The Use of Panoramic Radiography for Condyle Shape Detection on Menopausal Periods in Dentistry Radiology Installation – Dental and Oral Hospital of Universitas Sumatera Utara

Panoramic radiographs can be used to detect temporomandibular morphology and condylar changes. This study shape-determines the female condyle in perimenopausal and postmenopausal using panoramic radiography. It used an observational survey technique with a sample of 80 people, consisting of 40 perimenopausal aged between 20 and 29, and 40 postmenopausal females aged over 52. The results on the perimenopausal condyle process obtained a round shape of 43.7%, an angle of 32.5%, and a pointed shape of 23.7%. Furthermore, the shape of the condylar process in postmenopause is 37.5% pointed, 30% angled, 25% round, and 7.5% flat. Data were analyzed using the Chi-Square test with a significance value of p<0.05. The results showed that changes in the size and shape of the condyles occur with age. There is a significant difference in the condyle shape between perimenopausal and postmenopausal periods.

SmartFit: Smartphone Application for Garment Fit Detection

The apparel e-commerce industry is growing day by day. In recent times, consumers are particularly interested in an easy and time-saving way of online apparel shopping. In addition, the COVID-19 pandemic has generated more need for an effective and convenient online shopping solution for consumers. However, online shopping, particularly online apparel shopping, has several challenges for consumers. These issues include sizing, fit, return, and cost concerns. Especially, the fit issue is one of the cardinal factors causing hesitance and drawback in online apparel purchases. The conventional method of clothing fit detection based on body shapes relies upon manual body measurements. Since no convenient and easy-to-use method has been proposed for body shape detection, we propose an interactive smartphone application, “SmartFit”, that will provide the optimal fitting clothing recommendation to the consumer by detecting their body shape. This optimal recommendation is provided by using image processing and machine learning that are solely dependent on smartphone images. Our preliminary assessment of the developed model shows an accuracy of 87.50% for body shape detection, producing a promising solution to the fit detection problem persisting in the digital apparel market.