Radiographic analyses of skeletal and dental parameters for age estimation in Malaysian population - Sigali, Waleed Mohammed Petro (2024)
To formulate a new prediction model for the age estimation range between 17-30 years old for both genders in the Malaysian population using oral parameters (root pulp visibility, periodontal ligament visibility, mental foramen and mandibular canal location) and stages of ossification of the medial clavicular epiphyseal cartilage. The study was a cross-sectional comparative study among the Malaysian population attending the clinic in the hospital set up at the Universiti Kebangsaan Malaysia and Universiti Sains Malaysia. Root pulp and periodontal ligament visibility showed a significant mean difference of age, mainly for stage 0 in the ethnic groups involved in the study between males and females. Measurements between right and left mental foramina and mental foramen to the inferior border of the mandible found a significant difference in mean between the ethnic groups after being adjusted for age and sex. There was also a significant difference in mean when measuring the distance from the midline to the right mental foramen between males and females after being adjusted for age and ethnic groups. Measurements from the right and left mandibular canal to the inferior border of the mandible found that there was a significant difference of mean between the ethnic groups after adjusted for age and sex, p-value < 0.001. The age prediction model using a combination of mandible variables, pulp, periodontal ligament visibility, mental foramen, and mandibular canal location showed R2 = 0.561. Hence, the model is considered to have a medium fit. Chest radiograph samples (390) showed a significant mean age difference between males and females, p-value = 0.017, using an independent t-test. Furthermore, there was a significant mean age difference between the ethnic groups, p-value < 0.001, by using a one-way ANOVA test. As for the age prediction model using right medial clavicle union, the ANOVA showed R2 = 0.767. This indicates that the final model explains 76.7% of the total variance in age. Hence, the model is considered to have an excellent fit. Using multi-factorial radiological methods showed good results while consuming less time and effort. Digital chest radiographs were a useful tool for predicting age.