Detection and Localization of Spine Disorders from Plain Radiography.
Spine disorders can cause severe functional limitations, including back pain, decreased pulmonary function, and increased mortality risk. Plain radiography is the first-line imaging modality to diagnose suspected spine disorders. Nevertheless, radiographical appearance is not always sufficient due t...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 6; pp. 2967 - 2983 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2024
|
| 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=182283986&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182283986 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2024 vid: 37 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 182283986 182283986 182283986 10.1007/s10278-024-01175-x 182283986 ppf: 2967 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Detection and Localization of Spine Disorders from Plain Radiography. aug: au: Yıldız Potter, İlkay Yeritsyan, Diana Rodriguez, Edward K. Wu, Jim S. Nazarian, Ara Vaziri, Ashkan affil: https://ror.org/01s2ng935 BioSensics, LLC, 57 Chapel Street, 02458, Newton, MA, USA sug: subj: Spinal Diseases Diagnosis Radiography Methods Human Deep Learning Diagnosis, Computer Assisted Convolutional Neural Networks Image Processing, Computer Assisted Fractures, Vertebral Compression Spondylolisthesis ROC Curve Predictive Value of Tests Sensitivity and Specificity Mann-Whitney U Test Confidence Intervals Nonparametric Statistics Funding Source Descriptive Statistics ab: Spine disorders can cause severe functional limitations, including back pain, decreased pulmonary function, and increased mortality risk. Plain radiography is the first-line imaging modality to diagnose suspected spine disorders. Nevertheless, radiographical appearance is not always sufficient due to highly variable patient and imaging parameters, which can lead to misdiagnosis or delayed diagnosis. Employing an accurate automated detection model can alleviate the workload of clinical experts, thereby reducing human errors, facilitating earlier detection, and improving diagnostic accuracy. To this end, deep learning-based computer-aided diagnosis (CAD) tools have significantly outperformed the accuracy of traditional CAD software. Motivated by these observations, we proposed a deep learning-based approach for end-to-end detection and localization of spine disorders from plain radiographs. In doing so, we took the first steps in employing state-of-the-art transformer networks to differentiate images of multiple spine disorders from healthy counterparts and localize the identified disorders, focusing on vertebral compression fractures (VCF) and spondylolisthesis due to their high prevalence and potential severity. The VCF dataset comprised 337 images, with VCFs collected from 138 subjects and 624 normal images collected from 337 subjects. The spondylolisthesis dataset comprised 413 images, with spondylolisthesis collected from 336 subjects and 782 normal images collected from 413 subjects. Transformer-based models exhibited 0.97 Area Under the Receiver Operating Characteristic Curve (AUC) in VCF detection and 0.95 AUC in spondylolisthesis detection. Further, transformers demonstrated significant performance improvements against existing end-to-end approaches by 4–14% AUC (p-values < 10−13) for VCF detection and by 14–20% AUC (p-values < 10−9) for spondylolisthesis detection. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|