A pilot study of a deep learning approach to detect marginal bone loss around implants

• Published in: BMC oral health Vol. 22; no. 1; pp. 11 - 8
• Main Authors: Liu, Min, Wang, Shimin, Chen, Hu, Liu, Yunsong
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 16.01.2022; BioMed Central; BMC
• Subjects: Accuracy; Artificial Intelligence; Automation; Bone implants; Bone loss; Bones; Clinical medicine; Complications and side effects; Datasets; Deep Learning; Density; Dental implant; Dental implants; Dental Implants - adverse effects; Dental prosthetics; Dentistry; Dentists; Health aspects; Humans; Implant dentures; Marginal bone loss; Methods; Neural networks; Neural Networks, Computer; Pilot Projects; Radiation; Radiography; Statistical analysis; Tomography; China; Deep learning; Marginal bone loss; Artificial intelligence; Dental implant
• ISSN: 1472-6831; 1472-6831
• DOI: 10.1186/s12903-021-02035-8

Recently, there has been considerable innovation in artificial intelligence (AI) for healthcare. Convolutional neural networks (CNNs) show excellent object detection and classification performance. This study assessed the accuracy of an artificial intelligence (AI) application for the detection of marginal bone loss on periapical radiographs. A Faster region-based convolutional neural network (R-CNN) was trained. Overall, 1670 periapical radiographic images were divided into training (n = 1370), validation (n = 150), and test (n = 150) datasets. The system was evaluated in terms of sensitivity, specificity, the mistake diagnostic rate, the omission diagnostic rate, and the positive predictive value. Kappa (κ) statistics were compared between the system and dental clinicians. Evaluation metrics of AI system is equal to resident dentist. The agreement between the AI system and expert is moderate to substantial (κ = 0.547 and 0.568 for bone loss sites and bone loss implants, respectively) for detecting marginal bone loss around dental implants. This AI system based on Faster R-CNN analysis of periapical radiographs is a highly promising auxiliary diagnostic tool for peri-implant bone loss detection.