Artificial Intelligence Techniques for Automatic Detection of Peri‑implant Marginal Bone Remodeling in Intraoral Radiographs
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2023
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Springer
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Vera, M., Gómez-Silva, M.J., Vera, V. et al. Artificial Intelligence Techniques for Automatic Detection of Peri-implant Marginal Bone Remodeling in Intraoral Radiographs. J Digit Imaging (2023). https://doi.org/10.1007/s10278-023-00880-3
Abstract
Peri-implantitis can cause marginal bone remodeling around implants. The aim is to develop an automatic image processing approach based on two artificial intelligence (AI) techniques in intraoral (periapical and bitewing) radiographs to assist
dentists in determining bone loss. The first is a deep learning (DL) object-detector (YOLOv3) to roughly identify (no exact
localization is required) two objects: prosthesis (crown) and implant (screw). The second is an image understanding-based
(IU) process to fine-tune lines on screw edges and to identify significant points (intensity bone changes, intersections
between screw and crown). Distances between these points are used to compute bone loss. A total of 2920 radiographs
were used for training (50%) and testing (50%) the DL process. The mAP@0.5 metric is used for performance evaluation of
DL considering periapical/bitewing and screws/crowns in upper and lower jaws, with scores ranging from 0.537 to 0.898
(sufficient because DL only needs an approximation). The IU performance is assessed with 50% of the testing radiographs
through the t test statistical method, obtaining p values of 0.0106 (line fitting) and 0.0213 (significant point detection). The
IU performance is satisfactory, as these values are in accordance with the statistical average/standard deviation in pixels
for line fitting (2.75/1.01) and for significant point detection (2.63/1.28) according to the expert criteria of dentists, who
establish the ground-truth lines and significant points. In conclusion, AI methods have good prospects for automatic bone
loss detection in intraoral radiographs to assist dental specialists in diagnosing peri-implantitis.