Farah Rashid

Objective: The aim of this study was to determine whether smartphone- and intraoral camera-derived CIELAB parameters reproduce spectrophotometric colour variation across ICDAS defined occlusal caries severity, agree with spectrophotometric measurements, and support machine learning classification and cross-modality transfer. Methods: A total of 298 extracted posterior teeth were classified as sound (ICDAS 0), initial (ICDAS 1–2), or moderate (ICDAS 3–4) lesions. Spectrophotometric measurements were obtained from five predefined occlusal sites per tooth. Each tooth was also imaged using a smartphone and intraoral camera under standardised conditions, and five corresponding regions of interest were analysed for image-derived CIELAB values. CIELAB patterns and agreement were assessed using Linear Mixed-Effects models, Intraclass Correlation Coefficients (ICCs), and Bland–Altman analysis. Nineteen tooth-level colour and spatial variation features were evaluated using five classifiers with stratified fivefold tooth-level cross-validation and identical fold assignments across modalities. Cross-modality transfer was assessed using spectrophotometer-trained models applied to aligned image-derived features. Results: Spectrophotometry showed the most consistent severity-related decrease in L* and increases in a* and b*. Smartphone measurements broadly reproduced the L* pattern and differentiated severity through a*, whereas intraoral cameras showed limited separation for L* and b*. Agreement was device- and coordinate-dependent; the highest ICCs were observed for smartphone L* (0.785) and intraoral camera a* (0.844), while systematic bias and wide limits of agreement indicated non-interchangeability. Smartphone-derived features achieved the highest balanced accuracy (0.836–0.918), followed by the intraoral camera (0.639–0.664) and spectrophotometry (0.443–0.552). Cross-modality transfer remained partial, with prediction agreement of 54.0–74.5% (k = 0.263–0.541). Conclusions: Image-derived CIELAB features captured lesion-related colour information but were not directly interchangeable with spectrophotometry. Image derived features performed best when the model was trained and tested using data from the same measurement method. Cross-device generalisability across different camera systems remains to be established.

Objectives: This study evaluated whether spectrophotometric CIELAB color measurements can support machine-learning (ML) classification of occlusal caries severity based on ICDAS categories. Methods: Three hundred extracted human teeth were visually classified using ICDAS (0–4) and served as labels for supervised learning. Five occlusal sites per tooth were measured using a spectrophotometer under standardized conditions. Site-level CIELAB values were transformed into engineered color features and aggregated at the tooth level. Teeth were categorised as sound (ICDAS 0), initial carious lesions (1–2), and moderate carious lesions (3–4). Five ML models: Random Forest (RF), XGBoost, CatBoost, Multilayer Perceptron (MLP), and a Deep Sets architecture were trained. Performance was evaluated using accuracy, balanced accuracy (BA), and macro-F1, with additional binary analyses for early (ICDAS 1–4 vs. 0) and operative lesion detection (ICDAS 3–4 vs. 0–2), based on the included ICDAS range. Learning curve analysis was performed to evaluate the effect of training data size on model performance. Results: Deep Sets achieved the highest multiclass performance (BA = 0.89) followed by the MLP (BA = 0.72). Tree-based models demonstrated lower performance overall. For early lesion detection, all models showed high sensitivity (SE) but reduced specificity (SP). At the operative level, the MLP achieved 100% SE and moderate SP. Learning-curve analysis showed that neural models benefited most from increased training data, whereas tree-based models showed limited improvement. Conclusion: Under controlled in-vitro conditions, spectrophotometric CIELAB measurements enabled machine-learning classification of occlusal caries severity. Deep sets achieved the highest overall performance, supporting the potential of color-based approaches as adjunctive tools for caries assessment. Clinical relevance: Spectrophotometric tooth color measurements may support machine learning classification of occlusal caries severity under controlled conditions, using an image-independent approach. Such color-based methods may complement visual assessment by providing a more standardized and reproducible evaluation of enamel changes following further validation.

Objective: The International Caries Detection and Assessment System (ICDAS) is widely used for early caries detection but relies on visual interpretation. Although spectrophotometry objectively quantifies tooth color, variation across ICDAS categories and its relationship with lesion severity remain unclear. The present in vitro study therefore characterized spectrophotometric measurements across ICDAS categories, evaluated their association with severity, and quantified differences across categories. Materials and Methods: Three hundred unrestored extracted posterior teeth were classified as ICDAS 0–4 by a calibrated examiner. CIELAB values were obtained with a Spectrophotometer at five standardized sites per tooth under controlled lighting and aggregated to tooth‐level. Differences across categories were analyzed using ANOVA/Kruskal‐Wallis with correlation and regression analysis. Color differences (ΔE00) were calculated relative to the ICDAS 0 (reference) and interpreted using published perceptibility and acceptability thresholds. Results: Color parameters differed significantly across ICDAS categories ( p < 0.001). ΔE00 increased from ICDAS 2, exceeding reported perceptibility and acceptability thresholds. With increasing severity, L* decreased while a* and b* increased with significant monotonic associations for all color parameters ( ρ = −0.58 to 0.62; p < 0.001). Conclusions: Statistically significant differences in spectrophotometric color measurements were observed across caries severity categories, with progressive reductions in lightness and corresponding increases in red and yellow chromatic components. Overall color differences were greater in higher severity categories and exceeded perceptibility and acceptability thresholds from moderate lesion stages onward. Clinical Significance: Spectrophotometric color assessment provides an objective framework for quantifying enamel changes associated with caries progression. Color differences exceeding established perceptibility and acceptability thresholds indicate clinically appreciable changes beyond early lesion stages, supporting the use of color‐based metrics as an adjunct to conventional visual assessment.

Objective: This systematic review and meta-analysis compare the diagnostic accuracy of intraoral scanners (IOS) and intraoral cameras (IOC) against traditional radiographic and histological methods for caries detection. Due to variation in sensitivity (SE) and specificity (SP) among diagnostic tools, this study evaluated their performance based on lesion type, lesion location, and examiner-dependent factors. Methods: A comprehensive literature search was conducted using keyword-based search strings in Scopus, Web of Science, PubMed, Cochrane Library, and Dentistry & Oral Sciences Source, following PRISMA-DTA guidelines. Studies comparing IOS or IOC to radiographic or histological reference standards were included. Three independent reviewers assessed study quality using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist, with disagreements resolved by discussion and Fleiss’s kappa (κ) analysis. Meta-analysis was performed using a bivariate random-effects model to estimate pooled SE and SP. Subgroup analyses examined the impact of lesion type, lesion location, and examiner-dependent variabilities, using the Python programming language. Results: Twenty-eight studies met the inclusion criteria, with 16 studies included in the meta-analysis. Pooled SE and SP of IOS and IOC were 50%, indicating moderate diagnostic accuracy. Radiographic methods had the highest SP, making them more effective at avoiding false positives. In contrast, IOSs and IOCs demonstrated higher SE for occlusal and dentin lesions, suggesting their utility in early caries detection. Lesion location significantly influenced diagnostic accuracy, with IOS and IOC showing superior SE for occlusal lesions but lower SE for supragingival lesions. Examiner-dependent differences in SE reached up to 32%, emphasizing the need for structured training and calibration protocols. Conclusions: IOSs and IOCs showed promise for early caries detection due to their higher SE for occlusal and dentin lesions. However, their lower SP compared to radiographic methods raises concerns about false positives. Standardized examiner training and improved diagnostic protocols are essential to enhance the reliability of these digital imaging techniques.

Objective: To investigate the compressive, dimensional, and antimicrobial properties of thermoplastic Acrylonitrile Butadiene Styrene (ABS) 3D printed hollow blocks following chemical disinfection. Methods: Forty-two 3D printed ABS cubes were immersed in tap water, 0.12% chlorhexidine gluconate, 3% hydrogen peroxide, 5% sodium bicarbonate, 0.5% sodium hypochlorite, and commercial Potassium Caroate (Polident) for 28 days (4 cycles). Linear Outer (OM) and Inner (IM) dimensions, Root Mean Square (RMS), and mass were recorded before and after each immersion cycle. An additional set of seven cubes was untreated and served as a control. Fourier transform infrared spectroscopy (FTIR) was executed on one randomly selected sample from each group before and after immersion. Following the completion of the immersion cycles, an evaluation of compressive strength was performed using a universal testing machine. Subsequently, from each group, a single ABS cube was randomly selected for the introduction of Streptococcus mutans and Candida albicans, followed by a 14-day incubation period with Scanning Electron Microscope (SEM) evaluation. Results: There were no significant differences (p > 0.05) between OM, IM, and compressive strength measurements (F = 1.036, p = 0.443) across all groups. RMS values increased for OM and decreased for IM. Notably, cubes that underwent immersion in a 0.12% chlorhexidine gluconate solution displayed considerable changes in mass (p < 0.05), exhibiting a low positive correlation (ρ = 0.339). The 0.12% chlorhexidine gluconate group exhibited the emergence of a new OH peak (3000–3500) in FTIR, whereas the 3% hydrogen peroxide group experienced the disappearance of the styrene peak (1300–1500). Exposure of ABS to C. albicans and S. mutans demonstrated clear surfaces under SEM with 0.12% chlorhexidine gluconate, 0.5% sodium hypochlorite, and Polident. Conclusions: Disinfection with 0.5% sodium hypochlorite and Potassium Caroate produced minimal mechanical changes and resisted growth of C. albicans and S. mutans. 0.12% chlorhexidine gluconate and 3% hydrogen peroxide altered dimensional and chemical compositions of 3D printed ABS following disinfection.

Objectives: The purpose of this bibliometric analysis is to identify the 100 most cited articles and delve into citation metrics to gain insights into the evolving trends in shade‐matching methods in dentistry.Material and Methods: Following PRISMA guidelines, two reviewers conducted a structured search in Scopus using keyword‐based search strings. The top 100 articles were selected based on predefined criteria, and their bibliometric data were extracted. Harzing's Publish Or Perish and VOSviewer were used to generate the bibliographic network.Results: Between 1989 and 2017, the top 100 articles were published and obtained citations ranging from 560 to 48. Twenty‐eight articles received over 100 citations, deemed classic. The Journal of Prosthetic Dentistry and the United States of America were the primary contributors. In‐vitro experimental studies employing restorative material samples were prevalent, with spectrophotometers being the favored method of color analysis. Using Python, Spearman's correlation coefficient resealed positive correlations between citation count and age of publication (r = 0.16, p = 0.12) and between citation count and the impact factor of the journal (r = 0.35, p < 0.05). However, a negative correlation was observed between citation density and age of publication (r = −0.46, p < 0.05).Conclusions: This study demonstrated a significant upward trajectory of citation count in shade‐matching in dentistry, reflecting high‐quality scientific contributions.

Purpose. To evaluate the influence of edible liquids on the characteristic properties of 3D printable materials compared to conventionally used dental resin acrylic. Method. Dental polymethyl methacrylate (PMMA) specimens were fabricated from preformed molds while polylactic acid (PLA) and polyethylene terephthalate glycol (PETG) specimens were 3D printed using fused deposition modelling at 0.1 mm layer thickness. All specimen forms adhered to ISO 37:2017 and ISO 604:2002 specifications. Specimens underwent controlled immersion for 180 hr into different media (no immersion (control), oil, soda, milk, and caffeine). Changes in mass (g), plastic deformity (N/mm²), ultimate tensile stress (N), flexural deformity (N/mm²), and break force (N) were evaluated using analysis of variance. Results. There was an increase in mass for all specimens following immersion with significant interactions between immersion media and the materials. The materials exhibited significant differences in plastic deformity (F (df) = 156.632(2), P < 0.001), ultimate tensile stress (F (df) = 109.521(2), P < 0.001), and break force (F (df) = 319.785 (2), P < 0.001) with no significant interactions with immersion media (P > 0.05) on both accounts. Materials showed no significant differences in flexural deformity (F (df) = 2.693(2), P = 0.074) but with significant interactions (F (df) = 4.984(8), P < 0.001) between acrylic and immersion media. Conclusion. Commercially available 3D printable PLA and PETG filaments printed at 0.1 mm thickness possess approximately half the mechanical resilience as dental PMMA with comparable flexural deformity after exposure to edible liquids.

Purpose. This study assessed the impact of intraoral scanner type, operator, and data augmentation on the dimensional accuracy of in vitro dental cast digital scans. It also evaluated the validation accuracy of an unsupervised machine-learning model trained with these scans. Methods. Twenty-two dental casts were scanned using two handheld intraoral scanners and one laboratory scanner, resulting in 110 3D cast scans across five independent groups. The scans underwent uniform augmentation and were validated using Hausdorffs distance (HD) and root mean squared error (RMSE), with the laboratory scanner as reference. A 3-factor analysis of variance examined interactions between scanners, operators, and augmentation methods. Scans were divided into training and validation sets and processed through a pretrained 3D visual transformer, and validation accuracy was assessed for each of the five groups. Results. No significant differences in HD and RMSE were found across handheld scanners and operators. However, significant changes in RMSE were observed between native and augmented scans with no specific interaction between scanner or operator. The 3D visual transformer achieved 96.2 validation accuracy for differentiating upper and lower scans in the augmented dataset. Native scans lacked volumetric depth, preventing their use for deep learning. Conclusion. Scanner, operator, and processing method did not significantly affect the dimensional accuracy of 3D scans for unsupervised deep learning. However, data augmentation was crucial for processing intraoral scans in deep learning algorithms, introducing structural differences in the 3D scans. Clinical Significance. The specific type of intraoral scanner or the operator has no substantial influence on the quality of the generated 3D scans, but controlled data augmentation of the native scans is necessary to obtain reliable results with unsupervised deep learning.

The pursuit of aesthetic excellence in dentistry, shaped by societal trends and digital advancements, highlights the critical role of precise shade matching in restorative procedures. Although conventional methods are prevalent, challenges such as shade guide variability and subjective interpretation necessitate a re-evaluation in the face of emerging non-proximity digital instruments. This systematic review employs PRISMA protocols and keyword-based search strategies spanning the Scopus^®, PubMed.gov, and Web of Science^TM databases, with the last updated search carried out in October 2023. The study aimed to synthesise literature that identified digital non-proximity recording instruments and associated colour spaces in dentistry and compare the clinical outcomes of digital systems with spectrophotometers and conventional visual methods. Utilising predefined criteria and resolving disagreements between two reviewers through Cohen’s kappa calculator, the review assessed 85 articles, with 33 included in a PICO model for clinical comparisons. The results reveal that 42% of studies employed the CIELAB colour space. Despite the challenges in study quality, non-proximity digital instruments demonstrated more consistent clinical outcomes than visual methods, akin to spectrophotometers, emphasising their efficacy in controlled conditions. The review underscores the evolving landscape of dental shade matching, recognising technological advancements and advocating for methodological rigor in dental research.