Improving Dental Implant Outcomes: CNN-Based System Accurately Measures Degree of Peri-Implantitis Damage on Periapical Film

Abstract

As the popularity of dental implants continues to grow at a rate of about 14% per year, so do the risks associated with the procedure. Complications such as sinusitis and nerve damage are not uncommon, and inadequate cleaning can lead to peri-implantitis around the implant, jeopardizing its stability and potentially necessitating retreatment. To address this issue, this research proposes a new system for evaluating the degree of periodontal damage around implants using Periapical film (PA). The system utilizes two Convolutional Neural Networks (CNN) models to accurately detect the location of the implant and assess the extent of damage caused by peri-implantitis. One of the CNN models is designed to determine the location of the implant in the PA with an accuracy of up to 89.31%, while the other model is responsible for assessing the degree of Peri-implantitis damage around the implant, achieving an accuracy of 90.45%. The system combines image cropping based on position information obtained from the first CNN with image enhancement techniques such as Histogram Equalization and Adaptive Histogram Equalization (AHE) to improve the visibility of the implant and gums. The result is a more accurate assessment of whether peri-implantitis has eroded to the first thread, a critical indicator of implant stability. To ensure the ethical and regulatory standards of our research, this proposal has been certified by the Institutional Review Board (IRB) under number 202102023B0C503. With no existing technology to evaluate Peri-implantitis damage around dental implants, this CNN-based system has the potential to revolutionize implant dentistry and improve patient outcomes.

Advanced Dental Implant System Classification with Pre-trained CNN Models and Multi-branch Spectral Channel Attention Networks

“…Chen et al [20] proposed a novel method for assessing peri-implantitis damage utilizing periapical films (PA) and CNN models. With its high accuracy in implant localization and peri-implantitis damage assessment, the CNN-based method offers potential for precise evaluation of peri-implantitis damage, aiding in implant dentistry and patient care.…”

“…The VGG16 scheme [19] yielded an accuracy of 91.6%, with recall and F1-score values of 90.5% and 91.4%, respectively. The CNN scheme [20,21] displayed an accuracy of 87.2%, with precision, recall, and F1-score values of 87.3%, 86.7%, and 86.3%, respectively. Finally, the MBSCA-MAO scheme showcased an accuracy of 95.7%, with precision, recall, and F1-score values of 95.2%, 94.3%, and 95.6%, respectively.…”

Classification of the Relative Position between the Third Molar and the Inferior Alveolar Nerve Using a Convolutional Neural Network Based on Transfer Learning

“…The improvement in efficiency and quality provides clinicians more time for patient care. Furthermore, periapical (PA) image is utilized as an adjunct diagnostic tool, particularly in the assessment of conditions such as apical lesions [10], implants [11], and furcation involvement [12]. DPR images refer to extraoral radiographs [13], typically encompassing all teeth alongside adjacent skeletal structures and nerves, providing two-dimensional information about dental and maxillofacial osseous anatomy.…”

Deep Learning-Based Recognition of Periodontitis and Dental Caries in Dental X-ray Images

“…AI has shown great potential to assist in disease diagnosis and treatment planning in dentistry [ 2 , 3 , 4 ]. Deep learning models have demonstrated outstanding abilities in learning complex patterns from large image datasets, giving rise to numerous applications in the field of dentistry [ 2 , 5 , 6 , 7 , 8 , 9 ]. Deep learning of dental radiographs has emerged as an efficient and precise method for detecting dental diseases.…”