Abstract
Objective Dental implants are considered the optimum solution to replace missing teeth and
restore the mouth's function and aesthetics. Surgical planning of the implant position
is critical to avoid damage to vital anatomical structures; however, the manual measurement
of the edentulous (toothless) bone on cone beam computed tomography (CBCT) images
is time-consuming and is subject to human error. An automated process has the potential
to reduce human errors and save time and costs. This study developed an artificial
intelligence (AI) solution to identify and delineate edentulous alveolar bone on CBCT
images before implant placement.
Materials and Methods After obtaining the ethical approval, CBCT images were extracted from the database
of the University Dental Hospital Sharjah based on predefined selection criteria.
Manual segmentation of the edentulous span was done by three operators using ITK-SNAP
software. A supervised machine learning approach was undertaken to develop a segmentation
model on a “U-Net” convolutional neural network (CNN) in the Medical Open Network
for Artificial Intelligence (MONAI) framework. Out of the 43 labeled cases, 33 were
utilized to train the model, and 10 were used for testing the model's performance.
Statistical Analysis The degree of 3D spatial overlap between the segmentation made by human investigators
and the model's segmentation was measured by the dice similarity coefficient (DSC).
Results The sample consisted mainly of lower molars and premolars. DSC yielded an average
value of 0.89 for training and 0.78 for testing. Unilateral edentulous areas, comprising
75% of the sample, resulted in a better DSC (0.91) than bilateral cases (0.73).
Conclusion Segmentation of the edentulous spans on CBCT images was successfully conducted by
machine learning with good accuracy compared to manual segmentation. Unlike traditional
AI object detection models that identify objects present in the image, this model
identifies missing objects. Finally, challenges in data collection and labeling are
discussed, together with an outlook at the prospective stages of a larger project
for a complete AI solution for automated implant planning.
Keywords
artificial intelligence - CBCT segmentation - dental implants - implant planning -
implantology
