@article{nokey,

title = {Leveraging Generative Artificial Intelligence in Teaching, Scholarship and Dental Education: Use Cases and Reflections},

author = {Jonas Bianchi and Meixun Zheng},

url = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/ocr.12949?getft_integrator=scopus&utm_source=scopus},

doi = {10.1111/ocr.12949},

year  = {2025},

date = {2025-05-30},

urldate = {2025-05-30},

journal = {Orthodontics and Craniofacial Research},

pages = {1-8},

abstract = {The objective of this narrative review is to explore the role of generative artificial intelligence (genAI) in dental education, highlighting its emerging applications, potential benefits and implementation challenges. Since the launch of ChatGPT in 2022, genAI tools have gained traction in academic and clinical settings, enabling content generation, translation, summarisation, 

exam preparation and basic clinical planning. This review presents a series of illustrative use cases demonstrating how genAI has been integrated into teaching, research and clinical workflows in dental and orthodontic training. Each example underscores how AI can support faculty in course design, assist students with learning complex concepts and provide real time feedback for exam analysis and academic writing. However, the implementation of genAI is not without limitations. The review addresses common concerns, including misinformation, data privacy, fabricated references and ethical use in clinical contexts. Although the benefits of genAI are promising, this review emphasises the importance of human oversight and institutional policies to guide ethical and effective use. In conclusion, genAI offers valuable support in dental education when used responsibly. Continued dialogue among educators, students and policymakers is essential to ensure that AI tools are integrated thoughtfully 

and equitably into academic practice.},

keywords = {artificial intelligence, dental education, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{Kim2025,

title = {Craniofacial growth prediction models based on cephalometric landmarks in Korean and American children},

author = {Jong-Hak Kim and Jun-Ho Moon and Jeffrey Roseth and Heeyeon Suh and Heesoo Oh and Shin-Jae Lee},

url = {https://angle-orthodontist.kglmeridian.com/view/journals/angl/95/2/article-p219.xml},

doi = {10.2319/052324-399.1},

year  = {2025},

date = {2025-01-13},

journal = {The Angle Orthodontist},

volume = {95},

issue = {2},

pages = {219-226},

abstract = {Objectives: To compare differences in craniofacial growth prediction results for Korean and American children according to growth prediction models developed using Korean and American longitudinal growth data. Materials and Methods: Growth prediction models based on cephalometric landmarks were built for each population using longitudinally taken lateral cephalograms of Korean children and American children of northern European origin. The sample sizes of the serial datasets were 679 and 1257 for Korean and American children, respectively. On each cephalogram, 78 cephalometric landmarks were identified. The prediction models were based on the partial least squares method with 160 input and 154 output variables. For each group, growth was predicted by applying the prediction models developed using data from the same and different populations. The growth prediction results were compared and analyzed. Results: The growth prediction results obtained with the prediction model developed using data from the same population were more accurate (P<.0001). The results distinctively visualized the discrepancies in the growth prediction results if different population types were not considered. Conclusions: Applying a growth prediction model generated using data from the same population may be desirable. (Angle Orthod. 2025;95:219–226.)},

keywords = {artificial intelligence, Growth prediction, Longitudinal studies, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2023c,

title = {Automatic landmark identification in cone‐beam computed tomography. },

author = {Gillot M and Miranda F and Baquero B and Ruellas A and Gurgel M and Al Turkestani N and et al},

url = {https://pubmed.ncbi.nlm.nih.gov/36811276/},

doi = {10.1111/ocr.12642},

year  = {2023},

date = {2023-11-26},

journal = {Orthod Craniofac Res},

volume = {26},

issue = {4},

pages = {560-567},

abstract = {Objective: To present and validate an open-source fully automated landmark placement (ALICBCT) tool for cone-beam computed tomography scans.



Materials and methods: One hundred and forty-three large and medium field of view cone-beam computed tomography (CBCT) were used to train and test a novel approach, called ALICBCT that reformulates landmark detection as a classification problem through a virtual agent placed inside volumetric images. The landmark agents were trained to navigate in a multi-scale volumetric space to reach the estimated landmark position. The agent movements decision relies on a combination of DenseNet feature network and fully connected layers. For each CBCT, 32 ground truth landmark positions were identified by 2 clinician experts. After validation of the 32 landmarks, new models were trained to identify a total of 119 landmarks that are commonly used in clinical studies for the quantification of changes in bone morphology and tooth position.



Results: Our method achieved a high accuracy with an average of 1.54 ± 0.87 mm error for the 32 landmark positions with rare failures, taking an average of 4.2 second computation time to identify each landmark in one large 3D-CBCT scan using a conventional GPU.



Conclusion: The ALICBCT algorithm is a robust automatic identification tool that has been deployed for clinical and research use as an extension in the 3D Slicer platform allowing continuous updates for increased precision.},

keywords = {anatomic landmarks, fiducial markers, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2023e,

title = {Integrative Risk Predictors of Temporomandibular Joint Osteoarthritis Progression.},

author = {Cai L and Al Turkestani N and Cevidanes L and Bianchi J and Gurgel M and Najarian K and et al},

url = {https://doi.org/10.1117/12.2651940},

doi = {10.1117/12.2651940},

year  = {2023},

date = {2023-04-03},

journal = {Semantic Scholar},

abstract = {In this paper we propose feature selection and machine learning approaches to identify a combination of features for risk prediction of Temporomandibular Joint (TMJ) disease progression. In a sample of 32 TMJ osteoarthritis and 38 controls, feature selection of 5 clinical comorbidities, 43 quantitative imaging, 28 biological features and was performed using Maximum Relevance Minimum Redundancy, Chi-Square and Least Absolute Shrinkage and Selection Operator (LASSO) and Recursive Feature Elimination. We compared the performance of learning using concave and convex kernels (LUCCK), Support Vector Machine (SVM) and Random Forest (RF) approaches to predict disease cure/improvement or persistence/worsening. We show that the SVM model using LASSO achieves area under the curve (AUC), sensitivity and precision of 0.92±0.08, 0.85±0.19 and 0.76 ±0.18, respectively. Baseline levels of headaches, lower back pain, restless sleep, muscle soreness, articular fossa bone surface/bone volume and trabecular separation, condylar High Gray Level Run Emphasis and Short Run High Gray Level Emphasis, saliva levels of 6Ckine, Osteoprotegerin (OPG) and Angiogenin, and serum levels of 6ckine and Brain Derived Neurotrophic Factor (BDNF) were the most frequently occurring features to predict more severe TMJ osteoarthritis prognosis.

},

keywords = {machine learning, temporomandibular joint},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2022e,

title = {Osteoarthritis Diagnosis Integrating Whole Joint Radiomics and Clinical Features for Robust Learning Models using Biological Privileged Information},

author = {Al Turkestani N and Cai L and Cevidanes L and Bianchi J and Zhang W and Gurgel M and Gillot M and Baguero B and Najarian K and Soroushmehr R },

url = {https://www.researchsquare.com/article/rs-1840348/v1},

doi = {https://doi.org/10.21203/rs.3.rs-1840348/v1},

year  = {2022},

date = {2022-07-14},

journal = {Research Square},

abstract = {This paper proposes a machine learning model using privileged information (LUPI) and normalized mutual information feature selection method (NMIFS) to build a robust and accurate framework to diagnose patients with Temporomandibular Joint Osteoarthritis (TMJ OA). To build such a model, we employ clinical, quantitative imaging and additional biological markers as privileged information. We show that clinical features play a leading role in the TMJ OA diagnosis and quantitative imaging features, extracted from cone-beam computerized tomography (CBCT) scans, improve the model performance. As the proposed LUPI model employs biological data in the training phase (which boosted the model performance), this data is unnecessary for the testing stage, indicating the model can be widely used even when only clinical and imaging data are collected. The model was validated using 5-fold stratified cross-validation with hyperparameter tuning to avoid the bias of data splitting. Our method achieved an AUC, specificity and precision of 0.81, 0.79 and 0.77, respectively.},

keywords = {feature selection, machine learning, osteoarthritis, temporomandibular joint},

pubstate = {published},

tppubtype = {article}

}