@article{Bianchi2023j,

title = {Interpretable artificial intelligence for classification of alveolar bone defect in patients with cleft lip and palate. },

author = {Miranda F and Choudhari V and Barone S and Anchling L and Hutin N and Gurgel M and et al},

url = {https://doi.org/10.1038/s41598-023-43125-7},

doi = {10.1038/s41598-023-43125-7},

year  = {2023},

date = {2023-09-22},

journal = {Scientific Reports},

volume = {15861},

abstract = {Cleft lip and/or palate (CLP) is the most common congenital craniofacial anomaly and requires bone grafting of the alveolar cleft. This study aimed to develop a novel classification algorithm to assess the severity of alveolar bone defects in patients with CLP using three-dimensional (3D) surface models and to demonstrate through an interpretable artificial intelligence (AI)-based algorithm the decisions provided by the classifier. Cone-beam computed tomography scans of 194 patients with CLP were used to train and test the performance of an automatic classification of the severity of alveolar bone defect. The shape, height, and width of the alveolar bone defect were assessed in automatically segmented maxillary 3D surface models to determine the ground truth classification index of its severity. The novel classifier algorithm renders the 3D surface models from different viewpoints and captures 2D image snapshots fed into a 2D Convolutional Neural Network. An interpretable AI algorithm was developed that uses features from each view and aggregated via Attention Layers to explain the classification. The precision, recall and F-1 score were 0.823, 0.816, and 0.817, respectively, with agreement ranging from 97.4 to 100% on the severity index within 1 group difference. The new classifier and interpretable AI algorithm presented satisfactory accuracy to classify the severity of alveolar bone defect morphology using 3D surface models of patients with CLP and graphically displaying the features that were considered during the deep learning model's classification decision.},

keywords = {3D landmark identification, alveolar bone defect, artificial intelligence, cleft lip, cleft lip and palate},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2023g,

title = {Artificial Intelligence Applications in Dentistry},

author = {Bianchi J},

url = {https://doi.org/10.1080/19424396.2023.2204566},

year  = {2023},

date = {2023-05-31},

journal = {Journal of the California Dental Association },

volume = {51},

issue = {1},

keywords = {artificial intelligence, CHAT-GPT, DALL-E AI system, dentistry, OpenAI},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2023f,

title = {Artificial intelligence applications in orthodontics. },

author = {Miranda F and Barone S and Gillot M and Baquero B and Anchling L and Hutlin B and et al},

url = {https://doi.org/10.1080/19424396.2023.2195585},

year  = {2023},

date = {2023-04-13},

urldate = {2023-04-13},

journal = {Journal of the California Dental Association },

volume = {51},

issue = {1},

abstract = {Objective

This manuscript describes strategies for assessment of precision of several diagnostic artificial intelligence (AI) tools in orthodontics, available open-source image analysis platforms, as well as the use of three-dimensional (3D) surface models and superimpositions.



Results

The advances described in this manuscript present perspectives on the controversies of whether AI is smarter than clinicians and may replace human clinical decisions. A thorough orthodontic diagnosis requires comprehensive 3D analysis of the interrelationships among the dentition, craniofacial skeleton and soft tissues. Forecasts have indicated that 3D printing technology will provide more than 60% of all dental treatment needs by 2025, and orthodontic companies as well as remote monitoring companies are already using AI technology, being it essential that the clinicians are prepared and knowledgeable with the technology advances now available.



Conclusions

The AI applications in orthodontics rely on the implementation into diagnostic image records, data analysis for clinical practice and research applications. Continuous training and validation of the AI orthodontic image tools are essential for improving the performance and generalizability of these methods.},

keywords = {artificial intelligence, imaging, orthodontics, three-dimensional},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2022d,

title = {Quantitative bone imaging biomarkers and joint space analysis of the articular Fossa in temporomandibular joint osteoarthritis using artificial intelligence models},

author = {Mackie T and Al Turkestani N and Bianchi J and Li T and Ruellas A and Gurgel M and Benavides E and Soki F and Cevidanes L},

url = {https://www.frontiersin.org/articles/10.3389/fdmed.2022.1007011/full},

doi = {https://doi.org/10.3389/fdmed.2022.1007011},

year  = {2022},

date = {2022-09-19},

journal = {Frontiers in Dental Medicine},

abstract = {Temporomandibular joint osteoarthritis (TMJ OA) is a disease with a multifactorial etiology, involving many pathophysiological processes, and requiring comprehensive assessments to characterize progressive cartilage degradation, subchondral bone remodeling, and chronic pain. This study aimed to integrate quantitative biomarkers of bone texture and morphometry of the articular fossa and joint space to advance the role of imaging phenotypes for diagnosis of Temporomandibular Joint Osteoarthritis (TMJ OA) in early to moderate stages by improving the performance of machine-learning algorithms to detect TMJ OA status. Ninety-two patients were prospectively enrolled (184 h-CBCT scans of the right and left mandibular condyles), divided into two groups: 46 control and 46 TMJ OA subjects. No significant difference in the articular fossa radiomic biomarkers was found between TMJ OA and control patients. The superior condyle-to-fossa distance (p < 0.05) was significantly smaller in diseased patients. The interaction effects of the articular fossa radiomic biomarkers enhanced the performance of machine-learning algorithms to detect TMJ OA status. The LightGBM model achieved an AUC 0.842 to diagnose the TMJ OA status with Headaches and Range of Mouth Opening Without Pain ranked as top features, and top interactions of VE-cadherin in Serum and Angiogenin in Saliva, TGF-β1 in Saliva and Headaches, Gender and Muscle Soreness, PA1 in Saliva and Range of Mouth Opening Without Pain, Lateral Condyle Grey Level Non-Uniformity and Lateral Fossa Short Run Emphasis, TGF-β1 in Serum and Lateral Fossa Trabeculae number, MMP3 in Serum and VEGF in Serum, Headaches and Lateral Fossa Trabecular spacing, Headaches and PA1 in Saliva, and Headaches and BDNF in Saliva. Our preliminary results indicate that condyle imaging features may be more important in regards to main effects, but the fossa imaging features may have a larger contribution in terms of interaction effects. More studies are needed to optimize and further enhance machine-learning algorithms to detect early markers of disease, improve prediction of disease progression and severity to ultimately better serve clinical decision support systems in the treatment of patients with TMJ OA.},

keywords = {articular fossa, artificial intelligence, hr-CBCT, imaging biomarkers, joint space, temporomandibular osteoarthritis},

pubstate = {published},

tppubtype = {article}

}