Tuntiwong, Kuson
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Item type:Publication, Sex estimation from panoramic dental radiographs in a Thai population using image analysis(2025-12-01); Sritart, HiranyaBackground: In forensic investigation, sexual dimorphism plays a significant role in estimating the biological sex of unidentified human remains. In cases involving disasters or severely decomposed or fragmented bodies, teeth are highly valued for their resilience to environmental conditions. However, significant gaps remain in fully utilizing these characteristics for accurately estimating sex. Therefore, this study primarily aimed to address these gaps, investigating dental metric traits specific to the Thai population to provide population-specific norms for sex estimation. Results: This study analyzed 246 panoramic dental radiographs (123 males and 123 females), with measurements of crown width and root length obtained using ImageJ software under standardized conditions. Statistical analysis using independent t-tests revealed significant differences between sexes in several teeth. The mandibular right canine (FDI 43) showed the most statistically significant difference between sexes in both crown width and root length (p < 0.001), with a percentage of sexual dimorphism in root length of 8.69%. This finding indicates that FDI 43 is a particularly reliable marker for sex estimation in this population. Conclusion: These findings underscore the importance of crown width and root length as reliable indicators for sex estimation. Given the population-specific nature of sexual dimorphism, this study provides essential baseline data for the Thai population. Its results contribute to improving the accuracy and reliability of forensic identification using dental radiographs. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Automated Crack Detection in Monolithic Zirconia Crowns Using Acoustic Emission and Deep Learning Techniques(2024-09-01); ; Monolithic zirconia (MZ) crowns are widely utilized in dental restorations, particularly for substantial tooth structure loss. Inspection, tactile, and radiographic examinations can be time-consuming and error-prone, which may delay diagnosis. Consequently, an objective, automatic, and reliable process is required for identifying dental crown defects. This study aimed to explore the potential of transforming acoustic emission (AE) signals to continuous wavelet transform (CWT), combined with Conventional Neural Network (CNN) to assist in crack detection. A new CNN image segmentation model, based on multi-class semantic segmentation using Inception-ResNet-v2, was developed. Real-time detection of AE signals under loads, which induce cracking, provided significant insights into crack formation in MZ crowns. Pencil lead breaking (PLB) was used to simulate crack propagation. The CWT and CNN models were used to automate the crack classification process. The Inception-ResNet-v2 architecture with transfer learning categorized the cracks in MZ crowns into five groups: labial, palatal, incisal, left, and right. After 2000 epochs, with a learning rate of 0.0001, the model achieved an accuracy of 99.4667%, demonstrating that deep learning significantly improved the localization of cracks in MZ crowns. This development can potentially aid dentists in clinical decision-making by facilitating the early detection and prevention of crack failures. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Disparities in healthcare services and spatial assessments of mobile health clinics in the border regions of thailand(2021-10-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Smart Diagnostics: Hierarchical Deep Learning of Acoustic Emission Signals for Early Crack Detection in Zirconia Dental Structures(2026-05-01); ; Sritart, HiranyaMonolithic zirconia restorations are frequently affected by the unnoticed growth of subcritical cracks, a failure process that is not captured by traditional imaging methods like radiographs and ultrasounds in sophisticated dental architectures. To address this evaluative inadequacy, this research introduces a hierarchical deep learning framework for microcrack detection and spatial localization. We promote a hierarchical deep learning system that integrates Acoustic Emission (AE) detection alongside signal processing. Raw AE signals utilized during dynamic loading are enhanced via Kalman filtering and Continuous Wavelet Transform (CWT) to construct high-fidelity time–frequency scalograms. The diagnostic pipeline operates in two stages: first, a hybrid CNN–BiGRU network with temporal attention fulfills zirconia component-level classification; second, a ResNet-18 backbone integrated with Bidirectional LSTM and Multi-Head Attention precisely localizes defects across five anatomical crown regions. This hierarchical design effectively captures the non-stationary, transient nature of fracture-induced stress waves. The framework achieved an F1-score of 99.00% and an AUC of 0.994, significantly outperforming conventional convolutional networks. By enabling predictive maintenance through early, non-invasive damage localization, this study demonstrates a promising laboratory framework for AE-based crack detection in zirconia dental structures and prosthetics and toward enhanced clinical reliability in digital dentistry. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Airborne Dental Material Particulates and Occupational Exposure: Computational and Field Insights into Airflow Dynamics and Control Strategies(2025-11-01); Occupational exposure to airborne polymethacrylate (PMMA) particles during dental laboratory procedures poses an underexplored health risk. This study presents the first integrated Computational Fluid Dynamics (CFD) and real-time particle monitoring investigation of 0.5 µm PMMA particle dispersion during mechanical polishing in an actual clinic. We quantitatively assessed particle behavior in 30 s exposure scenarios by examining the effects of dental professional work orientations and comparing two mitigation strategies, rear-inlet portable air cleaners (PACs) and a Box Dust Collector (BC), with an emphasis on the safety of both personnel and patients. The findings establish that operatory airflow is a primary safety determinant: aligning the workflow with the main airflow (0°). Furthermore, the combined use of PACs and BC demonstrated synergistic superiority, achieving the optimal reduction in peak concentrations and airborne residence time. PACs alone reduced working zone concentrations by up to 80%, while BC provided a crucial 40–60 s delay in initial plume dispersion. We conclude that effective exposure control requires a proactive, two-stage engineering defense: source confinement augmented by continuous ambient filtration. This research provides a robust, evidence-based foundation for defining airflow-aware ergonomic and combined engineering standards in the evolving digital era of dentistry.