Association between patient-, tooth- and treatment-level factors and root canal treatment failure: A retrospective longitudinal and machine learning study

Radiographic outcome of endodontic treatment and retreatment of teeth with apical periodontitis using two different root canal irrigants. A prospective cohort study

AIM

The aim of this prospective cohort study was to compare the radiographic outcome of endodontic treatment and retreatment of teeth with apical periodontitis using either 1% sodium hypochlorite (NaOCl) or 2% chlorhexidine digluconate (CHX) for root canal irrigation.

MATERIALS AND METHODS

In the years from 2013 to 2015 standard irrigation varied by semester between NaOCl and CHX at the Department of Endodontics at the Faculty of Dentistry. During that time, 912 teeth received treatment for apical periodontitis in 744 patients, of whom 532 responded to the request for a 1-year follow-up. Only one tooth per person (the most distally located) were included; 285 teeth treated with NaOCl and 247 with CHX. One hundred cases were then randomly sampled from each irrigation group and analysed for outcome by periapical index (PAI) scoring using criteria for success, uncertain and failure. Clinical and other radiographic parameters were scored or recorded and analysed for associations with radiographic outcome using chi-square, ANOVA and regression analyses.

RESULTS

Success rates (PAI score 1 or 2 at control) were nearly identical for the two irrigation liquids. The use of irrigating solution also did not significantly influence the outcome in chi-square analyses of subgroups of teeth or regression analyses with other variables included. Ordinal regression analysis established that preoperative lesion size or preoperative PAI score were significantly associated with outcome, and teeth in the mandible had significantly better outcomes than in the maxilla.

CONCLUSIONS

No significant differences in the radiographic outcome using either 1% NaOCl or 2% CHX as irrigants were found. The outcome was better for teeth with small lesions or lower PAI scores at completion of treatment and for mandibular teeth.

Experimental validation of computer-vision methods for the successful detection of endodontic treatment obturation and progression from noisy radiographs

PURPOSE

(1) To evaluate the effects of denoising and data balancing on deep learning to detect endodontic treatment outcomes from radiographs. (2) To develop and train a deep-learning model and classifier to predict obturation quality from radiomics.

METHODS

The study conformed to the STARD 2015 and MI-CLAIMS 2021 guidelines. 250 deidentified dental radiographs were collected and augmented to produce 2226 images. The dataset was classified according to endodontic treatment outcomes following a set of customized criteria. The dataset was denoised and balanced, and processed with YOLOv5s, YOLOv5x, and YOLOv7 models of real-time deep-learning computer vision. Diagnostic test parameters such as sensitivity (Sn), specificity (Sp), accuracy (Ac), precision, recall, mean average precision (mAP), and confidence were evaluated.

RESULTS

Overall accuracy for all the deep-learning models was above 85%. Imbalanced datasets with noise removal led to YOLOv5x's prediction accuracy to drop to 72%, while balancing and noise removal led to all three models performing at over 95% accuracy. mAP saw an improvement from 52 to 92% following balancing and denoising.

CONCLUSION

The current study of computer vision applied to radiomic datasets successfully classified endodontic treatment obturation and mishaps according to a custom progressive classification system and serves as a foundation to larger research on the subject matter.

Second Opinion for Non-Surgical Root Canal Treatment Prognosis Using Machine Learning Models

Although the association between risk factors and non-surgical root canal treatment (NSRCT) failure has been extensively studied, methods to predict the outcomes of NSRCT are in an early stage, and dentists currently make the treatment prognosis based mainly on their clinical experience. Since this involves different sources of error, we investigated the use of machine learning (ML) models as a second opinion to support the clinical decision on whether to perform NSRCT. We undertook a retrospective study of 119 confirmed and not previously treated Apical Periodontitis cases that received the same treatment by the same specialist. For each patient, we recorded the variables from a newly proposed data collection template and defined a binary outcome: Success if the lesion clears and failure otherwise. We conducted tests for detecting the association between the variables and the outcome and selected a set of variables as the initial inputs into four ML algorithms: Logistic Regression (LR), Random Forest (RF), Naive-Bayes (NB), and K Nearest Neighbors (KNN). According to our results, RF and KNN significantly improve (p-values < 0.05) the sensitivity and accuracy of the dentist's treatment prognosis. Taking our results as a proof of concept, we conclude that future randomized clinical trials are worth designing to test the clinical utility of ML models as a second opinion for NSRCT prognosis.

Impact of systemic health on treatment outcomes in endodontics

BACKGROUND

The healing of periapical lesions after root canal treatment (RCT) is not the result of the curative action of the treatment. The process of healing begins with inflammation, and is resolved by the clearance of the immunogen that induces the immune response. Then, the periapical tissue itself carries out the healing of the periapical lesion, by repair or by a combination of repair and regeneration, depending on the host's reparative response working properly. The ultimate objective of RCT is to achieve wound healing by removing the source of bacterial antigens and toxins, allowing chronic inflammatory tissue to become reparative tissue. Some systemic conditions increase the susceptibility of the host to infection or impair the tissue reparative response, maintaining the inflammatory process and periapical bone resorption after RCT. This can cause the failure of RCT and even the need for extraction of the affected tooth.

OBJECTIVE

To analyse the scientific literature on the possible influence of systemic conditions on the treatment outcomes in endodontics, as well as to discuss the biological mechanisms that may be involved.

METHODS

The search was carried out in PubMed, SCOPUS and EMBASE. The inclusion criteria established were original scientific articles reporting data about some systemic condition in relation to treatment outcomes in endodontics, including clinical studies and studies carried out in animal models.

RESULTS

Systemic factors (age, nutrition, stress, hormones, smoking habits), and systemic diseases, such as diabetes, cardiovascular diseases, osteoporosis, HIV infection, inflammatory bowel disease, and others, can influence or interfere in the repair of periapical tissues after RCT.

DISCUSSION

Some of these systemic diseases can alter bone turnover and fibroblast function, preventing or delaying periapical wound healing. Others can alter the microvasculature, reducing nutrients and oxygen supply to periapical tissues. As a result, these systemic conditions can decrease the success rate of RCT and provoke incomplete wound healing (typically granulomatous tissue formation) in the periapical region.

CONCLUSIONS

The results of this narrative review show worse success rate of RCT, with higher percentage of postoperative radiolucent periapical lesions and higher proportion of non-retained teeth (RFT), associated with several systemic conditions, such as smoking habits and diabetes.

Developments and Performance of Artificial Intelligence Models Designed for Application in Endodontics: A Systematic Review

Technological advancements in health sciences have led to enormous developments in artificial intelligence (AI) models designed for application in health sectors. This article aimed at reporting on the application and performances of AI models that have been designed for application in endodontics. Renowned online databases, primarily PubMed, Scopus, Web of Science, Embase, and Cochrane and secondarily Google Scholar and the Saudi Digital Library, were accessed for articles relevant to the research question that were published from 1 January 2000 to 30 November 2022. In the last 5 years, there has been a significant increase in the number of articles reporting on AI models applied for endodontics. AI models have been developed for determining working length, vertical root fractures, root canal failures, root morphology, and thrust force and torque in canal preparation; detecting pulpal diseases; detecting and diagnosing periapical lesions; predicting postoperative pain, curative effect after treatment, and case difficulty; and segmenting pulp cavities. Most of the included studies (n = 21) were developed using convolutional neural networks. Among the included studies. datasets that were used were mostly cone-beam computed tomography images, followed by periapical radiographs and panoramic radiographs. Thirty-seven original research articles that fulfilled the eligibility criteria were critically assessed in accordance with QUADAS-2 guidelines, which revealed a low risk of bias in the patient selection domain in most of the studies (risk of bias: 90%; applicability: 70%). The certainty of the evidence was assessed using the GRADE approach. These models can be used as supplementary tools in clinical practice in order to expedite the clinical decision-making process and enhance the treatment modality and clinical operation.

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.