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This study evaluated the shear-peel bond strength and mode of bond failure of 3 curing devices (plasma arc light, argon laser, and conventional halogen light) and 2 orthodontic bracket adhesives with different filler contents (Transbond XT and Adhesive Precoated [APC]). Observations of microleakage were also reported. Ninety human adolescent premolars were randomly divided into 6 groups, and standardized brackets were bonded according to the manufacturers' recommendations. The plasma arc light produced significantly (P =.006) higher bond strength than did the halogen light or the argon laser when Transbond was used. When APC was used, the plasma arc light and the halogen light produced similar results, and they both produced significantly (P =.015) higher bond strengths than did the argon laser. Overall, the APC showed substantially less variation in bond strength than did the Transbond. Although all curing methods showed significant microleakage (P <.001), differences among the 3 curing lights occurred only when APC was used. Microscopic evaluations demonstrated that 95% of the specimens failed for adhesion at the bracket or tooth surface; the argon laser produced the highest adhesive remnant index scores. On the basis of bond strength and microleakage results, the plasma arc light was comparable with or superior to the other curing devices, depending on the adhesive used.

Objectives: To determine the relative effects of Herbst appliance therapy in hypo-and hyperdivergent patients. Materials and Methods: The treated group included 45 growing Class II, division 1, patients treated with stainless steel crown Herbst appliances, followed by fixed edgewise appliances. The untreated control group consisted of 45 Class II, division 1, subjects, matched to the treated sample based on Angle classification, age, sex, and pretreatment mandibular plane angle (MPA). Subjects were categorized as hypo-or hyperdivergent based on their MPAs. Pre-and posttreatment cephalograms were traced and superimposed on cranial base and mandibular structures. Results: The primary effect of the Herbst in terms of maxillomandibular correction was in the maxilla. It significantly restricted maxillary growth, producing a ''headgear effect.'' Mandibular treatment changes depended on divergence. Hyperdivergent patients experienced a deleterious backward true mandibular rotation with Herbst treatment. Hypodivergent patients, as well as untreated hypo-and hyperdivergent controls, underwent forward true mandibular rotation. However, hypodivergent chins did not advance any more than expected for untreated hypodivergent Class II patients. Conclusions: Hypo-and hyperdivergent patients benefit from the Herbst's headgear effect. While the mandibular growth of hypodivergent patients overcomes the negative rotational effects, hyperdivergent patients undergo a deleterious backward mandibular rotation and increases in facial height.

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Over the past two decades, artificial intelligence (AI) and machine learning (ML) have undergone considerable development. There have been various applications in medicine and dentistry. Their application in orthodontics has progressed slowly, despite promising results. The available literature pertaining to the orthodontic applications of AI and ML has not been adequately synthesized and reviewed. This review article provides orthodontists with an overview of AI and ML, along with their applications. It describes state-of-the-art applications in the areas of orthodontic diagnosis, treatment planning, growth evaluations, and in the prediction of treatment outcomes. AI and ML are powerful tools that can be utilized to overcome some of the clinical problems that orthodontists face daily. With the availability of more data, better AI and ML systems should be expected to be developed that will help orthodontists practice more efficiently and improve the quality of care.