Automated Detection of the Thoracic Ossification of the Posterior Longitudinal Ligament Using Deep Learning and Plain Radiographs.
Ossification of the ligaments progresses slowly in the initial stages, and most patients are unaware of the disease until obvious myelopathy symptoms appear. Consequently, treatment and clinical outcomes are not satisfactory. This study is aimed at developing an automated system for the detection of...
| Publicado en: | BioMed Research International pp. 1 - 8 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
11/27/2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=174098620&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 174098620 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 11/27/2023 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 174098620 174098620 174098620 10.1155/2023/8495937 174098620 ppf: 1 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated Detection of the Thoracic Ossification of the Posterior Longitudinal Ligament Using Deep Learning and Plain Radiographs. aug: au: Ito, Sadayuki Nakashima, Hiroaki Segi, Naoki Ouchida, Jun Oda, Masahiro Yamauchi, Ippei Oishi, Ryotaro Miyairi, Yuichi Mori, Kensaku Imagama, Shiro affil: Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan sug: subj: Automation Methods Thoracic Vertebrae Ossification, Heterotopic Complications Longitudinal Ligaments Pathology Deep Learning Radiography Methods Human Retrospective Design Outcomes (Health Care) Spinal Cord Diseases ab: Ossification of the ligaments progresses slowly in the initial stages, and most patients are unaware of the disease until obvious myelopathy symptoms appear. Consequently, treatment and clinical outcomes are not satisfactory. This study is aimed at developing an automated system for the detection of the thoracic ossification of the posterior longitudinal ligament (OPLL) using deep learning and plain radiography. We retrospectively reviewed the data of 146 patients with thoracic OPLL and 150 control cases without thoracic OPLL. Plain lateral thoracic radiographs were used for object detection, training, and validation. Thereafter, an object detection system was developed, and its accuracy was calculated. The performance of the proposed system was compared with that of two spine surgeons. The accuracy of the proposed object detection model based on plain lateral thoracic radiographs was 83.4%, whereas the accuracies of spine surgeons 1 and 2 were 80.4% and 77.4%, respectively. Our findings indicate that our automated system, which uses a deep learning-based method based on plain radiographs, can accurately detect thoracic OPLL. This system has the potential to improve the diagnostic accuracy of thoracic OPLL. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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