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Detection of extremity chronic traumatic osteomyelitis by machine learning based on computed-tomography images: A retrospective study.
Wu, Yifan; Lu, Xin; Hong, Jianqiao; Lin, Weijie; Chen, Shiming; Mou, Shenghong; Feng, Gang; Yan, Ruijian; Cheng, Zhiyuan.
Afiliación
  • Wu Y; Department of Surgery, Zhejiang University Hospital.
  • Lu X; College of Information Science & Electronic Engineering, Key Lab. of Advanced Micro/Nano Electronics Devices & Smart Systems of Zhejiang, Zhejiang University.
  • Hong J; Department of Orthopedic Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Lin W; College of Information Science & Electronic Engineering, Key Lab. of Advanced Micro/Nano Electronics Devices & Smart Systems of Zhejiang, Zhejiang University.
  • Chen S; Department of Surgery, Shaoxing Second Hospital, Shaoxing, Zhejiang Province, China.
  • Mou S; College of Information Science & Electronic Engineering, Key Lab. of Advanced Micro/Nano Electronics Devices & Smart Systems of Zhejiang, Zhejiang University.
  • Feng G; Department of Orthopedic Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Yan R; Department of Orthopedic Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Cheng Z; College of Information Science & Electronic Engineering, Key Lab. of Advanced Micro/Nano Electronics Devices & Smart Systems of Zhejiang, Zhejiang University.
Medicine (Baltimore) ; 99(9): e19239, 2020 Feb.
Article en En | MEDLINE | ID: mdl-32118728
Despite the availability of a series of tests, detection of chronic traumatic osteomyelitis is still exhausting in clinical practice. We hypothesized that machine learning based on computed-tomography (CT) images would provide better diagnostic performance for extremity traumatic chronic osteomyelitis than the serological biomarker alone. A retrospective study was carried out to collect medical data from patients with extremity traumatic osteomyelitis according to the criteria of musculoskeletal infection society. In each patient, serum levels of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and D-dimer were measured and CT scan of the extremity was conducted 7 days after admission preoperatively. A deep residual network (ResNet) machine learning model was established for recognition of bone lesion on the CT image. A total of 28,718 CT images from 163 adult patients were included. Then, we randomly extracted 80% of all CT images from each patient for training, 10% for validation, and 10% for testing. Our results showed that machine learning (83.4%) outperformed CRP (53.2%), ESR (68.8%), and D-dimer (68.1%) separately in accuracy. Meanwhile, machine learning (88.0%) demonstrated highest sensitivity when compared with CRP (50.6%), ESR (73.0%), and D-dimer (51.7%). Considering the specificity, machine learning (77.0%) is better than CRP (59.4%) and ESR (62.2%), but not D-dimer (83.8%). Our findings indicated that machine learning based on CT images is an effective and promising avenue for detection of chronic traumatic osteomyelitis in the extremity.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Osteomielitis / Extremidades Tipo de estudio: Diagnostic_studies / Evaluation_studies / Observational_studies / Prognostic_studies Límite: Female / Humans / Male / Middle aged País/Región como asunto: Asia Idioma: En Revista: Medicine (Baltimore) Año: 2020 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Osteomielitis / Extremidades Tipo de estudio: Diagnostic_studies / Evaluation_studies / Observational_studies / Prognostic_studies Límite: Female / Humans / Male / Middle aged País/Región como asunto: Asia Idioma: En Revista: Medicine (Baltimore) Año: 2020 Tipo del documento: Article Pais de publicación: Estados Unidos