Ethical Considerations on Artificial Intelligence in Dentistry: A Framework and Checklist.

Personalized medicine refers to the tailoring of diagnostics and therapeutics to individuals based on one's biological, social, and behavioral characteristics. While personalized dental medicine is still far from being a reality, advanced artificial intelligence (AI) technologies with improved data analytic approaches are expected to integrate diverse data from the individual, setting, and system levels, which may facilitate a deeper understanding of the interaction of these multi level data and therefore bring us closer to more personalized, predictive, preventive, and participatory dentistry, also known as P4 dentistry. In the field of dentomaxillofacial imaging, a wide range of AI applications, including several commercially available software options, have been proposed to assist dentists in the diagnosis and treatment planning of various dentomaxillofacial diseases, with performance similar or even superior to that of specialists. Notably, the impact of these dental AI applications on treatment decision, clinical and patient-reported outcomes, and cost-effectiveness has so far been assessed sparsely. Such information should be further investigated in future studies to provide patients, providers, and healthcare organizers a clearer picture of the true usefulness of AI in daily dental practice.

Personalized dental medicine, artificial intelligence and their relevance for dentomaxillofacial imaging.

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. 

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

The purpose of this study was to evaluate the accuracy of automatic cephalometric landmark localization and measurements using cephalometric analysis via artificial intelligence (AI) compared with computer-assisted manual analysis.Reconstructed lateral cephalograms (RLCs) from cone-beam computed tomography (CBCT) in 85 patients were selected. Computer-assisted manual analysis (Dolphin Imaging 11.9) and AI automatic analysis (Planmeca Romexis 6.2) were used to locate 19 landmarks and obtain 23 measurements. Mean radial error (MRE) and successful detection rate (SDR) values were calculated to assess the accuracy of automatic landmark digitization. Paired t tests and Bland‒Altman plots were used to compare the differences and consistencies in cephalometric measurements between manual and automatic analysis programs.The MRE for 19 cephalometric landmarks was 2.07 ± 1.35 mm with the automatic program. The average SDR within 1 mm, 2 mm, 2.5 mm, 3 and 4 mm were 18.82%, 58.58%, 71.70%, 82.04% and 91.39%, respectively. Soft tissue landmarks (1.54 ± 0.85 mm) had the most consistency, while dental landmarks (2.37 ± 1.55 mm) had the most variation. In total, 15 out of 23 measurements were within the clinically acceptable level of accuracy, 2 mm or 2°. The rates of consistency within the 95% limits of agreement were all above 90% for all measurement parameters.Automatic analysis software collects cephalometric measurements almost effectively enough to be acceptable in clinical work. Nevertheless, automatic cephalometry is not capable of completely replacing manual tracing. Additional manual supervision and adjustment for automatic programs can increase accuracy and efficiency. 

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Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence

Distribution of important medicinal plant species in Nepal under past, present, and future climatic conditions

Artificial Intelligence has created a huge impact in different areas of dentistry. Automated cephalometric analysis is one of the major applications of artificial intelligence in the field of orthodontics. Various automated cephalometric software have been developed which utilizes artificial intelligence and claim to be reliable. The purpose of this study was to compare the linear and angular cephalometric measurements obtained from web-based fully automated Artificial Intelligence (AI) driven platform "WebCeph"™ with that from manual tracing and evaluate the validity and reliability of automated cephalometric measurements obtained from "WebCeph"™.Thirty pre-treatment lateral cephalograms of patients were randomly selected. For manual tracing, digital images of same cephalograms were printed using compatible X-ray printer. After calibration, a total of 18 landmarks was plotted and 12 measurements (8 angular and 4 linear) were obtained using standard protocols. The digital images of each cephalogram were uploaded to "WebCeph"™ server. After image calibration, the automated cephalometric measurements obtained through AI digitization were downloaded for each image. Intraclass correlation coefficient (ICC) was used to determine agreement between the measurements obtained from two methods. ICC value < 0.75 was considered as poor to moderate agreement while an ICC value between 0.75 and 0.90 was considered as good agreement. Agreement was rated as excellent when ICC value > 0.90 was obtained.All the measurements had ICC value above 0.75. A higher ICC value > 0.9 was obtained for seven parameters i.e. ANB, FMA, IMPA/L1 to MP (°), LL to E-line, L1 to NB (mm), L1 to NB (°), S-N to Go-Gn whereas five parameters i.e. UL to E-line, U1 to NA (mm), SNA, SNB, U1 to NA (°) showed ICC value between 0.75 and 0.90.A good agreement was found between the cephalometric measurements obtained from "WebCeph"™ and manual tracing. 

/pdf/evaluation-of-fully-automated-cephalometric-measurements-3mqggmgk.pdf

Evaluation of fully automated cephalometric measurements obtained from web-based artificial intelligence driven platform

Ethnomedicinal landscape: distribution of used medicinal plant species in Nepal

/pdf/evaluation-of-fully-automated-cephalometric-measurements-ipd4bcwp.pdf

Introduction: Orthodontic treatment is usually perceived as a lengthy treatment as average treatment time usually last more than a year and may go up to even five years. So, in orthodontic practice the optimum treatment result with minimal treatment time should be the goal of clinicians. Treatment duration varies based on different clinico-environmental factors. Materials & Method: Total 200 cases were randomly selected from the archives of debonded treatmnent records from the department of orthodontics, Dhulikhel Hospital, Kathmandu University Teaching Hospital. Out of the 200 records, 103 records were fit for the study based on previously set inclusion and exclusion criteria. The selected cases were divided into three different groups: extraction n= 28 (extraction of 4 premolars), partial extraction= 19, (extraction of 1-3 teeth) and non-extraction n=56. Furthermore, the cases were also divided into Class I, II and III malocclusion patterns. After doing the test of normality, descriptive statistics, independent samples t test and ANOVA test were performed to compare the treatment duration with respect to gender, malocclusion pattern and treatment modalities. Result: Out of the 103 selected cases, maximum number of cases were of Class I(60) followed by Class II(37) and Class III(6). There was no statistical significance on treatment duration among male and female subjects (p= 0.933) as well as among different malocclusion pattern( p= 0.255). On the contrary, there was statistical difference on treatment duration among non extraction, partial extraction and extraction groups ( p=0.0004). The average treatment duration for non extraction, partial extraction and extraction group were 22, 28 and 29 months respectively. Conclusion: Orthodontic treatment duration is shorter for therapies done by non extraction than extraction. The average treatment duration for non extraction and extraction therapy is 22 and 29 months respectively. The treatment duration is not affected by gender and type of malocclusion.

/pdf/comparison-of-orthodontic-treatment-duration-among-3uqmxccq6o.pdf

Comparison of Orthodontic Treatment Duration Among Extraction Versus Non Extraction Therapies

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Evaluation of fully automated cephalometric measurements obtained from web-based artificial intelligence driven platform

Ethnomedicinal landscape: distribution of used medicinal plant species in Nepal

Evaluation of fully automated cephalometric measurements obtained from web-based artificial intelligence driven platform

Comparison of Orthodontic Treatment Duration Among Extraction Versus Non Extraction Therapies