Characterization and Safety Profile of a New Combined Advanced Therapeutic Medical Product Platelet Lysate-Based Fibrin Hydrogel for Mesenchymal Stromal Cell Local Delivery in Regenerative Medicine.

Adipose-derived mesenchymal stromal cells (ASC) transplant to recover the optimal tissue structure/function relationship is a promising strategy to regenerate tissue lesions. Because filling local tissue defects by injection alone is often challenging, designing adequate cell carriers with suitable characteristics is critical for in situ ASC delivery. The aim of this study was to optimize the generation phase of a platelet-lysate-based fibrin hydrogel (PLFH) as a proper carrier for in situ ASC implantation and (1) to investigate in vitro PLFH biomechanical properties, cell viability, proliferation and migration sustainability, and (2) to comprehensively assess the local in vivo PLFH/ASC safety profile (local tolerance, ASC fate, biodistribution and toxicity). We first defined the experimental conditions to enhance physicochemical properties and microscopic features of PLFH as an adequate ASC vehicle. When ASC were mixed with PLFH, in vitro assays exhibited hydrogel supporting cell migration, viability and proliferation. In vivo local subcutaneous and subgingival PLFH/ASC administration in nude mice allowed us to generate biosafety data, including biodegradability, tolerance, ASC fate and engraftment, and the absence of biodistribution and toxicity to non-target tissues. Our data strongly suggest that this novel combined ATMP for in situ administration is safe with an efficient local ASC engraftment, supporting the further development for human clinical cell therapy.

Gingival Orofacial Granulomatosis Clinical and 2D/3D Microscopy Features after Orthodontic Therapy: A Pediatric Case Report.

Orofacial granulomatosis (OFG) represents a heterogeneous group of rare orofacial diseases. When affecting gingiva, it appears as a chronic soft tissue inflammation, sometimes combined with the enlargement and swelling of other intraoral sites, including the lips. Gingival biopsy highlights noncaseating granulomatous inflammation, similar to that observed in Crohn's disease and sarcoidosis. At present, the etiology of OFG remains uncertain, although the involvement of the genetic background and environmental triggers, such as oral conditions or therapies (including orthodontic treatment), has been suggested. The present study reports the results of a detailed clinical and 2D/3D microscopy investigation of a case of gingival orofacial granulomatosis in an 8-year-old male patient after orthodontic therapy. Intraoral examination showed an erythematous hyperplasia of the whole gingiva with a granular appearance occurring a few weeks after the installation of a quad-helix. Peri-oral inspection revealed upper labial swelling and angular cheilitis. General investigations did not report ongoing extra-oral disturbances with the exception of a weakly positive anti-Saccharomyces cerevicae IgG auto-antibody. Two- and three-dimensional microscopic investigations confirmed the presence of gingival orofacial granulomatosis. Daily corticoid mouthwashes over a period of 3 months resulted in a slight improvement in clinical signs, despite an intermittent inflammation recurrence. This study brings new insights into the microscopic features of gingival orofacial granulomatosis, thus providing key elements to oral practitioners to ensure accurate and timely OFG diagnosis. The accurate diagnosis of OFG allows targeted management of symptoms and patient monitoring over time, along with early detection and treatment of extra-oral manifestations, such as Crohn's disease.

Periodontal Cell Therapy: A Systematic Review and Meta-analysis.

BACKGROUND: Periodontitis is a chronic inflammatory disease characterized by the loss of tooth-supporting tissues (or periodontium) leading to the formation of periodontal pocket then to tooth loss. Conventional therapies that involve tooth root debridement are still disappointing because they are more centered on periodontal repair than disease pathophysiology causes. The meta-analysis we present here focused on the results of experimental studies that investigated periodontal mesenchymal stromal cells (MSCs) therapy, a promising strategy to regenerate tissue, given to their immunomodulatory and trophic properties. METHODS: Using PubMed database and ICTRP search portal, 84 animal and 3 randomized human studies were analyzed. RESULTS: Overall, our results highlighted that MSCs grafting, regardless of their tissue origin, enhances periodontal regeneration. A defect morphology suitable for an initial clot stabilization increases the procedure efficacy, especially if cells are carried using a vehicle from natural origin. Nevertheless, methodological biases have been highlighted and still limit the translation to human with high prognosis and regulatory considerations. Besides, because only 2 randomized human trials demonstrated the efficacy of the procedure, further studies are needed to investigate periodontal regeneration procedures on experimental models closer to human pathophysiology. CONCLUSION: Although MSCs grafting in periodontal disease demonstrated therapeutic benefits in animal, it is critical to define more accurately protocols translatable to human and focus on the treatment of the pathology as a whole rather than on the restitution of the sole destroyed tissues.

Comparison of different machine learning approaches to predict dental age using Demirjian's staging approach.

CONTEXT: Dental age, one of the indicators of biological age, is inferred by radiological methods. Two of the most commonly used methods are using Demirjian's radiographic stages of permanent teeth excluding the third molar (Demirjian's and Willems' method). The major drawbacks of these methods are that they are based on population-specific conversion tables and may tend to over- or underestimate dental age in other populations. Machine learning (ML) methods make it possible to create complex data schemas more simply while keeping the same annotation system. The objectives of this study are to compare (1) the capacity of ten machine learning algorithms to predict dental age in children using the seven left permanent mandibular teeth compared to reference methods and (2) the capacity of ten machine learning algorithms to predict dental age from childhood to young adulthood using the seven left permanent mandibular teeth and the four third molars. METHODS: Using a large radiological database of 3605 orthopantomograms (1734 females and 1871 males) of healthy French patients aged between 2 and 24 years, seven left permanent mandibular teeth and the 4 third molars were assessed using Demirjian's stages. Dental age estimation was then performed using Demirjian's reference method and various ML regression methods. Two analyses were performed: with the 7 left mandibular teeth without third molars for the under 16 age group and with the third molars for the entire study population. The different methods were compared using mean error, mean absolute error, root mean square error as metrics, and the Bland-Altman graph. RESULTS: All ML methods had a mean absolute error (MAE) under 0.811 years. With Demirjian's and Willems' methods, the MAE was 1.107 and 0.927 years, respectively. Except for the Bayesian ridge regression that gives poorer accuracy, there was no statistical difference between all ML tested. CONCLUSION: Compared to the two reference methods, all the ML methods based on the maturation stages defined by Demirjian were more accurate in estimating dental age. These results support the use of ML algorithms instead of using standard population tables.

A geometric morphometric approach to the study of variation of shovel-shaped incisors.

OBJECTIVES: The scoring and analysis of dental nonmetric traits are predominantly accomplished by using the Arizona State University Dental Anthropology System (ASUDAS), a standard protocol based on strict definitions and three-dimensional dental plaques. However, visual scoring, even when controlled by strict definitions of features, visual reference, and the experience of the observer, includes an unavoidable part of subjectivity. In this methodological contribution, we propose a new quantitative geometric morphometric approach to quickly and efficiently assess the variation of shoveling in modern human maxillary central incisors (UI1). MATERIALS AND METHODS: We analyzed 87 modern human UI1s by means of virtual imaging and the ASU-UI1 dental plaque grades using geometric morphometrics by placing semilandmarks on the labial crown aspect. The modern human sample was composed of individuals from Europe, Africa, and Asia and included representatives of all seven grades defined by the ASUDAS method. RESULTS: Our results highlighted some limitations in the use of the current UI1 ASUDAS plaque, indicating that it did not necessarily represent an objective gradient of expression of a nonmetric tooth feature. Rating of shoveling tended to be more prone to intra- and interobserver bias for the highest grades. In addition, our analyses suggest that the observers were strongly influenced by the depth of the lingual crown aspect when assessing the shoveling. DISCUSSION: In this context, our results provide a reliable and reproducible framework reinforced by statistical results supporting the fact that open scale numerical measurements can complement the ASUDAS method.

The effects of periodontal treatment on diabetic patients: The DIAPERIO randomized controlled trial.

AIM: To assess whether periodontal treatment can lead to clinical, glycaemic control and quality of life improvements in metabolically unbalanced diabetic patients (type 1 or type 2) diagnosed with periodontitis. METHODS: In this open-labelled randomized controlled trial, diabetic subjects (n = 91) were given "immediate" or "delayed" periodontal treatment (full-mouth non-surgical scaling and root planing, systemic antibiotics, and oral health instructions). The main outcome was the effect on glycated haemoglobin (HbA1C ) and fructosamine levels. The General Oral Health Assessment Index and the SF-36 index were used to assess quality of life (QoL). RESULTS: Periodontal health significantly improved after periodontal treatment (p < 0.001). Periodontal treatment seemed to be safe but had no significant effects on glycaemic control based on HbA1C (adjusted mean difference with a 95% confidence interval (aMD) of 0.04 [-0.16;0.24]) and fructosamine levels (aMD 5.0 [-10.2;20.2]). There was no obvious evidence of improvement in general QoL after periodontal treatment. However, there was significant improvement in oral health-related QoL (aMD 7.0 [2.4;11.6], p = 0.003). CONCLUSION: Although periodontal treatment showed no clinical effect on glycaemic control in this trial, important data were provided to support periodontal care among diabetic patients. Periodontal treatment is safe and improves oral health-related QoL in patients living with diabetes. ISRCTN15334496.

Spatial and temporal structure of the clinical research based on mesenchymal stromal cells: A network analysis.

BACKGROUND AIMS: Using innovative tools derived from social network analysis, the aims of this study were (i) to decipher the spatial and temporal structure of the research centers network dedicated to the therapeutic uses of mesenchymal stromal cells (MSCs) and (ii) to measure the influence of fields of applications, cellular sources and industry funding on network topography. METHODS: From each trial using MSCs reported on ClinicalTrials.gov, all research centers were extracted. Networks were generated using Cytoscape 3.2.2, where each center was assimilated to a node, and one trial to an edge connecting two nodes. RESULTS: The analysis included 563 studies. An independent segregation was obvious between continents. Asian, South American and African centers were significantly more isolated than other centers. Isolated centers had fewer advanced phases (P <0.001), completed studies (P = 0.01) and industry-supported studies (P <0.001). Various thematic priorities among continents were identified: the cardiovascular, digestive and nervous system diseases were strongly studied by North America, Europe and Asia, respectively. The choice of cellular sources also affected the network topography; North America was primarily involved in bone-marrow-derived MSC research, whereas Europe and Asia dominated the use of adipose-derived MSCs. Industrial funding was the highest for North American centers (90.5%). CONCLUSIONS: Strengthening of international standards and statements with institutional, federal and industrial partners is necessary. More connections would facilitate the transfer of knowledge, sharing of resources, mobility of researchers and advancement of trials. Developing partnerships between industry and academic centers seems beneficial to the advancement of trials across different phases and would facilitate the translation of research discoveries.

Comparison between pediatric and adult adipose mesenchymal stromal cells.

BACKGROUND: Adipose-derived mesenchymalstromal cells (ASC) are currently tested in regenerative medicine to promote tissue reconstruction after injury. Regardingautologous purpose, the possible loss of therapeutic function and cell properties during aging have been questioned in adults. To date no reliable information is available concerning ASC from pediatric patients and a better knowledge is required for clinical applications. METHODS: Subcutaneous adipose tissue was collected from 27 donors (0-1 years old) and 50 donors (1-12 years old) and compared with adult ASC for in vitro characteristics. ASC were then tested in a mouse model of limb ischemia. RESULTS: Cells from the stromal vascular fraction (SVF) and subsequent cultured ASC were prepared. Only a greater amount in SVF cell number and ASC proliferative rate were found. Cell phenotype, colony formingunit-fibroblast (CFU-F) content, immunomodulation effect and adipogenic, osteoblastic and angiogenic potentials were not significantly different. In vivo, pediatric ASC induced an increase in microangiographic score in a mouse model of limb ischemia, even though improvement in vascular density was not significantly correlated to limb rescue. Finally messengerRNA (mRNA) analysis using a microarray approach identified that only 305 genes were differentially expressed (217 down- and 88 up-regulated) in pediatric versus adult ASC, confirming that ASC from both age groups shared very close intrinsic properties. CONCLUSION: This is the first study reporting a comparative analysis of ASC from a large number of donors and showing that their in vitro and in vivo properties were similar and maintained during aging.

Trichomonas tenax and periodontal diseases: a concise review.

Periodontal diseases (gingivitis and periodontitis), result from a disruption of the host-oral microbiome homoeostasis. Whereas the pathological role of some specific bacterial strains during periodontal diseases is well documented, the impact of parasites in periodontium pathophysiology is still under debate. This review aims to collect data about the prevalence and the potential role of Trichomonas tenax during periodontal diseases. Data from 47 studies revealed that T. tenax prevalence in diseased periodontium ranged from 0 to 94·1%. The prevalence of oral protozoan infections was found to be largely greater in patients with periodontal diseases than with healthy periodontium. The parasite detection was mainly performed by direct microscopy. Trichomonas tenax presence was clearly correlated with periodontal disease. The high heterogeneity of its periodontal prevalence may be correlated with the diversity of the population screened (age, sex, systemic diseases), and the methods used for diagnosis. This protozoan seems to have the capacity to be involved in the inflammatory process of gum disease. Animal experimentation, using relevant physiopathological models of periodontitis, needs to be performed to investigate the ability of T. tenax to cause and/or worsen the disease. Further investigations using standardized experimental designs of epidemiologic studies are also needed.

Clinical research activity in periodontal medicine: a systematic mapping of trial registers.

AIM: The primary aim of the study was to systematically map registration records on periodontal medicine in clinical trial registers. The secondary aim was to assess the evolution of periodontal medicine in clinical periodontal research as a whole. MATERIAL AND METHODS: We searched all registration records related to periodontology in the World Health Organization International Clinical Trials Registry Platform. For registration records classified in the field of periodontal medicine, we assigned the 2015 MeSH(®) term for the most precisely corresponding systemic condition. RESULTS: Fifty-seven systemic conditions have been hypothesized to be linked with periodontal diseases, covering nearly 2% of the diseases indexed in MeSH. In addition to diabetes, cardiovascular disease or preterm birth, other systemic conditions have been the subject of registration records, such as anaemia, liver diseases, dyspepsia or ankylosing spondylitis. A trend towards increasing diversification of systemic conditions has appeared over time. About a third of registration records in clinical periodontal research deals with periodontal medicine. CONCLUSIONS: Periodontal medicine now constitutes an important part of clinical periodontal research. Research activity in periodontal medicine has grown continuously since the early 2000s, and exploration of registers gives a useful up-to-date snapshot of this constantly evolving field of research.

Oral Health Education in Children before Dental Treatment under General Anesthesia.

OBJECTIVE: The aim of this study was to evaluate the attitude of parents towards the oral health of their children before oral rehabilitation under general anesthesia (GA). STUDY DESIGN: Children receiving dental treatment under GA between November 2013 and July 2014 in the Pediatric Dentistry Department (University Hospital Center, Toulouse, France) were enrolled in an oral health preventive program. An anonymous questionnaire was self-administered by the parents during the pre-operative session. RESULTS: The sample comprised 67 children with a mean age of 4.8 years. 48 % of the parents had difficulties in maintaining the oral hygiene of their child. Two thirds of them reported a lack of cooperation. An adult cleaned the child's teeth in 43% of the cases. 14% of the study population brushed their teeth twice a day or more. In addition, half of the parents reported that they modified food consumption or teeth cleaning habits of their children since the initial consultation. CONCLUSIONS: This study suggests a low compliance of parents and children with the recommendations on oral hygiene and food consumption given at the initial visit and demonstrates the feasibility of a preventive program in this population.

The oral organ: A new vision of the mouth as a whole for a gerophysiological approach to healthy aging.

This article brings a new perspective on oral physiology by presenting the oral organ as an integrated entity within the entire organism and its surrounding environment. Rather than considering the mouth solely as a collection of discrete functions, this novel approach emphasizes its role as a dynamic interphase, supporting interactions between the body and external factors. As a resilient ecosystem, the equilibrium of mouth ecological niches is the result of a large number of interconnected factors including the heterogeneity of different oral structures, diversity of resources, external and internal pressures and biological actors. The manuscript seeks to deepen the understanding of age-related changes within the oral cavity and throughout the organism, aligning with the evolving field of gerophysiology. The strategic position and fundamental function of the mouth make it an invaluable target for early prevention, diagnosis, treatment, and even reversal of aging effects throughout the entire organism. Recognizing the oral cavity capacity for sensory perception, element capture and information processing underscores its vital role in continuous health monitoring. Overall, this integrated understanding of the oral physiology aims at advancing comprehensive approaches to the oral healthcare and promoting broader awareness of its implications on the overall well-being.

Oral health-related quality of life among outpatients with rheumatoid arthritis.

OBJECTIVES: The aims of the Oral Status And Rheumatoid Arthritis (OSARA) cross-sectional study were to study the oral health-related quality of life and to assess the associated factors in a population of outpatients with rheumatoid arthritis in France. METHODS: The data were collected by five trained and standardised dentists who asked each subject the questions of a socio-demographic, behavioural and medical questionnaire, which was completed with the medical records, and performed the dental examination. Each subject filled out two self-assessment questionnaires: the Health Assessment Questionnaire and the General Oral Health Assessment Index. RESULTS: Seventy-three subjects were included. The mean age of the participants was 60.2 ± 11.9 years and 75.3% were women. For 58.3% of the subjects, their self-perceived oral health-related quality of life was described as poor. The logistic regression analysis found that a small number of teeth and marked difficulties in dressing and grooming were associated with bad oral health-related quality of life [ORa = 10.5 (1.96-56.19) and ORa = 4.3 (1.15-15.77), respectively]. CONCLUSIONS: More care should be given to the prevention of dental diseases in order to improve the oral health-related quality of life of patients with rheumatoid arthritis and their self-esteem, which will already be heavily affected.

Explanations as a New Metric for Feature Selection: A Systematic Approach.

With the extensive use of Machine Learning (ML) in the biomedical field, there was an increasing need for Explainable Artificial Intelligence (XAI) to improve transparency and reveal complex hidden relationships between variables for medical practitioners, while meeting regulatory requirements. Feature Selection (FS) is widely used as a part of a biomedical ML pipeline to significantly reduce the number of variables while preserving as much information as possible. However, the choice of FS methods affects the entire pipeline including the final prediction explanations, whereas very few works investigate the relationship between FS and model explanations. Through a systematic workflow performed on 145 datasets and an illustration on medical data, the present work demonstrated the promising complementarity of two metrics based on explanations (using ranking and influence changes) in addition to accuracy and retention rate to select the most appropriate FS/ML models. Measuring how much explanations differ with/without FS are particularly promising for FS methods recommendation. While reliefF generally performs the best on average, the optimal choice may vary for each dataset. Positioning FS methods in a tridimensional space, integrating explanations-based metrics, accuracy and retention rate, would allow the user to choose the priorities to be given on each of the dimensions. In biomedical applications, where each medical condition may have its own preferences, this framework will make it possible to offer the healthcare professional the appropriate FS technique, to select the variables that have an important explainable impact, even if this comes at the expense of a limited drop of accuracy.

Explainable machine learning framework to predict personalized physiological aging.

Attaining personalized healthy aging requires accurate monitoring of physiological changes and identifying subclinical markers that predict accelerated or delayed aging. Classic biostatistical methods most rely on supervised variables to estimate physiological aging and do not capture the full complexity of inter-parameter interactions. Machine learning (ML) is promising, but its black box nature eludes direct understanding, substantially limiting physician confidence and clinical usage. Using a broad population dataset from the National Health and Nutrition Examination Survey (NHANES) study including routine biological variables and after selection of XGBoost as the most appropriate algorithm, we created an innovative explainable ML framework to determine a Personalized physiological age (PPA). PPA predicted both chronic disease and mortality independently of chronological age. Twenty-six variables were sufficient to predict PPA. Using SHapley Additive exPlanations (SHAP), we implemented a precise quantitative associated metric for each variable explaining physiological (i.e., accelerated or delayed) deviations from age-specific normative data. Among the variables, glycated hemoglobin (HbA1c) displays a major relative weight in the estimation of PPA. Finally, clustering profiles of identical contextualized explanations reveal different aging trajectories opening opportunities to specific clinical follow-up. These data show that PPA is a robust, quantitative and explainable ML-based metric that monitors personalized health status. Our approach also provides a complete framework applicable to different datasets or variables, allowing precision physiological age estimation.

Doctors in Medical Data Sciences: A New Curriculum.

Machine Learning (ML), a branch of Artificial Intelligence, which is competing with human experts in many specialized biomedical fields and will play an increasing role in precision medicine. As with any other technological advances in medicine, the keys to understanding must be integrated into practitioner training. To respond to this challenge, this viewpoint discusses some necessary changes in the health studies curriculum that could help practitioners to interpret decisions the made by a machine and question them in relation to the patient's medical context. The complexity of technology and the inherent criticality of its use in medicine also necessitate a new medical profession. To achieve this objective, this viewpoint will propose new medical practitioners with skills in both medicine and data science: the Doctor in Medical Data Sciences.

Improvement of Mucosal Lesion Diagnosis with Machine Learning Based on Medical and Semiological Data: An Observational Study.

Despite artificial intelligence used in skin dermatology diagnosis is booming, application in oral pathology remains to be developed. Early diagnosis and therefore early management, remain key points in the successful management of oral mucosa cancers. The objective was to develop and evaluate a machine learning algorithm that allows the prediction of oral mucosa lesions diagnosis. This cohort study included patients followed between January 2015 and December 2020 in the oral mucosal pathology consultation of the Toulouse University Hospital. Photographs and demographic and medical data were collected from each patient to constitute clinical cases. A machine learning model was then developed and optimized and compared to 5 models classically used in the field. A total of 299 patients representing 1242 records of oral mucosa lesions were used to train and evaluate machine learning models. Our model reached a mean accuracy of 0.84 for diagnostic prediction. The specificity and sensitivity range from 0.89 to 1.00 and 0.72 to 0.92, respectively. The other models were proven to be less efficient in performing this task. These results suggest the utility of machine learning-based tools in diagnosing oral mucosal lesions with high accuracy. Moreover, the results of this study confirm that the consideration of clinical data and medical history, in addition to the lesion itself, appears to play an important role.

Systemic Periodontal Risk Score Using an Innovative Machine Learning Strategy: An Observational Study.

Early diagnosis is crucial for individuals who are susceptible to tooth-supporting tissue diseases (e.g., periodontitis) that may lead to tooth loss, so as to prevent systemic implications and maintain quality of life. The aim of this study was to propose a personalized explainable machine learning algorithm, solely based on non-invasive predictors that can easily be collected in a clinic, to identify subjects at risk of developing periodontal diseases. To this end, the individual data and periodontal health of 532 subjects was assessed. A machine learning pipeline combining a feature selection step, multilayer perceptron, and SHapley Additive exPlanations (SHAP) explainability, was used to build the algorithm. The prediction scores for healthy periodontium and periodontitis gave final F1-scores of 0.74 and 0.68, respectively, while gingival inflammation was harder to predict (F1-score of 0.32). Age, body mass index, smoking habits, systemic pathologies, diet, alcohol, educational level, and hormonal status were found to be the most contributive variables for periodontal health prediction. The algorithm clearly shows different risk profiles before and after 35 years of age and suggests transition ages in the predisposition to developing gingival inflammation or periodontitis. This innovative approach to systemic periodontal disease risk profiles, combining both ML and up-to-date explainability algorithms, paves the way for new periodontal health prediction strategies.

Place of a new radiological index in predicting pulp exposure before intervention for deep carious lesions.

BACKGROUND: During interventions for deep caries lesions without severe symptoms, preserving pulpal vitality is important to ensure treatment success, improve organ prognosis, and decrease cost-effectiveness. Current pre-operative radiographs allow visual estimation but not accurate measurement of lesion depth. PURPOSE: Investigate the ability of ratio 'remaining/total dentin thickness' (RDT/TDT, as determined on pre-operative radiographs) to predict pulp exposure during excavation. METHODS: This retrospective study (January 2018-June 2020) analyzed data on 360 patients. Four independent raters examined standard pre-operative radiographs and their contrasted versions. Lines put at the dentino-enamel junction, the floor of the carious lesion, and the pulp chamber wall allowed deriving RDT/TDT. Inter-rater agreements and concordance were assessed. A logistic regression accounting for measurement errors provided odds ratios that estimated the ability of the RDT/TDT to predict pulp exposure. RESULTS: The median RDT/TDT ratio ranges were 16.8-26.5% on standard and 16.2-24.6% on contrasted radiographs. Inter-rater agreements on RDT/TDT were rather poor and inter-rater reliability was low and similar in standard and contrasted radiographs: the concordance correlation coefficients (95% CIs) were estimated at 0.46 (0.40; 0.51) and 0.46 (0.40; 0.52), respectively. The risk of pulp exposure increased by 2.5 times [odds ratio (95% CI) 2.57 (2.06; 3.20)] per 10-point decrease of the ratio on standard radiographs vs. 4.15 (3.15; 5.46) on contrasted radiographs. CONCLUSION: RDT/TDT ratio is potentially helpful in predicting pulp exposure. However, the measurement errors on RDT and TDT being non-negligible and the interrater agreements poor, there is still place for advances through development of an automated process that will improve reliability and reproducibility of pulp exposure risk assessment. CLINICAL TRIAL: Trial registration number. ClinicalTrials.gov NCT04607395, October 29, 2020.