Dental age assessment of 3-15-year-old Saudi children and adolescents using Demirjian's method-A radiographic study

Dental age assessment plays a pivotal role in clinical practice, demographic studies, forensics, and courts of law but is affected by ethnic and geographic variations. The aim was to determine the population-specific weighted scores needed when dental age is estimated using Demirjian's method for Saudi children and adolescents between the ages of 3 and 15 years. Design: A total of 298 panoramic radiographs were collected from Saudi Arabia. Dental age was assessed using Demirjian's method (1973). Chronological age was determined from the date of birth and the date on which the panoramic radiograph was taken for each individual. Between 3- and 15-years age group, the Saudi boys had an estimated age of 9.07 ± 1.96 years and chronological age of 8.49 ± 2.30 years. The Saudi girls had an estimated age of 9.22 ± 1.93 years and chronological age of 8.78 ± 2.32 years. With Demirjian's method, the Saudi boys were 0.57 ± 1.48 years, and girls were 0.44 ± 1.66 years ahead of their chronological ages (p < .05). New population-specific weighted scores were developed to convert dental age according to Demirjian's method into estimated ages in the contemporary Saudi Arabian population. This study can be used for further research and comparisons with other population groups, regions or communities.

EEG electrode selection for person identification thru a genetic-algorithm method

New biometric identification techniques are continually being developed to meet various applications. Electroencephalography (EEG) signals may provide a reasonable option for this type of identification due its unique features that overcome the lacks of other common methods. Currently, however, the processing load for such signals requires considerable time and labor. New methods and algorithms have attempted to reduce EEG processing time, including a reduction of the number of electrodes and segmenting the EEG data into its typical frequency bands. This work complements other efforts by proposing a genetic algorithm to reduce the number of necessary electrodes for measurements by EEG devices. Using a public EEG dataset of 109 subjects who underwent relaxation with eye-open and eye-closed stimuli, we aimed to determine the minimum set of electrodes required for optimum identification accuracy in each EEG sub-band of both stimuli. The results were encouraging and it was possible to accurately identify a subject using about 10 out of 64 electrodes. Moreover, higher frequency bands required a fewer number of electrodes for identification compared with lower frequency bands.