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AI-Powered Diagnosis and Treatment Plans in Dentistry and Orofacial Fields

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A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 17321

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Dr. Antonino Lo Giudice

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Department of General Surgery and Medical-Surgical Specialties, School of Dentistry, Unit of Orthodontics, University of Catania, 95131 Catania, Italy
Interests: 3D imaging; CBCT; digital anatomical segmentation; facial scan; intraoral scan; cephalometry; craniofacial development imaging; CAD-CAM; diagnostic digital workflow; RMI; functional orthodontic appliances; dentofacial orthopedics; interceptive orthodontics; elastodontics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Rosalia Leonardi

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Unit of Orthodontics, Department of General Surgery and Medical-Surgical Specialties, School of Dentistry, University of Catania, 95131 Catania, Italy
Interests: 3D imaging; CBCT; artificial intelligence (AI); digital anatomical segmentation; facial scan; intra-oral scan; cephalometry; craniofacial development imaging; CAD-CAM; diagnostic digital workflow; RMI; functional orthodontic appliances; dentofacial orthopedics
Special Issues, Collections and Topics in MDPI journals

Dr. Victor Díaz-Flores García

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Guest Editor

Department of Pre-Clinical Dentistry, School of Biomedical Sciences, Universidad Europea de Madrid, Villaviciosa de Odón, 28670 Madrid, Spain
Interests: patient safety; legal dentistry; artificial intelligence

Dr. Yolanda Freire

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Department of Pre-Clinical Dentistry, School of Biomedical Sciences, Universidad Europea de Madrid, Villaviciosa de Odón, 28670 Madrid, Spain
Interests: artificial intelligence; dentistry

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has emerged as a transformative force in healthcare, revolutionizing the way diseases are diagnosed and treated. In the dentistry and orofacial fields, AI offers immense potential for enhancing precision, efficiency, and personalization in diagnosis and treatment planning. This Special Issue aims to explore the latest advancements, challenges, and future directions in leveraging AI for diagnosis and treatment plans in the context of dentistry and orofacial disciplines.

This Special Issue will encompass a broad range of topics related to AI applications in dentistry and orofacial fields, including but not limited to the following:

AI-powered diagnostic tools: Development and validation of AI algorithms for the accurate and early diagnosis of dental and orofacial conditions.

Machine learning in treatment planning: Utilizing machine learning models to optimize and personalize treatment plans based on patient-specific data.

Integration of AI in imaging analysis: Enhancing diagnostic imaging interpretation through AI-based analysis in radiology and 3D imaging in dentistry.

Predictive modeling for oral diseases: AI-driven approaches to predict the onset and progression of oral diseases, enabling proactive and preventive interventions.

Virtual and augmented reality in treatment simulation: Applications of AI-enhanced virtual and augmented reality for simulating treatment outcomes and patient education.

Ethical considerations and challenges: Exploration of ethical implications, patient privacy, and regulatory considerations associated with the integration of AI in dentistry and orofacial treatment planning.

We invite researchers, practitioners, and experts in the field to submit original research articles, reviews, and case studies that contribute to the understanding and advancement of AI in the dentistry and orofacial fields. Submissions should adhere to the journal's guidelines and will undergo a rigorous peer review process.

Dr. Antonino Lo Giudice
Prof. Dr. Rosalia Leonardi
Dr. Victor Díaz-Flores García
Dr. Yolanda Freire
Guest Editors

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

artificial intelligence
AI
digital dentistry
digital systems
CBCT
CAD-CAM

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Published Papers (11 papers)

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Research

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12 pages, 2215 KiB

Open AccessArticle

A Three-Stage Fusion Neural Network for Predicting the Risk of Root Fracture—A Pilot Study

by Yung-Ming Kuo, Liang-Yin Kuo, Hsun-Yu Huang, Tsen-Yu Sung, Chun-Hung Yang, Wan-Ting Chang and Chien-Shun Lo

Bioengineering 2025, 12(5), 447; https://doi.org/10.3390/bioengineering12050447 - 24 Apr 2025

Viewed by 239

Abstract

Predicting the risk of root fractures following root canal therapy requires diagnosis of the dental history and status of patients. However, dental history is a kind of categorical data type that is not easy to combine with numerical data to obtain good performance in deep learning. The accuracy of support vector machine (SVM) and artificial neural networks (ANNs) is 71.7% and 73.1%, respectively. In this study, a three-stage fusion neural network (TSFNN) is proposed to improve the multiple types of clinical data in the dental field based on ANNs. Clinical data were obtained from 145 teeth, comprising 97 fractured teeth and 48 nonfractured teeth. Each dataset contained 17 items, which were divided into 10 categorical items and 7 numerical items. TSFNN combines numerical and categorical NN with batch normalization and embedding layer techniques and can produce the accuracy of 82.1% and a 19.1% improvement in F1-score. It shows impressive performance in predicting the risk of root fracture. Furthermore, due to the limited amount of clinical data, it is believed that such a pilot study can effectively improve the results when the amount of clinical data is insufficient. Full article

(This article belongs to the Special Issue AI-Powered Diagnosis and Treatment Plans in Dentistry and Orofacial Fields)

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16 pages, 3643 KiB

Open AccessArticle

2D Pose Estimation vs. Inertial Measurement Unit-Based Motion Capture in Ergonomics: Assessing Postural Risk in Dental Assistants

by Steven Simon, Jonna Meining, Laura Laurendi, Thorsten Berkefeld, Jonas Dully, Carlo Dindorf and Michael Fröhlich

Bioengineering 2025, 12(4), 403; https://doi.org/10.3390/bioengineering12040403 - 10 Apr 2025

Viewed by 322

Abstract

The dental profession has a high prevalence of musculoskeletal disorders because daily working life is characterized by many monotonous and one-sided physical exertions. Inertial measurement unit (IMU)-based motion capture (MoCap) is increasingly utilized for assessing workplace postural risk. However, practical alternatives are needed because it is time-consuming and relatively cost intensive for ergonomists. This study compared two measurement technologies: IMU-based MoCap and a time-effective alternative, two-dimensional (2D) pose estimation. Forty-five dental assistant students (all female) were included (age: 19.56 ± 5.91 years; height: 165.00 ± 6.35 cm; weight: 63.41 ± 13.87 kg; BMI: 21.56 ± 4.63 kg/m²). A 30 s IMU-based MoCap and image-based pose estimation in the sagittal and frontal planes were performed during a representative experimental task. Data were analyzed using Cohen’s weighted kappa and Bland–Altman plots. There was a significant moderate agreement between the Rapid Upper Limb Assessment (RULA) score from IMU-based MoCap and pose estimation (κ = 0.461, pB = 0.006), but no significant poor agreement (p > 0.05) regarding the body regions of the upper arm, lower arm, wrist, neck, and trunk. These findings indicate that IMU-based MoCap and pose estimation moderately align when assessing the overall RULA score but not for specific body parts. While pose estimation might be useful for quick general posture assessment, it may not be reliable for evaluating joint-level differences, especially in body areas such as the upper extremities. Future research should focus on refining video-based pose estimation for real-time postural risk assessment in the workplace. Full article

(This article belongs to the Special Issue AI-Powered Diagnosis and Treatment Plans in Dentistry and Orofacial Fields)

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14 pages, 5065 KiB

Open AccessArticle

Accuracy Evaluation of a Three-Dimensional Face Reconstruction Model Based on the Hifi3D Face Model and Clinical Two-Dimensional Images

by Yujia Xiao, Bochun Mao, Jianglong Nie, Jiayi Liu, Shuo Wang, Dawei Liu and Yanheng Zhou

Bioengineering 2024, 11(12), 1174; https://doi.org/10.3390/bioengineering11121174 - 21 Nov 2024

Viewed by 1305

Abstract

Three-dimensional (3D) facial models have been increasingly applied in orthodontics, orthognathic surgery, and various medical fields. This study proposed an approach to reconstructing 3D facial models from standard orthodontic frontal and lateral images, providing an efficient way to expand 3D databases. A total of 23 participants (average age 20.70 ± 5.36 years) were enrolled. Based on the Hifi3D face model, 3D reconstructions were generated and compared with corresponding face scans to evaluate their accuracy. Root mean square error (RMSE) values were calculated for the entire face, nine specific facial regions, and eight anatomical landmarks. Clinical feasibility was further assessed by comparing six angular and thirteen linear measurements between the reconstructed and scanned models. The RMSE of the reconstruction model was 2.00 ± 0.38 mm (95% CI: 1.84–2.17 mm). High accuracy was achieved for the forehead, nose, upper lip, paranasal region, and right cheek (mean RMSE < 2 mm). The forehead area showed the smallest deviation, at 1.52 ± 0.88 mm (95% CI: 1.14–1.90 mm). In contrast, the lower lip, chin, and left cheek exhibited average RMSEs exceeding 2 mm. The mean deviation across landmarks was below 2 mm, with the Prn displaying the smallest error at 1.18 ± 1.10 mm (95% CI: 0.71–1.65 mm). The largest discrepancies were observed along the Z-axis (Z > Y > X). Significant differences (p < 0.05) emerged between groups in the nasolabial, nasal, and nasofrontal angles, while the other 13 linear and 3 angular measurements showed no statistical differences (p > 0.05). This study explored the feasibility of reconstructing accurate 3D models from 2D photos. Compared to facial scan models, the Hifi3D face model demonstrated a 2 mm deviation, with potential for enriching 3D databases for subjective evaluations, patient education, and communication. However, caution is advised when applying this model to clinical measurements, especially angle assessments. Full article

(This article belongs to the Special Issue AI-Powered Diagnosis and Treatment Plans in Dentistry and Orofacial Fields)

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9 pages, 2627 KiB

Open AccessArticle

Artificial Intelligence Diagnosing of Oral Lichen Planus: A Comparative Study

by Sensen Yu, Wansu Sun, Dawei Mi, Siyu Jin, Xing Wu, Baojian Xin, Hengguo Zhang, Yuanyin Wang, Xiaoyu Sun and Xin He

Bioengineering 2024, 11(11), 1159; https://doi.org/10.3390/bioengineering11111159 - 18 Nov 2024

Cited by 4 | Viewed by 1567

Abstract

Early diagnosis of oral lichen planus (OLP) is challenging, which traditionally is dependent on clinical experience and subjective interpretation. Artificial intelligence (AI) technology has been widely applied in objective and rapid diagnoses. In this study, we aim to investigate the potential of AI diagnosis in OLP and evaluate its effectiveness in improving diagnostic accuracy and accelerating clinical decision making. A total of 128 confirmed OLP patients were included, and lesion images from various anatomical sites were collected. The diagnosis was performed using AI platforms, including ChatGPT-4O, ChatGPT (Diagram-Date extension), and Claude Opus, for AI directly identification and AI pre-training identification. After OLP feature training, the diagnostic accuracy of the AI platforms significantly improved, with the overall recognition rates of ChatGPT-4O, ChatGPT (Diagram-Date extension), and Claude Opus increasing from 59%, 68%, and 15% to 77%, 80%, and 50%, respectively. Additionally, the pre-training recognition rates for buccal mucosa reached 94%, 93%, and 56%, respectively. However, the AI platforms performed less effectively when recognizing lesions in less common sites and complex cases; for instance, the pre-training recognition rates for the gums were only 60%, 60%, and 20%, demonstrating significant limitations. The study highlights the strengths and limitations of different AI technologies and provides a reference for future AI applications in oral medicine. Full article

(This article belongs to the Special Issue AI-Powered Diagnosis and Treatment Plans in Dentistry and Orofacial Fields)

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13 pages, 1564 KiB

Open AccessArticle

Research and Application of Deep Learning Models with Multi-Scale Feature Fusion for Lesion Segmentation in Oral Mucosal Diseases

by Rui Zhang, Miao Lu, Jiayuan Zhang, Xiaoyan Chen, Fudong Zhu, Xiang Tian, Yaowu Chen and Yuqi Cao

Bioengineering 2024, 11(11), 1107; https://doi.org/10.3390/bioengineering11111107 - 2 Nov 2024

Cited by 1 | Viewed by 1520

Abstract

Given the complexity of oral mucosal disease diagnosis and the limitations in the precision of traditional object detection methods, this study aims to develop a high-accuracy artificial intelligence-assisted diagnostic approach based on the SegFormer semantic segmentation model. This method is designed to automatically segment lesion areas in white-light images of oral mucosal diseases, providing objective and quantifiable evidence for clinical diagnosis. This study utilized a dataset of oral mucosal diseases provided by the Affiliated Stomatological Hospital of Zhejiang University School of Medicine, comprising 838 high-resolution images of three diseases: oral lichen planus, oral leukoplakia, and oral submucous fibrosis. These images were annotated at the pixel level by oral specialists using Labelme software (v5.5.0) to construct a semantic segmentation dataset. This study designed a SegFormer model based on the Transformer architecture, employed cross-validation to divide training and testing sets, and compared SegFormer models of different capacities with classical segmentation models such as UNet and DeepLabV3. Quantitative metrics including the Dice coefficient and mIoU were evaluated, and a qualitative visual analysis of the segmentation results was performed to comprehensively assess model performance. The SegFormer-B2 model achieved optimal performance on the test set, with a Dice coefficient of 0.710 and mIoU of 0.786, significantly outperforming other comparative algorithms. The visual results demonstrate that this model could accurately segment the lesion areas of three common oral mucosal diseases. The SegFormer model proposed in this study effectively achieves the precise automatic segmentation of three common oral mucosal diseases, providing a reliable auxiliary tool for clinical diagnosis. It shows promising prospects in improving the efficiency and accuracy of oral mucosal disease diagnosis and has potential clinical application value. Full article

(This article belongs to the Special Issue AI-Powered Diagnosis and Treatment Plans in Dentistry and Orofacial Fields)

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18 pages, 52572 KiB

Open AccessArticle

LMVSegRNN and Poseidon3D: Addressing Challenging Teeth Segmentation Cases in 3D Dental Surface Orthodontic Scans

by Tibor Kubík and Michal Španěl

Bioengineering 2024, 11(10), 1014; https://doi.org/10.3390/bioengineering11101014 - 11 Oct 2024

Viewed by 1416

Abstract

The segmentation of teeth in 3D dental scans is difficult due to variations in teeth shapes, misalignments, occlusions, or the present dental appliances. Existing methods consistently adhere to geometric representations, omitting the perceptual aspects of the inputs. In addition, current works often lack evaluation on anatomically complex cases due to the unavailability of such datasets. We present a projection-based approach towards accurate teeth segmentation that operates in a detect-and-segment manner locally on each tooth in a multi-view fashion. Information is spatially correlated via recurrent units. We show that a projection-based framework can precisely segment teeth in cases with anatomical anomalies with negligible information loss. It outperforms point-based, edge-based, and Graph Cut-based geometric approaches, achieving an average weighted IoU score of

0.97122 \pm 0.038

and a Hausdorff distance at 95 percentile of

0.49012 \pm 0.571

mm. We also release Poseidon’s Teeth 3D (Poseidon3D), a novel dataset of real orthodontic cases with various dental anomalies like teeth crowding and missing teeth. Full article

(This article belongs to the Special Issue AI-Powered Diagnosis and Treatment Plans in Dentistry and Orofacial Fields)

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13 pages, 1280 KiB

Open AccessArticle

Exploring the Practical Applications of Artificial Intelligence, Deep Learning, and Machine Learning in Maxillofacial Surgery: A Comprehensive Analysis of Published Works

by Ladislav Czako, Barbora Sufliarsky, Kristian Simko, Marek Sovis, Ivana Vidova, Julia Farska, Michaela Lifková, Tomas Hamar and Branislav Galis

Bioengineering 2024, 11(7), 679; https://doi.org/10.3390/bioengineering11070679 - 3 Jul 2024

Cited by 3 | Viewed by 2761

Abstract

Artificial intelligence (AI), deep learning (DL), and machine learning (ML) are computer, machine, and engineering systems that mimic human intelligence to devise procedures. These technologies also provide opportunities to advance diagnostics and planning in human medicine and dentistry. The purpose of this literature review was to ascertain the applicability and significance of AI and to highlight its uses in maxillofacial surgery. Our primary inclusion criterion was an original paper written in English focusing on the use of AI, DL, or ML in maxillofacial surgery. The sources were PubMed, Scopus, and Web of Science, and the queries were made on the 31 December 2023. The search strings used were “artificial intelligence maxillofacial surgery”, “machine learning maxillofacial surgery”, and “deep learning maxillofacial surgery”. Following the removal of duplicates, the remaining search results were screened by three independent operators to minimize the risk of bias. A total of 324 publications from 1992 to 2023 were finally selected. These were calculated according to the year of publication with a continuous increase (excluding 2012 and 2013) and R² = 0.9295. Generally, in orthognathic dentistry and maxillofacial surgery, AI and ML have gained popularity over the past few decades. When we included the keywords “planning in maxillofacial surgery” and “planning in orthognathic surgery”, the number significantly increased to 7535 publications. The first publication appeared in 1965, with an increasing trend (excluding 2014–2018), with an R² value of 0.8642. These technologies have been found to be useful in diagnosis and treatment planning in head and neck surgical oncology, cosmetic and aesthetic surgery, and oral pathology. In orthognathic surgery, they have been utilized for diagnosis, treatment planning, assessment of treatment needs, and cephalometric analyses, among other applications. This review confirms that the current use of AI and ML in maxillofacial surgery is focused mainly on evaluating digital diagnostic methods, especially radiology, treatment plans, and postoperative results. However, as these technologies become integrated into maxillofacial surgery and robotic surgery in the head and neck region, it is expected that they will be gradually utilized to plan and comprehensively evaluate the success of maxillofacial surgeries. Full article

(This article belongs to the Special Issue AI-Powered Diagnosis and Treatment Plans in Dentistry and Orofacial Fields)

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17 pages, 4127 KiB

Open AccessArticle

Inferior Alveolar Nerve Canal Segmentation on CBCT Using U-Net with Frequency Attentions

by Zhiyang Liu, Dong Yang, Minghao Zhang, Guohua Liu, Qian Zhang and Xiaonan Li

Bioengineering 2024, 11(4), 354; https://doi.org/10.3390/bioengineering11040354 - 5 Apr 2024

Cited by 4 | Viewed by 2076

Abstract

Accurate inferior alveolar nerve (IAN) canal segmentation has been considered a crucial task in dentistry. Failing to accurately identify the position of the IAN canal may lead to nerve injury during dental procedures. While IAN canals can be detected from dental cone beam computed tomography, they are usually difficult for dentists to precisely identify as the canals are thin, small, and span across many slices. This paper focuses on improving accuracy in segmenting the IAN canals. By integrating our proposed frequency-domain attention mechanism in UNet, the proposed frequency attention UNet (FAUNet) is able to achieve

75.55 %

and

81.35 %

in the Dice and surface Dice coefficients, respectively, which are much higher than other competitive methods, by adding only 224 parameters to the classical UNet. Compared to the classical UNet, our proposed FAUNet achieves a

2.39 %

and

2.82 %

gain in the Dice coefficient and the surface Dice coefficient, respectively. The potential advantage of developing attention in the frequency domain is also discussed, which revealed that the frequency-domain attention mechanisms can achieve better performance than their spatial-domain counterparts. Full article

(This article belongs to the Special Issue AI-Powered Diagnosis and Treatment Plans in Dentistry and Orofacial Fields)

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10 pages, 12696 KiB

Open AccessArticle

A Comparative Analysis of Artificial Intelligence and Manual Methods for Three-Dimensional Anatomical Landmark Identification in Dentofacial Treatment Planning

by Hee-Ju Ahn, Soo-Hwan Byun, Sae-Hoon Baek, Sang-Yoon Park, Sang-Min Yi, In-Young Park, Sung-Woon On, Jong-Cheol Kim and Byoung-Eun Yang

Bioengineering 2024, 11(4), 318; https://doi.org/10.3390/bioengineering11040318 - 27 Mar 2024

Cited by 4 | Viewed by 1946

Abstract

With the growing demand for orthognathic surgery and other facial treatments, the accurate identification of anatomical landmarks has become crucial. Recent advancements have shifted towards using three-dimensional radiologic analysis instead of traditional two-dimensional methods, as it allows for more precise treatment planning, primarily relying on direct identification by clinicians. However, manual tracing can be time-consuming, mainly when dealing with a large number of patients. This study compared the accuracy and reliability of identifying anatomical landmarks using artificial intelligence (AI) and manual identification. Thirty patients over 19 years old who underwent pre-orthodontic and orthognathic surgery treatment and had pre-orthodontic three-dimensional radiologic scans were selected. Thirteen anatomical indicators were identified using both AI and manual methods. The landmarks were identified by AI and four experienced clinicians, and multiple ANOVA was performed to analyze the results. The study results revealed minimal significant differences between AI and manual tracing, with a maximum deviation of less than 2.83 mm. This indicates that utilizing AI to identify anatomical landmarks can be a reliable method in planning orthognathic surgery. Our findings suggest that using AI for anatomical landmark identification can enhance treatment accuracy and reliability, ultimately benefiting clinicians and patients. Full article

(This article belongs to the Special Issue AI-Powered Diagnosis and Treatment Plans in Dentistry and Orofacial Fields)

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Review

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15 pages, 3242 KiB

Open AccessReview

Unlocking the Potentials of Large Language Models in Orthodontics: A Scoping Review

by Jie Zheng, Xiaoqian Ding, Jingya Jane Pu, Sze Man Chung, Qi Yong H. Ai, Kuo Feng Hung and Zhiyi Shan

Bioengineering 2024, 11(11), 1145; https://doi.org/10.3390/bioengineering11111145 - 13 Nov 2024

Viewed by 1612

Abstract

(1) Background: In recent years, large language models (LLMs) such as ChatGPT have gained significant attention in various fields, including dentistry. This scoping review aims to examine the current applications and explore potential uses of LLMs in the orthodontic domain, shedding light on how they might improve dental healthcare. (2) Methods: We carried out a comprehensive search in five electronic databases, namely PubMed, Scopus, Embase, ProQuest and Web of Science. Two authors independently screened articles and performed data extraction according to the eligibility criteria, following the PRISMA-ScR guideline. The main findings from the included articles were synthesized and analyzed in a narrative way. (3) Results: A total of 706 articles were searched, and 12 papers were eventually included. The applications of LLMs include improving diagnostic and treatment efficiency in orthodontics as well as enhancing communication with patients. (4) Conclusions: There is emerging research in countries worldwide on the use of LLMs in orthodontics, suggesting an upward trend in their acceptance within this field. However, the potential application of LLMs remains in its early stage, with a noticeable lack of extensive studies and tailored products to address specific clinical needs. Full article

(This article belongs to the Special Issue AI-Powered Diagnosis and Treatment Plans in Dentistry and Orofacial Fields)

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Other

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22 pages, 863 KiB

Open AccessSystematic Review

The Accuracy of Algorithms Used by Artificial Intelligence in Cephalometric Points Detection: A Systematic Review

by Júlia Ribas-Sabartés, Meritxell Sánchez-Molins and Nuno Gustavo d’Oliveira

Bioengineering 2024, 11(12), 1286; https://doi.org/10.3390/bioengineering11121286 - 18 Dec 2024

Cited by 1 | Viewed by 1459

Abstract

The use of artificial intelligence in orthodontics is emerging as a tool for localizing cephalometric points in two-dimensional X-rays. AI systems are being evaluated for their accuracy and efficiency compared to conventional methods performed by professionals. The main objective of this study is to identify the artificial intelligence algorithms that yield the best results for cephalometric landmark localization, along with their learning system. A literature search was conducted across PubMed-MEDLINE, Cochrane, Scopus, IEEE Xplore, and Web of Science. Observational and experimental studies from 2013 to 2023 assessing the detection of at least 13 cephalometric landmarks in two-dimensional radiographs were included. Studies requiring advanced computer engineering knowledge or involving patients with anomalies, syndromes, or orthodontic appliances, were excluded. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Newcastle–Ottawa Scale (NOS) tools. Of 385 references, 13 studies met the inclusion criteria (1 diagnostic accuracy study and 12 retrospective cohorts). Six were high-risk, and seven were low-risk. Convolutional neural networks (CNN)-based AI algorithms showed point localization accuracy ranging from 64.3 to 97.3%, with a mean error of 1.04 mm ± 0.89 to 3.40 mm ± 1.57, within the clinical range of 2 mm. YOLOv3 demonstrated improvements over its earlier version. CNN have proven to be the most effective AI system for detecting cephalometric points in radiographic images. Although CNN-based algorithms generate results very quickly and reproducibly, they still do not achieve the accuracy of orthodontists. Full article

(This article belongs to the Special Issue AI-Powered Diagnosis and Treatment Plans in Dentistry and Orofacial Fields)

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