ABSTRACT
Background@#Mandibular split fractures, in which the fracture occurs exclusively in the posterior wall, are uncommon. This study aimed to enhance clinicians’ understanding of mandibular split fractures and offer insights for future research. @*Methods@#This study included six patients who visited our hospital between January 2020 and June 2023 and were diagnosed with mandibular split fractures. We retrospectively collected data from patients’ medical records on their age, sex, symptoms, mechanism, impact site, associated injuries, and treatment method, as well as the location, pattern, and number of fractures observed on computed tomography (CT) and panoramic images. The frequency of split fractures among all mandibular fractures was calculated. @*Results@#The six patients included three men (50%) and three women (50%), ranging in age from 20 to 71 years (mean age, 49.8 years). The split fractures were located in the symphysis in one patient (16.7%), symphysis to parasymphysis in two patients (33.3%), parasymphysis in one patient (16.7%), and parasymphysis to the body in two patients (33.3%). Four patients (66.7%) had condylar head fractures, while two patients (33.3%) had single split fractures. The mechanism of trauma was a slip-down incident in four cases (66.7%), while two cases (33.3%) were caused by motorcycle traffic accidents. Four patients (67%) underwent intermaxillary fixation, while two patients (33%) improved with conservative treatment. Split fractures were diagnosed in all six patients on CT, whereas the fracture line was not clearly visible on panoramic images. Mandibular split fractures accounted for 5.6% of all mandibular fractures. @*Conclusion@#This study provides insights into the clinical characteristics of rare mandibular split fractures and the diagnostic imaging findings. Furthermore, CT scans and three-dimensional image synthesis-instead of panoramic images-may be essential for accurately diagnosing mandibular fractures, including mandibular split fractures, in the future.
ABSTRACT
Strange attractor can be constructed from time series data such as heart sound. In the areas of the recognition and diagnosis of abnormal heart sounds, signal presentation method is very useful because good features can be detected from good presentation. This paper examines efficiency in diagnosing abnormal heart sounds of the two different methods for constructing attractor. Nine different heart sounds from typical clinical conditions were used for this study. The first method was constructing attractors using original heart sounds, and the second was modifying the original sounds by autocorrelation and they were then applied to the orignal sounds as to cross correlation checks. Attractors could be constructed using signals generated by these methods, and values of fractal dimensions would then be calculated which has been a well known method to measure characteristics of attractors. The results showed that the second method appeared to provide more efficient way to correctly classify abnormal heart sounds.
Subject(s)
Diagnosis , Fractals , Heart Sounds , HeartABSTRACT
Strange attractor can be constructed from time series data such as heart sound. In the area of the recognition and diagnosis problem, signal presentation method is very important because good features can be detected from good presentation. This paper discusses a way to extract a cycle from strange attractor and introduce new attractor construction method using autocorrelation value of the heart rate. The result shows well-formed attractor and good ability for extraction features. Largest Lyapunov Exponent is used to check whether the attractors provide distinguish abilities among different types of heart rate. The result shows good points that can be applied to some areas of human signal processing.