YOLOv8 Algorithm-aided Detection of Rib Fracture on Multiplane Reconstruction Images.

IF 1.1 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Shihong Liu, Wei Zhang, Gang Wu
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引用次数: 0

Abstract

Objective: This study aimed to develop and assess the performance of a YOLOv8 algorithm-aided detection model for identifying rib fractures on multiplane reconstruction (MPR) images, addressing the limitations of current AI models and the labor-intensive nature of manual diagnosis.

Methods: Ethical approval was obtained, and a dataset comprising 624 MPR images, confirmed by CT, was collected from three regions of Tongji Hospital between May 2020 and May 2023. The images were categorized into training, validation, and external test sets. A musculoskeletal radiologist labeled the images, and a YOLOV8n model was trained and validated using these datasets. The performance metrics, including sensitivity, specificity, accuracy, precision, recall, and F1 score, were calculated.

Results: The refined YOLO model demonstrated high diagnostic accuracy, with sensitivity, specificity, and accuracy rates of 96%, 97%, and 97%, respectively. The AI model significantly outperformed the radiologist in terms of diagnostic speed, with an average interpretation time of 2.02 seconds for 144 images compared to 288 seconds required by the radiologist.

Conclusion: The YOLOv8 algorithm shows promise in expediting the diagnosis of rib fractures on MPR images with high accuracy, potentially improving clinical efficiency and reducing the workload for radiologists. Future work will focus on enhancing the model with more feature learning capabilities and integrating it into the PACS system for human-computer interaction.

基于YOLOv8算法的肋骨骨折多平面重建图像辅助检测。
目的:本研究旨在开发和评估YOLOv8算法辅助检测模型在多平面重建(MPR)图像上识别肋骨骨折的性能,解决当前人工智能模型的局限性和人工诊断的劳动密集型性质。方法:获得伦理批准,收集同济医院2020年5月至2023年5月三个区域经CT确认的624张MPR图像数据集。图像被分类为训练集、验证集和外部测试集。肌肉骨骼放射科医生对图像进行标记,并使用这些数据集训练和验证YOLOV8n模型。计算性能指标,包括敏感性、特异性、准确性、精密度、召回率和F1评分。结果:改进后的YOLO模型诊断准确率较高,敏感性96%,特异性97%,准确率97%。人工智能模型在诊断速度方面明显优于放射科医生,144张图像的平均解释时间为2.02秒,而放射科医生需要288秒。结论:YOLOv8算法有望在MPR图像上快速、准确地诊断肋骨骨折,有可能提高临床效率,减少放射科医生的工作量。未来的工作将集中在增强模型的特征学习能力,并将其集成到PACS系统中进行人机交互。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.60
自引率
0.00%
发文量
246
审稿时长
1 months
期刊介绍: Current Medical Imaging Reviews publishes frontier review articles, original research articles, drug clinical trial studies and guest edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation and therapeutic applications related to all modern medical imaging techniques. The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis.
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