Najla al Turkestani

University of Michigan | U-M · Department of Cariology, Restorative Sciences and Endodontics

Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative disease characterized by chronic pain and impaired jaw function. The complexity of TMJ OA has hindered the development of prognostic tools, posing a significant challenge in timely, patient-specific management. Addressing this gap, our research employs a comprehensive, mult...

This paper proposes a machine learning model using privileged information (LUPI) and normalized mutual information feature selection method (NMIFS) to build a robust and accurate framework to diagnose patients with Temporomandibular Joint Osteoarthritis (TMJ OA). To build such a model, we employ clinical, quantitative imaging and additional biologi...

Objective(s) This study aims to evaluate the influence of the piezocision surgery in the orthodontic biomechanics, as well as in the magnitude and direction of tooth movement in the mandibular arch using novel artificial intelligence (AI)‐automated tools. Materials and Methods Nineteen patients, who had piezocision performed in the lower arch at t...

This paper presents a novel method for the automatic registration of Intra Oral Scans (IOS). Our approach uses deep learning techniques and alignment algorithms such as the Iterative Closest Point (ICP) to automatically align IOS at different time points for the same subject. For the proposed registration methods; firstly, crown segmentation is per...

Automated clinical decision support systems rely on accurate analysis of three-dimensional (3D) medical and dental images to assist clinicians in diagnosis, treatment planning, intervention, and assessment of growth and treatment effects. However, analyzing longitudinal 3D images requires standardized orientation and registration, which can be labo...

Cleft lip and/or palate (CLP) is the most common congenital craniofacial anomaly and requires bone grafting of the alveolar cleft. This study aimed to develop a novel classification algorithm to assess the severity of alveolar bone defects in patients with CLP using three-dimensional (3D) surface models and to demonstrate through an interpretable a...

This work aimed to evaluate the effect of Semaphorin 4D (SEMA4D) signaling through Plexin B1 on the vasculogenic differentiation of dental pulp stem cells. We assessed the protein expression of SEMA4D and Plexin B1 in dental pulp stem cells (DPSC) from permanent human teeth and stem cells from human exfoliated deciduous (SHED) teeth using Western b...

Objective This manuscript describes strategies for assessment of precision of several diagnostic artificial intelligence (AI) tools in orthodontics, available open-source image analysis platforms, as well as the use of three-dimensional (3D) surface models and superimpositions. Results The advances described in this manuscript present perspectives...

Treatment effects occurring during Class II malocclusion treatment with the clear aligner mandibular advancement protocol were evaluated in two growing patients: one male (12 years, 3 months) and one female (11 years, 9 months). Both patients presented with full cusp Class II molar and canine relationships. Intraoral scans and cone-beam computed to...

Objective: To present and validate an open-source fully automated landmark placement (ALICBCT) tool for cone-beam computed tomography scans. Material and methods: One hundred and forty-three large and medium field of view cone-beam computed tomography (CBCT) were used to train and test a novel approach, called ALICBCT that reformulates landmark...

With the advent of 3D printing and additive manufacturing of dental devices, IntraOral scanners (IOS) have gained wide adoption in dental practices and allowed for efficient workflows in clinical settings. Accurate automatic identification of dental landmarks in IOS is required to aid dental researchers and clinicians to plan and assess tooth posit...

Osteoarthritis of the temporomandibular joint (TMJ OA) is the most common disorder of the TMJ. A clinical decision support (CDS) system designed to detect TMJ OA could function as a useful screening tool as part of regular check-ups to detect early onset. This study implements a CDS concept model based on Random Forest and dubbed RF to predict TMJ...

In the digital dentistry era, new tools, algorithms, data science approaches, and computer applications are available to researchers and clinicians. However, there is also a strong need for better knowledge and understanding of multisource data applications, including three-dimensional imaging information such as cone-beam computed tomography image...

The segmentation of medical and dental images is a fundamental step in automated clinical decision support systems. It supports the entire clinical workflow from diagnosis, therapy planning, intervention, and follow-up. In this paper, we propose a novel tool to accurately process a full-face segmentation in about 5 minutes that would otherwise requ...

Introduction Orthodontists, surgeons, and patients have taken an interest in using clear aligners in combination with orthognathic surgery. This study aimed to evaluate the accuracy of tooth movements with clear aligners during presurgical orthodontics using novel 3-dimensional superimposition techniques. Methods The study sample consisted of 20 p...

Temporomandibular joint osteoarthritis (TMJ OA) is a disease with a multifactorial etiology, involving many pathophysiological processes, and requiring comprehensive assessments to characterize progressive cartilage degradation, subchondral bone remodeling, and chronic pain. This study aimed to integrate quantitative biomarkers of bone texture and...

This paper proposes a machine learning model using privileged information (LUPI) and normalized mutual information feature selection method (NMIFS) to build a robust and accurate framework to diagnose patients with Temporomandibular Joint Osteoarthritis (TMJ OA). To build such a model, we employ clinical, quantitative imaging and additional biologi...

Objective: To compare the transverse dental and skeletal changes in patients treated with bone anchored palatal expander (bone-borne, BB) compared to patients treated with tooth and bone anchored palatal expanders (tooth-bone borne, TBB) using Cone Beam Computer Tomography (CBCT) and 3D image analysis. Methods: The sample comprised 30 patients w...

Introduction The objective was to determine the skeletal and dental changes with microimplant assisted rapid palatal expansion (MARPE) appliances in growing (GR) and nongrowing (NG) patients using cone-beam computed tomography and 3-dimensional imaging analysis. Methods The sample consisted of 25 patients with transverse maxillary discrepancy trea...

Osteoarthritis is a chronic disease that affects the temporomandibular joint (TMJ), causing chronic pain and disability. To diagnose patients suffering from this disease before advanced degradation of the bone, we developed a diagnostic tool called TMJOAI. This machine learning based algorithm is capable of classifying the health status TMJ in of p...

In this paper, machine learning approaches are proposed to support dental researchers and clinicians to study the shape and position of dental crowns and roots, by implementing a Patient Specific Classification and Prediction tool that includes RootCanalSeg and DentalModelSeg algorithms and then merges the output of these tools for intraoral scanni...

Diagnosis of temporomandibular joint (TMJ) Osteoarthritis (OA) before serious degradation of cartilage and subchondral bone occurs can help prevent chronic pain and disability. Clinical, radiomic, and protein markers collected from TMJ OA patients have been shown to be predictive of OA onset. Since protein data can often be unavailable for clinical...

In order to diagnose TMJ pathologies, we developed and tested a novel algorithm, MandSeg, that combines image processing and machine learning approaches for automatically segmenting the mandibular condyles and ramus. A deep neural network based on the U-Net architecture was trained for this task, using 109 cone-beam computed tomography (CBCT) scans...

This paper aims to combine two different imaging techniques to create an accurate 3D model representation of root canals and dental crowns. We combine Cone-Beam Computed Tomography (CBCT) (root canals) and Intra Oral Scans (IOS) (dental crowns). The Root Canal Segmentation algorithm relies on a U-Net architecture with 2D sliced images from CBCT sca...

Multimodal data allows supervised learning while considering multiple complementary views of a problem, improving final diagnostic performance of trained models. Data modalities that are missing or difficult to obtain in clinical situations can still be incorporated into model training using the learning using privileged information (LUPI) framewor...

Advancements in technology and data collection generated immense amounts of information from various sources such as health records, clinical examination, imaging, medical devices, as well as experimental and biological data. Proper management and analysis of these data via high‐end computing solutions, artificial intelligence and machine learning...

With the exponential growth of computational systems and increased patient data acquisition, dental research faces new challenges to manage a large quantity of information. For this reason, data science approaches are needed for the integrative diagnosis of multifactorial diseases, such as Temporomandibular joint (TMJ) Osteoarthritis (OA). The Data...

This study investigated the effect of bulk-fill composites on proximal contact tightness (PCT) of composite restorations using different matrix systems. 150/standardized-MO-ivorine cavity preparations were divided into 5 groups; Smart Dentin Replacement (SDR), SonicFill (SF), Tetric EvoCeram Bulk-Fill (TEB), G-ænial Universal Flo (GF) and Tetric Ev...