Ultrasonics in Dentistry

AbstractUltrasonic instruments have been used in dentistry since the 1950's. Initially they were used to cut teeth but very quickly they became established as an ultrasonic scaler which was used to remove deposits from the hard tissues of the tooth. This enabled the soft tissues around the tooth to return to health. The ultrasonic vibrations are generated in a thin metal probe and it is the working tip that is the active component of the instrument. Scanning laser vibrometry has shown that there is much variability in their movement which is related to the shape and cross sectional shape of the probe. The working instrument will also generate cavitation and microstreaming in the associated cooling water. This can be mapped out along the length of the instrument indicating which are the active areas. Ultrasonics has also found use for cleaning often inaccessible or different surfaces including root canal treatment and dental titanium implants. The use of ultrasonics to cut bone during different surgical techniques shows considerable promise. More research is indicated to determine how to maximize the efficiency of such instruments so that they are more clinically effective

The Economic Advantage of Preventative Health Care in Prisons

The Economic Advantage of Preventative Health Care in Prison

The automatic recognition and counting of cough

BACKGROUND: Cough recordings have been undertaken for many years but the analysis of cough frequency and the temporal relation to trigger factors have proven problematic. Because cough is episodic, data collection over many hours is required, along with real-time aural analysis which is equally time-consuming. A method has been developed for the automatic recognition and counting of coughs in sound recordings. METHODS: The Hull Automatic Cough Counter (HACC) is a program developed for the analysis of digital audio recordings. HACC uses digital signal processing (DSP) to calculate characteristic spectral coefficients of sound events, which are then classified into cough and non-cough events by the use of a probabilistic neural network (PNN). Parameters such as the total number of coughs and cough frequency as a function of time can be calculated from the results of the audio processing. Thirty three smoking subjects, 20 male and 13 female aged between 20 and 54 with a chronic troublesome cough were studied in the hour after rising using audio recordings. RESULTS: Using the graphical user interface (GUI), counting the number of coughs identified by HACC in an hour long recording, took an average of 1 minute 35 seconds, a 97.5% reduction in counting time. HACC achieved a sensitivity of 80% and a specificity of 96%. Reproducibility of repeated HACC analysis is 100%. CONCLUSION: An automated system for the analysis of sound files containing coughs and other non-cough events has been developed, with a high robustness and good degree of accuracy towards the number of actual coughs in the audio recording