A weakly supervised learning-based segmentation network for dental diseases

Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review

OBJECTIVES

Artificial intelligence (AI) is an emerging field in dentistry. AI is gradually being integrated into dentistry to improve clinical dental practice. The aims of this scoping review were to investigate the application of AI in image analysis for decision-making in clinical dentistry and identify trends and research gaps in the current literature.

MATERIAL AND METHODS

This review followed the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). An electronic literature search was performed through PubMed and Scopus. After removing duplicates, a preliminary screening based on titles and abstracts was performed. A full-text review and analysis were performed according to predefined inclusion criteria, and data were extracted from eligible articles.

RESULTS

Of the 1334 articles returned, 276 met the inclusion criteria (consisting of 601,122 images in total) and were included in the qualitative synthesis. Most of the included studies utilized convolutional neural networks (CNNs) on dental radiographs such as orthopantomograms (OPGs) and intraoral radiographs (bitewings and periapicals). AI was applied across all fields of dentistry - particularly oral medicine, oral surgery, and orthodontics - for direct clinical inference and segmentation. AI-based image analysis was use in several components of the clinical decision-making process, including diagnosis, detection or classification, prediction, and management.

CONCLUSIONS

A variety of machine learning and deep learning techniques are being used for dental image analysis to assist clinicians in making accurate diagnoses and choosing appropriate interventions in a timely manner.

Toward Digital Periodontal Health: Recent Advances and Future Perspectives

Periodontal diseases, ranging from gingivitis to periodontitis, are prevalent oral diseases affecting over 50% of the global population. These diseases arise from infections and inflammation of the gums and supporting bones, significantly impacting oral health. The established link between periodontal diseases and systemic diseases, such as cardiovascular diseases, underscores their importance as a public health concern. Consequently, the early detection and prevention of periodontal diseases have become critical objectives in healthcare, particularly through the integration of advanced artificial intelligence (AI) technologies. This paper aims to bridge the gap between clinical practices and cutting-edge technologies by providing a comprehensive review of current research. We examine the identification of causative factors, disease progression, and the role of AI in enhancing early detection and treatment. Our goal is to underscore the importance of early intervention in improving patient outcomes and to stimulate further interest among researchers, bioengineers, and AI specialists in the ongoing exploration of AI applications in periodontal disease diagnosis.

Weakly supervised salient object detection via image category annotation

The rapid development of deep learning has made a great progress in salient object detection task. Fully supervised methods need a large number of pixel-level annotations. To avoid laborious and consuming annotation, weakly supervised methods consider low-cost annotations such as category, bounding-box, scribble, etc. Due to simple annotation and existing large-scale classification datasets, the category annotation based methods have received more attention while still suffering from inaccurate detection. In this work, we proposed one weakly supervised method with category annotation. First, we proposed one coarse object location network (COLN) to roughly locate the object of an image with category annotation. Second, we refined the coarse object location to generate pixel-level pseudo-labels and proposed one quality check strategy to select high quality pseudo labels. To this end, we studied COLN twice followed by refinement to obtain a pseudo-labels pair and calculated the consistency of pseudo-label pairs to select high quality labels. Third, we proposed one multi-decoder neural network (MDN) for saliency detection supervised by pseudo-label pairs. The loss of each decoder and between decoders are both considered. Last but not least, we proposed one pseudo-labels update strategy to iteratively optimize pseudo-labels and saliency detection models. Performance evaluation on four public datasets shows that our method outperforms other image category annotation based work.