Assessment of deep learning technique for fully automated mandibular segmentation.

Introduction: This study aimed to assess the precision of an open-source, clinician-trained, and user-friendly convolutional neural network-based model for automatically segmenting the mandible.

Methods: A total of 55 cone-beam computed tomography scans that met the inclusion criteria were collected and divided into test and training groups. The MONAI (Medical Open Network for Artificial Intelligence) Label active learning tool extension was used to train the automatic model.

Accuracy of deep learning-based upper airway segmentation.

Introduction: In orthodontic treatments, accurately assessing the upper airway volume and morphology is essential for proper diagnosis and planning. Cone beam computed tomography (CBCT) is used for assessing upper airway volume through manual, semi-automatic, and automatic airway segmentation methods. This study evaluates upper airway segmentation accuracy by comparing the results of an automatic model and a semi-automatic method against the gold standard manual method.

3D evaluation of cranial and dentofacial morphological differences between individuals with mouth breathing and nasal breathing.

Introduction: The present study aimed to identify the morphological differences in cranial and dentofacial structures between individuals with mouth-breathing and nasal-breathing.

Materials And Methods: The study included 120 individuals, 60 each in the nasal breathing (NB) and mouth breathing (MB) groups. 3D stereophotogrammetry, lateral cephalometric radiographs, and intraoral examination results were recorded by the researchers to determine the morphological differences between the MB group and the NB group.

A comparative analysis of AI-based chatbots: Assessing data quality in orthognathic surgery related patient information.

Introduction: The aim of the current study is to evaluate the quality, reliability, readability, and similarity of data provided by different AI-based chatbots in the field of orthognathic surgery.

Materials And Methods: Guidelines on orthognathic surgery were reviewed, and a list of questions for patients to ask chatbots was produced by two reasearchers. The questions were categorized into 'General Information and Procedure' and 'Results and Recovery', with 30 questions in each category.

The Quality of CLP-Related Information for Patients Provided by ChatGPT.

Objective: To assess the quality, reliability, readability, and similarity of the data that a recently created NLP-based artificial intelligence model ChatGPT 4 provides to users in Cleft Lip and Palate (CLP)-related information.

Design: In the evaluation of the responses provided by the OpenAI ChatGPT to the CLP-related 50 questions, several tools were utilized, including the Ensuring Quality Information for Patients (EQIP) tool, Reliability Scoring System (Adapted from DISCERN), Flesh Reading Ease Formula (FRES) and Flesch-Kinkaid Reading Grade Level (FKRGL) formulas, Global Quality Scale (GQS), and Similarity Index with plagiarism-detection tool. Jamovi (The Jamovi Project, 2022, version 2.

The effect of layer thıckness on the accuracy of the dıfferent ın-house clear alıgner attachments.

Objective: The accuracy of the attachments, one of the key components of clear aligner therapy, is important for obtaining more precise tooth movement. The aim of this study was to evaluate the accuracy of the ovoid, hemi-ellipsoid, and vertical rectangular attachments produced by the digital light-processing(DLP) 3-dimensional printing technologies with 25 µm, 75 µm, and 125 µm layer thickness.

Materials And Methods: The ovoid, hemi-ellipsoid, and vertical rectangular attachments were positioned onto the convex surface of the central incisor by the software.

Evaluation of an automated approach for facial midline detection and asymmetry assessment: A preliminary study.

Objective: To examine the level of agreement between the conventional method and a machine-learning approach to facial midline determination and asymmetry assessment.

Settings And Sample Population: The study included a total of 90 samples (53 females; 37 males) with different levels of mandibular asymmetry.

Materials And Methods: Two researchers placed predefined soft tissue landmarks individually on selected facial frontal photographs and created 10 reference lines.