Automated segmentation of dental restorations using deep learning: exploring data augmentation techniques.

Objectives: Deep learning has revolutionized image analysis for dentistry. Automated segmentation of dental radiographs is of great importance towards digital dentistry. The performance of deep learning models heavily relies on the quality and diversity of the training data.

Improving resolution of panoramic radiographs: super-resolution concept.

Objectives: Dental imaging plays a key role in the diagnosis and treatment of dental conditions, yet limitations regarding the quality and resolution of dental radiographs sometimes hinder precise analysis. Super-resolution with deep learning refers to a set of techniques used to enhance the resolution of images beyond their original size or quality using deep neural networks instead of traditional image interpolation methods which often result in blurred or pixelated images when attempting to increase resolution. Leveraging advancements in technology, this study aims to enhance the resolution of dental panoramic radiographs, thereby enabling more accurate diagnoses and treatment planning.

The role of deep learning for periapical lesion detection on panoramic radiographs.

Objective: This work aimed to detect automatically periapical lesion on panoramic radiographs (PRs) using deep learning.

Methods: 454 objects in 357 PRs were anonymized and manually labeled. They are then pre-processed to improve image quality and enhancement purposes.

Automated detection of dental restorations using deep learning on panoramic radiographs.

Objectives: Automatically detecting dental conditions using Artificial intelligence (AI) and reporting it visually are now a need for treatment planning and dental health management. This work presents a comprehensive computer-aided detection system to detect dental restorations.

Methods: The state-of-art ten different deep-learning detection models were used including R-CNN, Faster R-CNN, SSD, YOLOv3, and RetinaNet as detectors.

Spatial Transcriptomics as a Novel Approach to Redefine Electrical Stimulation Safety.

Current standards for safe delivery of electrical stimulation to the central nervous system are based on foundational studies which examined post-mortem tissue for histological signs of damage. This set of observations and the subsequently proposed limits to safe stimulation, termed the "Shannon limits," allow for a simple calculation (using charge per phase and charge density) to determine the intensity of electrical stimulation that can be delivered safely to brain tissue. In the three decades since the Shannon limits were reported, advances in molecular biology have allowed for more nuanced and detailed approaches to be used to expand current understanding of the physiological effects of stimulation.

Deep Learning Based Detection Tool for Impacted Mandibular Third Molar Teeth.

Third molar impacted teeth are a common issue with all ages, possibly causing tooth decay, root resorption, and pain. This study was aimed at developing a computer-assisted detection system based on deep convolutional neural networks for the detection of third molar impacted teeth using different architectures and to evaluate the potential usefulness and accuracy of the proposed solutions on panoramic radiographs. A total of 440 panoramic radiographs from 300 patients were randomly divided.

Omitting routine gastric residual checks may help to accelerate enteral feeds and postnatal growth in stable preterm infants.

Background: The prefeed gastric residual check (GRC) when increasing the amounts of feeds given via orogastric and nasogastric tubes as a precaution for necrotizing enterocolitis (NEC) and intestinal intolerance is a routine procedure. However, it is mostly misleading, and recently, there has been a tendency not to check prefeed residuals.

Methods: We changed our nutrition protocol at the end of 2018 to start minimal enteral feeds (MEFs) and increase feeds without GRCs.

Wild edible plants in Yeşilli (Mardin-Turkey), a multicultural area.

Background: The Yeşilli district (Mardin) is located in the southeastern of Turkey and hosts different cultures. The objective of this study was to record the traditional knowledge of wild edible plants used by indigenous people in Yeşilli, where no ethnobotanical studies have been conducted previously.

Methods: An ethnobotanical study was carried out in Yeşilli district in March 2017-March 2019 to document the traditional knowledge of wild edible plants.

Comparison Between Prospectively Electrocardiogram-Gated High-Pitch Mode and Retrospectively Electrocardiogram-Gated Mode for Dual-Source CT Coronary Angiography.

Background: We aimed to report the image quality, relationship between heart rate and image quality, amount of contrast agent given to the patients and radiation doses in coronary CT angiography (CTA) obtained by using high-pitch prospectively ECG-gated "Flash Spiral" technique (method A) or retrospectively ECG-gated technique (method B) using 128×2-slice dual-source CT.

Material/methods: A total of 110 patients who were evaluated with method A and method B technique with a 128×2-detector dual-source CT device were included in the study. Patients were divided into three groups based on their heart rates during the procedure, and a relationship between heart rate and image quality were evaluated.