Caries Detection Research Articles

Detection of caries around restorations on bitewings using deep learning

ObjectiveSecondary caries lesions adjacent to restorations, a leading cause of restoration failure, require accurate diagnostic methods to ensure an optimal treatment outcome. Traditional diagnostic strategies rely on visual inspection complemented by radiographs. Recent advancements in artificial intelligence (AI), particularly deep learning, provide potential improvements in caries detection. This study aimed to develop a convolutional neural network (CNN)-based algorithm for detecting primary caries and secondary caries around restorations using bitewings. MethodsClinical data from 7 general dental practices in the Netherlands, comprising 425 bitewings of 383 patients, were utilized. The study used the Mask-RCNN architecture, for instance, segmentation, supported by the Swin Transformer backbone. After data augmentation, model training was performed through a ten-fold cross-validation. The diagnostic accuracy of the algorithm was evaluated by calculating the area under the Free-Response Receiver Operating Characteristics curve, sensitivity, precision, and F1 scores. ResultsThe model achieved areas under FROC curves of 0.806 and 0.804, and F1-scores of 0.689 and 0.719 for primary and secondary caries detection, respectively. ConclusionAn accurate CNN-based automated system was developed to detect primary and secondary caries lesions on bitewings, highlighting a significant advancement in automated caries diagnostics. Clinical significanceAn accurate algorithm that integrates the detection of both primary and secondary caries will permit the development of automated systems to aid clinicians in their daily clinical practice.

Occlusal Caries Detection with Intraoral Scanners in Pediatric Dentistry: A Comparative Clinical Study.

Background: The aim of this clinical study was to compare the occlusal caries detection (OCD) performance of the intraoral scanners (IOSs) Trios 4 (TIO, 3Shape) and Emerald S (EME, Planmeca) and the Diagnocam (DIA, KaVo) with the established visual (WHO) examination (VIS, reference method). Methods: Between 08/2022 and 02/2023, 60 children (mean age 9.6 ± 2.5 years) were examined as part of their regular dental checkups. OCD was performed at the tooth level, separately for primary and permanent unrestored teeth. Furthermore, two thresholds were analyzed: sound versus overall caries (pooled data of enamel and dentin caries, TH1) and pooled data of sound and enamel caries versus dentin caries (TH2). Results: The best agreement with the reference method (reliability) in both dentitions was obtained for DIA (ĸ = 0.829/ĸ = 0.846; primary/permanent teeth), followed by EME (ĸ = 0.827/ĸ = 0.837) and TIO (ĸ = 0.714/ĸ = 0.680). Similar results were shown for the diagnostic quality (sensitivity, specificity and area under the curve of the receiver operating characteristic curve), with higher values for TH1 than for TH2. Both IOSs and the DIA showed worse results than the reference method VIS. Conclusions: Currently, IOS should be used as an additional caries detection tool, especially for visualization, and cannot be recommended as a basic tool for diagnosis or invasive/noninvasive therapy decisions in OCD.

Automatic caries detection in bitewing radiographs—Part II: experimental comparison

ObjectiveThe objective of this study was to compare the detection of caries in bitewing radiographs by multiple dentists with an automatic method and to evaluate the detection performance in the absence of a reliable ground truth.Materials and methodsFour experts and three novices marked caries using bounding boxes in 100 bitewing radiographs. The same dataset was processed by an automatic object detection deep learning method. All annotators were compared in terms of the number of errors and intersection over union (IoU) using pairwise comparisons, with respect to the consensus standard, and with respect to the annotator of the training dataset of the automatic method.ResultsThe number of lesions marked by experts in 100 images varied between 241 and 425. Pairwise comparisons showed that the automatic method outperformed all dentists except the original annotator in the mean number of errors, while being among the best in terms of IoU. With respect to a consensus standard, the performance of the automatic method was best in terms of the number of errors and slightly below average in terms of IoU. Compared with the original annotator, the automatic method had the highest IoU and only one expert made fewer errors.ConclusionsThe automatic method consistently outperformed novices and performed as well as highly experienced dentists.Clinical significanceThe consensus in caries detection between experts is low. An automatic method based on deep learning can improve both the accuracy and repeatability of caries detection, providing a useful second opinion even for very experienced dentists.

Al-Based Detection of Dental Caries: Comparative Analysis with Clinical Examination

ABSTRACT Dental caries pose a significant public health concern, affecting a vast population globally. Traditional clinical examination methods, although reliable, can be subject to human error and time-consuming. Artificial intelligence (AI) technologies have emerged as promising tools to enhance diagnostic accuracy and efficiency. This study explores the potential of AI in revolutionizing dental caries detection. Materials and Methods: A cohort of 50 patients with varying degrees of dental caries participated in this comparative analysis. Clinical examination by dental professionals served as the gold standard for caries detection. AI algorithms were trained using dental images, and their performance was evaluated against the clinical examination results. Results: The AI-based detection system demonstrated a sensitivity of 92% and a specificity of 85% in identifying dental caries, with an overall accuracy of 88%. The clinical examination yielded a sensitivity of 86% and a specificity of 90%, resulting in an overall accuracy of 88%. Statistical analysis indicated no significant difference between AI-based detection and clinical examination (P > 0.05). Conclusion: AI technology exhibits promise as an adjunctive tool for dental practitioners, potentially reducing diagnostic errors and improving efficiency. Integrating AI into routine dental practice may aid in early caries detection and promote better oral health outcomes.

Preliminary Study of Dental Caries Detection by Deep Neural Network Applying Domain-Specific Transfer Learning

PurposeThe purpose of this study is to confirm whether it is possible to acquire a certain degree of diagnostic ability even with a small dataset using domain-specific transfer learning. In this study, we constructed a simulated caries detection model on panoramic tomography using transfer learning.MethodsA simulated caries model was trained and validated using 1094 trimmed intraoral images. A convolutional neural network (CNN) with three convolution and three max pooling layers was developed. We applied this caries detection model to 50 panoramic images and evaluated its diagnostic performance.ResultsThe diagnostic performance of the CNN model on the intraoral film was as follows: C0 84.6%; C1 90.6%; C2 88.6%. Finally, we tested 50 panoramic images with simulated caries insertion. The diagnostic performance of the CNN model on the panoramic image was as follows: C0 75.0%, C1 80.0%, C2 80.0%, and overall diagnostic accuracy was 78.0%. The diagnostic performance of the caries detection model constructed only with panoramic images was much lower than that of the intraoral film.ConclusionDomain-specific transfer learning methods may be useful for saving datasets and training time (179/250).

Evaluating the Accuracy of AI-Based Software vs Human Interpretation in the Diagnosis of Dental Caries Using Intraoral Radiographs: An RCT

ABSTRACT Background: Dental caries is a prevalent oral health issue, often diagnosed through intraoral radiographs. The accuracy of Artificial Intelligence (AI) in diagnosing dental caries from these radiographs is a subject of growing interest Materials and Methods: In this RCT, 200 intraoral radiographs were collected from patients seeking dental care. These radiographs were independently evaluated by both AI-based software and experienced human dentists. The software utilized deep learning algorithms to analyze the radiographs for signs of dental caries. The performance of both AI and human interpretations was compared by calculating sensitivity, specificity, and overall accuracy. Arbitrary values of 85% sensitivity, 90% specificity, and 88% overall accuracy were set as benchmarks. Results: The AI-based software demonstrated a sensitivity of 88%, a specificity of 91%, and an overall accuracy of 89% in diagnosing dental caries from intraoral radiographs. Human interpretation, however, yielded a sensitivity of 84%, a specificity of 88%, and an overall accuracy of 86%. The AI-based software performed consistently close to or above the predefined benchmarks, while human interpretation showed slightly lower accuracy rates Conclusion: This RCT suggests that AI-based software is a valuable tool for diagnosing dental caries from intraoral radiographs, with performance comparable to or exceeding that of experienced human dentists. The consistent accuracy of AI in this context highlights its potential as an adjunctive diagnostic tool, which can aid dental professionals in more efficient and precise caries detection.

Artificial intelligence and dental panoramic radiographs: where are we now?

Bielefeld Academic Search Engine (BASE), Google Scholar Association for Computing Machinery: Guide to Computing Literature (ACM) and National Library of Medicine: PubMed databases were searched for systematic reviews. This study addressed a structured PICO question (Population, Intervention, Comparison, Outcome). Population was panoramic radiographs in human subjects. Intervention was use of artificial intelligence (AI) diagnostics, compared to human-only diagnosis. Quantitative or qualitative AI efficiency was the outcome. Systematic reviews were considered if they stated 'systematic review' in their title or abstract, were published in English and were not bound by a certain time frame. No supplemental primary studies were included. Screening and removal of duplicates were performed using the Rayyan tool. Data were extracted from each systematic review by two authors, with a third author having the deciding vote in cases of inconsistency. Cohen's Kappa co-efficient was used to measure reliability between authors, resulting in almost perfect agreement. The risk of bias was accounted for using the ROBIS method which resulted in one paper being rejected, so only 11 included in results. Data were then grouped into seven domains which were detected by AI: teeth identification and numbering, detection of periapical lesions, periodontal bone loss, osteoporosis, maxillary sinusitis, dental caries, and other tasks. The effectiveness of the AI systems was assessed by various outcome metrics - accuracy, sensitivity, specificity, and precision being the most common variables. Results of this overview show a significant increase in accuracy of AI in analysing OPTs between 1988-2023. Latest AI models are most accurate in teeth identification and numbering (93.67%) whilst caries detection and osteoporosis showed 91.5% and 89.29% accuracy, respectively. Accurate results were also observed for the detection of maxillary sinusitis and periodontal bone loss. However, given the heterogeneity of source studies used in these systematic reviews, results should be interpreted with caution. With improving AI technology, its use in dental radiology can be increasingly effective in supporting dentists in the detection of different pathologies. This overview has shown that systematic reviews of AI can quickly become outdated and that results of any systematic review should be treated with caution as this field advances. As such, regular updating and ongoing research is required.

Accuracy of an intraoral scanner with near-infrared imaging feature in detection of interproximal caries of permanent teeth: An in vivo validation.

This study aimed to evaluate the accuracy of an intraoral scanner with near-infrared imaging (NIRI) feature in the diagnosis of interproximal caries and to compare it with the visual-tactile method (VTM), bitewing radiography (BWR), and panoramic radiography (PR). Six hundred thirty-nine interproximal surfaces (mesial-distal) of posterior teeth from 22 volunteers were examined. Results were scored by VTM, BWR, PR, and NIRI. Lesions were scored as 0 for no-caries, 1 for early-enamel lesion (EEL), and 2 for lesions involving dentino-enamel junction (DEJ). McNemar, Kappa, and Fleis Kappa tests were used to evaluate the agreement levels. Pearson's Chi-square test was used to determine the matching rates after validation. A good level of agreement was observed between examination methods (Ƙ = 0.613; p < 0.001). In pairwise comparisons, a moderate agreement was seen between all the methods for lesions with DEJ involvement, while a statistically good agreement was observed between BWR and NIRI (Ƙ = 0.675; p < 0.001). As a result of validation, the accuracy of NIRI for molars was considered 85.2% and 75.7% for premolars in EELs, 85.2% for molars, and 70% for premolars regarding the lesions involving DEJ. Intraoral scanners with the NIRI feature may be used for diagnosing interproximal caries, especially for permanent molars. Early detection of proximal caries is one of the most essential topics forming the basis of preventive dentistry. This study investigates a caries diagnostic tool integrated into intraoral scanners to diagnose interproximal caries. A caries diagnostic tool integrated into an intraoral scanner may prevent the harmful effects of ionizing radiation in early caries diagnosis and may improve the patient's oral health status.

Host-Guest Mechanism via Induced Fit Fullerene Complexation in Porphin Receptor to Probe Salivary Alpha-Amylase in Dental Caries for Clinical Applications.

Salivary α-amylase is the most abundant protein of human saliva that potentially binds to streptococcus and other bacteria via specific surface-exposed α-amylase-binding proteins and plays a significant role in caries development. The detection of α-amylase in saliva can be used as a bioindicator of caries development. Herein, a facile strategy has been applied, tailoring the photochemical properties of 5,10,15,20-tetrakis(4-hydroxyphenyl)-21H,23H-porphine (TPPOH) and the fullerene C60 complex. The fluorescence emission of TPPOH is quenched by starch-coated fullerene C60 via charge-transfer effects, as determined by UV absorption and fluorescence spectroscopic studies. The starch-coated C60 has been thoroughly characterized via Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), optical microscopy, thermal gravimetric analysis (TGA), static water contact angle measurements, and zeta potential measurements. The analytical response of the assay showed a linear fluorescent response in α-amylase concentrations ranging from 0.001-0.1 Units/mL, with an LOD of 0.001 Units/mL. The applicability of the method was tested using artificial saliva with quantitative recoveries in the range 95-100%. The practicability of the procedure was verified by inspecting saliva samples of real clinical samples covering all age groups. We believe that the proposed method can serve as an alternative analytical method for caries detection and risk assessment that would also minimize the cost of professional preventive measures and treatments.

Diagnostic accuracy of tele-dentistry in screening children for dental caries by community health workers in a lower-middle-income country.

Tele-dentistry can be useful for dental caries screening of children, especially in lower-middle-income countries (LMICs). To evaluate the diagnostic accuracy of mobile phone photographs taken by a community health worker (CHW) for caries detection in Iran. Children aged 6-12 years were visually examined by a paediatric dentist. Following dental examinations, intraoral photographs were taken by a trained CHW. Two remote dentists assessed intraoral photographs for dental caries. Diagnostic accuracy of tele-dentistry for caries detection was evaluated. In addition, the questionnaire about oral health and parents' views towards tele-dentistry was prepared. One hundred thirty-one children aged 8.74 ± 1.62 years participated. The caries prevalence was 30% for the whole dentition. Tele-dentistry demonstrated high accuracy, with a sensitivity exceeding 80% and specificity exceeding 90%. The inter-rater reliability for remote dentists' assessments to the gold standard dental examination ranged from substantial to almost perfect (kappa: 75%-93%). Additionally, 80% of parents whose children participated in this study had positive views towards tele-dentistry. Tele-dentistry was shown to be an alternative approach to clinical examinations for caries detection among school children. Employing non-dental care professionals in tele-dentistry has been emerged as a reliable and cost-effective approach, especially in LMICs.

Behaviour change intervention for toothbrushing (lesson and text messages) to prevent dental caries in secondary school pupils: The BRIGHT randomized control trial.

This multicentre, assessor-blinded, two-arm cluster randomized trial evaluated the clinical and cost-effectiveness of a behaviour change intervention promoting toothbrushing for preventing dental caries in UK secondary schools. Pupils aged 11-13 years with their own mobile telephone attending secondary schools with above average free school meals eligibility were randomized (at year-group level) to receive a lesson and twice-daily text messages or to usual care. Year-groups (n = 84) from 42 schools including 4680 pupils (intervention, n = 2262; control, n = 2418) were randomized. In 2383 participants with valid data at baseline and 2.5 years, the primary outcome of presence of at least one treated or untreated carious lesion (D4-6 MFT [Decayed, Missing and Filled Teeth] in permanent teeth using International Caries Detection and Assessment System) was 44.6% in the intervention group and 43.0% in control (odds ratio [OR] 1.04, 95% CI 0.85-1.26, p = .72). There were no statistically significant differences in secondary outcomes of presence of at least one treated or untreated carious lesion (D1-6 MFT), number of D4-6 MFT and D1-6 MFT, plaque and bleeding scores or health-related- (Child Health Utility 9D) or oral health-related- quality of life (CARIES-QC). However, twice-daily toothbrushing, reported by 77.6% of pupils at baseline, increased at 6 months (intervention, 86.9%; control, 83.0%; OR 1.30, 95% CI 1.03-1.63, p = .03), but returned to no difference at 2.5 years (intervention, 81.0%; control, 79.9%; OR 1.05, 95% CI 0.84-1.30, p = .69). Estimated incremental costs and quality-adjusted life-years (QALYs) of the intervention, relative to control, were £1.02 (95% CI -1.29 to 3.23) and -0.003 (95% CI -0.009 to 0.002), respectively, with a 7% chance of being cost-effective (£20 000/QALY gained threshold). There was no evidence of statistically significant difference for caries prevalence at 2.5-years. The intervention's positive 6-month toothbrushing behaviour change did not translate into caries reduction. (ISRCTN 12139369). COVID-19 pandemic adversly affected follow-up.

Sleep disorders, anxiety and obesity associated with untreated dental caries in children eight to ten years of age.

To evaluate the presence of sleep disorders, obesity and anxiety associated with cavitated carious lesions in children aged 8 to 10 years. A cross-sectional study was carried out in the Northeast of Brazil. The sample was comprised of 793 schoolchildren randomly selected from public and private schools. Calibrated examiners (Kappa >0.80) performed the clinical examination of dental caries using the International Caries Detection and Assessment System and applied the Revised Children's Manifest Anxiety Scale and the Sleep Disturbance Scale for Children questionnaires. The anthropometric variables evaluated were weight and height. Negative binomial regressions (α ≤ 0.05) were performed. A Directed Acyclic Graph was prepared using DAGitty software (version 3.0), to select the co-variables for the statistical fits. The prevalence of tooth decay was 52.8%. The mean number of tooth surfaces with cavitated caries was 2.2(2.8), 58.9% of the schoolchildren had some type of sleep disorder, while 20.2% were anxious and 29.1% were obese. Sleep disturbance (RR = 1.38; 95% CI: 1.05-1.83), general anxiety (RR = 1.71; 95% CI: 1.32-2.21), obesity (RR = 1.48; 95% CI: 1.17-1.86) were associated with dental caries in the final model. The presence of carious lesions was higher in children with sleep disorders, anxiety, obesity, and those who experienced dry mouth.

Detection of caries lesions using a water-sensitive STIR sequence in dental MRI

In clinical practice, diagnosis of suspected carious lesions is verified by using conventional dental radiography (DR), including panoramic radiography (OPT), bitewing imaging, and dental X-ray. The aim of this study was to evaluate the use of magnetic resonance imaging (MRI) for caries visualization. Fourteen patients with clinically suspected carious lesions, verified by standardized dental examination including DR and OPT, were imaged with 3D isotropic T2-weighted STIR (short tau inversion recovery) and T1 FFE Black bone sequences. Intensities of dental caries, hard tissue and pulp were measured and calculated as aSNR (apparent signal to noise ratio) and aHTMCNR (apparent hard tissue to muscle contrast to noise ratio) in both sequences. Imaging findings were then correlated to clinical examination results. In STIR as well as in T1 FFE black bone images, aSNR and aHTMCNR was significantly higher in carious lesions than in healthy hard tissue (p < 0.001). Using water-sensitive STIR sequence allowed for detecting significantly lower aSNR and aHTMCNR in carious teeth compared to healthy teeth (p = 0.01). The use of MRI for the detection of caries is a promising imaging technique that may complement clinical exams and traditional imaging.

Rapid detection of non-normal teeth on dental X-ray images using improved Mask R-CNN with attention mechanism.

Dental health has been getting increased attention. Timely detection of non-normal teeth (caries, residual root, retainer, teeth filling, etc.) is of great importance for people's health, well-being, and quality of life. This work proposes a rapid detection of non-normal teeth based on improved Mask R-CNN, aiming to achieve comprehensive screening of non-normal teeth on dental X-ray images. An improved Mask R-CNN based on attention mechanism was used to develop a non-normal teeth detection method trained on a high-quality annotated dataset, which can segment the whole mask of each non-normal tooth on the dental X-ray image immediately. The average precision (AP) of the proposed non-normal teeth detection was 0.795 with an intersection-over-union of 0.5 and max detections (maxDets) of 32, which was higher than that of the typical Mask R-CNN method (AP = 0.750). In addition, validation experiments showed that the evaluation metrics (AP, recall, precision-recall (P-R) curve) of the proposed method were superior to those of the Mask R-CNN method. Furthermore, the experimental results indicated that proposed method exhibited a high sensitivity (95.65%) in detecting secondary caries. The proposed method took about 0.12s to segment non-normal teeth on one dental X-ray image using the laptop (8G memory, NVIDIA RTX 3060 graphics processing unit), which was much faster than conventional manual methods. The proposed method enhances the accuracy and efficiency of abnormal tooth diagnosis for practitioners, while also facilitating early detection and treatment of dental caries to substantially lower patient costs. Additionally, it can enable rapid and objective evaluation of student performance in dental examinations.

Near-infrared light transillumination for occlusal caries detection in dog teeth: A comparative study

The aim of this study is to compare the effectiveness of visual, radiologic, and near-infrared light transillumination caries detection methods on caries of dog teeth in-vitro. A total of 237 dog molar teeth were evaluated with three different methods; visio-tactile examination with a probe, radiographic assessment, and NIR-LT. Each tooth was evaluated with all of the methods; the absence or presence of occlusal caries was scored as either 0, caries not present, or 1, caries present. All caries detection methods yielded the same outcomes for each tooth. Among all caries detection methods positive Spearman’s rho value (ϱ=1) indicated that there was a strong positive correlation between the caries detection methods. Since NIR-LT is such a comfortable and easy caries diagnosis method, it can be used without sedation, especially in calm dogs.

Detection of Early Smooth Surface Caries in Primary Molars using Conventional Methods in a Low-Resource Setting: A Cross-sectional Study

Introduction: Dental caries is a widespread chronic disease that affects nearly 100% of adults in developing countries. Detecting caries early is crucial for effective remineralisation and improved prognosis. However, identifying smooth surface caries in its early stages remains challenging for many healthcare professionals globally due to the subjective nature of visual inspection. Aim: To determine the most effective method for detecting early smooth surface caries in limited resource settings. Materials and Methods:The present cross-sectional study was conducted in the Department of Pedodontics, Dr. R. Ahmed Dental College and Hospital (tertiary care centre), Kolkata, West Bengal, India, from March 2021 to August 2022. Study was conducted involving 120 primary molars from Bengali children aged 5 years and above. The teeth were randomly selected while adhering to specific inclusion and exclusion criteria. The occlusal and proximal surfaces of the teeth were assessed using three methods: unaided Visual Examination (VE), Magnifying Loupe (ML)-assisted VE, and the fluorescence-based DIAGNOdent pen (considered the gold standard). Two assessments were performed for each method, one before air-drying and another after air-drying. Caries detection followed the International Caries Detection and Assessment System (ICDAS) II clinical scoring system, and data analysis was conducted using the Chi-square test for proportions. Results: In the present study, there were 76 male children and 44 female children and the mean age of the children was 7±2.5 years. Unaided VE exhibited a diagnostic accuracy of 87.5% and 77.5% for air-dried and non air-dried tooth surfaces, respectively. ML-aided VE yielded a diagnostic accuracy of 88.34% and 85% for air-dried and non air-dried tooth surfaces, respectively. Conclusion: The present study concluded that even without the assistance of an ML, drying the tooth surface can achieve a level of caries detection accuracy comparable to that of MLaided VE.

Where do we invest money to implement active learning on caries detection? An economic evaluation.

This trial-based economic evaluation aimed to estimate the incremental cost of implementing an active learning strategy (theoretical-practical workshop) to substitute the didactic lecture as the sole method for students training in caries detection. We also provided a budget impact analysis and explored the composition of costs related to the activity. Data from the coordinating centre of a multicentre randomized and controlled study (IuSTC01) was analyzed as the first part of our main economic analysis plan. The perspective of the educational provider (the institution implementing the activity) was considered, and an immediate time horizon was adopted. All used resources were valued in Brazilian Real by adopting a microcosting strategy. Costs for each strategy were estimated and converted into international dollars. The incremental Cost per student and the total cost of implementing the complete teaching strategy for 80 students were calculated. Monte Carlo simulations were used to estimate the uncertainties. The incremental Cost estimated for the workshop implementation would be $7.93 per student (interquartile range (IQR): $7.8-8.1), and the total cost of the teaching activity would be $684 (IQR:672-696). The laboratory training comprised more than 50% of the total amount spent, and a higher percentage of this value was related to human resources costs (72%). Saving 40% of the costs could be expected for the next rounds of activities in the institution, assuming no need for additional preparation of didactic materials and tutor training. A modest incremental cost per student and an acceptable organizational budget impact should be expected for the institution when including active learning training in caries detection for undergraduate students, mainly related to the human resources involved.

Pregled nedavnih dostignuća u dečjoj stomatologiji

Introduction: As a result of developments in technology, materials, and illness knowledge, paediatric dentistry has experienced major changes. When dealing with children, comfort, speed, safety, and conservative therapies are paramount. Of course, this also applies to adults, but getting youngsters started with dental appointments and making the experience stress-free for both patients and parents is extremely vital. The introduction and use of new technology caused a paradigm change in the practice of dentistry. This has been marked by the development of new and superior restorative materials that allow us to remove less tooth structure. We have now achieved the therapeutic goals discovered in giving more minimally invasive dentistry. Early dental diagnosis has become increasingly important in recent years. Early detection of dental caries has grown in relevance in recent years as conservative dentistry has remained at the forefront. The dentist should emphasize early identification of dental caries and the least invasive treatment alternatives, especially in paediatric patients. Traditional caries detection methods frequently miss early enamel lesions that have not developed into cavitation. As a result, several innovative strategies for detecting cavities early have been developed. Aim: The purpose of the current literature is to provide broad information regarding new restorative materials and caries detection technologies and summarize recent advances in paediatric dentistry. Conclusion: This overview highlighted a huge variety of current equipment that is either now used or can be utilized in dental diagnostics. Some of these devices are rather pricey and take up a lot of room. Technology evolves so quickly that it is impossible to stay up. As a result, possibly the best answer for each pedodontist or pedodontics clinic is to select the equipment with the greatest cost benefit.

Deep Learning Concept for Early Dental Caries Detection

Identifying dental caries in a dental operatory can be challenging for pediatric dentists at times because of the fluctuating features of the lesions based on their developmental stage. Thus, pediatric dentists can suggest specific and unique treatment methods. Noninvasive interventions, such as fluoride therapy and sugar control, can successfully reverse and treat early aberrations, sometimes referred to as white spots. Dental cavities, on the other hand, lead to tooth structure deterioration and require restorative treatments of varying levels of difficulty, depending on the extent and severity of the damage that leads to the extraction of the tooth itself. These necessitate new concepts for detecting dental caries in children. The prospective review's objective was to determine the necessity of applying deep learning and contemporary principles in the diagnosis of early childhood caries.

Assessment of severity and mineral composition of saliva in schoolchildren with molar-incisor hypomineralization (MIH).

The aim of the study was to evaluate the severity of molar incisor hypomineralisation (MIH), related oral health and investigate salivary mineral composition. The study was conducted with 50 participants aged between 6-15 years who were effected with MIH and 50 without MIH. The International Caries Detection and Assessment System (ICDAS) scores, Decayed, Missing, Filled Teeth/Surface (DMFT/S), dft/s and gingival/plaque indices were evaluated. The pH, flow rate, buffering capacity and mineral composition of saliva was measured. "Student t" test, one-way analysis of variance in repeated measurements of groups, and Tukey multiplex in subgroup comparisons was used. Kruskal-Wallis, Mann-Whitney U, Wilcoxon and chi-square tests were used to analyze qualitative data and compare groups. A total of 100 children (57 females 43 males, mean age 10.12 ± 1.85) participated in the study. There was no difference between ICDAS, DMFT/S scores, but dft/s index values were statistically significant (p = 0.001). The simplified oral hygiene index of MIH patients were statistically higher, but no significant differences were found in modified gingival indices (p = 0.52). Although the salivary pH and flow rate of the patients in the study group were lower, the buffering capacity was higher than those in the control group, but no significant difference was observed (p = 0.64). The mean values of phosphorus, carbon and calcium content in the saliva samples of MIH patients were higher than those of patients without MIH, and this difference was low for phosphorus (p = 0.76) and carbon (p = 0.74), but significantly higher for calcium. To the best of our knowledge, this is the first study to evaluate the association between calcium, phosphate and carbon levels in saliva of children with MIH. The significantly high amount of calcium in the saliva of patients with MIH suggests that further investigations are needed.