AIMC Topic: Dentition, Mixed

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Accuracy of AI-based binary classification for detecting malocclusion in the mixed dentition stage.

PloS one
BACKGROUND: Malocclusion is a common anomaly and is frequently observed in children and adults. Early detection and treatment of malocclusion is necessary to prevent and minimize complications. Therefore, developing a tool to check dentition at an ea...

Predictive variables analysis for the tongue crib treatment of anterior crossbite in mixed dentition.

BMC oral health
OBJECTIVE: This study aimed to identify key prognostic variables and to develop and validate a clinical prediction model for pre-treatment assessment of tongue crib applicability.

Deep learning approach for tooth numbering and restoration detection on pediatric periapical radiographs in mixed dentition.

Clinical oral investigations
OBJECTIVES: Accurate tooth numbering and restoration detection on periapical radiographs in mixed dentition are critical to the treatment planning process. They also improve the speed and accuracy of treatment processes by automating the early diagno...

A novel deep learning-based model for automated tooth detection and numbering in mixed and permanent dentition in occlusal photographs.

BMC oral health
BACKGROUND: While artificial intelligence-driven approaches have shown great promise in dental diagnosis and treatment planning, most research focuses on dental radiographs. Only three studies have explored automated tooth numbering in oral photograp...

Artificial intelligence system for automatic tooth detection and numbering in the mixed dentition in CBCT.

European journal of paediatric dentistry
AIM: To evaluate the effectiveness and accuracy of artificial intelligence (AI) by automating tooth segmentation in CBCT volumes of paediatric patients with mixed dentition, using nnU-Netv2 algorithm.

Fully automated method for three-dimensional segmentation and fine classification of mixed dentition in cone-beam computed tomography using deep learning.

Journal of dentistry
OBJECTIVE: To establish a high-precision, automated model using deep learning for the fine classification and three-dimensional (3D) segmentation of mixed dentition in cone-beam computed tomography (CBCT) images.

A deep-learning system for diagnosing ectopic eruption.

Journal of dentistry
OBJECTIVES: To construct a diagnostic model for mixed dentition using a multistage deep-learning network to predict potential ectopic eruption in permanent teeth by integrating dentition segmentation into the process of automatic classification of de...

YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition.

BMC medical imaging
OBJECTIVES: In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and...

Automatic segmentation and detection of ectopic eruption of first permanent molars on panoramic radiographs based on nnU-Net.

International journal of paediatric dentistry
AIM: The purpose of this research was to present an artificial intelligence (AI) model, which can automatically segment and detect ectopic eruption of first permanent molars (EMMs) in early mixed dentition on panoramic radiographs using the no-new-Ne...

Detecting the presence of supernumerary teeth during the early mixed dentition stage using deep learning algorithms: A pilot study.

International journal of paediatric dentistry
BACKGROUND: Supernumerary teeth are a common anomaly and are frequently observed in paediatric patients. To prevent or minimize complications, early diagnosis and treatment is ideal in children with supernumerary teeth.