Self-Assessment of acute rib fracture detection system from chest X-ray: Preliminary study for early radiological diagnosis.

Authors

Lee HK,Kim HS,Kim SG,Park JY

Affiliations (3)

  • Department of Thoracic and Cardiovascular Surgery, Hallym University Sacred Heart Hospital, Hallym University Medical Center, Anyang-si, Gyeonggi-do, Republic of Korea.
  • Division of Nephrology, Department of Internal medicine, Hallym University Sacred Heart Hospital, Hallym University Medical Center, Anyang-si, Gyeonggi-do, Republic of Korea.
  • Department of Orthopedic Surgery, Hallym University Sacred Heart Hospital, Hallym University Medical Center, Anyang-si, Gyeonggi-do, Republic of Korea.

Abstract

ObjectiveDetecting and accurately diagnosing rib fractures in chest radiographs is a challenging and time-consuming task for radiologists. This study presents a novel deep learning system designed to automate the detection and segmentation of rib fractures in chest radiographs.MethodsThe proposed method combines CenterNet with HRNet v2 for precise fracture region identification and HRNet-W48 with contextual representation to enhance rib segmentation. A dataset consisting of 1006 chest radiographs from a tertiary hospital in Korea was used, with a split of 7:2:1 for training, validation, and testing.ResultsThe rib fracture detection component achieved a sensitivity of 0.7171, indicating its effectiveness in identifying fractures. Additionally, the rib segmentation performance was measured by a dice score of 0.86, demonstrating its accuracy in delineating rib structures. Visual assessment results further highlight the model's capability to pinpoint fractures and segment ribs accurately.ConclusionThis innovative approach holds promise for improving rib fracture detection and rib segmentation, offering potential benefits in clinical practice for more efficient and accurate diagnosis in the field of medical image analysis.

Topics

Journal Article

Ready to Sharpen Your Edge?

Join hundreds of your peers who rely on RadAI Slice. Get the essential weekly briefing that empowers you to navigate the future of radiology.

We respect your privacy. Unsubscribe at any time.