A Precision Smart Healthcare System With Deep Learning for Real-Time Radiographic Localization and Severity Assessment of Peri-Implantitis

Abstract

Peri-implantitis is a common complication associated with the growing use of dental implants. Clinicians often rely on periapical radiographs for its diagnosis. Recent studies have explored the use of image analysis and artificial intelligence (AI) to reduce the diagnostic workload and time. However, the low quality of periapical images and inconsistent angulation across serial radiographs complicate clinical assessment of peri-implant bone changes, making it challenging for AI to accurately evaluate the severity of peri-implantitis. To address this issue, this study proposes a novel system for the identification and localization of peri-implantitis using periapical radiographs. The study utilizes the YOLOv8 oriented bounding boxes (OBB) model to accurately identify dental implant locations, significantly improving localization accuracy (98.48%) compared to previous research. Since peri-implantitis is diagnosed unilaterally, the algorithm splits the implant in X-ray images to facilitate better analysis. Subsequent steps enhanced the visibility of symptoms by using histogram equalization and coloring the implant parts. The convolutional neural networks (CNN) model, particularly EfficientNet-B0, further improved the detection accuracy (94.05%). In addition, an AI-based method was introduced to assess the severity of peri-implantitis by classifying thread damage, achieving 90.48% accuracy. This deep learning approach using CNN models significantly reduces interpretation time for X-rays, easing the dentist’s workload, minimizing misdiagnosis risks, lowering healthcare costs, and benefiting more patients.