Publications by authors named "Khalid Ayidh Alqahtani"
Article Synopsis
- The study assessed the knowledge and awareness of dental students and interns in Saudi Arabia about human papillomavirus (HPV) and oral cancer through a web-based survey of 451 participants.
- Results showed that dental interns had significantly better knowledge than undergraduate dental students in identifying oral cancer locations and symptoms, as well as understanding HPV's connection to AIDS and common warts.
- The study concluded that while overall knowledge about HPV and oral cancer is present, it is not optimal, highlighting the need for improved educational programs and training for dental students in Saudi Arabia.
The primary purpose of this study was to accurately assess linear, volumetric and morphological changes of maxillary teeth roots following multi-segments Le Fort I osteotomy. A secondary objective was to assess whether patient- and/or treatment-related factors might influence root remodeling. A total of 60 patients (590 teeth) who underwent combined orthodontic and orthognathic surgery were studied retrospectively.
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- This study focuses on developing a convolutional neural network (CNN) model to automatically segment teeth in intraoral scanned (IOS) images, addressing the limitations of current methods in handling various dental conditions.
- The research utilized 761 IOS images and implemented a 3D U-Net pipeline, which completed tooth segmentation in an average of 31.7 seconds per jaw while achieving a high accuracy score of 91% on the segmentation task.
- Results indicated that the refined AI (R-AI) method was more efficient and reliable than semi-automatic methods, significantly reducing time spent on segmentation and making the online platform a suitable option for clinical use.
The purpose of this study was to report root remodeling/resorption percentages of maxillary teeth following the different maxillary osteotomies; i.e. one-piece, two-pieces, three-pieces Le Fort I, surgically assisted rapid palatal expansion (SARPE).
View Article and Find Full Text PDFObjective: Three-dimensional (3D) quantitative assessment of external root resorption (ERR) following combined orthodontic-orthognathic surgical treatment is vital for ensuring an optimal long-term tooth prognosis. In this era, lack of evidence exists applying automated 3D approaches for assessing ERR. Therefore, this study aimed to validate a protocol for 3D quantification of ERR on cone-beam computed tomography (CBCT) images following combined orthodontic-orthognathic surgical treatment.
View Article and Find Full Text PDFObjective: Tooth segmentation and classification from cone-beam computed tomography (CBCT) is a prerequisite for diagnosis and treatment planning in the majority of digital dental workflows. However, an accurate and efficient segmentation of teeth in the presence of metal artefacts still remains a challenge. Therefore, the following study aimed to validate an automated deep convolutional neural network (CNN)-based tool for the segmentation and classification of teeth with orthodontic brackets on CBCT images.
View Article and Find Full Text PDFImpact of orthognathic surgery on root resorption: A systematic review.
J Stomatol Oral Maxillofac Surg
October 2022
Objective: This systematic review was performed to assess the potential influence of orthognathic surgery on root resorption (RR).
Material And Methods: An electronic search was conducted using PubMed, Web of Science, Cochrane Central and Embase for articles published up to April 2022. Following inclusion and exclusion criteria, a total of six articles were selected that reported on RR following orthognathic surgery.
Objectives: The present study aims to describe the relationship between upper first molar roots and maxillary sinus, for the first time with a truly three-dimensional approach.
Methods: From a retrospective cone-beam computed tomography (CBCT) sample of the upper jaw, a total of 105 upper first molars in contact with maxillary sinus from 74 patients (male 24, female 50, mean age 42) were included in the present study. Segmentation of the upper first molar and maxillary sinus in CBCT was performed utilizing a semiautomatic livewire segmentation tool in MeVisLab v.
Objectives: Automatic tooth segmentation and classification from cone beam computed tomography (CBCT) have become an integral component of the digital dental workflows. Therefore, the aim of this study was to develop and validate a deep learning approach for an automatic tooth segmentation and classification from CBCT images.
Methods: A dataset of 186 CBCT scans was acquired from two CBCT machines with different acquisition settings.