The aim of the current study was to find, by means of panoramic radiographs, a viable statistical method to forecast the duration of orthodontic traction of impacted maxillary cuspids. The treatment sample consisted of 51 palatal impacted cuspids (19 unilateral and 32 bilateral) in 35 patients (aged between 10.5 and 17.5 y) with a cervical vertebral maturation between cervical stage 1 and 4. Each patient underwent the same combined surgical-orthodontic technique. Anamnestic data as well as pretreatment panoramic radiograph and cephalogram with European Board of Orthodontics analysis were recorded for each case. Eight radiographic indicators were derived from panoramic films to define the reliable position of the impacted cuspid. Multiple regression analysis was used. All cuspids were successfully treated with an average traction time of 99 days (range, 33-188 d). The pretreatment radiographic features assessed on the panoramic radiographs did not significantly affect the duration of traction. The formula based on α angle, d1 distance, and S sector forecasted an average traction time of 123 days (range, 63-210 d), which is longer than the real time. No relevant correlations were found between orthodontic traction time and pretreatment radiograph parameters derived from panoramic film at the beginning of the treatment. The classic formula elaborated by Crescini could not be applied to the patients of this study, who were treated with the Easy Cuspid method.
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http://dx.doi.org/10.1097/SCS.0000000000001506 | DOI Listing |
J Cardiovasc Electrophysiol
January 2025
McGill University Health Centre, Montreal, Canada.
Background: Electrographic flow (EGF) mapping allows for the visualization of global atrial wavefront propagations. One mechanism of initiation and maintenance of atrial fibrillation (AF) is stimulation from EGF-identified focal sources that serve as driver sites of fibrillatory conduction. Electrographic flow consistency (EGFC) further quantifies the concordance of observed wavefront patterns, indicating that a healthier substrate shows more organized wavefront propagation and higher EGFC.
View Article and Find Full Text PDFJ Sci Med Sport
December 2024
Department of Kinesiology, Michigan State University, USA. Electronic address:
Objectives: Monitoring body composition can help to optimize performance in female athletes. This study aimed to create a conversion equation between dual-energy X-ray absorptiometry-measured body fat percentage and ultrasound-measured subcutaneous thigh fat thickness in Division I female athletes as a more accessible, cost-effective alternative.
Design: Cross-sectional study.
Imaging Sci Dent
December 2024
Department of Biostatistics and Medical Informatics, Faculty of Medicine, Suleyman Demirel University, Isparta, Turkey.
Purpose: Periarticular and generalized osteoporosis are well-known comorbidities of rheumatoid arthritis (RA), associated with either the disease itself or glucocorticoid therapy. This study was performed to quantitatively evaluate changes in the jawbones of patients with RA using fractal analysis (FA).
Materials And Methods: The study comprised 186 participants, including 144 women and 42 men.
Biomimetics (Basel)
December 2024
Sussex AI, School of Engineering and Informatics, University of Sussex, Brighton BN1 9QJ, UK.
Visual navigation is a key capability for robots and animals. Inspired by the navigational prowess of social insects, a family of insect-inspired route navigation algorithms-familiarity-based algorithms-have been developed that use stored panoramic images collected during a training route to subsequently derive directional information during route recapitulation. However, unlike the ants that inspire them, these algorithms ignore the sequence in which the training images are acquired so that all temporal information/correlation is lost.
View Article and Find Full Text PDFJ Med Internet Res
December 2024
Department of Oral and Maxillofacial Surgery, Facial Plastic Surgery, University Medical Centre of the Johannes Gutenberg-University Mainz, Mainz, Germany.
Background: Structured and standardized documentation is critical for accurately recording diagnostic findings, treatment plans, and patient progress in health care. Manual documentation can be labor-intensive and error-prone, especially under time constraints, prompting interest in the potential of artificial intelligence (AI) to automate and optimize these processes, particularly in medical documentation.
Objective: This study aimed to assess the effectiveness of ChatGPT (OpenAI) in generating radiology reports from dental panoramic radiographs, comparing the performance of AI-generated reports with those manually created by dental students.
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