ArticleEuropean radiology experimental2024
Quality control of elbow joint radiography using a YOLOv8-based artificial intelligence technology.
Article in European radiology experimental, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- U-Net-Based Automated Quality Control of Knee Radiographs: Dual-Center Validation and Clinical Intervention.Journal of imaging informatics in medicine · 2026Article
- Automated quality evaluation of dental panoramic radiographs using deep learning.Imaging science in dentistry · 2025Article
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Authors and funding
10 authors.
Funding
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Abstract
backgroundTo explore an artificial intelligence (AI) technology employing YOLOv8 for quality control (QC) on elbow joint radiographs.
methodsFrom January 2022 to August 2023, 2643 consecutive elbow radiographs were collected and randomly assigned to the training, validation, and test sets in a 6:2:2 ratio. We proposed the anteroposterior (AP) and lateral (LAT) models to identify target detection boxes and key points on elbow radiographs using YOLOv8. These identifications were transformed into five quality standards: (1) AP elbow positioning coordinates (X
resultsThe AP and LAT models demonstrated high precision, recall, and mean average precision for identifying boxes and points. AI and physicians showed high intraclass correlation coefficient (ICC) in evaluating: AP coordinates X
conclusionYOLOv8-based AI technology is feasible for QC of elbow radiography with high performance. RELEVANCE STATEMENT: This study proposed and validated a YOLOv8-based AI model for automated quality control in elbow radiography, obtaining high efficiency in clinical settings. KEY POINTS: QC of elbow joint radiography is important for detecting diseases. Models based on YOLOv8 are proposed and perform well in image QC. Models offer objective and efficient solutions for QC in elbow joint radiographs.
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