Applying artificial intelligence to detect and analyse oral and maxillofacial bone loss—A scoping review

Farajollahi, Mehran; Safarian, Mohammad Sadegh; Hatami, Masoud; Nejad, Azadeh Esmaeil; Peters, Ove A.

doi:10.1111/aej.12775

Aust Endodontic J

2023

DOI: 10.1111/aej.12775

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Applying artificial intelligence to detect and analyse oral and maxillofacial bone loss—A scoping review

Mehran Farajollahi

Mohammad Sadegh Safarian

Masoud Hatami

et al.

Abstract: Radiographic evaluation of bone changes is one of the main tools in the diagnosis of many oral and maxillofacial diseases. However, this approach to assessment has limitations in accuracy, inconsistency and comparatively low diagnostic efficiency. Recently, artificial intelligence (AI)‐based algorithms like deep learning networks have been introduced as a solution to overcome these challenges. Based on recent studies, AI can improve the detection accuracy of an expert clinician for periapical pathology, period… Show more

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Cited by 3 publications

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Artificial Intelligence Application in a Case of Mandibular Third Molar Impaction: A Systematic Review of the Literature

Assiri,

Hameed,

Alqarni

et al. 2024

JCM

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Objective: This systematic review aims to summarize the evidence on the use and applicability of AI in impacted mandibular third molars. Methods: Searches were performed in the following databases: PubMed, Scopus, and Google Scholar. The study protocol is registered at the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY202460081). The retrieved articles were subjected to an exhaustive review based on the inclusion and exclusion criteria for the study. Articles on the use of AI for diagnosis, treatment, and treatment planning in patients with impacted mandibular third molars were included. Results: Twenty-one articles were selected and evaluated using the Scottish Intercollegiate Guidelines Network (SIGN) evidence quality scale. Most of the analyzed studies dealt with using AI to determine the relationship between the mandibular canal and the impacted mandibular third molar. The average quality of the articles included in this review was 2+, which indicated that the level of evidence, according to the SIGN protocol, was B. Conclusions: Compared to human observers, AI models have demonstrated decent performance in determining the morphology, anatomy, and relationship of the impaction with the inferior alveolar nerve canal. However, the prediction of eruptions and future horizons of AI models are still in the early developmental stages. Additional studies estimating the eruption in mixed and permanent dentition are warranted to establish a comprehensive model for identifying, diagnosing, and predicting third molar eruptions and determining the treatment outcomes in the case of impacted teeth. This will help clinicians make better decisions and achieve better treatment outcomes.

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Artificial Intelligence Application in a Case of Mandibular Third Molar Impaction: A Systematic Review of the Literature

Assiri,

Hameed,

Alqarni

et al. 2024

JCM

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Evaluation of artificial intelligence for detecting periapical lesions on panoramic radiographs

Ünal,

Keser,

Namdar

et al. 2024

Balkan J Dent Med

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Background/Aim: The aim of this study is to evaluate the function of diagnostic computer software designed for the detection of periapical lesions on panoramic images with deep learning methods. Material and Methods: In our study, more than 500 adult retrospective panoramic radiography images obtained randomly were evaluated, and periapical lesions were labeled on the radiographs with the ResultLab.Ai labeling program (ResultLab.AI, Istanbul, Turkey). All images have been rechecked and verified by Oral, Dental and Maxillofacial Radiology experts. The model used is a U-Net based architecture customized for dental radiographs and optimized for fast operation. What is meant by customization is the structures called "Attention Gate" added to the architecture to draw the model's attention to the lesions. Results: Mask estimation was made separately for each image and F1 and IoU scores were calculated by comparing them with the marked masks. A list was created from the calculated F1 and IoU scores. The F1 and IoU score of the entire data set was calculated by taking the average of the values in this list. In IoU score calculation, Keras library's Mean IoU metric was used to calculate for 2 classes. In the F1 score calculation, the F1 score metric of the SKLearn library was used to calculate the unweighted average for 2 classes. As a result, the IoU-Score was obtained as 0.8578 and the F1-Score as 0.8587. Conclusions: Detection of periapical lesions on panoramic radiographs with an artificial intelligence approach will help practitioners reach definitive conclusions even in lesions that are likely to be overlooked. In addition, success rates for deep learning methods improve as data set size grows. As the number of images rises, the training models' success rates will follow.

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Assessing the Accuracy, Completeness, and Reliability of Artificial Intelligence-Generated Responses in Dentistry: A Pilot Study Evaluating the ChatGPT Model

Molena,

Macedo,

Ijaz

et al. 2024

Cureus

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Background: Artificial intelligence (AI) can be a tool in the diagnosis and acquisition of knowledge, particularly in dentistry, sparking debates on its application in clinical decision-making.Objective: This study aims to evaluate the accuracy, completeness, and reliability of the responses generated by Chatbot Generative Pre-Trained Transformer (ChatGPT) 3.5 in dentistry using expert-formulated questions.Materials and methods: Experts were invited to create three questions, answers, and respective references according to specialized fields of activity. The Likert scale was used to evaluate agreement levels between experts and ChatGPT responses. Statistical analysis compared descriptive and binary question groups in terms of accuracy and completeness. Questions with low accuracy underwent re-evaluation, and subsequent responses were compared for improvement. The Wilcoxon test was utilized (α = 0.05).Results: Ten experts across six dental specialties generated 30 binary and descriptive dental questions and references. The accuracy score had a median of 5.50 and a mean of 4.17. For completeness, the median was 2.00 and the mean was 2.07. No difference was observed between descriptive and binary responses for accuracy and completeness. However, re-evaluated responses showed a significant improvement with a significant difference in accuracy (median 5.50 vs. 6.00; mean 4.17 vs. 4.80; p=0.042) and completeness (median 2.0 vs. 2.0; mean 2.07 vs. 2.30; p=0.011). References were more incorrect than correct, with no differences between descriptive and binary questions.Conclusions: ChatGPT initially demonstrated good accuracy and completeness, which was further improved with machine learning (ML) over time. However, some inaccurate answers and references persisted. Human critical discernment continues to be essential to facing complex clinical cases and advancing theoretical knowledge and evidence-based practice.

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Applying artificial intelligence to detect and analyse oral and maxillofacial bone loss—A scoping review

Cited by 3 publications

References 91 publications

Artificial Intelligence Application in a Case of Mandibular Third Molar Impaction: A Systematic Review of the Literature

Artificial Intelligence Application in a Case of Mandibular Third Molar Impaction: A Systematic Review of the Literature

Evaluation of artificial intelligence for detecting periapical lesions on panoramic radiographs

Assessing the Accuracy, Completeness, and Reliability of Artificial Intelligence-Generated Responses in Dentistry: A Pilot Study Evaluating the ChatGPT Model

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