Diagnosis Of Caries Research Articles

Advanced Lasers and Their Applications in Dentistry.

The development of laser technology has revolutionized dentistry, offering complementary and alternative approaches to traditional techniques. Lasers have been successfully integrated into various dental procedures, enhancing treatment outcomes and patient care. Several types of lasers can increase the acid resistance of enamel, thus preventing caries. Laser fluorescence has been utilized for the pre-operative diagnosis of dental caries, enabling early detection and effective treatment planning. The therapeutic application of lasers in caries treatment aligns with the contemporary philosophy of minimally invasive procedures. Clinicians can use laser Doppler flowmetry as a supplementary tool for pulp vitality testing by detecting pulpal blood flow. Lasers are also employed in various pulp-related interventions, such as managing dentine hypersensitivity and performing root canal therapy. These procedures benefit from the precision and reduced invasiveness provided by laser technology. Furthermore, laser fluorescence serves as an additional tool for subgingival calculus detection. High-power and low-power lasers are used in both nonsurgical and surgical therapies to treat periodontal and peri-implant diseases, oral mucosa conditions, and even cancer based on their specific properties. Lasers are also utilized to accelerate bone regeneration, promote adhesive strength, and remove ceramic brackets. In summary, laser technology has significantly impacted contemporary dentistry by facilitating early diagnosis, minimally invasive treatments, and precise operative procedures, ultimately improving patient outcomes and expanding the scope of dental practice.

The Most Promising Alternative to Mercury-containing Dental Restorations

The European Union Environmental Commission report and the United Nation Minamata Convention have legislated for phasing-out of mercury-containing dental materials use (dental amalgams) by 2030.1, 2 Several Scandinavian countries have already banned the use of dental amalgams, and this ban is expected to grow to other countries worldwide.2, 3 This creates a market gap for materials which can be used as an alternative to dental amalgams.4 Varieties of mercury-free dental materials have been clinically approved as posterior restorations; such as (1) resin-based composite materials, and (2) glass-based materials, including glass ionomers and glass hybrids. Using these types of dental materials will decrease the mercury’s risks on human’s health and also contribute significantly in the reduction of the environmental mercury pollution.5, 6 Recently, several clinical approaches to treat dental caries lesions have shifted the focus to preserving tooth structure and use of adhesive materials. Considering this, Minimally Intervention Dentistry (MID) has become a crucial concept which includes three aspects to fulfil the requirement of preventive and restorative dentistry; these are early caries diagnosis, enhance remineralisation and minimal cutting of tooth surfaces.7, 8 This approach is identified as an Atraumatic Restorative Treatment (ART). The ART technique was adopted in 1994 by the World Health Organisation (WHO) as an alternative technique for dental caries management in the developed countries either for treating deciduous and/or permanent dentitions.9 The high viscous glass ionomer cement (GIC) is approved as an ideal candidate material to be used with ART technique; such as GC Fuji IX®, KetacTM Molar and Glass Carbomer®.10, 11 One of the main advances in dental materials field is based on the modifications of the GIC chemical compositions, by introducing a bioactive glass in different particle sizes (for promoting remineralising ability) and/or using a highly molecular weight of poly- acrylic acid (for enhancing matrix strength).12 When bioactive glass-containing dental materials come into contact with body fluid, will undergo to a sequence of bioactivity reactions; which is summarised into (1) ion exchange, (2) dissolution and (3) precipitation stages, leading to formation of apatite crystals in fractions identical to those of the natural bone and tooth components.13 Formation of apatite crystals [hydroxyapatite-Ca10(PO4)6OH2 or fluorapatite-Ca10(PO4)6F2] in the bioactive glass-containing GICs occurs via delivering of calcium (Ca2+), phosphate (PO43), hydroxyl (OH-) and/or fluoride (F-) species at the interfaces between the GIC/tooth surfaces and/or the GIC/oral environment.14 For this reason, the bioactive glass-containing GICs are considered as the most promising alternative restorative materials for posterior cavities.

Diagnosis of approximal caries in children with convolutional neural networks based detection algorithms on radiographs: A pilot study.

Approximal caries diagnosis in children is difficult, and artificial intelligence-based research in pediatric dentistry is scarce. To create a convolutional neural network (CNN)-based diagnostic system for the prompt and efficient identification of approximal caries in pediatric patients aged 5-12 years. Pediatric patients' digital periapical radiographic images were collected to create a unique dataset. Various augmentation methods were used, and approximal caries in the augmented images were labeled by a pediatric dentist to minimize labeling errors. The dataset consisted of 830 data labeled for approximal caries on 415 images, which were divided into 80% training and 20% testing sets. After comparing 13 detection algorithms, including the latest YOLOv8, the most appropriate one was selected for the proposed system, which was then evaluated based on various performance metrics. The proposed detection system achieved a precision of 91.2%, an accuracy of 90.8%, a recall of 89.3%, and an F1 value of 90.24% after 300 iterations, utilizing a learning rate of 0.01. Approximal caries has been successfully detected with the developed system. Future efforts will focus on augmenting the dataset and expanding the sample size to enhance the efficacy of the system.

An overview of artificial intelligence based automated diagnosis in paediatric dentistry.

Artificial intelligence (AI) is a subfield of computer science with the goal of creating intelligent machines (1) Machine learning is a branch of artificial intelligence. In machine learning a datasets are used for training diagnostic algorithms. This review comprehensively explains the applications of AI in the diagnosis in paediatric dentistry. The online database searches were performed between 25th May 2024 to 1st July 2024. Original research studies that focus on the automated diagnosis or predicted the outcome in Paediatric dentistry using AI were included in this review. AI is being used in varied domains of paediatric dentistry like diagnosis of supernumerary and submerged teeth, early diagnosis of dental caries, diagnosis of dental plaques, assessment of bone age, forensic dentistry and preventive oral dental healthcare kit. The field of AI, deep machine learning and CNN's is an upcoming and newer area, with new developments this will open up areas for more sophisticated algorithms in multiple layers to predict accurately, when compared to experienced Paediatric dentists.

Dentists’ Treatment Decisions Concerning Restorations in Adult Patients in North Norway: A Cross-Sectional Tromsø 7 Study

Plain Language SummaryA randomized population of adults over 40 years, selected among the citizens of Tromsø County in North Norway, were examined by dentists, resulting in a study group of 3,653. Clinical examinations were executed, clinical photos and X-rays taken of each patient in a dental clinic. This is part of the Tromsø 7 study. A specially designed data programme was developed, and 17 trained dentists were able to assess the clinical state of restorations. All the dentists were working as clinical teachers in dental schools. The purpose was to decide what kind of treatment they thought best for unacceptable restorations. Should they be replaced or repaired if revision was needed. Only 3.3% of all examined teeth were proposed for restoration revision, which seemed to be rather low. Among the suggested revisions, approx. 25% were recommended for repair, while 75% was assigned for replacement. Tooth-coloured material, i.e., composite, was recommended in almost 75% of the cases. Different kinds of crowns counted for 25%. A considerable difference in the number of treatment suggestions was noted among the dentists. Statistical analyses demonstrated that 16% of the difference in suggestions for restoration re-treatment depended on different dentist opinions. Secondary caries and restoration fracture were found to be the most used indications for re-treatment. Need for restoration revision seems low in North Norway. However, there is a tendency towards larger and more indirect restorations. The diagnosis of secondary caries is still a matter of uncertainty.

AI-based prediction and classification of root caries using radiographic images.

Root surface caries, commonly known as root decay, is a common dental disorder that affects tooth roots. Like enamel-based tooth decay, root caries attack exposed root surfaces caused by gum recession or periodontal disease. Older persons with gum recession, tooth loss, or poor oral hygiene may be more likely to develop this disorder. Dental root caries must be diagnosed early to improve treatment and prevention. This research will examine radiographic image-based AI-based root caries prediction algorithms. Saveetha Dental College supplied 200 root surface radiographs. An expert dentist and dental radiologist confirmed one hundred teeth with root caries and 100 without. Edited and segmented radiographic images. Orange, a machine learning squeeze net embedding model with Naive Bayes, Logistic Regression, and neural networks, was used to assess prediction accuracy. Training and test data were split 80/20. Cross-validation, confusion matrix, and ROC analysis assessed model performance. This study examined precision and recall. Naïve bayes and logistic regression have 96% and 100% accuracy, but class accuracy is -94% and 100% in image classification of root caries was seen. AI-based root caries prediction utilizing radiographic images would improve dental care by diagnosing and treating early, accurately, and personalized. With appropriate deployment, research, and ethics, AI integration in dentistry could benefit practitioners and patients. Dental professionals and AI experts must work together to maximize this new technology.AI integration in dentistry can significantly improve root caries diagnosis and treatment by predicting root caries using radiographic images. This early detection reduces treatment need and time. Collaboration between dental professionals and AI experts is crucial for maximizing benefits.

Social Vulnerability Index and Dental Caries in Children: An Exploratory Study.

This retrospective cross-sectional study evaluated the association between caries outcomes in a pediatric population visiting a dental clinic and the social vulnerability index, an area-based measure capturing 4 main social determinants of health: socioeconomic status, household composition/disability, minority status/language, and housing/transportation. The Centers for Disease Control Social Vulnerability Index (SVI) and electronic dental record data of children (0 to 18 y) reporting a caries diagnosis at the Children's Hospital Colorado in 2020 were extracted for 9,201 children. Logistic regressions were used to test the association between SVI and the presence or absence of dental caries, adjusting for age, sex, ethnicity, and race. Children with a caries diagnosis had a greater mean overall SVI percentile (62.0, standard deviation [SD] = 29.1) compared with patients without a caries diagnosis (59.1, SD = 29.8; P < 0.001). With each 10-point increase in the overall SVI percentile, having a caries diagnosis visit was 2.7% more likely compared with having a visit without a caries diagnosis (odds ratio [OR] 1.027, 95% confidence interval [CI] 1.012, 1.042; P = 0.0004). Those with an overall SVI percentile between 51 and 75 were 23% more likely to have a caries diagnosis compared with those with a percentile ≤25 (OR 1.23, 95% CI 1.07, 1.42; P = 0.003), and those with a percentile >75 were 23.6% more likely to have a caries diagnosis compared with those with a percentile ≤25 (OR 1.236, 95% CI 1.09, 1.40; P = 0.001). Children (0 to 18 y) living in socially vulnerable environments or areas were more likely to have a caries diagnosis at their dental exam. This study showed an association between social determinants of health demonstrating social vulnerability and dental caries in children. Ultimately, understanding upstream factors for children living in socially vulnerable areas could support policymakers in creating more effective policies to support socially vulnerable populations.

Is there an association between salivary immune and microbial profile with dental health in systematically healthy children?

ObjectiveThis study aimed to characterize the inflammatory profile of systemically healthy children’s saliva and its association with clinical diagnoses of caries and gingival inflammation.Materials and methodsUnstimulated saliva was collected from 100 children before clinical dental examinations. The saliva samples were analyzed for total protein and specific inflammatory cytokines (IL-10, IL-8, IL-6, and TNFα) with Bradford and ELISA assays, respectively. Salivary bacteria were quantified using a quantitative real-time polymerase chain assay. The salivary values were then correlated with age, DMFT index, plaque index (PI), and gingival index (GI).ResultsThe mean age of the cohort was 8.08 ± 0.23 years with 49% females, the mean DMF of the cohort was 2.64 ± 0.31, the mean GI was 0.51 ± 0.06, and the mean PI was 1.33 ± 0.07. Significant correlations were found between PI with DMFT and GI. Children with DMFT > 2 had significantly higher levels of IL-8 compared with children with DMFT ≤ 2. IL-6 and TNFα were significantly higher among children with PI > 1 than among children with PI ≤ 1.ConclusionsSalivary cytokine were found to be associate with clinical parameters as DMFT and PI, thus may be a potential tool that reflects dental health status.Clinical relevanceThe presence of salivary cytokines in children may reflect evaluation of dental caries and oral inflammation.

A survey on radiographic diagnosis and interpretation of proximal dental caries

A survey on radiographic diagnosis and interpretation of proximal dental caries

Methods used for caries detection and diagnosis in Ontario dental practices: a cross-sectional survey

BackgroundEarly detection of caries is essential for applying non-surgical treatment procedures and preventing the formation of cavitated lesions leading to unnecessary removal of tooth structure. Understanding dentists’ preferences for caries detection tools can inform stakeholders about their strategies and knowledge of contemporary, evidence-based caries management approaches. However, there is a lack of research exploring the detection methods of caries commonly used by dentists in Ontario, Canada. The objective of this study was to investigate the methods of caries detection and diagnosis preferred by dentists in Ontario.MethodsA 21-item self-reported survey was mailed to one thousand Ontario dental practices in the Winter of 2022. Descriptive and bivariate data analysis were performed to determine the associations between: demographics and professional practice characteristics (explanatory variables), and methods for detecting and diagnosing dental caries (outcome variables) using SPSS Statistics 29.0.ResultsA total of 325 dentists (33%) responded to the survey, with 274 answering all of the questions completely. The highest proportion of respondents were 35–44 years of age (32.8%) and male (53.4%). More than half of the respondents reported using a dental explorer to assess primary occlusal caries (57.6%), secondary caries (57.1%), and cervical caries (57.5%). Likewise, 57.9% of the participants reported using dental radiographs to diagnose proximal caries. Among additional caries detection tools, digital radiography (89.8%) and traditional radiography (84.7%) were the most used methods/modalities, while cone beam computed tomography was the least (12.8%). Most study participants did not use any caries classification system (77.7%) or caries risk assessment tool (85.3%).ConclusionsParticipants preferred conventional methods for caries detection, instead of contemporary visual-tactile caries lesions classification and/or caries risk assessment systems. These findings indicate a need for continuing dental education programs tailored to evidence-based caries management approaches.

Tactile sensation in relation to roughness and reflection of active initial lesions in primary (deciduous) and permanent dentition in vitro

ObjectivesThis study evaluated whether a relationship exist between tactile sensation, roughness and reflection intensity in active enamel lesions of primary (deciduous) and permanent dentition. MethodsFreshly extracted teeth of the primary (n=29) and permanent (n=60) dentition of patients who underwent serial extractions under general anesthesia due to multiple deep caries lesions showing active lesions (International Caries Detection and Assessment System scores of 2) were selected. The mean linear (Ra), area-related (Sa), volume-related (Vmc) roughness and vertical reflection intensity (VRI) of sound (S) and carious (C) areas were determined by using a 3D-laser-scanning-microscope and a multi-sensor microscope with two different chromatic-confocal optics. Furthermore, two blinded examiners evaluated the roughness by tactile examination using three different explorers (S23H,405CP11, S3C). ResultsMean differences (95%CI) between S and C for teeth of the primary dentition were: Ra:-1.9(-2.3;-0.4)µm, Sa:-31.8(-1.8;0.0)µm, Vmc:-1.8(-1.6;-0.0)ml/m2, VRI:29(20;43) and for teeth of the permanent dentition: Ra:-4.0(-2.5;-1.0)µm, Sa:-4.8(-3.0;-1.1)µm, Vmc:-4.6(-3.4;-0.5)ml/m2, VRI:34(19;44) differing significantly between S and C (p<0.05,Wilcoxon test). No significant difference was observed between 1st and 2nd dentition (p>0.05, Kruskal-Wallis test) as well as commercial and experimental optic (p>0.05). The highest positive predictive value (PPV) was achieved by examiner 1 with explorer S3C (1st dentition 67%;2nd dentition 100%;pooled dentition 88%)), while examiner 2 revealed the highest PPV with explorer S23H (89%;86%;88%). ConclusionDifferences in roughness and reflectance between sound and caries-active enamel surfaces could be evaluated in both primary and permanent dentition. These differences could also be reliably detected using three different explorers with good validity. However, the most predictive explorer seems to differ between examiners. Clinical SignificanceIn both primary (deciduous) and permanent dentition active caries lesions exhibit significantly higher roughness and lower vertical reflection intensity compared with sound enamel. These differences are detectable by blind tactile examination and objective methods such as 3D-laser-scanning or multi-sensor microscopy, highlighting their utility in caries diagnosis in both dentitions.

Evaluation of DIAGNOdent pen for initial occlusal caries diagnosis in permanent teeth

BackgroundTo verify the validity of diagnosing initial caries on occlusal surface of permanent posterior teeth by laser fluorescence instrument DIAGNOdent pen.MethodsPatients from School of Stomatology in Wuhan University were selected and their posterior teeth were examined using DIAGNOdent pen and the International Caries Detection and Assessment System (ICDAS II) by an experienced dentist. After teeth extraction, histological criteria were used to determine the severity of the lesions. The sensitivity, specificity, accuracy, the area under the curve (AUC), and correlation of DIAGNOdent pen and ICDAS II were analyzed compared with histological criteria. Examiners’ agreement was measured.ResultsThe sensitivity range was 0.440-1 while that of specificity was 0.750–0.994. The accuracy and AUC were above 80% and 0.7 respectively. Consistency of examiners’ kappa values of ICDAS II, DIAGNOdent pen, and histological criteria were ranged from 0.629 to 0.840.ConclusionsICDAS II and DIAGNOdent pen can be effectively used in tandem or independently for the assessment of initial caries.

Inconsistent decision making in dental caries diagnosis and treatment: A case-based questionnaire survey.

Delayed implementation of new knowledge into clinical practice poses patient safety risks. This study investigates agreement on use of the dental caries interventions, sealing, and stepwise excavation. A cross-sectional questionnaire survey, based on 11 constructed cases with descriptions of patient symptoms, radiographic, and clinical findings. Interrater agreement on dental caries- and pulp diagnoses and interventions were measured with Cohen's and Light's κ. The data collection period was September 28 to November 5, 2021. To explore variations in use and knowledge factors, we examined Danish dentists' attitudes towardcontinuing education. Based on 243 responses, moderate interrater agreement for dental caries and pulp diagnoses and weak agreement on interventions were seen. The agreement with the gold standard for caries was moderate. No agreement was found for dental pulp diagnosis, and for interventions the agreement was weak. No pattern in agreement with the gold standard was seen in relation to case difficulty level. The majority reported knowing of and using stepwise excavation, in conflict with findings that less than half chose stepwise excavation in cases, where considered appropriate. One in four (25%) reported to be unfamiliar with sealing, and half (50%) use sealing regularly. Better access to continuing education and for universities to offer continuing education as alternatives to one-sidedprivate market were requested. Some patients may receive too radical treatment despite available less invasive evidence-based effective treatments. Dentists acknowledge the importance of continuing education. Easier access and perhaps more incentives for seeking out high-quality continuing education from trustworthy sources are needed.

Cross-sectional survey and analysis of factors influencing the prevalence of dental caries among older individuals aged 65-74 in Guangdong Province in 2021

BackgroundThe prevalence of oral diseases is subject to change over time. In 2021, Guangdong Province conducted its fourth survey assessing the oral health status of individuals aged 65–74.Materials and methodsEvaluation criteria and potential influencing factors were identified. A sample of residents aged 65–74 from 13 designated monitoring sites in Guangdong Province was randomly selected for the study. Spearman correlation analysis was employed to investigate the clinical correlation between influencing factors and evaluation criteria. Negative binomial and zero-inflated negative binomial regression models were utilized to examine the factors influencing caries prevalence. In contrast, logistic regression was employed to identify the risk factors for caries occurrence. A p-value of ≤0.05 was considered statistically significant.ResultsThe prevalence of caries rate of crowns, roots, and teeth were 76.36%, 52.25%, and 79.2%, respectively. Individuals with periodontal pockets exhibited a significantly higher risk of root caries. The presence of dental calculus significantly exacerbated the occurrence of crown, root, and dental caries, and increased the risk of crown and dental caries. Consuming sweet foods once or more a week notably increased the average root decayed score (D of roots), the prevalence rate of root caries, and the D score of the Decayed, Missing, and Filled teeth [DMFT] index in individuals already afflicted with this condition. Similarly, the consumption of sweetened drinks significantly elevated the risk of crown and root caries, exacerbating overall caries progression. Frequencies of manual toothbrush and toothpick cleaning showed a negative correlation with average tooth missing score (MT). In contrast, the frequency of manual/electric toothbrush and toothpick cleaning was negatively correlated with the DMFT index. Engaging in dental diagnosis and treatment behaviors significantly increased the number of filled crowns (F), MT, and DMFT scores while reducing the prevalence of dental caries.ConclusionsIn Guangdong Province, caries prevalence among older individuals aged 65–74 remains substantial. Relevant professionals and institutions must provide comprehensive guidance and assistance to the older population, emphasizing the importance of reducing the consumption of sweets and sweetened beverages, adopting correct tooth brushing techniques and frequency (at least twice daily), timely treatment of periodontal diseases, conducting regular epidemiological caries surveys, and addressing economic barriers to accessing caries diagnosis and treatment services.

Artificial Intelligence in Dental Caries Diagnosis and Detection: An Umbrella Review.

Dental caries is largely preventable, yet an important global health issue. Numerous systematic reviews have summarized the efficacy of artificial intelligence (AI) models for the diagnosis and detection of dental caries. Therefore, this umbrella review aimed to synthesize the results of systematic reviews on the application and effectiveness of AI models in diagnosing and detecting dental caries. MEDLINE/PubMed, IEEE Explore, Embase, and Cochrane Database of Systematic Reviews were searched to retrieve studies. Two authors independently screened the articles based on eligibility criteria and then, appraised the included articles. The findings are summarized in tabulation form and discussed using the narrative method. A total of 1249 entries were identified out of which 7 were finally included. The most often employed AI algorithms were the multilayer perceptron, support vector machine (SVM), and neural networks. The algorithms were built to perform the segmentation, classification, caries detection, diagnosis, and caries prediction from several sources, including periapical radiographs, panoramic radiographs, smartphone images, bitewing radiographs, near-infrared light transillumination images, and so forth. Convoluted neural networks (CNN) demonstrated high sensitivity, specificity, and area under the curve in the caries detection, segmentation, and classification tests. Notably, AI in conjunction with periapical and panoramic radiography images yielded better accuracy in detecting and diagnosing dental caries. AI models, especially convolutional neural network (CNN)-based models, have an enormous amount of potential for accurate, objective dental caries diagnosis and detection. However, ethical considerations and cautious adoption remain critical to its successful integration into routine practice.

Recent Advances in Digital Diagnosis of Caries

Recent Advances in Digital Diagnosis of Caries

Diş Çürüklerinde Yapay Zeka Kullanımının Bibliyometrik Analizi

Background: The aim of this study is to examine the development trends and dynamics of research on the use of artificial intelligence in dental caries diagnosis, to identify the strengths and limitations of the existing literature, and to guide future research. Methods: A literature search was conducted using the Web of Science database, covering articles published before 3 June 2024. Pilot searches were conducted and 883 studies were reached. After the specified scanning and filtering processes, the study was carried out on 270 publications. In the bibliometric analysis, the Biblioshiny R package as well as the features of Web of Science and VOSviewer software were used for visualizations. Microsoft Excel was used to tabulate the data. Results: There is a general increase in the number of articles published each year. A total of 3081 citations were made to publications on the use of artificial intelligence in cariology. The average number of citations per article was found to be 11.41, and the H index was 29. The most cited country was Germany (581 citations), and the most influential author was Falk Schwendicke. On the basis of institutions, the highest contribution was made by Charite University Medicine Berlin (19 articles, 475 citations). Conclusion: Since 2008, and particularly since 2018, the utilisation of artificial intelligence (AI) in the investigation of dental caries and oral and dental diseases has garnered increasing interest. Artificial Intelligence (AI) can be said to be a groundbreaking discovery that will be increasingly applied in various branches of dentistry.

The Prevalence of Candida albicans and Malassezia globosa in Preschool Children with Severe Early Childhood Caries: A Case-Control Study.

Background: This cross-sectional study aimed to identify the prevalence of Candida albicans and Malassezia globosa in children with severe early childhood caries and caries-free children in Hong Kong. Methods: This study first recruited a total of 80 children aged between 48 and 72 months old, 40 children with severe early childhood caries, and 40 caries-free children. The children were then further divided into four groups, with 20 children in each group: Group 1: Severe early childhood caries-C. albicans, Group 2: Severe early childhood caries-M. globosa, Group 3: Caries-free-C. albicans and Group 4: Caries-free-M. globosa. Saliva, plaque, and caries lesion samples were collected from participants with severe early childhood caries, while only saliva and plaque samples were collected from caries-free participants. Caries status of the primary molars was assessed using WHO's decayed, missing, and filled tooth index, and the severity of cavitated lesions was determined based on International Caries Diagnosis and Assessment System criteria as caries code 5 or 6. The samples were analyzed using an Internal Transcribed Space and Quantitative Real-Time Polymerase Chain Reaction. Results:C. albicans was more prevalent in saliva and plaque samples of severe early childhood caries than in the caries-free group. Proportion of C. albicans in both saliva and plaque samples differed significantly between severe early childhood caries and caries-free groups (p < 0.05). Within the severe early childhood caries group, the proportion of children with C. albicans varied between 6 and 46%. No significant difference in M. globosa load was found between plaque samples of the severe early childhood caries and caries-free groups (p = 0.159). Conversely, no significant difference in M. globosa load was observed between saliva samples of severe early childhood caries and caries-free groups (p = 0.051). Conclusions: This study demonstrated a strong association between C. albicans and severe early childhood caries. M. globosa was detected in both the caries-free and severe early childhood caries groups, albeit at low levels.

Early diagnosis of early childhood caries: A simple novel tool (MAAC charts) for parents and caretakers

Early Childhood Caries (ECC) is a condition associated with nearly 123 risk factors. Among all the risk factors, enamel defects or enamel hypoplasia (ED/EH) has been shown to be the primary risk factor. ED/EH comprises the earliest changes that manifest on newly erupted primary tooth surfaces soon after its eruption. ED/EH appears as white lines or patches, either as demarcated or diffuse opacities on the enamel surface. It is often observed on the labial and buccal surfaces of the newly erupted primary maxillary incisors. A simple self-report tool for mothers/caretakers, healthcare professionals, and primary care workers to diagnose early changes in ECC may facilitate early diagnosis. The MAAC chart was developed to prevent ECC in children with cleft lip and palate. These charts, primarily developed to educate parents, caregivers, healthcare professionals, and primary health care providers, illustrate the variations in ED/EH on the enamel surface (demarcated and diffuse opacities on various maxillary teeth) as a collage of pictures organized as incisors and canines. This paper describes a novel and simple, tool called ‘MAAC charts’ providing guidance on early signs of Early childhood caries (ECC) for health professionals, parents and caretakers. MAAC charts can be useful for Pediatric dentists while providing anticipatory guidance during early first dental visits in their office. Pediatric dentists can suggest paediatricians to use these MAAC charts, in vaccinations clinics and various in/out patient settings to provide oral health related anticipatory guidance. Pediatric dentists can use these MAAC charts in large scale public health campaigns focused on preventing ECC.

Diagnosis of dental caries based on attenuation coefficients analysis of optical coherence tomography images.

Quantitative analysis of optical attenuation based on optical coherence tomographyimages will offer an effective method to enhance diagnostic capabilities. In this paper, the optical attenuation in demineralized caries specimens was calculated to distinguish between normal teeth and carious teeth and further to differentiate the severity of caries, and thus come to the half-automated diagnosis of dental caries. Results show that the attenuation coefficient in carious regions is approximately , while that of normal teeth is about . Attenuation coefficient of carious regions is 35% higher than that of normal teeth. Moreover, five classes of caries were qualified and classified based on the optical attenuation coefficient. Compared with the healthy teeth, there is a noticeable disparity in the attenuation coefficients of carious teeth, both on the surface and at the dentinoenamel junction. This study provides a method for accurate caries diagnosis, particularly in detection of early lesions and subtle structural changes.