AIMC Topic: Cone-Beam Computed Tomography
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Different machine learning methods based on maxillary sinus in sex estimation for northwestern Chinese Han population.
International journal of legal medicine
May 18, 2024
BACKGROUND & OBJECTIVE: Sex estimation is a critical aspect of forensic expertise. Some special anatomical structures, such as the maxillary sinus, can still maintain integrity in harsh environmental conditions and may be served as a basis for sex es...
An overview of artificial intelligence based applications for assisting digital data acquisition and implant planning procedures.
Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.]
May 17, 2024
OBJECTIVES: To provide an overview of the current artificial intelligence (AI) based applications for assisting digital data acquisition and implant planning procedures.
New perspectives in the differential diagnosis of jaw lesions: Machine learning and inflammatory biomarkers.
Journal of stomatology, oral and maxillofacial surgery
May 7, 2024
This study aimed to assess the diagnostic performance of a machine learning approach that utilized radiomic features extracted from Cone Beam Computer Tomography (CBCT) images and inflammatory biomarkers for distinguishing between Dentigerous Cysts (...
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...
Artificial intelligence vs. semi-automated segmentation for assessment of dental periapical lesion volume index score: A cone-beam CT study.
Computers in biology and medicine
Apr 28, 2024
INTRODUCTION: Cone beam computed tomography periapical volume index (CBCTPAVI) is a categorisation tool to assess periapical lesion size in three-dimensions and predict treatment outcomes. This index was determined using a time-consuming semi-automat...
Accuracy of machine learning in the diagnosis of odontogenic cysts and tumors: a systematic review and meta-analysis.
Oral radiology
Mar 26, 2024
BACKGROUND: The recent impact of artificial intelligence in diagnostic services has been enormous. Machine learning tools offer an innovative alternative to diagnose cysts and tumors radiographically that pose certain challenges due to the near simil...
Surveying the landscape of diagnostic imaging in dentistry's future: Four emerging technologies with promise.
Journal of the American Dental Association (1939)
Mar 23, 2024
BACKGROUND: Advances in digital radiography for both intraoral and panoramic imaging and cone-beam computed tomography have led the way to an increase in diagnostic capabilities for the dental care profession. In this article, the authors provide inf...
Diagnostic capability of artificial intelligence tools for detecting and classifying odontogenic cysts and tumors: a systematic review and meta-analysis.
Oral surgery, oral medicine, oral pathology and oral radiology
Mar 19, 2024
OBJECTIVE: To evaluate the diagnostic capability of artificial intelligence (AI) for detecting and classifying odontogenic cysts and tumors, with special emphasis on odontogenic keratocyst (OKC) and ameloblastoma.
GADNN: a revolutionary hybrid deep learning neural network for age and sex determination utilizing cone beam computed tomography images of maxillary and frontal sinuses.
BMC medical research methodology
Feb 27, 2024
INTRODUCTION: The determination of identity factors such as age and sex has gained significance in both criminal and civil cases. Paranasal sinuses like frontal and maxillary sinuses, are resistant to trauma and can aid profiling. We developed a deep...
Fully automated deep learning model for detecting proximity of mandibular third molar root to inferior alveolar canal using panoramic radiographs.
Oral surgery, oral medicine, oral pathology and oral radiology
Feb 20, 2024
OBJECTIVE: This study endeavored to develop a novel, fully automated deep-learning model to determine the topographic relationship between mandibular third molar (MM3) roots and the inferior alveolar canal (IAC) using panoramic radiographs (PRs).
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