Computer-Aided Diagnosis for Determining Sagittal Spinal Curvatures Using Deep Learning and Radiography.
Analyzing spinal curvatures manually is time-consuming and tedious for clinicians, and intra-observer and inter-observer variability can affect manual measurements. In this study, we developed and evaluated the performance of an automated deep learning–based computer-aided diagnosis (CAD) tool for m...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 4; pp. 846 - 860 |
|---|---|
| Autores principales: | , , , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2022
|
| 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=159195612&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159195612 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2022 vid: 35 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 159195612 155682878 159195612 159195612 10.1007/s10278-022-00592-0 159195612 ppf: 846 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Computer-Aided Diagnosis for Determining Sagittal Spinal Curvatures Using Deep Learning and Radiography. aug: au: Lee, Hyo Min Kim, Young Jae Cho, Je Bok Jeon, Ji Young Kim, Kwang Gi affil: Department of Biomedical Engineering, College of Health Science, Gachon University, Seongnam, South Korea sug: subj: Spinal Curvatures Radiography Spinal Diseases Radiography Diagnosis, Computer Assisted Deep Learning Algorithms Evaluation Predictive Value of Tests Evaluation Human Thoracic Vertebrae Pathology Lumbar Vertebrae Pathology Kyphosis Diagnosis Lordosis Diagnosis Sensitivity and Specificity Descriptive Statistics Pearson's Correlation Coefficient Intraclass Correlation Coefficient Reliability Intrarater Reliability Interrater Reliability ab: Analyzing spinal curvatures manually is time-consuming and tedious for clinicians, and intra-observer and inter-observer variability can affect manual measurements. In this study, we developed and evaluated the performance of an automated deep learning–based computer-aided diagnosis (CAD) tool for measuring the sagittal alignment of the spine from X-ray images. The CAD system proposed here performs two functions: deep learning–based lateral spine segmentation and automatic analysis of thoracic kyphosis and lumbar lordosis angles. We utilized 322 datasets with data augmentation for learning and fivefold cross-validation. The segmentation model was based on U-Net, which has multiple applications in medical image processing. Here, we utilized parameter equations and trigonometric functions to design spinal angle measurement algorithms. The kyphosis (T4–T12) and lordosis angle (L1–S1, L1–L5) were automatically measured to help diagnose kyphosis and lordosis. The segmentation model had precision, sensitivity, and dice similarity coefficient values of 90.53 ± 4.61%, 89.53 ± 1.8%, and 90.22 ± 0.62%, respectively. The performance of the CAD algorithm was also verified with the Pearson correlation, Bland–Altman, and intra-class correlation coefficient (ICC) analysis. The proposed angle measurement algorithm exhibited high similarity and reliability during verification. Therefore, CAD can help clinicians in reaching a diagnosis by analyzing the sagittal spinal curvatures while reducing observer-based variability and the required time or effort. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|