Supporting data for Aspects of Artificial Intelligence (AI) in Dentistry

Four studies were conducted in this PhD project. In the first two studies, a novel AI algorithm with 3D-DCGAN for designing dental crowns was proposed and evaluated. Dental crowns designed by AI algorithm on pre-molars and molars were compared respectively to natural teeth, CEREC biogeneric design and technician design with the parameters of cusp angle, 3D volume discrepancy, occlusal contact point number and area, and<i> in silico </i>fatigue load. The results revealed that the AI algorithm can design a dental crown mimicking natural tooth morphology, such that the performance of load outweigh than other designs. The latter two studies examined the application of AI image segmentation. In the third study, the AI tool was used to quantitatively measure the initial bacterial adhesion on scanning electron microscope images. To evaluate the efficiency of different dental suction systems in the COVID-19 pandemic, in the fourth study this AI tool was used to measure the number and area of aerosols/droplets produced by a high-speed dental handpiece powered by an electrical surgical motor. The AI tool was shown to be accurate and efficient in measuring and detecting for these purposes, able to find a new relationship, and can be an alternative method in evaluation of initial bacterial adhesion and dental aerosol/droplet measurement.