from the Department o f Orthopedics, "The standard diagnostic method involves X-ray imaging, but subtl e fractures can be missed, leading to delayed or incorrect diagnoses. Deep learn ing, a subset of artificial intelligence, has demonstrated the ability to automa te medical image analysis tasks, potentially improving fracture identification a ccuracy and reducing the need for additional and cost-intensive imaging modaliti es (Schwarz et al. 2023). This study aims to develop a deep learning-based diagn ostic support system for distal humerus fractures using conventional X-ray image s. The primary objective of this study is to determine whether deep learning can provide reliable image-based fracture detection recommendations for distal hume rus fractures. Between March 2017 and March 2022, our tertiary hospital's PACS d ata were evaluated for conventional radiography images of the anteroposterior (A P) and lateral elbow for suspected traumatic distal humerus fractures.The data set consisted of 4931 images of patients seven years and older, after excluding paediatric images below seven years due to the absence of ossification centres. Two senior orthopaedic surgeons with 12 + years of experience reviewed and label led the images as fractured or normal. The data set was split into training sets (79.88%) and validation tests (20.1%). Image pre-proc essing was performed by cropping the images to 224 x 224 pixels around the capit ellum, and the deep learning algorithm architecture used was ResNet18. The deep learning model demonstrated an accuracy of 69.14% in the validatio n test set, with a specificity of 95.89% and a positive predictive value (PPV) of 99.47%. However, the sensitivity was 61.49% , indicating that the model had a relatively high false negative rate. ROC analy sis showed an AUC of 0.787 when deep learning AI was the reference and an AUC of 0.580 when the most senior orthopaedic surgeon was the reference. The performan ce of the model was compared with that of other orthopaedic surgeons of varying experience levels, showing varying levels of diagnostic precision. The developed deep learningbased diagnostic support system shows potential for accurately di agnosing distal humerus fractures using AP and lateral elbow radiographs. The mo del's specificity and PPV indicate its ability to mark out occult lesions and ha s a high false positive rate."
NagpurIndiaAsiaArtificial Intellig enceEmerging TechnologiesHealth and MedicineMachine LearningOrthopedics
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