Accuracy of one-step automated orthodontic diagnosis model using a convolutional neural network and lateral cephalogram images with different qualities obtained from nationwide multi-hospitals

Artificial Intelligence in Dentistry: A Narrative Review of Diagnostic and Therapeutic Applications

Advancements in digital and precision medicine have fostered the rapid development of artificial intelligence (AI) applications, including machine learning, artificial neural networks (ANN), and deep learning, within the field of dentistry, particularly in imaging diagnosis and treatment. This review examines the progress of AI across various domains of dentistry, focusing on its role in enhancing diagnostics and optimizing treatment for oral diseases such as endodontic disease, periodontal disease, oral implantology, orthodontics, prosthodontic treatment, and oral and maxillofacial surgery. Additionally, it discusses the emerging opportunities and challenges associated with these technologies. The findings indicate that AI can be effectively utilized in numerous aspects of oral healthcare, including prevention, early screening, accurate diagnosis, treatment plan design assistance, treatment execution, follow-up monitoring, and prognosis assessment. However, notable challenges persist, including issues related to inaccurate data annotation, limited capability for fine-grained feature expression, a lack of universally applicable models, potential biases in learning algorithms, and legal risks pertaining to medical malpractice and data privacy breaches. Looking forward, future research is expected to concentrate on overcoming these challenges to enhance the accuracy and applicability of AI in diagnosing and treating oral diseases. This review aims to provide a comprehensive overview of the current state of AI in dentistry and to identify pathways for its effective integration into clinical practice.

Development of a diagnostic classification model for lateral cephalograms based on multitask learning

OBJECTIVES

This study aimed to develop a cephalometric classification method based on multitask learning for eight diagnostic classifications.

METHODS

This study was retrospective. A total of 3,310 lateral cephalograms were collected to construct a dataset. Eight clinical classifications were employed, including sagittal and vertical skeletal facial patterns, maxillary and mandibular anteroposterior positions, inclinations of upper and lower incisors, as well as their anteroposterior positions. The images were manually annotated for initially classification, which was verified by senior orthodontists. The data were randomly divided into training, validation, and test sets at a ratio of approximately 8:1:1. The multitask learning classification model was constructed based on the ResNeXt50_32 × 4d network and consisted of shared layers and task-specific layers. The performance of the model was evaluated using classification accuracy, precision, sensitivity, specificity and area under the curve (AUC).

RESULTS

This model could perform eight clinical diagnostic classifications on cephalograms within an average of 0.0096 s. The accuracy of the six classifications was 0.8-0.9, and the accuracy of the two classifications was 0.75-0.8. The overall AUC values for each classification exceeded 0.9.

CONCLUSIONS

An automatic diagnostic classification model for lateral cephalograms was established based on multitask learning to achieve simultaneous classification of eight common clinical diagnostic items. The multitask learning model achieved better classification performance and reduced the computational costs, providing a novel perspective and reference for addressing such problems.

Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms

BACKGROUND

Malocclusion, characterized by dental misalignment and improper occlusal relationships, significantly impacts oral health and daily functioning, with a global prevalence of 56%. Lateral cephalogram is a crucial diagnostic tool in orthodontic treatment, providing insights into various structural characteristics.

METHODS

This study introduces a pre-training approach using multi-center lateral cephalograms for self-supervised learning, aimed at improving model generalization across diverse clinical data domains. Additionally, a multi-attribute classification network is proposed, leveraging attribute correlations to optimize parameters and enhance classification performance.

RESULTS

Comprehensive evaluation on both public and clinical datasets showcases the superiority of the proposed framework, achieving an impressive average accuracy of 90.02%. The developed Self-supervised Pre-training and Multi-Attribute (SPMA) network achieves a best match ratio (MR) score of 71.38% and a low Hamming loss (HL) of 0.0425%, demonstrating its efficacy in orthodontic diagnosis from lateral cephalograms.

CONCLUSIONS

This work contributes significantly to advancing automated diagnostic tools in orthodontics, addressing the critical need for accurate and efficient malocclusion diagnosis. The outcomes not only improve the efficiency and accuracy of diagnosis, but also have the potential to reduce healthcare costs associated with orthodontic treatments.

Artificial intelligence for orthodontic diagnosis and treatment planning: A scoping review

OBJECTIVES

To provide an overview of artificial intelligence (AI) applications in orthodontic diagnosis and treatment planning, and to evaluate whether AI improves accuracy, reliability, and time efficiency compared to expert-based manual approaches, while highlighting its current limitations.

DATA

This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist.

SOURCES

An electronic search was performed on PubMed, Web of Science, and Embase electronic databases. Additional studies were identified from Google Scholar and by hand searching through included studies. The search was carried out until June 2023 without restriction of language and publication year.

STUDY SELECTION

After applying the selection criteria, 71 articles were included in the review. The main research areas were classified into three domains based on the purpose of AI: diagnostics (n = 29), landmark identification (n = 20) and treatment planning (n = 22).

CONCLUSION

This scoping review shows that AI can be used in various orthodontic diagnosis and treatment planning applications, with anatomical landmark detection being the most studied domain. While AI shows potential in improving time efficiency and reducing operator variability, the accuracy and reliability have not yet consistently surpassed those of expert clinicians. At all moments, human supervision remains essential. Further advances and optimizations are necessary to strive towards automated patient-specific treatment planning.

CLINICAL SIGNIFICANCE

AI in orthodontics has shown its ability to serve as a decision-support system, thereby enhancing the efficiency of diagnostics and treatment planning within orthodontics digital workflow."

Accuracy of artificial intelligence-assisted growth prediction in skeletal Class I preadolescent patients using serial lateral cephalograms for a 2-year growth interval

OBJECTIVE

To investigate the accuracy of artificial intelligence-assisted growth prediction using a convolutional neural network (CNN) algorithm and longitudinal lateral cephalograms (Lat-cephs).

MATERIALS AND METHODS

A total of 198 Japanese preadolescent children, who had skeletal Class I malocclusion and whose Lat-cephs were available at age 8 years (T0) and 10 years (T1), were allocated into the training, validation, and test phases (n = 161, n = 17, n = 20). Orthodontists and the CNN model identified 28 hard-tissue landmarks (HTL) and 19 soft-tissue landmarks (STL). The mean prediction error values were defined as 'excellent,' 'very good,' 'good,' 'acceptable,' and 'unsatisfactory' (criteria: 0.5 mm, 1.0 mm, 1.5 mm, and 2.0 mm, respectively). The degree of accurate prediction percentage (APP) was defined as 'very high,' 'high,' 'medium,' and 'low' (criteria: 90%, 70%, and 50%, respectively) according to the percentage of subjects that showed the error range within 1.5 mm.

RESULTS

All HTLs showed acceptable-to-excellent mean PE values, while the STLs Pog', Gn', and Me' showed unsatisfactory values, and the rest showed good-to-acceptable values. Regarding the degree of APP, HTLs Ba, ramus posterior, Pm, Pog, B-point, Me, and mandibular first molar root apex exhibited low APPs. The STLs labrale superius, lower embrasure, lower lip, point of lower profile, B', Pog,' Gn' and Me' also exhibited low APPs. The remainder of HTLs and STLs showed medium-to-very high APPs.

CONCLUSION

Despite the possibility of using the CNN model to predict growth, further studies are needed to improve the prediction accuracy in HTLs and STLs of the chin area.

Accuracy and clinical validity of automated cephalometric analysis using convolutional neural networks

BACKGROUND

This study aimed to assess the error range of cephalometric measurements based on the landmarks detected using cascaded CNNs and determine how horizontal and vertical positional errors of individual landmarks affect lateral cephalometric measurements.

METHODS

In total, 120 lateral cephalograms were obtained consecutively from patients (mean age, 32.5 ± 11.6) who visited the Asan Medical Center, Seoul, Korea, for orthodontic treatment between 2019 and 2021. An automated lateral cephalometric analysis model previously developed from a nationwide multi-centre database was used to digitize the lateral cephalograms. The horizontal and vertical landmark position error attributable to the AI model was defined as the distance between the landmark identified by the human and that identified by the AI model on the x- and y-axes. The differences between the cephalometric measurements based on the landmarks identified by the AI model vs those identified by the human examiner were assessed. The association between the lateral cephalometric measurements and the positioning errors in the landmarks comprising the cephalometric measurement was assessed.

RESULTS

The mean difference in the angular and linear measurements based on AI vs human landmark localization was .99 ± 1.05°, and .80 ± .82 mm, respectively. Significant differences between the measurements derived from AI-based and human localization were observed for all cephalometric variables except SNA, pog-Nperp, facial angle, SN-GoGn, FMA, Bjork sum, U1-SN, U1-FH, IMPA, L1-NB (angular) and interincisal angle.

CONCLUSIONS

The errors in landmark positions, especially those that define reference planes, may significantly affect cephalometric measurements. The possibility of errors generated by automated lateral cephalometric analysis systems should be considered when using such systems for orthodontic diagnoses.

Orthognathic surgical planning using graph CNN with dual embedding module: External validations with multi-hospital datasets

BACKGROUND AND OBJECTIVE

Despite recent development of AI, prediction of the surgical movement in the maxilla and mandible by OGS might be more difficult than that of tooth movement by orthodontic treatment. To evaluate the prediction accuracy of the surgical movement using pairs of pre-(T0) and post-surgical (T1) lateral cephalograms (lat-ceph) of orthognathic surgery (OGS) patients and dual embedding module-graph convolution neural network (DEM-GCNN) model.

METHODS

599 pairs from 3 institutions were used as training, internal validation, and internal test sets and 201 pairs from other 6 institutions were used as external test set. DEM-GCNN model (IEM, learning the lat-ceph images; LTEM, learning the landmarks) was developed to predict the amount and direction of surgical movement of ANS and PNS in the maxilla and B-point and Md1crown in the mandible. The distance between T1 landmark coordinates actually moved by OGS (ground truth) and predicted by DEM-GCNN model and pre-existed CNN-based Model-C (learning the lat-ceph images) was compared.

RESULTS

In both internal and external tests, DEM-GCNN did not exhibit significant difference from ground truth in all landmarks (ANS, PNS, B-point, Md1crown, all P > 0.05). When the accumulated successful detection rate for each landmark was compared, DEM-GCNN showed higher values than Model-C in both the internal and external tests. In violin plots exhibiting the error distribution of the prediction results, both internal and external tests showed that DEM-GCNN had significant performance improvement in PNS, ANS, B-point, Md1crown than Model-C. DEM-GCNN showed significantly lower prediction error values than Model-C (one-jaw surgery, B-point, Md1crown, all P < 0.005; two-jaw surgery, PNS, ANS, all P < 0.05; B point, Md1crown, all P < 0.005).

CONCLUSION

We developed a robust OGS planning model with maximized generalizability despite diverse qualities of lat-cephs from 9 institutions.