Artificial Intelligence (AI)-Based Systems for Automatic Skeletal Maturity Assessment through Bone and Teeth Analysis: A Revolution in the Radiological Workflow?

Caloro, Elena; Cè, Maurizio; Gibelli, Daniele; Palamenghi, Andrea; Martinenghi, C; Oliva, Giancarlo; Cellina, Michaela

doi:10.3390/app13063860

Cited by 6 publications

(2 citation statements)

References 117 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although these deep learning models obtained favorable prediction accuracy, interpreting their prediction outcomes is challenging due to their “black box” nature, making the decision-making process difficult to explain [ 17 ]. Most recently, it has been raised that quantitative textural imaging analysis could be utilized to predict the growth of specific organs or the progression of lesions [ 84 , 85 ]. The integration of ML and quantitative imaging feature analysis (i.e., radiomics) may potentially advance the personalized diagnosis and treatment of Class III malocclusion, enhancing the understanding of the underlying mechanisms of maxillofacial growth.…”

Section: Current States and Future Prospectsmentioning

confidence: 99%

Radiographic Imaging for the Diagnosis and Treatment of Patients with Skeletal Class III Malocclusion

Li,

Hung,

et al. 2024

Diagnostics

View full text Add to dashboard Cite

Skeletal Class III malocclusion is one type of dentofacial deformity that significantly affects patients’ facial aesthetics and oral health. The orthodontic treatment of skeletal Class III malocclusion presents challenges due to uncertainties surrounding mandibular growth patterns and treatment outcomes. In recent years, disease-specific radiographic features have garnered interest from researchers in various fields including orthodontics, for their exceptional performance in enhancing diagnostic precision and treatment effect predictability. The aim of this narrative review is to provide an overview of the valuable radiographic features in the diagnosis and management of skeletal Class III malocclusion. Based on the existing literature, a series of analyses on lateral cephalograms have been concluded to identify the significant variables related to facial type classification, growth prediction, and decision-making for tooth extractions and orthognathic surgery in patients with skeletal Class III malocclusion. Furthermore, we summarize the parameters regarding the inter-maxillary relationship, as well as different anatomical structures including the maxilla, mandible, craniofacial base, and soft tissues from conventional and machine learning statistical models. Several distinct radiographic features for Class III malocclusion have also been preliminarily observed using cone beam computed tomography (CBCT) and magnetic resonance imaging (MRI).

show abstract

Section: Current States and Future Prospectsmentioning

confidence: 99%

Radiographic Imaging for the Diagnosis and Treatment of Patients with Skeletal Class III Malocclusion

Li,

Hung,

et al. 2024

Diagnostics

View full text Add to dashboard Cite

show abstract

“…OPT also allows the workup of orthodontic therapy through the assessment of dental and skeletal relationships, crowding investigation, and the identification of missing or extra teeth [1][2][3]. Moreover, OPTs are routinely used to assess bone age [4] and for forensic purposes [5].…”

Section: Introductionmentioning

confidence: 99%

Teeth Segmentation in Panoramic Dental X-ray Using Mask Regional Convolutional Neural Network

Rubiu,

Bologna,

Cellina

et al. 2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

Background and purpose: Accurate instance segmentation of teeth in panoramic dental X-rays is a challenging task due to variations in tooth morphology and overlapping regions. In this study, we propose a new algorithm, for instance, segmentation of the different teeth in panoramic dental X-rays. Methods: An instance segmentation model was trained using the architecture of a Mask Region-based Convolutional Neural Network (Mask-RCNN). The data for the training, validation, and testing were taken from the Tuft dental database (1000 panoramic dental radiographs). The number of the predicted label was 52 (20 deciduous and 32 permanent). The size of the training, validation, and test sets were 760, 190, and 70 images, respectively, and the split was performed randomly. The model was trained for 300 epochs, using a batch size of 10, a base learning rate of 0.001, and a warm-up multistep learning rate scheduler (gamma = 0.1). Data augmentation was performed by changing the brightness, contrast, crop, and image size. The percentage of correctly detected teeth and Dice in the test set were used as the quality metrics for the model. Results: In the test set, the percentage of correctly classified teeth was 98.4%, while the Dice score was 0.87. For both the left mandibular central and lateral incisor permanent teeth, the Dice index result was 0.91 and the accuracy was 100%. For the permanent teeth right mandibular first molar, mandibular second molar, and third molar, the Dice indexes were 0.92, 0.93, and 0.78, respectively, with an accuracy of 100% for all three different teeth. For deciduous teeth, the Dice indexes for the right mandibular lateral incisor, right mandibular canine, and right mandibular first molar were 0.89, 0.91, and 0.85, respectively, with an accuracy of 100%. Conclusions: A successful instance segmentation model for teeth identification in panoramic dental X-ray was developed and validated. This model may help speed up and automate tasks like teeth counting and identifying specific missing teeth, improving the current clinical practice.

show abstract

Charting the growth through intelligence: A SWOC analysis on AI-assisted radiologic bone age estimation

Jani,

Patel

2024

Int J Legal Med

View full text Add to dashboard Cite

Artificial Intelligence (AI)-Based Systems for Automatic Skeletal Maturity Assessment through Bone and Teeth Analysis: A Revolution in the Radiological Workflow?

Cited by 6 publications

References 117 publications

Radiographic Imaging for the Diagnosis and Treatment of Patients with Skeletal Class III Malocclusion

Radiographic Imaging for the Diagnosis and Treatment of Patients with Skeletal Class III Malocclusion

Teeth Segmentation in Panoramic Dental X-ray Using Mask Regional Convolutional Neural Network

Charting the growth through intelligence: A SWOC analysis on AI-assisted radiologic bone age estimation

Contact Info

Product

Resources

About