A comprehensive artificial intelligence framework for dental diagnosis and charting

Abstract

Background The aim of this study was to develop artificial intelligence (AI) guided framework to recognize tooth numbers in panoramic and intraoral radiographs (periapical and bitewing) without prior domain knowledge and arrange the intraoral radiographs into a full mouth series (FMS) arrangement template. This model can be integrated with different diseases diagnosis models, such as periodontitis or caries, to facilitate clinical examinations and diagnoses. Methods The framework utilized image segmentation models to generate the masks of bone area, tooth, and cementoenamel junction (CEJ) lines from intraoral radiographs. These masks were used to detect and extract teeth bounding boxes utilizing several image analysis methods. Then, individual teeth were matched with a patient’s panoramic images (if available) or tooth repositories for assigning tooth numbers using the multi-scale matching strategy. This framework was tested on 1240 intraoral radiographs different from the training and internal validation cohort to avoid data snooping. Besides, a web interface was designed to generate a report for different dental abnormalities with tooth numbers to evaluate this framework’s practicality in clinical settings. Results The proposed method achieved the following precision and recall via panoramic view: 0.96 and 0.96 (via panoramic view) and 0.87 and 0.87 (via repository match) by handling tooth shape variation and outperforming other state-of-the-art methods. Additionally, the proposed framework could accurately arrange a set of intraoral radiographs into an FMS arrangement template based on positions and tooth numbers with an accuracy of 95% for periapical images and 90% for bitewing images. The accuracy of this framework was also 94% in the images with missing teeth and 89% with restorations. Conclusions The proposed tooth numbering model is robust and self-contained and can also be integrated with other dental diagnosis modules, such as alveolar bone assessment and caries detection. This artificial intelligence-based tooth detection and tooth number assignment in dental radiographs will help dentists with enhanced communication, documentation, and treatment planning accurately. In addition, the proposed framework can correctly specify detailed diagnostic information associated with a single tooth without human intervention.

Deep learning for classifying the stages of periodontitis on dental images: a systematic review and meta-analysis

“…2 , respectively. Nearly half of the included studies did not have clear information on whether patients were consecutively or randomly enrolled, resulting in 42.9% of the articles (12/27) showing an unclear risk of bias in the patient selection domain [ 20 , 30 , 32 , 34 – 36 , 38 , 45 , 48 , 52 , 37 , 42 ]. Two studies were rated as having a high risk of bias, with one [ 29 ] designed to be a case-control study with a convenient sample collection and the other [ 31 ] using inappropriate exclusion criteria.…”

“…Three studies used an external dataset to evaluate the performance of the algorithms [ 29 , 43 , 48 ]. In addition, three studies used public databases [ 35 – 37 ]. In terms of dental image modality, the studies employed periapical radiograph images, panoramic dental radiographs, and CBCT images to classify periodontitis, among which panoramic radiographs were used the most (15/27) [ 28 – 30 , 32 , 33 , 35 , 36 , 38 , 39 , 42 , 47 – 51 ] and only one study used CBCT [ 44 ].…”

Hidden Health Issues: The Role Of AI In Diagnosing Oral Infections And Inflammation

“…These models can identify potential areas of interest for more thorough evaluation with higher precision and effectiveness [21]. Consequently, integrating AI into diagnostic processes represents a pivotal step toward improving care quality and patient outcomes [22].…”

“…In the patient's case, AI can clarify ambiguous images, thus enhancing the overall objectivity and trust. Lastly, AI-based diagnostic tools can enhance collaboration between healthcare providers and dental professionals [22].…”

Deep learning to support the detection and classification of teeth, dental caries and restorations: a review

“…Bouchahma et al (2019) study combined panoramic and periapical; Kyventidis e Angelopoulos (2021) combined periapical and bitewing;(Kabir et al, 2022) combined panoramic, periapical and bitewing; Salunke et al (2022) used RadioVisioGraphy (RVG) and Rashid et al (2022) manipulated panoramic and high-definition photographs of the mouth. Li et al (2022) did not specify the type of radiograph.…”