An AI-Based Image Quality Control Framework for Knee Radiographs.
Image quality control (QC) is crucial for the accurate diagnosis of knee diseases using radiographs. However, the manual QC process is subjective, labor intensive, and time-consuming. In this study, we aimed to develop an artificial intelligence (AI) model to automate the QC procedure typically perf...
| Published in: | Journal of Digital Imaging Vol. 36; no. 5; pp. 2278 - 2290 |
|---|---|
| Main Authors: | , , , , , , , , , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Springer Nature
Oct2023
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=171950861&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 171950861 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2023 vid: 36 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 171950861 164039810 171950861 171950861 10.1007/s10278-023-00853-6 171950861 ppf: 2278 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An AI-Based Image Quality Control Framework for Knee Radiographs. aug: au: Sun, Hongbiao Wang, Wenwen He, Fujin Wang, Duanrui Liu, Xiaoqing Xu, Shaochun Zhao, Baolian Li, Qingchu Wang, Xiang Jiang, Qinling Zhang, Rong Liu, Shiyuan Xiao, Yi affil: https://ror.org/0103dxn66 Department of Radiology, Shanghai Changzheng Hospital, Naval Medical University, No.415 Fengyang Road, Huangpu District, 200003, Shanghai, China sug: subj: Knee Radiography Radiographic Image Enhancement Artificial Intelligence Utilization Theory Construction Quality Control (Technology) Human Prospective Studies Intraclass Correlation Coefficient Deep Learning Funding Source ab: Image quality control (QC) is crucial for the accurate diagnosis of knee diseases using radiographs. However, the manual QC process is subjective, labor intensive, and time-consuming. In this study, we aimed to develop an artificial intelligence (AI) model to automate the QC procedure typically performed by clinicians. We proposed an AI-based fully automatic QC model for knee radiographs using high-resolution net (HR-Net) to identify predefined key points in images. We then performed geometric calculations to transform the identified key points into three QC criteria, namely, anteroposterior (AP)/lateral (LAT) overlap ratios and LAT flexion angle. The proposed model was trained and validated using 2212 knee plain radiographs from 1208 patients and an additional 1572 knee radiographs from 753 patients collected from six external centers for further external validation. For the internal validation cohort, the proposed AI model and clinicians showed high intraclass consistency coefficients (ICCs) for AP/LAT fibular head overlap and LAT knee flexion angle of 0.952, 0.895, and 0.993, respectively. For the external validation cohort, the ICCs were also high, with values of 0.934, 0.856, and 0.991, respectively. There were no significant differences between the AI model and clinicians in any of the three QC criteria, and the AI model required significantly less measurement time than clinicians. The experimental results demonstrated that the AI model performed comparably to clinicians and required less time. Therefore, the proposed AI-based model has great potential as a convenient tool for clinical practice by automating the QC procedure for knee radiographs. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|