Intelligent detection and grading diagnosis of fresh rib fractures based on deep learning.

IF 2.9 3区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

BMC Medical Imaging Pub Date : 2025-03-24 DOI:10.1186/s12880-025-01641-0

Tongxin Li, Mingyi Liao, Yong Fu, Fanghong Zhang, Luya Shen, Junliang Che, Shulei Wu, Jie Liu, Wei Wu, Ping He, Qingyuan Xu, Yi Wu

{"title":"Intelligent detection and grading diagnosis of fresh rib fractures based on deep learning.","authors":"Tongxin Li, Mingyi Liao, Yong Fu, Fanghong Zhang, Luya Shen, Junliang Che, Shulei Wu, Jie Liu, Wei Wu, Ping He, Qingyuan Xu, Yi Wu","doi":"10.1186/s12880-025-01641-0","DOIUrl":null,"url":null,"abstract":"Background: Accurate detection and grading of fresh rib fractures are crucial for patient management but remain challenging due to the complexity of rib structures on CT images.Methods: Chest CT images from 383 patients with rib fractures were retrospectively analyzed. The dataset was divided into a training set (n = 306) and an internal testing set (n = 77). An external testing set of 50 patients from the public RibFrac dataset was included. Fractures were classified into severe and non-severe categories. A modified YOLO-based deep learning model was developed for detection and grading. Performance was compared with thoracic surgeons using precision, recall, mAP50, and F1 score.Results: The deep learning model showed excellent performance in diagnosing fresh rib fractures. For all fractures types in internal test set, the precision, recall, mAP50, and F1 score were 0.963, 0.934, 0.972, and 0.948, respectively. The model outperformed thoracic surgeons of varying experience levels (all p < 0.01).Conclusion: The proposed deep learning model can automatically detect and grade fresh rib fractures with accuracy comparable to that of physicians. This model helps improve diagnostic accuracy, reduce physician workload, save medical resources, and strengthen health care in resource-limited areas.Clinical trial number: Not applicable.","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"98"},"PeriodicalIF":2.9000,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934624/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Medical Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12880-025-01641-0","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}

引用次数: 0

Abstract

Background: Accurate detection and grading of fresh rib fractures are crucial for patient management but remain challenging due to the complexity of rib structures on CT images.

Methods: Chest CT images from 383 patients with rib fractures were retrospectively analyzed. The dataset was divided into a training set (n = 306) and an internal testing set (n = 77). An external testing set of 50 patients from the public RibFrac dataset was included. Fractures were classified into severe and non-severe categories. A modified YOLO-based deep learning model was developed for detection and grading. Performance was compared with thoracic surgeons using precision, recall, mAP50, and F1 score.

Results: The deep learning model showed excellent performance in diagnosing fresh rib fractures. For all fractures types in internal test set, the precision, recall, mAP50, and F1 score were 0.963, 0.934, 0.972, and 0.948, respectively. The model outperformed thoracic surgeons of varying experience levels (all p < 0.01).

Conclusion: The proposed deep learning model can automatically detect and grade fresh rib fractures with accuracy comparable to that of physicians. This model helps improve diagnostic accuracy, reduce physician workload, save medical resources, and strengthen health care in resource-limited areas.

Clinical trial number: Not applicable.

查看原文本刊更多论文

基于深度学习的新鲜肋骨骨折智能检测与分级诊断。

背景：新鲜肋骨骨折的准确检测和分级对患者治疗至关重要，但由于CT图像上肋骨结构的复杂性，仍然具有挑战性。方法：对383例肋骨骨折患者的胸部CT图像进行回顾性分析。数据集分为训练集（n = 306）和内部测试集（n = 77）。包括来自RibFrac公共数据集的50名患者的外部测试集。骨折分为严重型和非严重型。开发了一种改进的基于yolo的深度学习模型用于检测和分级。通过准确性、召回率、mAP50和F1评分对胸外科医生的表现进行比较。结果：深度学习模型对新鲜肋骨骨折的诊断效果良好。对于内部测试集的所有骨折类型，精度、召回率、mAP50和F1评分分别为0.963、0.934、0.972和0.948。结论：所提出的深度学习模型可以自动检测新鲜肋骨骨折并对其进行分级，其准确性与内科医生相当。该模型有助于提高诊断准确性，减少医生工作量，节省医疗资源，加强资源有限地区的卫生保健。临床试验号：不适用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

4.60

自引率

3.70%

发文量

198

审稿时长

27 weeks

期刊介绍： BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.