The relationship between endodontic case complexity and treatment outcomes

A comparison of radiographically determined periapical healing and tooth survival outcomes of root canal (re)treatment performed in two care pathways within the United Kingdom Armed Forces

AIMS

To compare radiographic periapical healing and tooth survival outcomes of root canal (re)treatment performed within two care pathways (Routine Dental Care and Referred Treatment Pathway), in the United Kingdom Armed Forces (UKAF), and determine the effects of endodontic complexity on outcomes.

METHODOLOGY

This retrospective cohort study included 1466 teeth in 1252 personnel who received root canal (re)treatment between 2015 and 2020. General Dental Practitioners treated 661 teeth (573 patients) (Routine cohort), whilst Dentists with a Special Interest treated 805 teeth (678 patients) (Referred cohort). The latter group were graduates of an MSc programme in Endodontics with 4-8 years of postgraduation experience. Case complexity was retrospectively determined for each tooth using the endodontic component of Restorative Index of Treatment Need (RIOTN) guidelines. Periapical healing was determined using loose radiographic criteria. The data were analysed using chi-square tests, univariate logistic regression and Cox proportional hazards models.

RESULTS

A significantly (p < 0.0001) larger proportion of cases of low complexity had undergone root canal treatment within the Routine versus Referred cohort. The odds of periapical healing was significantly higher within the Referred versus Routine cohort, regardless of analyses using pooled (OR = 1.17; 95% CI: 1.11, 1.22) or moderate complexity (OR = 4.71; 95% CI: 2.73, 8.11) data. Within the Routine cohort, anterior teeth had higher odds of periapical healing than posterior teeth (OR = 1.13; 95% CI: 1.04, 1.22). The 60-month cumulative tooth survival was lower (p = 0.03) in the Routine (90.5%) than the Referred (96.0%) cohort. Within the Routine cohort, the hazard of tooth loss was higher amongst posterior teeth (HR = 4.03; 95% CI: 1.92, 8.45) but lower if posterior teeth had cast restorations (HR = 0.36; 95% CI: 0.19, 0.70). For the Referred cohort, posterior teeth restored with cast restoration (vs not) had significantly lower risk of tooth loss (HR = 0.21; 95% CI: 0.08, 0.55).

CONCLUSIONS

For UKAF patients, root canal (re)treatment provided within the Referred pathway was significantly more likely to achieve periapical healing and better tooth survival than those provided within the Routine pathway. Posterior teeth restored with an indirect restoration had a higher proportion of tooth survival. This study supported the utility of the endodontic component of RIOTN for assessing case complexity.

Deep learning for determining the difficulty of endodontic treatment: a pilot study

BACKGROUND

To develop and validate a deep learning model for automated assessment of endodontic case difficulty from periapical radiographs.

METHODS

A dataset of 1,386 periapical radiographs was compiled from two clinical sites. Two dentists and two endodontists annotated the radiographs for difficulty using the "simple assessment" criteria from the American Association of Endodontists' case difficulty assessment form in the Endocase application. A classification task labeled cases as "easy" or "hard", while regression predicted overall difficulty scores. Convolutional neural networks (i.e. VGG16, ResNet18, ResNet50, ResNext50, and Inception v2) were used, with a baseline model trained via transfer learning from ImageNet weights. Other models was pre-trained using self-supervised contrastive learning (i.e. BYOL, SimCLR, MoCo, and DINO) on 20,295 unlabeled dental radiographs to learn representation without manual labels. Both models were evaluated using 10-fold cross-validation, with performance compared to seven human examiners (three general dentists and four endodontists) on a hold-out test set.

RESULTS

The baseline VGG16 model attained 87.62% accuracy in classifying difficulty. Self-supervised pretraining did not improve performance. Regression predicted scores with ± 3.21 score error. All models outperformed human raters, with poor inter-examiner reliability.

CONCLUSION

This pilot study demonstrated the feasibility of automated endodontic difficulty assessment via deep learning models.

Expert consensus on difficulty assessment of endodontic therapy

Endodontic diseases are a kind of chronic infectious oral disease. Common endodontic treatment concepts are based on the removal of inflamed or necrotic pulp tissue and the replacement by gutta-percha. However, it is very essential for endodontic treatment to debride the root canal system and prevent the root canal system from bacterial reinfection after root canal therapy (RCT). Recent research, encompassing bacterial etiology and advanced imaging techniques, contributes to our understanding of the root canal system's anatomy intricacies and the technique sensitivity of RCT. Success in RCT hinges on factors like patients, infection severity, root canal anatomy, and treatment techniques. Therefore, improving disease management is a key issue to combat endodontic diseases and cure periapical lesions. The clinical difficulty assessment system of RCT is established based on patient conditions, tooth conditions, root canal configuration, and root canal needing retreatment, and emphasizes pre-treatment risk assessment for optimal outcomes. The findings suggest that the presence of risk factors may correlate with the challenge of achieving the high standard required for RCT. These insights contribute not only to improve education but also aid practitioners in treatment planning and referral decision-making within the field of endodontics.

Antifungal and genotoxic effects of Thymus serpyllum as a root canal irrigant

OBJECTIVES

The aim of this study was the assessment of the efficiency of the ethyl acetate (EthOAc) extract of Thymus serpyllum against Candida albicans and to compare it with sodium hypochlorite (NaOCl) and chlorhexidine (CHX), as well as their genotoxic effect.

MATERIAL AND METHODS

The antifungal effectiveness of the EthOAc extract of Thymus serpyllum was determined using the agar disk diffusion method. The inhibition zones induced by the EthOAc extract were compared after 5 min, 60 min, and 24 h to those induced by standard solutions (2% CHX and 2% NaOCl). An in vitro genotoxicity assay was performed in cultured lymphocytes from the blood of human volunteers to observe micronuclei formation. Statistical analysis of the results was performed using the Kruskal-Wallis test and one-way analysis of variance.

RESULTS

The inhibition zone of combination of CHX with EthOAc extract of Thymus serpyllum against C. albicans was 29.7 mm after 5 min, 28.3 mm after 60 min, and 29 mm after 24 h. The inhibition zone of NaOCl in combination with EthOAc extract of Thymus serpyllum against C. albicans was 0 mm. The EthOAc extract of Thymus serpyllum did not show a genotoxic effect on lymphocyte cells.

CONCLUSIONS

The EthOAc extract of Thymus serpyllum in combination with CHX may be a useful root canal disinfection in endodontic therapy.

Incidence of missed middle mesial canal in endodontically treated mandibular molar teeth: A cone-beam computed tomography study

Background

In endodontic treatment, the aim is to completely determine, shape, and fill all root canals in a three-dimensional way. Missed canals lead to treatment failure. In mandibular molars, there may be an extra canal called the middle mesial canal between the mesiobuccal and mesiolingual canals.

Aim

The aim of this study was to evaluate the prevalence of missed middle mesial canals in root canal-treated mandibular molar teeth.

Materials and Methods

In this study, cone-beam computed tomography (CBCT) images of 1054 patients were analyzed. We identified 121 endodontically treated mandibular molars and evaluated the prevalence of missed canals.

Results

Although 33.05% of the root canal-treated teeth did not have a middle mesial canal, this canal was detected in the other 66.94%; 97.53% of teeth with a middle mesial canal could not be detected by clinicians.

Conclusion

Clinicians performing endodontic treatment of mandibular molar teeth should not ignore the presence of the middle mesial canal. Thus, it is very important for clinicians to have sufficient information about the localization, morphology, and variations of the middle mesial canal.

Effectiveness of endodontic complexity assessment tool (E-CAT) on the undergraduate students in an endodontic training program and its predictive capability on complications

INTRODUCTION

Dental students face a number of challenges when it comes to performing root canal treatments (RCTs). The Endodontic Complexity Assessment Tool (E-CAT) was developed to assist dental practitioners in assessing the complexity of RCTs before beginning treatment.

MATERIALS AND METHODS

The E-CAT was filled out independently by both the educator and the student. To allow educators to record scores and complexity classes, they transferred their and students' forms to the website https://www.e-cat.uk/. Students began endodontic treatment after learning about the complexity level of the case. The educators were responsible for recording any complications encountered in every case from the outset to 1 month after treatment.

RESULTS

A total of 70 students, 33 in fourth and 37 in fifth-grade, were included in the study. In the cases with higher E-CAT scores, complications such as misdiagnosed, faulty access cavity, furca or coronal third perforation, insufficient root canal instrumentation, working length loss, canal blockage, overpreparation, incomplete root canal filling and overfilling were experienced significantly more often compared to the cases with lower E-CAT scores (p < .05). The number of complications (r = .40, p < .001), treatment sessions (r = .44, p < .001), and teacher support (r = .24, p < .001) positively correlated with E-CAT score (p < .05).

CONCLUSION

The E-CAT is an effective tool for assisting dental students in understanding technical challenges, such as complex root canal anatomy and possible complications during treatment. Educators can also use e-CAT to pre-select clinical cases and standardise student training by offering cases of equal complexity.

Preoperative risk assessment does not allow to predict root filling length using machine learning: A longitudinal study

OBJECTIVES

First we aimed to identify significant associations between preoperative risk factors and achieving optimal root filling length (RFL) during orthograde root canal treatments (RCT) and second to predict successful RFL using machine learning.

METHODS

Teeth receiving RCT at one university clinic from 2016-2020 with complete documentation were included. Successful RFL was defined to be 0-2mm of the apex, suboptimal RFL >2mm or beyond the apex. Logistic regression (logR) was used for association analyses; logR and more advanced machine learning (random forest (RF), support vector machine (SVM), decision tree (DT), gradient boosting machine (GBM) and extreme gradient boosting (XGB)) were employed for predictive modeling.

RESULTS

555 completed RCT (343 patients, female/male 32.1/67.9%) were included. In our association analysis (involving the full dataset), unsuccessful RFL was more likely in undergraduate students (US): OR 2.74, 95% CI [1.61, 4.75], p < 0.001), teeth with indistinct canal paths (OR 11.04, [2.87, 44.88], p < 0.001), root canals reduced in size (OR 2.56, [1.49, 4.46], p < 0.01), retreatments (OR 3.13, [1.6, 6.41], p < 0.001). Subgroup analyses revealed that dentists were more successful in mitigating risks than undergraduate students. Prediction of RFL on a separate testset was limitedly possible regardless of the machine learning approach.

CONCLUSIONS

Achieving RFL is depending on the operator and several risk factors. The predictive performance on the technical outcome of a root canal treatment utilizing ML algorithms was insufficient.

CLINICAL SIGNIFICANCE

Preoperative risk assessment is a relevant step in endodontic treatment planning. Single radiographic risk factors were significantly associated with achieving (or not achieving) optimal RFL and showed higher predictive value than a more complex risk assessment form.

Restorative difficulty evaluation system of endodontically treated teeth

OBJECTIVE

This article provides an updated overview of restorative procedures of endodontically treated teeth.

CLINICAL CONSIDERATIONS

The different techniques and procedures to restore an endodontic treated tooth were considered in the last decades. While they are generally performed using bonding procedures in combination with or without the placement of a post into the root to build up the abutment, there has been a lack of interest in restorative difficulties that can be faced. Failures are represented such as debonding of the post, fracture of the root, decementation, and/or fracture of the restoration, microleakage of the margins. Essentially, the presence of a sufficient failure is considered a key point of a long prognosis. Different clinical factors can directly influence the type of restoration and the longevity of the treatment. The restorative difficulty evaluation system (RDES) is proposed in this article. This new system is composed of eight different clinical factors that are divided into six levels of difficulties. The RDES is composed of 1. Endodontic complexity and outcome, 2. Vertical amount of coronal residual structure and dimension of the pulp chamber, 3. Horizontal amount of coronal residual structure, 4. Restoration marginal seal, 5. Local interdisciplinary conditions, 6. the complexity of the treatment planning, 7. Functional need, 8. Dental wear and esthetic need.

CONCLUSION

This article reviews the RDES and outlines critical steps and tips for clinical success.

CLINICAL SIGNIFICANCE

The RDES allows to any clinician to evaluate restorative difficulties when an endodontic treated tooth must be restored, combines clinical aspects that can involve from the single tooth to a full mouth rehabilitation.

Machine learning models for prognosis prediction in endodontic microsurgery

OBJECTIVES

This study aimed to establish and validate machine learning models for prognosis prediction in endodontic microsurgery, avoiding treatment failure and supporting clinical decision-making.

METHODS

A total of 234 teeth from 178 patients were included in this study. We developed gradient boosting machine (GBM) and random forest (RF) models. For each model, 80% of the data were randomly selected for the training set and the remaining 20% were used as the test set. A stratified 5-fold cross-validation approach was used in model training and testing. Correlation analysis and importance ranking were conducted for feature selection. The predictive accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, and the area under the curve (AUC) of receiver operating characteristic (ROC) curves were calculated to evaluate the predictive performance.

RESULTS

There were eight important predictors, including tooth type, lesion size, type of bone defect, root filling density, root filling length, apical extension of post, age, and sex. For the GBM model, the predictive accuracy was 0.80, with a sensitivity of 0.92, specificity of 0.71, PPV of 0.71, NPV of 0.92, F1 of 0.80/0.80, and AUC of 0.88. For the RF model, the accuracy was 0.80, with a sensitivity of 0.85, specificity of 0.76, PPV of 0.73, NPV of 0.87, F1 of 0.79/0.81, and AUC of 0.83.

CONCLUSIONS

The trained models were developed by eight common variables, showing the potential ability to predict the prognosis of endodontic microsurgery. The GBM model outperformed the RF model slightly on our dataset.

CLINICAL SIGNIFICANCE

Clinicians can use machine learning models for preoperative analysis in endodontic microsurgery. The models are expected to improve the efficiency of clinical decision-making and assist in clinician-patient communication.

Evaluation of Quality of Endodontic Re-Treatment and Changes in Periapical Status

Background:

The present study was conducted to assess quality of root canal (RC) filling before and after RC re-treatment.

Materials and Methods:

Two hundred and thirty-eight radiographs of failed endodontic treatment were assessed. The periapical status of the endodontic treatment was evaluated with periapical index (PAI) scoring system. PAI <3 showed absence and PAI >3 showed presence of periapical lesion.

Results:

There was a statistically significant increase in scores 1 and 3 and decrease in scores 2, 4, 5, and 6 after treatment (P < 0.05). PAI score >3 was seen in 37% before which decreased to 16% after endodontic retreatment. 34.6% obturation was homogenous and 65.4% was nonhomogenous before endodontic retreatment. After endodontic retreatment, 95.2% became homogenous and 4.8% nonhomogenous. The reason for endodontic failure was furcation in 2%, iatrogenic causes in 3%, loss of coronal seal in 16%, periapical pathology in 25%, and inadequate root filling in 54%.

Conclusion:

There was significant improvement and decrease in size of periapical lesions in re-endodontic cases as compared to primary RC treated teeth.

Impact of endodontic case difficulty on operating time of single visit nonsurgical endodontic treatment under general anesthesia

Background

A Case Difficulty Assessment Form was designed for use in endodontic curricula, and to assist practitioners with treatment planning, referral and recording. The aim of this study was to determine how endodontic case difficulty factors influence the operating time of single-visit nonsurgical endodontic treatments under general anesthesia.

Methods

Data on 198 single-visit endodontic treatments (80 anterior teeth, 43 premolars, and 75 molars) performed under general anesthesia by a specialized practitioner were obtained from 119 special needs patients (mean [SD] age = 30.7 [14.7] years). Total duration of operation was analyzed with relation to demographic and dental factors and American Association of Endodontists (AAE) Case Difficulty Assessment factors. Mann–Whitney U test, t-test, and Kruskal–Wallis test were used to assess relationships between operating time and confounding factors (p < 0.05).

Results

High difficulty cases required significantly longer time to complete operations than treatments of minimal-to-moderate difficulty regardless of tooth type (p < 0.05). Demographic factors of the patients rarely influenced operating time length. Among variables included in the AAE Case Difficulty Assessment Form, tooth position, crown morphology, root morphology, canal appearance, and periodontal condition were significantly associated with increased operating time (p < 0.05).

Conclusions

A higher level of case difficulty contributed to increased duration of endodontic treatment under general anesthesia indicating that Endodontic Case Difficulty Assessment Form is useful for predicting the duration of nonsurgical endodontic treatment. Among many factors, complicated anatomic features of the treated teeth increased case complexity and extended operating time.

Intra- and Interobserver Agreement during the Assessment of the Different Stages of Root Development Using 4 Radiographic Classifications

INTRODUCTION

This study analyzed intra- and interobserver agreements during radiographic assessment of the different stages of root development using the dichotomous, Moorrees, Demirjian, and Cvek classifications, as well as the effect of the observer's level of experience on the result.

METHODS

Two hundred eighty-five digital periapical radiographs were examined via visual inspection by 3 experienced and 3 nonexperienced observers (n = 6) under strict technological and viewing conditions. After observer calibration, determination of the presence or absence of an open apex and the assignment of a root development stage according to the different subdivisions of the 4 indexes were performed. This evaluation was carried out by each observer in duplicate in the first round (n = 8) and repeated in the second round (n = 8). The 16 examinations performed by each observer (N = 96) were analyzed to determine the percentage of concordance followed by intraobserver, interobserver, and global observer agreement using the kappa coefficient and a weighted kappa. Additionally, to determine the level of concordance between the visual determination of an open or closed apex and the apical foramen width measured in millimeters, a dichotomized kappa coefficient was applied.

RESULTS

A good level of global observer agreement was found for the dichotomous, Demirjian, and Cvek classifications. However, a significantly low percentage of total concordance and global observer agreement (6.66% and 0.498, respectively) was obtained using the Moorrees classification, which was more pronounced among nonexperienced observers (0.247). Apical foramen width measurements indicated the presence of 143 roots with an open apex (50.2%) and 142 with a closed apex (49.8%), and the dichotomized kappa coefficient test revealed a good level of agreement during the visual determination of an open or closed apex (range, 0.611-0.636).

CONCLUSIONS

The classifications of Cvek and Demirjian provided reliable results when determining the different stages of root development. In contrast, the Moorrees classification provided the lowest agreement values, with a significant negative effect among nonexperienced observers. Finally, the visual estimation of the presence (or absence) of an open apex provided a good level of concordance with the radiographic apical foramen width.

Assessment of the quality of endodontic re-treatment and changes in periapical status on a postgraduate endodontic clinic

OBJECTIVE

The aim of this study was to assess endodontic retreatment outcomes based on quality of obturation and healing.

MATERIALS AND METHODS

A total number of 223 radiographs of patients who had received endodontic retreatment during the period (2008-2015) at a postgraduate teaching clinic were selected. Unreadable radiographs were all excluded. From the original sample of 223 in total 24 radiographs were discarded. The final sample thus consisted of 199 root canal fillings. All radiographs were individually evaluated for the density of the root filling as well as the distance between the end of the root canal filling and radiographic apex based on a six-point scoring system. Subsequently, patients were reviewed and follow up periapical radiographs were exposed. The outcome of healing was assessed using the Periapical Index (PAI) scoring system. The data were analysed using Chi Square test (p < 0.05).

RESULTS

The study revealed that 78.9 % of the endodontic retreatments were both homogeneity and length acceptable. The corresponding figure was only 13.1 % before endodontic retreatment. Conversely, homogeneity and length unacceptable before endodontic retreatment was 47.2 % reducing to a mere 2.5 % after retreatment. The results were statistically significant (P < 0.001). There was over 80 % improvement in periapical healing following endodontic retreatment and this was also statistically significant (P < 0.001).

CONCLUSION

There was a significant improvement in outcome after endodontic retreatment on the postgraduate endodontic clinic. The success rate of endodontic retreatment was over 70 % which is in line with the endodontic literature. Radiographic follow up confirmed some 81 % improvement in healing rate following endodontic retreatment.

CLINICAL SIGNIFICANCE

Endodontic re-treatment cases are normally categorised as high complexity and as such referral to specialist settings should be considered to help improve treatment outcomes.