Predicting postoperative facial swelling following impacted mandibular third molars extraction by using artificial neural networks evaluation

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.

Applications of artificial intelligence for surgical extraction in stomatology: a systematic review

OBJECTIVES

Artificial intelligence (AI) has been extensively used in the field of stomatology over the past several years. This study aimed to evaluate the effectiveness of AI-based models in the procedure, assessment, and treatment planning of surgical extraction.

STUDY DESIGN

Following Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, a comprehensive search was conducted on the Web of Science, PubMed/MEDLINE, Embase, and Scopus databases, covering English publications up to September 2023. Two reviewers performed the study selection and data extraction independently. Only original research studies utilizing AI in surgical extraction of stomatology were included. The Cochrane risk of bias tool for randomized trials (RoB 2) was selected to perform the quality assessment of the selected literature.

RESULTS

From 2,336 retrieved references, 35 studies were deemed eligible. Among them, 28 researchers reported the pioneering role of AI in segmentation, classification, and detection, aligning with clinical needs. In addition, another 7 studies suggested promising results in tooth extraction decision-making, but further model refinement and validation were required.

CONCLUSIONS

Integration of AI in stomatology surgical extraction has significantly progressed, enhancing decision-making accuracy. Combining and comparing algorithmic outcomes across studies is essential for determining optimal clinical applications in the future.

Prediction of extraction difficulty for impacted maxillary third molars with deep learning approach

OBJECTIVE

The aim of this study is to determine if a deep learning (DL) model can predict the surgical difficulty for impacted maxillary third molar tooth using panoramic images before surgery.

MATERIALS AND METHODS

The dataset consists of 708 panoramic radiographs of the patients who applied to the Oral and Maxillofacial Surgery Clinic for various reasons. Each maxillary third molar difficulty was scored based on dept (V), angulation (H), relation with maxillary sinus (S), and relation with ramus (R) on panoramic images. The YoloV5x architecture was used to perform automatic segmentation and classification. To prevent re-testing of images, participate in the training, the data set was subdivided as: 80 % training, 10 % validation, and 10 % test group.

RESULTS

Impacted Upper Third Molar Segmentation model showed best success on sensitivity, precision and F1 score with 0,9705, 0,9428 and 0,9565, respectively. S-model had a lesser sensitivity, precision and F1 score than the other models with 0,8974, 0,6194, 0,7329, respectively.

CONCLUSION

The results showed that the proposed DL model could be effective for predicting the surgical difficulty of an impacted maxillary third molar tooth using panoramic radiographs and this approach might help as a decision support mechanism for the clinicians in peri‑surgical period.

Imaging in Third Molar Surgery: A Clinical Update

Third molar surgery is one of the most common surgical procedures performed in oral and maxillofacial surgery. Considering the patient's young age and the often-elective nature of the procedure, a comprehensive preoperative evaluation of the surgical site, relying heavily on preoperative imaging, is key to providing accurate diagnostic work-up, evidence-based clinical decision making, and, when appropriate, indication-specific surgical planning. Given the rapid developments of dental imaging in the field, the aim of this article is to provide a comprehensive, up-to-date clinical overview of various imaging techniques related to perioperative imaging in third molar surgery, ranging from panoramic radiography to emerging technologies, such as photon-counting computed tomography and magnetic resonance imaging. Each modality's advantages, limitations, and recent improvements are evaluated, highlighting their role in treatment planning, complication prevention, and postoperative follow-ups. The integration of recent technological advances, including artificial intelligence and machine learning in biomedical imaging, coupled with a thorough preoperative clinical evaluation, marks another step towards personalized dentistry in high-risk third molar surgery. This approach enables minimally invasive surgical approaches while reducing inefficiencies and risks by incorporating additional imaging modality- and patient-specific parameters, potentially facilitating and improving patient management.

Advancements in oral and maxillofacial surgery medical images segmentation techniques: An overview

OBJECTIVES

This article reviews recent advances in computer-aided segmentation methods for oral and maxillofacial surgery and describes the advantages and limitations of these methods. The objective is to provide an invaluable resource for precise therapy and surgical planning in oral and maxillofacial surgery. Study selection, data and sources: This review includes full-text articles and conference proceedings reporting the application of segmentation methods in the field of oral and maxillofacial surgery. The research focuses on three aspects: tooth detection segmentation, mandibular canal segmentation and alveolar bone segmentation. The most commonly used imaging technique is CBCT, followed by conventional CT and Orthopantomography. A systematic electronic database search was performed up to July 2023 (Medline via PubMed, IEEE Xplore, ArXiv, Google Scholar were searched).

RESULTS

These segmentation methods can be mainly divided into two categories: traditional image processing and machine learning (including deep learning). Performance testing on a dataset of images labeled by medical professionals shows that it performs similarly to dentists' annotations, confirming its effectiveness. However, no studies have evaluated its practical application value.

CONCLUSION

Segmentation methods (particularly deep learning methods) have demonstrated unprecedented performance, while inherent challenges remain, including the scarcity and inconsistency of datasets, visible artifacts in images, unbalanced data distribution, and the "black box" nature.

CLINICAL SIGNIFICANCE

Accurate image segmentation is critical for precise treatment and surgical planning in oral and maxillofacial surgery. This review aims to facilitate more accurate and effective surgical treatment planning among dental researchers.

What is the current state of artificial intelligence applications in dentistry and orthodontics?

BACKGROUND

The use of Artificial Intelligence (AI) in the medical field has the potential to bring about significant improvements in patient care and outcomes. AI is being used in dentistry and more specifically in orthodontics through the development of diagnostic imaging tools, the development of treatment planning tools, and the development of robotic surgery. The aim of this study is to present the latest emerging AI softwares and applications in dental field to benefit from.

TYPES OF STUDIES REVIEWED

Search strategies were conducted in three electronic databases, with no date limits in the following databases up to April 30, 2023: MEDLINE, PUBMED, and GOOGLE® SCHOLAR for articles related to AI in dentistry & orthodontics. No inclusion and exclusion criteria were used for the selection of the articles. Most of the articles included (n = 79) are reviews of the literature, retro/prospective studies, systematic reviews and meta-analyses, and observational studies.

RESULTS

The use of AI in dentistry and orthodontics is a rapidly growing area of research and development, with the potential to revolutionize the field and bring about significant improvements in patient care and outcomes; this can save clinicians' chair-time and push for more individualized treatment plans. Results from the various studies reported in this review are suggestive that the accuracy of AI-based systems is quite promising and reliable.

PRACTICAL IMPLICATIONS

AI application in the healthcare field has proven to be efficient and helpful for the dentist to be more precise in diagnosis and clinical decision-making. These systems can simplify the tasks and provide results in quick time which can save dentists time and help them perform their duties more efficiently. These systems can be of greater aid and can be used as auxiliary support for dentists with lesser experience.

ResMIBCU-Net: an encoder-decoder network with residual blocks, modified inverted residual block, and bi-directional ConvLSTM for impacted tooth segmentation in panoramic X-ray images

OBJECTIVE

Impacted tooth is a common problem that can occur at any age, causing tooth decay, root resorption, and pain in the later stages. In recent years, major advances have been made in medical imaging segmentation using deep convolutional neural network-based networks. In this study, we report on the development of an artificial intelligence system for the automatic identification of impacted tooth from panoramic dental X-ray images.

METHODS

Among existing networks, in medical imaging segmentation, U-Net architectures are widely implemented. In this article, for dental X-ray image segmentation, blocks and convolutional block structures using inverted residual blocks are upgraded by taking advantage of U-Net's network capacity-intensive connections. At the same time, we propose a method for jumping connections in which bi-directional convolution long short-term memory is used instead of a simple connection. Assessment of the proposed artificial intelligence model performance was evaluated with accuracy, F1-score, intersection over union, and recall.

RESULTS

In the proposed method, experimental results are obtained with 99.82% accuracy, 91.59% F1-score, 84.48% intersection over union, and 90.71% recall.

CONCLUSION

Our findings show that our artificial intelligence system could help with future diagnostic support in clinical practice.

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.

Artificial Intelligence and Its Theranostic Applications in Dentistry

As new technologies emerge, they continue to have an impact on our daily lives, and artificial intelligence (AI) covers a wide range of applications. Because of the advancements in AI, it is now possible to analyse large amounts of data, which results in more accurate data and more effective decision-making. This article explains the fundamentals of AI and examines its development and present use. AI technology has had an impact on the healthcare sector as a result of the need for accurate diagnosis and improved patient care. An overview of the existing AI applications in clinical dentistry was provided. Comprehensive care involving artificial intelligence aims to provide cutting-edge research and innovations, as well as high-quality patient care, by enabling sophisticated decision support tools. The cornerstone of AI advancement in dentistry is creative inter-professional coordination among medical professionals, scientists, and engineers. Artificial intelligence will continue to be associated with dentistry from a wide angle despite potential misconceptions and worries about patient privacy. This is because precise treatment methods and quick data sharing are both essential in dentistry. Additionally, these developments will make it possible for patients, academicians, and healthcare professionals to exchange large data on health as well as provide insights that enhance patient care.

A Systematic Review of Artificial Intelligence Applications in Plastic Surgery: Looking to the Future

Artificial intelligence (AI) is presently employed in several medical specialties, particularly those that rely on large quantities of standardized data. The integration of AI in surgical subspecialties is under preclinical investigation but is yet to be widely implemented. Plastic surgeons collect standardized data in various settings and could benefit from AI. This systematic review investigates the current clinical applications of AI in plastic and reconstructive surgery.

METHODS

A comprehensive literature search of the Medline, EMBASE, Cochrane, and PubMed databases was conducted for AI studies with multiple search terms. Articles that progressed beyond the title and abstract screening were then subcategorized based on the plastic surgery subspecialty and AI application.

RESULTS

The systematic search yielded a total of 1820 articles. Forty-four studies met inclusion criteria warranting further analysis. Subcategorization of articles by plastic surgery subspecialties revealed that most studies fell into aesthetic and breast surgery (27%), craniofacial surgery (23%), or microsurgery (14%). Analysis of the research study phase of included articles indicated that the current research is primarily in phase 0 (discovery and invention; 43.2%), phase 1 (technical performance and safety; 27.3%), or phase 2 (efficacy, quality improvement, and algorithm performance in a medical setting; 27.3%). Only one study demonstrated translation to clinical practice.

CONCLUSIONS

The potential of AI to optimize clinical efficiency is being investigated in every subfield of plastic surgery, but much of the research to date remains in the preclinical status. Future implementation of AI into everyday clinical practice will require collaborative efforts.

Diagnosis of in vivo vertical root fracture using deep learning on cone-beam CT images

Objectives

Evaluating the diagnostic efficiency of deep learning models to diagnose vertical root fracture in vivo on cone-beam CT (CBCT) images.

Materials and methods

The CBCT images of 276 teeth (138 VRF teeth and 138 non-VRF teeth) were enrolled and analyzed retrospectively. The diagnostic results of these teeth were confirmed by two chief radiologists. There were two experimental groups: auto-selection group and manual selection group. A total of 552 regions of interest of teeth were cropped in manual selection group and 1118 regions of interest of teeth were cropped in auto-selection group. Three deep learning networks (ResNet50, VGG19 and DenseNet169) were used for diagnosis (3:1 for training and testing). The diagnostic efficiencies (accuracy, sensitivity, specificity, and area under the curve (AUC)) of three networks were calculated in two experiment groups. Meanwhile, 552 teeth images in manual selection group were diagnosed by a radiologist. The diagnostic efficiencies of the three deep learning network models in two experiment groups and the radiologist were calculated.

Results

In manual selection group, ResNet50 presented highest accuracy and sensitivity for diagnosing VRF teeth. The accuracy, sensitivity, specificity and AUC was 97.8%, 97.0%, 98.5%, and 0.99, the radiologist presented accuracy, sensitivity, and specificity as 95.3%, 96.4 and 94.2%. In auto-selection group, ResNet50 presented highest accuracy and sensitivity for diagnosing VRF teeth, the accuracy, sensitivity, specificity and AUC was 91.4%, 92.1%, 90.7% and 0.96.

Conclusion

In manual selection group, ResNet50 presented higher diagnostic efficiency in diagnosis of in vivo VRF teeth than VGG19, DensenNet169 and radiologist with 2 years of experience. In auto-selection group, Resnet50 also presented higher diagnostic efficiency in diagnosis of in vivo VRF teeth than VGG19 and DensenNet169. This makes it a promising auxiliary diagnostic technique to screen for VRF teeth.

Artificial intelligence for oral and maxillo-facial surgery: A narrative review

Artificial Intelligence (AI) is a set of technologies that simulate human cognition in order to address a specific problem. The improvement in computing speed, the exponential production and the routine collection of data have led to the rapid development of AI in the health sector. In this review, we propose to provide surgeons with the essential technical elements to help them understand the possibilities offered by AI and to review the current applications of AI for oral and maxillofacial surgery (OMFS). The review of the literature reveals a real research boom of AI in all fields in OMFS. The algorithms used are related to machine learning, with a strong representation of the convolutional neural networks specific to deep learning. The complex architecture of these networks gives them the capacity to extract and process the elementary characteristics of an image, and they are therefore particularly used for diagnostic purposes on medical imagery or facial photography. We identified representative articles dealing with AI algorithms providing assistance in diagnosis, therapeutic decision, preoperative planning, or prediction and evaluation of the outcomes. Thanks to their learning, classification, prediction and detection capabilities, AI algorithms complement human skills while limiting their imperfections. However, these algorithms should be subject to rigorous clinical evaluation, and ethical reflection on data protection should be systematically conducted.

Deep Learning Based Detection Tool for Impacted Mandibular Third Molar Teeth

Third molar impacted teeth are a common issue with all ages, possibly causing tooth decay, root resorption, and pain. This study was aimed at developing a computer-assisted detection system based on deep convolutional neural networks for the detection of third molar impacted teeth using different architectures and to evaluate the potential usefulness and accuracy of the proposed solutions on panoramic radiographs. A total of 440 panoramic radiographs from 300 patients were randomly divided. As a two-stage technique, Faster RCNN with ResNet50, AlexNet, and VGG16 as a backbone and one-stage technique YOLOv3 were used. The Faster-RCNN, as a detector, yielded a mAP@0.5 rate of 0.91 with ResNet50 backbone while VGG16 and AlexNet showed slightly lower performances: 0.87 and 0.86, respectively. The other detector, YOLO v3, provided the highest detection efficacy with a mAP@0.5 of 0.96. Recall and precision were 0.93 and 0.88, respectively, which supported its high performance. Considering the findings from different architectures, it was seen that the proposed one-stage detector YOLOv3 had excellent performance for impacted mandibular third molar tooth detection on panoramic radiographs. Promising results showed that diagnostic tools based on state-ofthe-art deep learning models were reliable and robust for clinical decision-making.

Intelligent systems for precision dental diagnosis and treatment planning – A review

Machines have changed the course of mankind. Simple machines were the basis of human civilization. Today with humongous technological development, machines are intelligent enough to carry out very complex nerve-racking tasks. The ability of a machine to learn from algorithms changed eventually into, the machine learning by itself, which constitutes artificial intelligence. Literature has plausible evidence for the use of intelligent systems in medical field. Artificial intelligence has been used in the multiple denominations of dentistry. These machines are used in the precision diagnosis, interpretation of medical images, accumulation of data, classification and compilation of records, determination of treatment and construction of a personalized treatment plan. Artificial intelligence can help in timely diagnosis of complex dental diseases which would ultimately aid in rapid commencement of treatment. Research helps us understand the effectiveness and challenges in the use of this technology. The apt use of intelligent systems could transform the entire medical system for the better.

The influence of flap design on patients' experiencing pain, swelling, and trismus after mandibular third molar surgery: a scoping systematic review

Third molar removal surgery usually comes accompanied by postoperative discomfort, which could be influenced by the surgical approach chosen. This scoping systematic review aimed at compiling the available evidence focused on the influence of flap design, including envelope flap (EF), triangular flap (TF), and modified triangular flap (MTF), on postoperative pain, swelling, and trismus, as primary outcome measures, and any result mentioning healing promotion or delay, as secondary outcome measure, after mandibular third molar extraction surgery. An electronic search, complemented by a manual search, of articles published from 1999 to 2020 was conducted in the Medline (PubMed), EMBASE and Web of Science databases including human randomized controlled trials, prospective, and retrospective studies with at least 15 patients. The risk of bias of the included studies was assessed either with the Cochrane’s Risk of Bias tool or with the Newcastle-Ottawa scale. Every step of the review was performed independently and in duplicate. The initial electronic search recovered 2102 articles. After applying the inclusion criteria, 12 articles were included. For patient’s perceived postoperative pain, TF and MTF frequently reported better results than EF. For swelling, the literature is divided, despite a trend favoring EF. For trismus, data showed that its occurrence is mostly associated with the duration of the surgery rather than with the chosen flap. For healing, the limited data is inconclusive. Finally, randomized studies showed a high risk of bias, whereas nonrandomized studies were mostly of good quality and low risk of bias. Although there was no clear consensus regarding the influence of different flap designs for third mandibular molar extraction on postoperative clinical morbidities; the surgeon’s experience, estimated surgical difficulty, molar position and orientation, and surg ery duration should be considered when choosing among the different flap designs.

Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review

Purpose

The study explored the clinical influence, effectiveness, limitations, and human comparison outcomes of machine learning in diagnosing (1) dental diseases, (2) periodontal diseases, (3) trauma and neuralgias, (4) cysts and tumors, (5) glandular disorders, and (6) bone and temporomandibular joint as possible causes of dental and orofacial pain.

Method

Scopus, PubMed, and Web of Science (all databases) were searched by 2 reviewers until 29^th October 2020. Articles were screened and narratively synthesized according to PRISMA-DTA guidelines based on predefined eligibility criteria. Articles that made direct reference test comparisons to human clinicians were evaluated using the MI-CLAIM checklist. The risk of bias was assessed by JBI-DTA critical appraisal, and certainty of the evidence was evaluated using the GRADE approach. Information regarding the quantification method of dental pain and disease, the conditional characteristics of both training and test data cohort in the machine learning, diagnostic outcomes, and diagnostic test comparisons with clinicians, where applicable, were extracted.

Results

34 eligible articles were found for data synthesis, of which 8 articles made direct reference comparisons to human clinicians. 7 papers scored over 13 (out of the evaluated 15 points) in the MI-CLAIM approach with all papers scoring 5+ (out of 7) in JBI-DTA appraisals. GRADE approach revealed serious risks of bias and inconsistencies with most studies containing more positive cases than their true prevalence in order to facilitate machine learning. Patient-perceived symptoms and clinical history were generally found to be less reliable than radiographs or histology for training accurate machine learning models. A low agreement level between clinicians training the models was suggested to have a negative impact on the prediction accuracy. Reference comparisons found nonspecialized clinicians with less than 3 years of experience to be disadvantaged against trained models.

Conclusion

Machine learning in dental and orofacial healthcare has shown respectable results in diagnosing diseases with symptomatic pain and with improved future iterations and can be used as a diagnostic aid in the clinics. The current review did not internally analyze the machine learning models and their respective algorithms, nor consider the confounding variables and factors responsible for shaping the orofacial disorders responsible for eliciting pain.

Comprehensive evaluation of the combined extracts of Epimedii Folium and Ligustri Lucidi Fructus for PMOP in ovariectomized rats based on MLP-ANN methods

ETHNOPHARMACOLOGICAL RELEVANCE

Kidney deficiency is the main pathogenesis of osteoporosis based on the theory of "kidney governing bones" in traditional Chinese medicine (TCM). Osteoporosis is a systemic disease; kidney deficiency influences the growth, aging and reproduction of human body, reflecting in endocrine, nerve, immunity, metabolism and other functions. Multi-target drugs composed of natural non-toxic products from kidney-reinforcing herbs, are being investigated for the treatment of osteoporosis. Therefore, it is necessary and imperative to develop an objective and comprehensive method to evaluate and compare the effects of herbs with listed drugs.

AIM OF THE STUDY

This study was designed to evaluate and compare the therapeutic effects and the underlying molecular mechanism of the combined extracts of Epimedii Folium and Ligustri Lucidi Fructus (EL) with Raloxifene hydrochloride (RH) in ovariectomy (OVX)-induced postmenopausal osteoporosis (PMOP) rats based on the multi-layer perception (MLP)-artificial neural network (ANN) model.

MATERIALS AND METHODS

Female SD rats were subjected to either sham surgery (n = 8) or bilateral OVX (n = 48). One week after recovering from surgery, the OVX-induced rats were randomly divided into three groups: OVX model group (n = 32, every 8 rats were killed at the end of the 5th, 9th, 11th or 13th week after OVX), EL group (treated with EL 0.35 g/kg, n = 8), and RH group (treated with RH 6.25 mg/kg, n = 8). The rats in the treatment groups were administrated once a day for 12 weeks, then sacrificed. We observed bone mass and quality, bone remodeling, the function of estrogen and TGF-β1/Smads pathway in all rats.

RESULTS

Both EL and RH could increase bone mineral density, enhance bone strength, relieve bone micro-structure degeneration, re-balance bone remodeling, regulate estrogen dysfunction, and up-regulate TGF-β1 expression. The evaluation of the MLP-ANN model showed that EL and RH had markedly anti-PMOP effects, and there was no significant difference in the comprehensive evaluation of anti-osteoporosis between the two drugs. However, RH had better effects on bone mass and quality and TGF-β1/Smads pathway than EL; EL had better effects on estrogen function than RH.

CONCLUSION

Combined extracts of Epimedii Folium and Ligustri Lucidi Fructus (EL) exhibited bone-protective effects on PMOP. The MLP-ANN method evaluated the efficacy of drugs more comprehensively, which provided a new direction for the evaluation and comparison of drugs.

Using deep learning to predict temporomandibular joint disc perforation based on magnetic resonance imaging

The goal of this study was to develop a deep learning-based algorithm to predict temporomandibular joint (TMJ) disc perforation based on the findings of magnetic resonance imaging (MRI) and to validate its performance through comparison with previously reported results. The study objects were obtained by reviewing medical records from January 2005 to June 2018. 299 joints from 289 patients were divided into perforated and non-perforated groups based on the existence of disc perforation confirmed during surgery. Experienced observers interpreted the TMJ MRI images to extract features. Data containing those features were applied to build and validate prediction models using random forest and multilayer perceptron (MLP) techniques, the latter using the Keras framework, a recent deep learning architecture. The area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the performances of the models. MLP produced the best performance (AUC 0.940), followed by random forest (AUC 0.918) and disc shape alone (AUC 0.791). The MLP and random forest were also superior to previously reported results using MRI (AUC 0.808) and MRI-based nomogram (AUC 0.889). Implementing deep learning showed superior performance in predicting disc perforation in TMJ compared to conventional methods and previous reports.

Efficacy of Commiphora myrrh mouthwash on early wound healing after tooth extraction: A randomized controlled trial

BACKGROUND

The early period after tooth extraction is a critical period for wound healing. Wound healing after tooth extraction is considered secondary intention healing. It passes through several stages in the following order: hemostasis, inflammatory phase, proliferative phase, and finally the remodeling phase.Wounds usually heal normally unless there is interference by local or systemic factors. In certain circumstances, early wound healing can be enhanced by several interventions such as antibiotics, mouthwashes, or topical medications. Myrrh has been used as a topical medication for promoting wound healing after tooth extraction. The purpose of this study was to assess the wound healing effect of myrrh mouthwash during the early post-extraction period.

METHODS

We enrolled 40 healthy adult patients in this study (20: study group and 20: control group). All the activities performed for each group were double-blinded. All the participants underwent dental extraction under local anesthesia using standard protocol. Next, the study group used Commiphora molmol (myrrh) extract as a mouthwash while the control group used normal saline mouthwash. The participants used the mouthwashes twice a day for 7 days starting from the first post-extraction day. Clinical examination data were recorded and analyzed using the Mann Whitney Wilcoxon test.

RESULTS

There was a statistically significant between-group difference in postoperative surgical-site edema, tenderness, and socket size, with the test group showing greater improvements.

CONCLUSIONS

Myrrh mouthwash has an enhancement effect on wound healing during the early period after tooth extraction.

Systemic Use of Arnica Montana for the Reduction of Postsurgical Sequels following Extraction of Impacted Mandibular 3rd Molars: A Pilot Study

Background

Postsurgical sequels (PSS) are a group of complications commonly encountered following invasive dental surgical procedures such as bone grafting procedures, external sinus grafting, and 3^rd molar extractions. These include pain, intraoral and extraoral bruising, and edema. The aim of this study is to evaluate the clinical efficacy of arnica montana (AM) in the management of PSS following extraction of impacted mandibular 3^rd molars. The investigators null hypothesis includes no significant role of AM in reducing PSS following dental extraction.

Materials and Methods

The investigators implemented a case-control pilot study enrolling twenty-three patients with impacted mandibular 3^rd molars. These patients were allocated to AM or control group. Baseline clinical measurements were collected and included: (1) length of the surgical procedure, (2) pain score, (3) maximum mouth opening, and (4) facial measurements to evaluate edema levels. Subjects in active group received systemic AM tablets following the manufacturer instructions. All study subjects were followed up on Days 2, 4, and 7. Data was analyzed for statistical significance.

Results

A total of 30 impacted mandibular 3^rd molars were extracted, in which 22 completed with AM. There were 16 females, and the average age was 26 years. On Day 2, subjects in the AM group reported significantly lower VAS compared to control group (3.09 ± 2.22 versus 4.75 ± 1.28). In addition, bleeding, extraoral bruising, edema, and decrease in maximum mouth opening were significantly less reported in the AM group.

Conclusions

This study describes the potential benefit of AM in reducing PSS following dental extractions.

Effect of platelet-rich fibrin on the control of alveolar osteitis, pain, trismus, soft tissue healing, and swelling following mandibular third molar surgery: an updated systematic review and meta-analysis

The purpose of this study was to estimate the effect of platelet-rich fibrin (PRF) on the control of alveolar osteitis (AO), pain, trismus, soft tissue healing, and swelling following mandibular third molar surgery. A comprehensive search of the literature was conducted through PubMed, Embase, Web of Science, and Cochrane Library up to May 2019. Randomized controlled studies conforming to the inclusion criteria were included. The record screening and data extraction were conducted by two authors independently. The risk of bias assessment was performed according to the guidelines recommended by the Cochrane Collaboration. The quantitative analysis was performed using RevMan version 5.3. Nineteen studies were included in the systematic review and 17 studies were eligible for the meta-analysis. The use of PRF significantly reduced the incidence of AO and postoperative pain when compared to the controls (AO: relative risk 0.43, 95% confidence interval (CI) 0.28 to 0.65, Z=3.90, P<0.0001 (I²=0%); pain: day 1, standardized mean difference (SMD) -1.12, 95% CI -1.87 to -0.37, Z=2.93, P=0.003 (I²=95%); day 3, SMD -0.93, 95% CI -1.48 to -0.38, Z=3.30, P=0.001 (I²=92%); day 7, SMD -1.84, 95% CI -2.98 to -0.71, Z=3.19, P=0.001 (I²=97%)). Additionally, the result showed a better soft tissue healing when PRF was used (mean difference -0.63, 95% CI -1.08 to -0.18, Z=2.76, P=0.006 (I²=90%)). The use of PRF reduced the incidence of AO and postoperative pain following third molar surgery. Furthermore, PRF may also improve the postoperative soft tissue healing.

Prediction of 3-month treatment outcome of IgG4-DS based on BP artificial neural network

OBJECTIVE

The study aimed to establish an effective back-Propagation artificial neural network (BP-ANN) model for automatic prediction of 3-month treatment outcome of IgG4-DS.

METHODS

A total of 26 IgG4-DS patients at Shanghai Ninth People's Hospital from January 2018 to December 2019 were involved in the study. They were all followed for >3 months. The primary outcome was reduction of serum IgG4 (sIgG4) after 3-month treatment. The association between risk factors and reduction of sIgG4 was analyzed by Spearman's rank correlation test. According to the R values, we built a BP-ANN model by MATLAB R2019b.

RESULTS

The average reduction of sIgG4 was 5.55 ± 5.03. After Spearman's rank correlation test, ESR, sIgG4, and sIgG were independently associated with reduction of sIgG4 (p < .05) and were selected as input variables. Take into account these parameters, BP-ANN model was developed and the coefficient of determination (R² ) model was 0.95512.

CONCLUSION

The BP-ANN model based on ESR, sIgG4, and sIgG could predict the 3-month reduction of sIgG4 for IgG4-DS patients. It showed potential clinical application value.

Developments, application, and performance of artificial intelligence in dentistry - A systematic review

Background/purpose

Artificial intelligence (AI) has made deep inroads into dentistry in the last few years. The aim of this systematic review was to identify the development of AI applications that are widely employed in dentistry and evaluate their performance in terms of diagnosis, clinical decision-making, and predicting the prognosis of the treatment.

Materials and methods

The literature for this paper was identified and selected by performing a thorough search in the electronic data bases like PubMed, Medline, Embase, Cochrane, Google scholar, Scopus, Web of science, and Saudi digital library published over the past two decades (January 2000–March 15, 2020).After applying inclusion and exclusion criteria, 43 articles were read in full and critically analyzed. Quality analysis was performed using QUADAS-2.

Results

AI technologies are widely implemented in a wide range of dentistry specialties. Most of the documented work is focused on AI models that rely on convolutional neural networks (CNNs) and artificial neural networks (ANNs). These AI models have been used in detection and diagnosis of dental caries, vertical root fractures, apical lesions, salivary gland diseases, maxillary sinusitis, maxillofacial cysts, cervical lymph nodes metastasis, osteoporosis, cancerous lesions, alveolar bone loss, predicting orthodontic extractions, need for orthodontic treatments, cephalometric analysis, age and gender determination.

Conclusion

These studies indicate that the performance of an AI based automated system is excellent. They mimic the precision and accuracy of trained specialists, in some studies it was found that these systems were even able to outmatch dental specialists in terms of performance and accuracy.

Welcome the "new kid on the block" into the family: artificial intelligence in oral and maxillofacial surgery

Recent decades have witnessed the genesis and progressive application of intelligent machines and computer programs that have the ability to process information and execute cognitive functions similar to those of human logic and reasoning such as problem solving and decision making. That is artificial intelligence (AI) in a nutshell as envisioned by John McCarthy, "the father of AI". Healthcare has welcomed AI, giving rise to collaborations such as the Moorfields Eye Hospital and Google's DeepMind division in the screening and predicting of retinal disease. The use of AI by the maxillofacial surgical fraternity is, however, limited. We wish to highlight the fact that surgeons are uniquely positioned to help drive these innovations rather than passively waiting for the technology to become useful.

Using artificial neural networks to predict future dryland responses to human and climate disturbances

Land degradation and sediment remobilisation in dryland environments is considered to be a significant global environmental problem. Given the potential for currently stabilised dune systems to reactivate under climate change and increased anthropogenic pressures, identifying the role of external disturbances in driving geomorphic response is vitally important. We developed a novel approach, using artificial neural networks (ANNs) applied to time series of historical reactivation-deposition events from the Nebraska Sandhills, to determine the relationship between historic periods of sand deposition in semi-arid grasslands and external climatic conditions, land use pressures and wildfire occurrence. We show that both vegetation growth and sediment re-deposition episodes can be accurately estimated. Sensitivity testing of individual factors shows that localised forcings (overgrazing and wildfire) have a statistically significant impact when the climate is held at present-day conditions. However, the dominant effect is climate-induced drought. Our approach has great potential for estimating future landscape sensitivity to climate and land use scenarios across a wide range of potentially fragile dryland environments.