This study evaluated the use of a deep-learning approach for automated detection and numbering deciduous teeth in children as depicted on panoramic radiographs.An artificial intelligence (AI) algorithm (CranioCatch, Eskisehir-Turkey) using Faster R-CNN Inception v2 (COCO) models were developed to automatically detect number seen pediatric radiographs. The was trained tested total 421 images. System performance assessed confusion matrix.The AI system successful detecting sensitivity precision...

Abstract Background Panoramic radiography is an imaging method for displaying maxillary and mandibular teeth together with their supporting structures. frequently used in dental due to its relatively low radiation dose, short time, burden the patient. We verified diagnostic performance of artificial intelligence (AI) system based on a deep convolutional neural network detect number panoramic radiographs. Methods The data set included 2482 anonymized radiographs from adults archive Eskisehir...

The purpose of the paper was assessment success an artificial intelligence (AI) algorithm formed on a deep-convolutional neural network (D-CNN) model for segmentation apical lesions dental panoramic radiographs. A total 470 anonymized radiographs were used to progress D-CNN AI based U-Net (CranioCatch, Eskisehir, Turkey) lesions. obtained from Radiology Archive Department Oral and Maxillofacial Faculty Dentistry Eskisehir Osmangazi University. implemented with PyTorch (version 1.4.0) In test...

Radiological examination has an important place in dental practice, and it is frequently used intraoral imaging. The correct numbering of teeth on radiographs a routine practice that takes time for the dentist. This study aimed to propose automatic detection system bitewing images using faster Region-based Convolutional Neural Networks (R-CNN) method.The included 1125 bite-wing patients who attended Faculty Dentistry Ordu University from 2018 2019. A R-CNN advanced object identification...

To evaluate the effectiveness and accuracy of artificial intelligence (AI) by automating tooth segmentation in CBCT volumes paediatric patients with mixed dentition, using nnU-Netv2 algorithm. Identifying numbering teeth, initial step treatment planning, demands an efficient method. AI models offer a promising approach dentition period play valuable role dentists' planning terms time effort.

Objectives: The aim of this study was to detect alveolar bone loss from dental panoramic radiographic images using artificial intelligence systems. Material and Methods: A total 2276 were used in study. While 1137 them belong cases with destruction, 1139 periodontally healthy. dataset is divided into three parts as training (n=1856) , validation (n=210) testing set (n= 210). All the data resized 1472x718 pixels before training. random sequence created open-source python programming language...

Objectives: The present study aimed to evaluate the performance of a Faster Region-based Convolutional Neural Network (R-CNN) algorithm for tooth detection and numbering on periapical images. Methods: data sets 1686 randomly selected radiographs patients were collected retrospectively. A pre-trained model (GoogLeNet Inception v3 CNN) was employed pre-processing, transfer learning techniques applied set training. consisted of: (1) Jaw classification model, (2) Region models, (3) Final using...

Segmentation of acute pulmonary embolism in computed tomography angiography using the deep learning methodIntroduction: Pulmonary is a type thromboembolism seen main artery and its branches.This study aimed to diagnose method tomographic (CTPA) perform segmentation data. Materials Methods:The CTPA images patients diagnosed with who underwent scheduled imaging were retrospectively evaluated.After data collection, areas that as embolisms axial section segmented.The dataset was divided into...

Every year, lung cancer contributes to a high percentage deaths in the world. Early detection of is important for its effective treatment, and non-invasive rapid methods are usually used diagnosis.In this study, we aimed detect using deep learning determine contribution classification carcinoma convolutional neural network (CNN).A total 301 patients diagnosed with pathologies our hospital were included study. In thorax, Computed Tomography (CT) was performed diagnostic purposes prior...

Objective: The purpose of the study was to create an artificial intelligence (AI) system for detecting idiopathic osteosclerosis (IO) on panoramic radiographs automatic, routine, and simple evaluations. Subject Methods: In this study, a deep learning method carried out with obtained from healthy patients. A total 493 anonymized were used develop AI (CranioCatch, Eskisehir, Turkey) detection IOs. acquired radiology archives Department Oral Maxillofacial Radiology, Faculty Dentistry, Eskisehir...

In the 21st century, which can be termed as artificial age of intelligence, machine learning techniques that become widespread and improve themselves given more quality services to humanity in many fields. As a result these developments, nowadays companies deliver their products customers via social media accounts. But not every customer is interested all product or service. Each customer's area interest different. Gender one main reasons for this difference. If gender user determined...

Amaç: Klasik veritabanı yöntemleri, sürekli biriken büyük veri kümeleri için yetersiz olabilir. Yapay zekanın ana alt kümelerinden biri olarak makine öğrenme (MÖ) bu sorunu çözebilir ve tıbbi çalışmalarda mevcut verilerden deneyim kazanarak özellik problemleri en iyi çözümü bulabilir. Alt üriner sistem disfonksiyonu (AÜSD) olan hastalarda klinik bulgularla renal skar (RS) arasında yüksek doğrulukla korelasyonu gösterebilecek bir yönteme ihtiyaç vardır. Bu çalışmada, AÜSD’lu çocuklarda MÖ...

<title>Abstract</title> The objective of the study is to determine hospitalized surgical patients’ opinions about communication skills physicians in building trust and factors affecting them. 201 patients staying at unit constituted sample study. data collection form consisted demographic Scale Trust Communication Patient-Physician Relationship. Descriptive statistics, Mann-Whitney U, Kruskal-Wallis, Bonferroni correction Spearman correlation coefficient have been used analyze data....

We introduce some algebraic structures such as singularity, commutators and central extension in modified categories of interest. Additionally, we the cat$^{1}$-objects with their connection to crossed modules these which gives rise unify many notions about (pre)crossed various algebras categories.

Aims of the Study: A radiographic examination is a significant part clinical routine for diagnosis, management, and follow-up disease. Artificial intelligence in dentistry shows that deep learning technique high enough quality effective to diagnose interpret images dental practice. For this purpose, it aimed evaluate diagnostic charting on panoramic radiography using deep-learning AI system study. Methods: 1084 anonymized radiographs were labeled 10 different situations including crown,...

Abstract Background Pulmonary embolism is a type of thromboembolism seen in the main pulmonary artery and its branches. This study aimed to diagnose acute using deep learning method computed tomographic angiography (CTPA) perform segmentation data. Methods The CTPA images patients diagnosed with who underwent scheduled imaging were retrospectively evaluated. After data collection, areas that as axial section segmented. dataset was divided into three parts training, validation, testing....

We implement GAP functions about groups with action on itself and investigate some basic properties of small order $&lt;32$.