Near-infrared Light Transillumination Research Articles

Near-infrared light transillumination (NIR-LT) vs clinical-visual inspection/FOTI to detect interproximal caries lesions in vivo.

To evaluate the ability of near-infrared light transillumination (NIR-LT) to detect interproximal enamel and dentinal caries lesions compared to clinical-visual inspection (VI) aided by fibre-optic transillumination (FOTI). From 170 Finnish adolescents aged 15-17 years, 5294 interproximal surfaces of premolars and molars were examined first clinical-visually aided by FOTI (VI&#43;FOTI) using the International Caries Detection and Assessment System (ICDAS) classification. Subsequently, the surfaces were examined using NIR-LT. The extent of lesions was determined using the modified NIR-LT classification based on the Söchtig criteria. For the analyses, data on upper and lower premolars and molars were combined. Distributions of lesions were presented as frequencies. Differences between VI&#43;FOTI and NIR-LT at the tooth and tooth surface levels were analysed by Chi-square and Fisher's exact tests. Sensitivity and specificity of the NIR-LT method to detect any lesion was performed using VI&#43;FOTI as the gold standard. By VI&#43;FOTI, 92.4% surfaces were classified as sound and by NIR-LT, 88.2%. Enamel caries lesions were found on 7.0% of the surfaces by VI&#43;FOTI and on 11.6% by NIR-LT. Enamel lesions identified by NIR-LT were nearly double for all examined teeth groups, except for lower molars it was 1.3-fold. In 66% of the surfaces, the differences between NIR-LT and VI&#43;FOTI findings were statistically significant (p<0.001). The sensitivity for all teeth of NIR-LT was 48.4% and the specificity was 91.1%. Radiation-free NIR-LT method shows considerable potential as a supplementary method for early detection of caries lesions among low caries prevalence adolescents.

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.

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.

Diagnosis of occlusal enamel caries using near-infrared light transillumination

Diagnosis of occlusal enamel caries using near-infrared light transillumination

Performance of near-infrared light transillumination in the detection of occlusal caries lesions in deciduous teeth

BackgroundThis study compared the performance of near-infrared light transillumination (NILT; DIAGNOcam [DC]) in detecting occlusal caries lesions in deciduous molars with the performance of the International Caries Detection and Assessment System (ICDAS), digital radiographic method (RX) and laser fluorescence (DIAGNOdent pen [LFpen]). MethodsFifty-seven extracted deciduous molars with no frank cavitation caries lesions were selected. One site per tooth was evaluated twice each by two examiners using all methods. Then, a histological examination was performed using a stereomicroscope. The performance of the methods was assessed with respect to two thresholds: D1 (all visible caries lesions affecting enamel) and D3 (caries lesions extended into dentin). ResultsA weighted kappa and intraclass correlation coefficients were calculated for intra- and inter-observer reproducibilities, varying from 0.31 (RX) to 0.89 (LFpen). The intra-observer reproducibility at D1 and D3 thresholds varied from 0.51 (ICDAS) to 0.85 (DC) and from 0.30 (ICDAS) to 1.00 (RX), respectively. The inter-observer reproducibility varied from 0.28 (RX) to 0.47 (DC) and from 0.22 (LFpen) to 0.75 (DC), respectively. At the D1 threshold, higher sensitivity and accuracy were found for LFpen (0.76/0.71) and ICDAS (0.65/0.66); and higher specificities were found for RX (0.94), DC (0.79), and ICDAS (0.71). At the D3 threshold, all methods exhibited lower sensitivities (0.13–0.35) and higher specificity, accuracy, and area under the ROC curve values (0.70–1.00). All methods showed a correlation with histological results, especially for ICDAS and DC. ConclusionsNILT technology (DC) and LFpen exhibited similar performance for the detection of occlusal caries lesions in deciduous molars when compared with ICDAS criteria.

Cost-effectiveness of school-based caries screening using transillumination

ObjectivesSchool-based screening for caries lesions usually only employs visual-tactile detection means (standard of care). Near-infrared-light-transillumination (NILT) could be used to support school-based screening and to identify early proximal caries, facilitating referral and appropriate non- or micro-invasive management in dental practice. MethodWe assessed the cost-effectiveness of NILT for school-based caries screening. A German mixed-payers’ perspective was adopted. A Markov model was used to simulate the consequences of true and false positive and negative detections and the subsequent decisions over the lifetime of initially 12 years old patients. Our health outcome was tooth retention in years. Costs were measured in Euro 2020. Monte-Carlo-microsimulations, univariate and probabilistic sensitivity analyses were conducted. The incremental cost-effectiveness ratio (ICER) and the cost-effectiveness-acceptability at different willingness-to-pay-thresholds were quantified. ResultsNILT was minimally more effective (tooth retention for a mean (2.5–97.5%) 56 (53–59) years) and minimally less costly (515 (416–616) Euro) than standard of care (56 (50–59) years; 526 (427–628 Euro)). The ICER was -503 Euro/year, i.e. school-based caries screening using NILT saved money at higher effectiveness in the modelled population. The cost-effectiveness of NILT increased for payers with a willingness-to-pay for additional tooth retention time. The biggest driver of costs were (avoided) tooth replacements later in life. ConclusionsNILT-based screening is likely to yield limited effectiveness gains and cost savings in the modelled populations. In countries where regular practice-based screening of children is less common than in Germany, the cost-effectiveness of NILT for school-based caries screening is likely higher. Clinical significanceNILT-based caries screening in German schools is unlikely to be cost-effective. In countries with different utilization patterns or generally higher caries prevalence and risk, this may differ.

Clinical Performance of Diagnostic Methods in Third Molar Teeth with Early Occlusal Caries.

The aim of this study was to compare the diagnostic performance of clinical visual examination (ICDAS II), digital periapical radiography (PR), near infrared light transillumination (NIR-LT), and laser fluorescence (LF) to microcomputed tomography (Micro-CT) which is the reference standard for the detection of non-cavitated occlusal enamel and dentin caries in third molar teeth. Potential participants were consecutively recruited. In this prospective study, 101 third molars of 101 patients were examined; the molars had non-cavitated occlusal caries which required extraction. ICDAS II, PR, NIR-LT and LF examinations were carried out by two blinded examiners. Reference standard was determined by micro-CT imaging seven days after extraction. Accuracy rate, sensitivity, specificity, predictive values and areas under receiver operating characteristic (ROC) curves (Az) were statistically analyzed. Nonparametric variables were subjected to the Kruskal-Wallis Test. Significance level was set as p &lt; 0.05. NIR-LT had the highest sensitivity (99.67-99.67%) and accuracy (78.22-77.23%) for the determination of occlusal enamel caries according to examiners 1 and 2, respectively. LF method had the highest sensitivity (70.83-54.17%) and accuracy (66.34-59.41%) for determining occlusal dentin caries according to examiners 1 and 2, respectively. The ROC curve (Az) value ranged between 0.524 and 0.726 for the different methods tested. Most effective methods for the diagnosis of occlusal enamel and dentin caries were determined to be NIR-LT and LF pen methods, respectively. The present prospective clinical study showed that NIR-LT and LF-Pen were a reliable modality for the detection of occlusal enamel and dentin caries without ionizing radiation.

Accuracy of high definition near infrared transillumination camera in detection of hidden proximal caries.

Early caries detection became mandatory in modern dentistry. However, the traditional methods in caries detection had many limitations. Hence,a novel approach based on Near Infrared technology was introduced to overcome such limitations. Proximal surfaces of 102 posterior teeth from 36 adult participants who fulfilled the eligibility criteria were assessed by two examiners using three diagnostic methods. Teeth were examined visually according to the criteria of the International Caries Detection and Assessment System (ICDAS-II) then examined by bitewing digital radiograph (BW) and near infrared light transillumination (NIRT) camera (Vista Proxi iX HD smart). Inter and intra observer agreements were assessed using Kappa test.Dignostic accuracy parameters and Area Under the ROC curve (AUROC) with 95% confidence interval (95% CI) were evaluated for the different caries assessment methods. The results of inter-observer agreement showed an excellent agreement in the different groups. There was a statistically significant difference in the score distribution between ICDASII and VistaCam modalities (P-value <0.05). While there was no statistically significant difference in the score distribution between bitewing radiography and VistaCam modalities (P-value >0.05). ROC curve analysis revealed that VistaCam when compared with ICDASII had sensitivity (99.0%), specificity (50.0 %), diagnostic accuracy (98.0%) and Area under the ROC curve (AUC) was 0.745 with 95% Confidence Interval (0.649 - 0.826).When VistaCam compared with bitewing radiography, it showed sensitivity (100.0%); specificity (40.0%), diagnostic accuracy (97.1%) and AUC (0.700) with 95% confidence interval (0.601 - 0.787). NIRT based diagnostic modality is a promising method for detection of hidden proximal lesions overcoming the hazards of radiograph. Key words:Bitewing radiography , ICDAS II , Near-infrared transillumination, proximal caries, VistaCam® iX Proxi.

Chapter 7: Technological Aids and Coronal Caries.

In recent decades, dentistry has developed significantly in all areas. While in the past, caries was mainly treated operatively, the today's management has shifted toward noninvasive, minimal invasive, and, only if needed, invasive treatment options. Aiming at enabling the most noninvasive or conservative treatment option requires early caries detection, which, however, remains challenging. The progression of early or noncavitated caries lesions can nowadays be successfully controlled, as well as lesions arrested by oral hygiene procedures combined with the use of fluorides, sealants, or resin infiltration. Methods such as near-infrared light transillumination, fibre-optic transillumination, digital fibre-optic transillumination, laser fluorescence, or quantitative light fluorescence measurements were introduced in the dental market to provide X-ray-free caries detection, assessment, and monitoring. For approximal surfaces that are not directly visible, bitewing radiography is still the standard in detecting caries lesions. The use of artificial intelligence has become the most recent technological aid for the detection of caries lesions on bitewing radiographs and clinical images and has to be understood as an emerging technology, which requires extensive research in the future. The aim of this chapter is to give an overview of different possibilities to detect coronal caries lesions and suggestions of how to improve this process.

Near-Infrared Light Transillumination for Diagnosing Proximal Caries

Near-Infrared Light Transillumination for Diagnosing Proximal Caries

Accuracy of different approaches for detecting proximal root caries lesions in vitro

ObjectivesThe objective was to evaluate the diagnostic accuracy of radiographic evaluation (XR), visual-tactile assessment (VT), laser-fluorescence (LF) (DIAGNOdent Pen/KaVo), and near-infrared-light transillumination (NILT) (DIAGNOcam/KaVo) on proximal root caries lesions in vitro.MethodsTwo-hundred extracted permanent premolars and molars with and without proximal root caries lesions were allocated to 50 diagnostic models simulating the proximal contacts between teeth and mounted in a phantom dummy head. Two independent examiners used the diagnostic approaches to detect any or advanced root caries lesions, with histologic evaluation of the lesions serving as reference. Receiver operating characteristic (ROC) curves were employed, and sensitivity, specificity, and the area under the ROC curve (AUC) are calculated. Significant differences in mean AUCs between approaches were assumed if p < 0.05 (two-sample t-test).ResultsNILT was not applicable for proximal root caries detection. The sensitivity/specificity to detect any lesions was 0.81/0.63 for XR, 0.76/0.88 for VT and 0.81/0.95 for LF, and the sensitivity/specificity to detect advanced lesions was 0.43/0.94 for XR, 0.66/0.99 for VT, and 0.83/0.78 for LF, respectively. For both, any and advanced root caries lesions, mean AUCs for LF and VT were significantly higher compared to XR (p < 0.05). For any root caries lesions, LF was significantly more accurate than VT (p = 0.01), but not for advanced root caries lesions (p = 0.59).ConclusionsUnder the in vitro conditions chosen, LF and VT were more accurate than XR to detect proximal root caries lesions, with LF being particularly useful for initial lesion stages.Clinical relevanceLF might be a useful diagnostic aid for proximal root caries diagnosis. Clinical studies are necessary to corroborate the findings.

Towards Trustworthy AI in Dentistry

Medical and dental artificial intelligence (AI) require the trust of both users and recipients of the AI to enhance implementation, acceptability, reach, and maintenance. Standardization is one strategy to generate such trust, with quality standards pushing for improvements in AI and reliable quality in a number of attributes. In the present brief review, we summarize ongoing activities from research and standardization that contribute to the trustworthiness of medical and, specifically, dental AI and discuss the role of standardization and some of its key elements. Furthermore, we discuss how explainable AI methods can support the development of trustworthy AI models in dentistry. In particular, we demonstrate the practical benefits of using explainable AI on the use case of caries prediction on near-infrared light transillumination images.

Application of Near-infrared Light Transillumination in Restorative Dentistry: A Review

This paper presents the various applications of near-infrared light transillumination (NILT) in dentistry. Untreated dental caries is considered the most prevalent health condition affecting both children and adults worldwide. Increased awareness and a paradigm shift toward utilization of minimally invasive treatment procedures and nonionizing radiation led to the discovery of newer techniques for screening and early diagnosis of demineralized lesions. Demineralized lesions detected early can be treated with minimally invasive treatment procedures such as the usage of fluoridated dentifrice to encourage remineralization and resin infiltration to arrest caries progression. NILT procedure involves noninvasive, nonionizing radiation and helps in the identification of early demineralized lesions using light transillumination. At near-infrared wavelengths, the enamel appears translucent and helps in visualizing and detecting demineralized lesions when long-wave light transilluminated against the tooth surface. The wavelength in the range of 1310 nm is considered best for the transillumination of lesions. This technique has been proven to be successful in the detection of carious and demineralized lesions. NILT can be used as a screening tool for the early detection of demineralized lesions and can be considered as an adjunct to bitewing radiographs. It can be advantageous in screening pregnant, growing adolescent patients and in cases where multiple follow-ups are needed and ionizing radiation must be avoided. Keywords: Dental caries, Early diagnosis, Ionizing radiation, Minimally invasive, Near-infrared, Occlusal caries, Transillumination.

Sensitivity and Specificity of Different Diagnostic Methods in Occlusal Caries Detection of Permanent Teeth among Paediatric Patients

Dental caries is a commonly progressive disease that proceeds through various degrees of severity that a dentist can detect. The aims of the in vivo study were to assess the accuracy of the individual model (near-infrared light transillumination [NILT] device, visual and radiographic examinations) in detecting occlusal caries, and to evaluate the performance of visual and NILT device combination for occlusal caries detection in deciding the treatment options. Fifty-two non-cavitated occlusal surfaces from 16 patients were assessed with three different diagnostic devices in random order. Identified lesions were prepared and validated. Logistic regression analysis was performed for each method. The sensitivity and specificity values for each method and the combined models were statistically measured using RStudio version 0.97.551. At the enamel level, visual detection was the most sensitive method (0.88), while NILT was the most specific (0.93). NILT scored the highest for sensitivity (0.93) at the dentine level and visual detection scored the highest for specificity (0.88). Visual detection + NILT model was significantly better (p = 0.04) compared to visual detection or NILT alone (df = 1). The visual-NILT combination is a superior model in detecting occlusal caries on permanent teeth. The model provided surplus value in caries detection hence improving the treatment decision-making in occlusal surfaces.

Generalizability of Deep Learning Models for Caries Detection in Near-Infrared Light Transillumination Images.

Objectives: The present study aimed to train deep convolutional neural networks (CNNs) to detect caries lesions on Near-Infrared Light Transillumination (NILT) imagery obtained either in vitro or in vivo and to assess the models’ generalizability. Methods: In vitro, 226 extracted posterior permanent human teeth were mounted in a diagnostic model in a dummy head. Then, NILT images were generated (DIAGNOcam, KaVo, Biberach), and images were segmented tooth-wise. In vivo, 1319 teeth from 56 patients were obtained and segmented similarly. Proximal caries lesions were annotated pixel-wise by three experienced dentists, reviewed by a fourth dentist, and then transformed into binary labels. We trained ResNet classification models on both in vivo and in vitro datasets and used 10-fold cross-validation for estimating the performance and generalizability of the models. We used GradCAM to increase explainability. Results: The tooth-level prevalence of caries lesions was 41% in vitro and 49% in vivo, respectively. Models trained and tested on in vivo data performed significantly better (mean ± SD accuracy: 0.78 ± 0.04) than those trained and tested on in vitro data (accuracy: 0.64 ± 0.15; p < 0.05). When tested in vitro, the models trained in vivo showed significantly lower accuracy (0.70 ± 0.01; p < 0.01). Similarly, when tested in vivo, models trained in vitro showed significantly lower accuracy (0.61 ± 0.04; p < 0.05). In both cases, this was due to decreases in sensitivity (by −27% for models trained in vivo and −10% for models trained in vitro). Conclusions: Using in vitro setups for generating NILT imagery and training CNNs comes with low accuracy and generalizability. Clinical significance: Studies employing in vitro imagery for developing deep learning models should be critically appraised for their generalizability. Applicable deep learning models for assessing NILT imagery should be trained on in vivo data.

Dental Caries Diagnosis and Detection Using Neural Networks: A Systematic Review.

Dental caries is the most prevalent dental disease worldwide, and neural networks and artificial intelligence are increasingly being used in the field of dentistry. This systematic review aims to identify the state of the art of neural networks in caries detection and diagnosis. A search was conducted in PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and ScienceDirect. Data extraction was performed independently by two reviewers. The quality of the selected studies was assessed using the Cochrane Handbook tool. Thirteen studies were included. Most of the included studies employed periapical, near-infrared light transillumination, and bitewing radiography. The image databases ranged from 87 to 3000 images, with a mean of 669 images. Seven of the included studies labeled the dental caries in each image by experienced dentists. Not all of the studies detailed how caries was defined, and not all detailed the type of carious lesion detected. Each study included in this review used a different neural network and different outcome metrics. All this variability complicates the conclusions that can be made about the reliability or not of a neural network to detect and diagnose caries. A comparison between neural network and dentist results is also necessary.

Laser fluorescence as an alternative for digital radiography in detecting caries lesions

Diagnosis of caries lesions consists of visual/tactile methods following the International Caries Detection and Assessment System (ICDAS) and radiographic methods (BW), however these methods have limitations for detecting caries at different depths and locations and is usually influenced by the examiner’s experience. New technologies have emerged to improve caries diagnosis methods, among these are methods that use optical principles to quantify differences between healthy and demineralized tissues, for example: quantitative light- -induced fluorescence (QLF), the “DIAGNOdent” caries detection pen, and VistaProof (fluorescence camera). This literature review aims to provide up-to-date information on the applicability of fluorescence-based methods in the diagnosis of caries. The results show that DIAGNOdent pen was the most effective method for diagnosing caries, wherein some authors indicate that it can be used as the only diagnostic method, but most recommend using it as a complementary method to ICDAS and BW. LFpen must be used in association with ICDAS and 2 articles indicate near-infrared light transillumination (NILT) as a substitute for BW, however there is still a need for further studies to reach a concrete decision. In conclusion, digital methods for the diagnosis of caries are efficient, but they do not replace visual/tactile methods and bitewing radiographs. Therefore, they should be used as a complementary diagnostic method to those already known and widely used and studied.

In vivo correlation of near-infrared transillumination and visual inspection with bitewing radiography for the detection of interproximal caries in permanent and primary teeth.

To evaluate near-infrared light transillumination (NILT) for interproximal caries detection in children by comparing the correlation between both NILT and visual inspection (ICDAS) with bitewing (BW) radiography and by investigating possible differences in caries detection with NILT between primary and permanent teeth. From 35 patients, 121 and 63 interproximal surfaces in, respectively, primary and permanent teeth were included. NILT images were obtained using DIAGNOcam™ (KaVo) and scored by two calibrated raters. A consensus diagnosis was reached for BW radiography; whereas, the ICDAS scores were obtained by one calibrated rater. Weighted Kappa (wκ) was used to evaluate inter- and intra-rater reliability of NILT and to evaluate the correlation between NILT, ICDAS and BW radiography. The correlation between NILT and BW radiography was moderate to substantial for primary teeth [Rater 1: wκ = 0.61 (95% CI = 0.49-0.75), Rater 2: wκ = 0.55 (95% CI = 0.41-0.69)] and fair for permanent teeth [Rater 1: wκ = 0.34 (95% CI = 0.15-0.53), Rater 2: wκ = 0.33 (95% CI = 0.08-0.58)]. The correlation between ICDAS and BW radiography was moderate for primary teeth [wκ = 0.49 (95% CI = 0.35-0.63)] and substantial for permanent teeth [wκ = 0.62 (95% CI = 0.32-0.92)]. No significant differences were found between primary and permanent teeth. NILT cannot be recommended as a single diagnostic tool for interproximal caries detection in primary teeth. The number of false negatives for dentine caries, especially in first primary molars, was too high. For the use in permanent teeth, NILT could be more accurate than BW radiography.

Clinical performance of clinical-visual examination, digital bitewing radiography, laser fluorescence, and near-infrared light transillumination for detection of non-cavitated proximal enamel and dentin caries.

The aim of this study was to compare the clinical performance of clinical-visual examination using the International Caries Detection and Assessment System (ICDAS) II, digital bitewing radiography, near-infrared light transillumination (NIR-LT), and laser fluorescence (LF) for the detection of non-cavitated proximal enamel and dentin caries. The study included 335 patients, aged 12-18years, with no cavities in the posterior teeth. Clinical-visual inspections of 335 non-cavitated proximal caries were performed by two examiners. For enamel caries, clinical validation included a combination of clinical-visual and digital bitewing radiography assessments. For dentin caries, the clinical validation was opening the cavity. The accuracy rate, sensitivity, specificity, predictive values, and areas under receiver operating characteristic curves were determined. The agreement between the examiners' measurements was calculated using the kappa coefficient. The sensitivity, specificity, and accuracy of the methods were compared using the McNemar test. The significance level was set at p < 0.05. Digital bitewing radiography had the highest sensitivity (0.96) and accuracy (0.96), and LF had the lowest sensitivity (0.38) and accuracy (0.39). After separation of the lesions into enamel and dentin caries, clinical-visual examination had the highest sensitivity (0.98) and accuracy (0.98) for enamel caries, while digital bitewing radiography had the highest sensitivity (0.97) and accuracy (0.97) for dentin caries. The NIR-LT method had a higher sensitivity for enamel caries (0.86). Each method also differed significantly from the others (p < 0.001). Digital bitewing radiography gave the best prediction of proximal enamel and dentin caries. NIR-LT showed good potential for detection of proximal caries.

Dental Images Recognition Technology and Applications: A Literature Review

Neural networks are increasingly being used in the field of dentistry. The aim of this literature review was to visualize the state of the art of artificial intelligence in dental applications, such as the detection of teeth, caries, filled teeth, crown, prosthesis, dental implants and endodontic treatment. A search was conducted in PubMed, the Institute of Electrical and Electronics Engineers (IEEE) Xplore and arXiv.org. Data extraction was performed independently by two reviewers. Eighteen studies were included. The variable teeth was the most analyzed (n = 9), followed by caries (n = 7). No studies detecting dental implants and filled teeth were found. Only two studies investigated endodontic applications. Panoramic radiographies were the most common image employed (n = 5), followed by periapical images (n = 3). Near-infrared light transillumination images were employed in two studies and bitewing and computed tomography (CT) were employed in one study. The included articles used a wide variety of neuronal networks to detect the described variables. In addition, the database used also had a great heterogeneity in the number of images. A standardized methodology should be used in order to increase the compatibility and robustness between studies because of the heterogeneity in the image database, type, neural architecture and results.