AIMC Topic: Dental Enamel
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From inconsistent annotations to ground truth: Aggregation strategies for annotations of proximal carious lesions in dental imagery.
Journal of dentistry
Mar 30, 2025
OBJECTIVES: Annotating carious lesions on images is challenging. For artificial intelligence (AI) applications, the aggregation of heterogeneous multi-examiner annotations into one single annotation (e.g. via majority voting, MV) is usually needed. W...
The effect of cryopreservation on enamel microcracks - A μCT analysis using a deep learning algorithm.
Cryobiology
Mar 13, 2025
To date, the effect of cryopreservation on microcracks in the dental enamel remains unclear. These enamel microcracks are very thin, at the limit of visibility and their segmentation is beyond the capabilities of traditional image analysis. The objec...
Clinically oriented automatic three-dimensional enamel segmentation via deep learning.
BMC oral health
Jan 24, 2025
BACKGROUND: Establishing accurate, reliable, and convenient methods for enamel segmentation and analysis is crucial for effectively planning endodontic, orthodontic, and restorative treatments, as well as exploring the evolutionary patterns of mammal...
The Influence of Surface Treatment on the Color of Enamel and Dentin: An In Vitro Study Using Machine Learning-Based Analysis.
Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.]
Jan 6, 2025
OBJECTIVE: To investigate how surface treatment affects the color of enamel and dentin, and to evaluate whether the color differences are acceptable.
The application of deep learning in early enamel demineralization detection.
PeerJ
Jan 2, 2025
OBJECTIVE: The study aims to develop a diagnostic model using intraoral photographs to accurately detect and classify early detection of enamel demineralization on tooth surfaces.
External validation of an artificial intelligence-based method for the detection and classification of molar incisor hypomineralisation in dental photographs.
Journal of dentistry
Jul 5, 2024
OBJECTIVES: This ex vivo diagnostic study aimed to externally validate an open-access artificial intelligence (AI)-based model for the detection, classification, localisation and segmentation of enamel/molar incisor hypomineralisation (EH/MIH).
Conquering class imbalances in deep learning-based segmentation of dental radiographs with different loss functions.
Journal of dentistry
May 10, 2024
OBJECTIVE: The imbalanced nature of real-world datasets is an ongoing challenge in the field of machine and deep learning. In medicine and in dentistry, most data samples represent patients not affected by pathologies, and on imagery, pathologic imag...
Machine learning assisted 5-part tooth segmentation method for CBCT-based dental age estimation in adults.
The Journal of forensic odonto-stomatology
Apr 30, 2024
BACKGROUND: The utilization of segmentation method using volumetric data in adults dental age estimation (DAE) from cone-beam computed tomography (CBCT) was further expanded by using current 5-Part Tooth Segmentation (SG) method. Additionally, superv...
Cervical wear pathobiology by robot-simulated 3-year toothbrushing - New methodological approach.
Archives of oral biology
Apr 24, 2024
OBJECTIVES: An ex-vivo study was aimed at (i) programming clinically validated robot three-year random toothbrushing, (ii) evaluating cervical macro- and microwear patterns on all tooth groups of different functional age, (iii) documenting and codifi...
Artificial neural networks reconstruct missing perikymata in worn teeth.
Anatomical record (Hoboken, N.J. : 2007)
Mar 11, 2024
Dental evolutionary studies in hominins are key to understanding how our ancestors and close fossil relatives grew from the early stages of embryogenesis into adults. In a sense, teeth are like an airplane's 'black box' as they record important varia...
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