Publication: Deep learning-based object detection of restorative dental instruments with potential implications for workflow automation and infection control in dental supply units
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Issued Date
2026-12-01
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-105027075057
Pubmed ID
41339466
Journal Title
Scientific Reports
Volume
16
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific Reports Vol.16 No.1 (2026)
Suggested Citation
Poomrittigul S., Mittong S., Thanathornwong B., Suebnukarn S. Deep learning-based object detection of restorative dental instruments with potential implications for workflow automation and infection control in dental supply units. Scientific Reports Vol.16 No.1 (2026). doi:10.1038/s41598-025-30774-z Retrieved from: https://hdl.handle.net/20.500.14740/55373
Corresponding Author(s)
Other Contributor(s)
Abstract
This study presents a proof-of-concept deep learning approach for automated detection and classification of restorative dental instruments on standardized trays, aiming to support workflow automation and infection control in dental supply units. A dataset comprising 1,000 images and 14,000 annotated instances of restorative dental instruments across 14 categories was developed. The YOLOv8 model was trained and evaluated on this dataset using standard object detection metrics, including precision, recall, and mean average precision at IoU thresholds 0.5 ([email protected]) and 0.5:0.95 (mAP@[0.5:0.95]). To assess model advancement, YOLOv8 performance was compared against its predecessors, YOLOv5, YOLOv6, and YOLOv7, under identical experimental settings. A session-level data split was implemented as the primary evaluation to minimize data leakage and provide a realistic estimate of generalization across unseen tray configurations. The YOLOv8 model achieved highest mean average precision [email protected] of 95.9% and mAP@[0.5:0.95] of 80.9%, demonstrating robust detection capability under both standard and stringent evaluation thresholds. Across instrument categories, YOLOv8 demonstrated precision ranging from 90.3% to 100% and recall from 80.6 to 98.5%. The findings demonstrate the feasibility of using YOLOv8 for automated restorative dental instrument detection as an early-stage tool for improving supply unit efficiency. While results indicate high detection accuracy and robustness, further validation in diverse clinical environments is needed. Future deployment should incorporate human-in-the-loop verification, audit trails, and error escalation mechanisms to ensure safe and accountable AI-assisted workflows.