Predicting postoperative pain following root canal treatment by using artificial neural network evaluation

Artificial intelligence in endodontics: Fundamental principles, workflow, and tasks

The integration of artificial intelligence (AI) in healthcare has seen significant advancements, particularly in areas requiring image interpretation. Endodontics, a specialty within dentistry, stands to benefit immensely from AI applications, especially in interpreting radiographic images. However, there is a knowledge gap among endodontists regarding the fundamentals of machine learning and deep learning, hindering the full utilization of AI in this field. This narrative review aims to: (A) elaborate on the basic principles of machine learning and deep learning and present the basics of neural network architectures; (B) explain the workflow for developing AI solutions, from data collection through clinical integration; (C) discuss specific AI tasks and applications relevant to endodontic diagnosis and treatment. The article shows that AI offers diverse practical applications in endodontics. Computer vision methods help analyse images while natural language processing extracts insights from text. With robust validation, these techniques can enhance diagnosis, treatment planning, education, and patient care. In conclusion, AI holds significant potential to benefit endodontic research, practice, and education. Successful integration requires an evolving partnership between clinicians, computer scientists, and industry.

Empowering Modern Dentistry: The Impact of Artificial Intelligence on Patient Care and Clinical Decision Making

Advancements in artificial intelligence (AI) are poised to catalyze a transformative shift across diverse dental disciplines including endodontics, oral radiology, orthodontics, pediatric dentistry, periodontology, prosthodontics, and restorative dentistry. This narrative review delineates the burgeoning role of AI in enhancing diagnostic precision, streamlining treatment planning, and potentially unveiling innovative therapeutic modalities, thereby elevating patient care standards. Recent analyses corroborate the superiority of AI-assisted methodologies over conventional techniques, affirming their capacity for personalization, accuracy, and efficiency in dental care. Central to these AI applications are convolutional neural networks and deep learning models, which have demonstrated efficacy in diagnosis, prognosis, and therapeutic decision making, in some instances surpassing traditional methods in complex cases. Despite these advancements, the integration of AI into clinical practice is accompanied by challenges, such as data security concerns, the demand for transparency in AI-generated outcomes, and the imperative for ongoing validation to establish the reliability and applicability of AI tools. This review underscores the prospective benefits of AI in dental practice, envisioning AI not as a replacement for dental professionals but as an adjunctive tool that fortifies the dental profession. While AI heralds improvements in diagnostics, treatment planning, and personalized care, ethical and practical considerations must be meticulously navigated to ensure responsible development of AI in dentistry.

The Use of Artificial Intelligence in Endodontics

Endodontics is the dental specialty foremost concerned with diseases of the pulp and periradicular tissues. Clinicians often face patients with varying symptoms, must critically assess radiographic images in 2 and 3 dimensions, derive complex diagnoses and decision making, and deliver sophisticated treatment. Paired with low intra- and interobserver agreement for radiographic interpretation and variations in treatment outcome resulting from nonstandardized clinical techniques, there exists an unmet need for support in the form of artificial intelligence (AI), providing automated biomedical image analysis, decision support, and assistance during treatment. In the past decade, there has been a steady increase in AI studies in endodontics but limited clinical application. This review focuses on critically assessing the recent advancements in endodontic AI research for clinical applications, including the detection and diagnosis of endodontic pathologies such as periapical lesions, fractures and resorptions, as well as clinical treatment outcome predictions. It discusses the benefits of AI-assisted diagnosis, treatment planning and execution, and future directions including augmented reality and robotics. It critically reviews the limitations and challenges imposed by the nature of endodontic data sets, AI transparency and generalization, and potential ethical dilemmas. In the near future, AI will significantly affect the everyday endodontic workflow, education, and continuous learning.

Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review

Artificial intelligence (AI) has been recently introduced into clinical dentistry, and it has assisted professionals in analyzing medical data with unprecedented speed and an accuracy level comparable to humans. With the help of AI, meaningful information can be extracted from dental databases, especially dental radiographs, to devise machine learning (a subset of AI) models. This study focuses on models that can diagnose and assist with clinical conditions such as oral cancers, early childhood caries, deciduous teeth numbering, periodontal bone loss, cysts, peri-implantitis, osteoporosis, locating minor apical foramen, orthodontic landmark identification, temporomandibular joint disorders, and more. The aim of the authors was to outline by means of a review the state-of-the-art applications of AI technologies in several dental subfields and to discuss the efficacy of machine learning algorithms, especially convolutional neural networks (CNNs), among different types of patients, such as pediatric cases, that were neglected by previous reviews. They performed an electronic search in PubMed, Google Scholar, Scopus, and Medline to locate relevant articles. They concluded that even though clinicians encounter challenges in implementing AI technologies, such as data management, limited processing capabilities, and biased outcomes, they have observed positive results, such as decreased diagnosis costs and time, as well as early cancer detection. Thus, further research and development should be considered to address the existing complications.

Ambiguous Faces of Water-Based Inclusion Compounds: L4(4)8(8) Intercalato-Clathrate Hydrate of Pt(II) Complex

Clathrate hydrates are among the most intensively studied H-bond inclusion compounds. Despite the broad definition for this class of compounds, their meaning commonly refers to closed polyhedral nanocages that encapsulate small guest molecules. On the other hand, larger solutes enforce another type of encapsulation because of the solute size effect. Herein, we report a series of structures containing various molecules encapsulated by intercalated water layers constructed of polycyclic moieties of L4(4)8(8) topology. We parametrized the corrugation of individual layers and characterized interactions governing their formation. We suggested that these could be categorized as two-dimensional clathrates based on the character of intra-layer interactions and the effects observed between entrapped molecules and water-based intercalators.

Artificial Intelligence and Its Application in Endodontics: A Review

AIM AND BACKGROUND

Artificial intelligence (AI) since it was introduced into dentistry, has become an important and valuable tool in many fields. It was applied in different specialties with different uses, for example, in diagnosis of oral cancer, periodontal disease and dental caries, and in the treatment planning and predicting the outcome of orthognathic surgeries. The aim of this comprehensive review is to report on the application and performance of AI models designed for application in the field of endodontics.

MATERIALS AND METHODS

PubMed, Web of Science, and Google Scholar were searched to collect the most relevant articles using terms, such as AI, endodontics, and dentistry. This review included 56 papers related to AI and its application in endodontics.

RESULT

The applications of AI were in detecting and diagnosing periapical lesions, assessing root fractures, working length determination, prediction for postoperative pain, studying root canal anatomy and decision-making in endodontics for retreatment. The accuracy of AI in performing these tasks can reach up to 90%.

CONCLUSION

Artificial intelligence has valuable applications in the field of modern endodontics with promising results. Larger and multicenter data sets can give external validity to the AI models.

CLINICAL SIGNIFICANCE

In the field of dentistry, AI models are specifically crafted to contribute to the diagnosis of oral diseases, ranging from common issues such as dental caries to more complex conditions like periodontal diseases and oral cancer. AI models can help in diagnosis, treatment planning, and in patient management in endodontics. Along with the modern tools like cone-beam computed tomography (CBCT), AI can be a valuable aid to the clinician. How to cite this article: Ahmed ZH, Almuharib AM, Abdulkarim AA, et al. Artificial Intelligence and Its Application in Endodontics: A Review. J Contemp Dent Pract 2023;24(11):912-917.

Effectiveness of root canal instrumentation for the treatment of apical periodontitis: A systematic review and meta-analysis

BACKGROUND

The development of endodontic instruments has rapidly advanced, but their impact on endodontic outcome parameters remains unclear.

OBJECTIVES

This systematic review aimed to answer the following PICOT questions: In patients with apical periodontitis (P) what is the effectiveness of root canal instrumentation ([Q1] performed with contemporary techniques [I] in comparison with 'traditional' techniques [C]] and ([Q2] performed with contemporary engine-driven NiTi instruments [I] compared with other types of contemporary engine-driven NiTi instruments [with different design and/or technology] [C]) in terms of clinical and patient-related outcomes (O)?

METHODS

After PROSPERO protocol registration, a literature search was conducted using Clarivate Analytics' Web of Science, Scopus, PubMed and Cochrane Central Register of Controlled Trials. Grey literature and major journal contents were examined. Two independent reviewers performed the study selection, data extraction and appraisal of included studies. A quantitative meta-analysis was considered, and statistical heterogeneity and overall quality of evidence were assessed.

RESULTS

Nine studies were identified showing substantial methodological differences. Five studies addressed PICOT 1 and three PICOT 2, whereas one study aimed both. A random-effects meta-analysis model was considered for the outcome 'radiographic evidence of normal periodontal ligament space or reduction of apical lesion size' (PICOT 1) based on three studies with 332 evaluable participants and showed that contemporary instrumentation was associated with a more favourable outcome (p = .005) compared with root canal preparation with stainless steel instruments (odds ratio = 2.07 [95%-confidence interval = 1.25-3.44]) with no evidence of statistical heterogeneity (I2  = 0%) but low quality of evidence.

DISCUSSION

Albeit a few studies fulfilled eligible criteria, no study had a low risk of bias. Compelling evidence indicating significantly different outcome rates using different endodontic instruments when treating teeth with apical periodontitis is lacking.

CONCLUSIONS

In terms of healing, the results of the meta-analysis determined the higher effectiveness of root canal instrumentation performed with contemporary techniques in comparison with conventional stainless steel instruments in patients with apical periodontitis followed for a minimum of 1 year with low quality of evidence. No differences could be demonstrated between preparations with traditional stainless steel and contemporary NiTi instruments for other clinical and patient-related outcomes.

REGISTRATION

PROSPERO (CRD42021274642).

Applications, functions, and accuracy of artificial intelligence in restorative dentistry: A literature review

OBJECTIVE

The applications of artificial intelligence (AI) are increasing in restorative dentistry; however, the AI performance is unclear for dental professionals. The purpose of this narrative review was to evaluate the applications, functions, and accuracy of AI in diverse aspects of restorative dentistry including caries detection, tooth preparation margin detection, tooth restoration design, metal structure casting, dental restoration/implant detection, removable partial denture design, and tooth shade determination.

OVERVIEW

An electronic search was performed on Medline/PubMed, Embase, Web of Science, Cochrane, Scopus, and Google Scholar databases. English-language articles, published from January 1, 2000, to March 1, 2022, relevant to the aforementioned aspects were selected using the key terms of artificial intelligence, machine learning, deep learning, artificial neural networks, convolutional neural networks, clustering, soft computing, automated planning, computational learning, computer vision, and automated reasoning as inclusion criteria. A manual search was also performed. Therefore, 157 articles were included, reviewed, and discussed.

CONCLUSIONS

Based on the current literature, the AI models have shown promising performance in the mentioned aspects when being compared with traditional approaches in terms of accuracy; however, as these models are still in development, more studies are required to validate their accuracy and apply them to routine clinical practice.

CLINICAL SIGNIFICANCE

AI with its specific functions has shown successful applications with acceptable accuracy in diverse aspects of restorative dentistry. The understanding of these functions may lead to novel applications with optimal accuracy for AI in restorative dentistry.

The implication of oversampling on the effectiveness of force signals in the fault detection of endodontic instruments during RCT

This work provides an innovative endodontic instrument fault detection methodology during root canal treatment (RCT). Sometimes, an endodontic instrument is prone to fracture from the tip, for causes uncertain the dentist's control. A comprehensive assessment and decision support system for an endodontist may avoid several breakages. This research proposes a machine learning and artificial intelligence-based approach that can help to diagnose instrument health. During the RCT, force signals are recorded using a dynamometer. From the acquired signals, statistical features are extracted. Because there are fewer instances of the minority class (i.e. faulty/moderate class), oversampling of datasets is required to avoid bias and overfitting. Therefore, the synthetic minority oversampling technique (SMOTE) is employed to increase the minority class. Further, evaluating the performance using the machine learning techniques, namely Gaussian Naïve Bayes (GNB), quadratic support vector machine (QSVM), fine k-nearest neighbor (FKNN), and ensemble bagged tree (EBT). The EBT model provides excellent performance relative to the GNB, QSVM, and FKNN. Machine learning (ML) algorithms can accurately detect endodontic instruments' faults by monitoring the force signals. The EBT and FKNN classifier is trained exceptionally well with an area under curve values of 1.0 and 0.99 and prediction accuracy of 98.95 and 97.56%, respectively. ML can potentially enhance clinical outcomes, boost learning, decrease process malfunctions, increase treatment efficacy, and enhance instrument performance, contributing to superior RCT processes. This work uses ML methodologies for fault detection of endodontic instruments, providing practitioners with an adequate decision support system.

Using artificial intelligence to improve pain assessment and pain management: a scoping review

CONTEXT

Over 20% of US adults report they experience pain on most days or every day. Uncontrolled pain has led to increased healthcare utilization, hospitalization, emergency visits, and financial burden. Recognizing, assessing, understanding, and treating pain using artificial intelligence (AI) approaches may improve patient outcomes and healthcare resource utilization. A comprehensive synthesis of the current use and outcomes of AI-based interventions focused on pain assessment and management will guide the development of future research.

OBJECTIVES

This review aims to investigate the state of the research on AI-based interventions designed to improve pain assessment and management for adult patients. We also ascertain the actual outcomes of Al-based interventions for adult patients.

METHODS

The electronic databases searched include Web of Science, CINAHL, PsycINFO, Cochrane CENTRAL, Scopus, IEEE Xplore, and ACM Digital Library. The search initially identified 6946 studies. After screening, 30 studies met the inclusion criteria. The Critical Appraisals Skills Programme was used to assess study quality.

RESULTS

This review provides evidence that machine learning, data mining, and natural language processing were used to improve efficient pain recognition and pain assessment, analyze self-reported pain data, predict pain, and help clinicians and patients to manage chronic pain more effectively.

CONCLUSIONS

Findings from this review suggest that using AI-based interventions has a positive effect on pain recognition, pain prediction, and pain self-management; however, most reports are only pilot studies. More pilot studies with physiological pain measures are required before these approaches are ready for large clinical trial.

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.

Artificial Neural Networks Coupled with MALDI-TOF MS Serum Fingerprinting To Classify and Diagnose Pathological Pain Subtypes in Preclinical Models

Pathological pain subtypes can be classified as either neuropathic pain, caused by a somatosensory nervous system lesion or disease, or nociplastic pain, which develops without evidence of somatosensory system damage. Since there is no gold standard for the diagnosis of pathological pain subtypes, the proper classification of individual patients is currently an unmet challenge for clinicians. While the determination of specific biomarkers for each condition by current biochemical techniques is a complex task, the use of multimolecular techniques, such as matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), combined with artificial intelligence allows specific fingerprints for pathological pain-subtypes to be obtained, which may be useful for diagnosis. We analyzed whether the information provided by the mass spectra of serum samples of four experimental models of neuropathic and nociplastic pain combined with their functional pain outcomes could enable pathological pain subtype classification by artificial neural networks. As a result, a simple and innovative clinical decision support method has been developed that combines MALDI-TOF MS serum spectra and pain evaluation with its subsequent data analysis by artificial neural networks and allows the identification and classification of pathological pain subtypes in experimental models with a high level of specificity.

Ultrasound-Triggered on Demand Lidocaine Release Relieves Postoperative Pain

Safe and non-invasive on-demand relief is a crucial and effective treatment for postoperative pain because it considers variable timing and intensity of anesthetics. Ultrasound modulation is a promising technique for this treatment because it allows convenient timed and non-invasive controlled drug release. Here, we created an ultrasound-triggered lidocaine (Lido) release platform using an amino acid hydrogel functioning as three-dimensional (3D) scaffold material (Lido-PPIX@ER hydrogel). It allows control of the timing, intensity and duration of lidocaine (Lido) to relieve postoperative pain. The hydrogel releases Lido due to the elevated reactive oxygen species (ROS) levels generated by PPIX under ultrasound triggering. The Lido-PPIX@ER hydrogel under individualized ultrasound triggering released lidocaine and provided effective analgesia for more than 72 h. The withdrawal threshold was higher than that in the control group at all time points measured. The hydrogel showed repeatable and adjustable ultrasound-triggered nerve blocks in vivo, the duration of which depended on the extent and intensity of insonation. On histopathology, no systemic effect or tissue reaction was observed in the ultrasound-triggered Lido-PPIX@ER hydrogel-treated group. The Lido-PPIX@ER hydrogel with individualized (highly variable) ultrasound triggering is a convenient and effective method that offers timed and spatiotemporally controlled Lido release to manage postoperative pain. This article presents the delivery system for a new effective strategy to reduce pain, remotely control pain, and offer timed and spatiotemporally controlled release of Lido to manage postoperative pain.

A critical analysis of research methods and experimental models to study pulpitis

Pulpitis is the inflammatory response of the dental pulp to a tooth insult, whether it is microbial, chemical, or physical in origin. It is traditionally referred to as reversible or irreversible, a classification for therapeutic purposes that determines the capability of the pulp to heal. Recently, new knowledge about dental pulp physiopathology led to orientate therapeutics towards more frequent preservation of pulp vitality. However, full adoption of these vital pulp therapies by dental practitioners will be achieved only following better understanding of cell and tissue mechanisms involved in pulpitis. The current narrative review aimed to discuss the contribution of the most significant experimental models developed to study pulpitis. Traditionally, in vitro two(2D)- or three(3D)-dimensional cell cultures or in vivo animal models were used to analyse the pulp response to pulpitis inducers at cell, tissue or organ level. In vitro 2D cell cultures were mainly used to decipher the specific roles of key actors of pulp inflammation such as bacterial by-products, pro-inflammatory cytokines, odontoblasts or pulp stem cells. However, these simple models did not reproduce the 3D organisation of the pulp tissue and, with rare exceptions, did not consider interactions between resident cell types. In vitro tissue/organ-based models were developed to better reflect the complexity of the pulp structure. Their major disadvantage is that they did not allow the analysis of blood supply and innervation participation. On the contrary, in vivo models have allowed researchers to identify key immune, vascular and nervous actors of pulpitis and to understand their function and interplay in the inflamed pulp. However, inflammation was mainly induced by iatrogenic dentine drilling associated with simple pulp exposure to the oral environment or stimulation by individual bacterial by-products for short periods. Clearly, these models did not reflect the long and progressive development of dental caries. Lastly, the substantial diversity of the existing models makes experimental data extrapolation to the clinical situation complicated. Therefore, improvement in the design and standardization of future models, for example by using novel molecular biomarkers, databased models and artificial intelligence, will be an essential step in building an incremental knowledge of pulpitis in the future.

Evaluation of Ozone Therapy in Endodontic Treatment of Teeth with Necrotic Pulp and Apical Periodontitis: A Randomized Clinical Trial

INTRODUCTION

The aim of this study was to compare the effect of different application techniques of ozone on the prevalence of postendodontic pain in patients undergoing single-visit root canal treatment.

METHODS

hundred eight patients with necrotic pulp in single-rooted teeth and apical periodontitis participated in the trial. A standard single-visit endodontics protocol was followed with 5.25% sodium hypochlorite and rotary nickel-titanium files. After shaping and cleaning, patients were randomly allocated into the following groups: group 1 (n = 21), ozone treatment with no activation (NA); group 2 (n = 22), ozone treatment with manual dynamic activation (MDA); group 3, (n = 21), ozone treatment with passive ultrasonic activation (PUA); group 4 (n = 23), ozone treatment with sonic activation (SA); and group 5 (n = 21), no ozone treatment (the control group). Patient levels of discomfort were recorded at 6 different time intervals using the visual analog scale (VAS). Comparison of the mean difference between the groups and time intervals was performed using 2-way analysis of variance followed by a post hoc Bonferroni test. The level of significance was set at 5%.

RESULTS

VAS scores were highest for the control > NA > MDA > SA > PUA groups. A statistically significant reduction in VAS scores was observed in the PUA and SA groups in comparison with the NA, control, and MDA groups. Timewise comparison showed a highly significant decline in VAS scores at all time intervals (P < .001).

CONCLUSIONS

Ultrasonic and sonic activation of ozone resulted in less pain in patients undergoing single-visit endodontics compared with no ozone treatment.