Predicting patient experience of Invisalign treatment: An analysis using artificial neural network

The Future of Orthodontics: Deep Learning Technologies

Deep learning has emerged as a revolutionary technical advancement in modern orthodontics, offering novel methods for diagnosis, treatment planning, and outcome prediction. Over the past 25 years, the field of dentistry has widely adopted information technology (IT), resulting in several benefits, including decreased expenses, increased efficiency, decreased need for human expertise, and reduced errors. The transition from preset rules to learning from real-world examples, particularly machine learning (ML) and artificial intelligence (AI), has greatly benefited the organization, analysis, and storage of medical data. Deep learning, a type of AI, enables robots to mimic human neural networks, allowing them to learn and make decisions independently without the need for explicit programming. Its ability to automate cephalometric analysis and enhance diagnosis through 3D imaging has revolutionized orthodontic operations. Deep learning models have the potential to significantly improve treatment outcomes and reduce human errors by accurately identifying anatomical characteristics on radiographs, thereby expediting analytical processes. Additionally, the use of 3D imaging technologies such as cone-beam computed tomography (CBCT) can facilitate precise treatment planning, allowing for comprehensive examinations of craniofacial architecture, tooth movements, and airway dimensions. In today's era of personalized medicine, deep learning's ability to customize treatments for individual patients has propelled the field of orthodontics forward tremendously. However, it is essential to address issues related to data privacy, model interpretability, and ethical considerations before orthodontic practices can use deep learning in an ethical and responsible manner. Modern orthodontics is evolving, thanks to the ability of deep learning to deliver more accurate, effective, and personalized orthodontic treatments, improving patient care as technology develops.

AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review

The advent of artificial intelligence (AI) in medicine has transformed various medical specialties, including orthodontics. AI has shown promising results in enhancing the accuracy of diagnoses, treatment planning, and predicting treatment outcomes. Its usage in orthodontic practices worldwide has increased with the availability of various AI applications and tools. This review explores the principles of AI, its applications in orthodontics, and its implementation in clinical practice. A comprehensive literature review was conducted, focusing on AI applications in dental diagnostics, cephalometric evaluation, skeletal age determination, temporomandibular joint (TMJ) evaluation, decision making, and patient telemonitoring. Due to study heterogeneity, no meta-analysis was possible. AI has demonstrated high efficacy in all these areas, but variations in performance and the need for manual supervision suggest caution in clinical settings. The complexity and unpredictability of AI algorithms call for cautious implementation and regular manual validation. Continuous AI learning, proper governance, and addressing privacy and ethical concerns are crucial for successful integration into orthodontic practice.

Effect of clear aligner attachment design on extrusion of maxillary lateral incisors: A multicenter, single-blind randomized clinical trial

INTRODUCTION

Extrusion of maxillary lateral incisors during aligner treatment is a difficult movement to achieve accurately. Despite recommendations regarding attachment design, few studies and no prospective trials compare predictability among attachments. This study aimed to compare the efficacy between optimized and horizontal attachment designs for achieving maxillary lateral incisor extrusion during clear aligner treatment.

METHODS

The study included maxillary lateral incisors in 3 orthodontic practices requiring at least 0.3 mm of extrusion during the first series of 20-25 aligners in patients aged ≥16 years who were scheduled to begin clear aligner treatment (Invisalign; Align Technology, San Jose, Calif). Teeth were randomly assigned to receive optimized (O), rectangular horizontal nonbeveled (H), rectangular horizontal incisally-beveled (HIB), or rectangular horizontal gingivally-beveled (HGB) attachments. After the first series, a blinded evaluator measured extrusion using superimpositions with initial and predicted models. Linear models determined the difference in the predicted extrusion percentage achieved on the basis of attachment design. Other covariates were patient age, sex, number of trays, and self-reported compliance.

RESULTS

Forty patients (74 teeth) were enrolled, and 38 patients (71 teeth) completed the study. Intraexaminer and interexaminer reliability for extrusion measurements was high (intraclass correlation coefficient, 0.985 and 0.991, respectively). The achieved extrusion was significantly less than predicted (mean, 73%; P <0.0001). The average achieved extrusion was 62%, 79%, 78%, and 78% for O, H, HIB, and HGB attachments, respectively, with H significantly more effective than O (P = 0.0403). Horizontal attachments (H, HIB, and HGB combined) were significantly more effective than O attachments (P = 0.0060), with an average difference in achieved extrusion of 14% of the predicted amount (95% confidence interval, 4-23; estimated 76% vs 62%). Horizontal attachments were an estimated 22% more effective than O attachments for extruding maxillary lateral incisors.

CONCLUSIONS

Horizontal attachments are more effective than O attachments for predicted maxillary lateral incisor extrusion between 0.3 and 2.5 mm. The 3 horizontal attachment designs evaluated performed similarly for achieving predicted extrusion.

TRIAL REGISTRATION

This randomized clinical trial was registered and reported at clinicaltrials.gov (NCT04968353).

PROTOCOL

The protocol was not published before trial commencement.

FUNDING

This study was funded in part by the Alexander Fellowship of the Virginia Commonwealth University School of Dentistry, the Southern Association of Orthodontists, and the Virginia Orthodontic Education and Research Foundation. No funding source influenced the study design, the collection, analysis or interpretation of data, writing of the report, or the decision to submit the article for publication.

Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives

In recent years, there has been the notable emergency of artificial intelligence (AI) as a transformative force in multiple domains, including orthodontics. This review aims to provide a comprehensive overview of the present state of AI applications in orthodontics, which can be categorized into the following domains: (1) diagnosis, including cephalometric analysis, dental analysis, facial analysis, skeletal-maturation-stage determination and upper-airway obstruction assessment; (2) treatment planning, including decision making for extractions and orthognathic surgery, and treatment outcome prediction; and (3) clinical practice, including practice guidance, remote care, and clinical documentation. We have witnessed a broadening of the application of AI in orthodontics, accompanied by advancements in its performance. Additionally, this review outlines the existing limitations within the field and offers future perspectives.