Pediatric pancreas segmentation from MRI scans with deep learning.

Objective: Our study aimed to evaluate and validate PanSegNet, a deep learning (DL) algorithm for pediatric pancreas segmentation on MRI in children with acute pancreatitis (AP), chronic pancreatitis (CP), and healthy controls.

Methods: With IRB approval, we retrospectively collected 84 MRI scans (1.5T/3T Siemens Aera/Verio) from children aged 2-19 years at Gazi University (2015-2024).

Three different protocols for pituitary suppression: progestins (dydrogesterone or medroxyprogesterone acetate) versus GnRH antagonist-balancing efficacy and cost in ovarian stimulation.

Purpose: To compare the clinical effectiveness and overall treatment cost of three ovarian stimulation protocols-dydrogesterone (DYD), medroxyprogesterone acetate (MPA), and GnRH antagonist-in women undergoing in vitro fertilization (IVF).

Methods: This prospective, multicenter cohort study was conducted at two IVF units from March 2023 to March 2024. A total of 307 women undergoing IVF were divided into three groups based on their pituitary suppression protocol: DYD (n = 99), MPA (n = 101), and GnRH antagonist (n = 107).

Comment on Dalboni da Rocha et al. Artificial Intelligence for Neuroimaging in Pediatric Cancer. 2025, , 622.

As researchers actively working in the field of pediatric neuroimaging and artificial intelligence, we were pleased to see your recent review, Artificial Intelligence for Neuroimaging in Pediatric Cancer, by Rocha et al [...

Multi-center evaluation of radiomics and deep learning to stratify malignancy risk of IPMNs.

Distinguishing high-risk intraductal papillary mucinous neoplasms (IPMNs), pancreatic cysts requiring surgery, from low-risk lesions remains a clinical challenge, often resulting in unnecessary procedures due to limited specificity of current methods. While radiomics and deep learning (DL) have been explored for pancreatic cancer, cyst-level malignancy risk stratification of IPMNs remains untapped. We conducted a multi-institutional study (seven centers, 359 T2W MRI images) to assess the feasibility of AI for predicting IPMN dysplasia grade using cyst-level image features.

Ethical framework for responsible foundational models in medical imaging.

The emergence of foundational models represents a paradigm shift in medical imaging, offering extraordinary capabilities in disease detection, diagnosis, and treatment planning. These large-scale artificial intelligence systems, trained on extensive multimodal and multi-center datasets, demonstrate remarkable versatility across diverse medical applications. However, their integration into clinical practice presents complex ethical challenges that extend beyond technical performance metrics.

Large Scale MRI Collection and Segmentation of Cirrhotic Liver.

Liver cirrhosis represents the end stage of chronic liver disease, characterized by extensive fibrosis and nodular regeneration that significantly increases mortality risk. While magnetic resonance imaging (MRI) offers a non-invasive assessment, accurately segmenting cirrhotic livers presents substantial challenges due to morphological alterations and heterogeneous signal characteristics. Deep learning approaches show promise for automating these tasks, but progress has been limited by the absence of large-scale, annotated datasets.

Machine learning based clinical decision tool to predict acute kidney injury and survival in therapeutic hypothermia treated neonates.

Therapeutic hypothermia (TH) significantly reduces mortality and morbidities in neonates with Neonatal Encephalopathy (NE). NE may result in neonatal death and multisystem organ impairment, including acute kidney injury (AKI). Our study aimed to utilize machine learning (ML) methods to predict the outcome of TH-treated NE neonates developing AKI and death during TH.

DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model.

Large-scale, big-variant, high-quality data are crucial for developing robust and successful deep-learning models for medical applications since they potentially enable better generalization performance and avoid overfitting. However, the scarcity of high-quality labeled data always presents significant challenges. This paper proposes a novel approach to address this challenge by developing controllable diffusion models for medical image synthesis, called DiffBoost.

A Conceptual Framework for Applying Ethical Principles of AI to Medical Practice.

Artificial Intelligence (AI) is reshaping healthcare through advancements in clinical decision support and diagnostic capabilities. While human expertise remains foundational to medical practice, AI-powered tools are increasingly matching or exceeding specialist-level performance across multiple domains, paving the way for a new era of democratized healthcare access. These systems promise to reduce disparities in care delivery across demographic, racial, and socioeconomic boundaries by providing high-quality diagnostic support at scale.

Comparison of search and rescue operations involving emergency physicians in devastating earthquakes in Turkey: a 24-year experience study.

Background: In the 21st century, disasters (particularly earthquakes, which remain the leading cause of death) continue to be among the foremost issues requiring global emergency response. While the impact of advancing technologies on the environmental and human damage caused by earthquakes is still a subject of debate, search and rescue (SAR) teams and emergency departments (ED), specifically emergency physicians (EPs), play a crucial role in the most acute management of the effects of these earthquakes on human life. This study aims to examine the injury dynamics of two catastrophic earthquakes that occurred in Turkey 24 years apart from the perspective of EPs, utilizing archival records from the SAR teams in which EPs served.

Advances for Managing Pancreatic Cystic Lesions: Integrating Imaging and AI Innovations.

Pancreatic cystic lesions (PCLs) represent a spectrum of non-neoplasms and neoplasms with varying malignant potential, posing significant challenges in diagnosis and management. While some PCLs are precursors to pancreatic cancer, others remain benign, necessitating accurate differentiation for optimal patient care. Conventional approaches to PCL management rely heavily on radiographic imaging, and endoscopic ultrasound (EUS) guided fine-needle aspiration (FNA), coupled with clinical and biochemical data.

Short-term effectiveness of epidermis dermis fascia kinesiotaping technique in myofascial pain syndrome on upper trapezius: A multi-center, double-blind, randomized clinical study.

Objectives: This study aims to investigate the efficacy of epidermis dermis fascia (EDF) kinesiotaping (KT) technique on pain intensity, number of active trigger points (TrPs), cervical range of motion (ROM) angles, and disability levels in patients with myofascial pain syndrome (MPS) on upper trapezius (UT) muscle.

Patients And Methods: Between January 2019 and January 2020, a total of 180 patients (21 males, 159 females; mean age: 35.9±9.

Large-scale multi-center CT and MRI segmentation of pancreas with deep learning.

Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective study, we collected a large dataset (767 scans from 499 participants) of T1-weighted (T1 W) and T2-weighted (T2 W) abdominal MRI series from five centers between March 2004 and November 2022.

Phthalate exposure induces cell death and ferroptosis in neonatal microglial cells.

Background/aim: Phthalates are the materials used for plasticizing polyvinyl chloride. Di-(2-Ethylhexyl) phthalate (DEHP) is one of the phthalates most frequently used in a wide range of applications, including medical equipment such as endotracheal and feeding tubes, intravenous catheters, central lines, extracorporeal membrane oxygenation sets, total parenteral nutrition bags, blood product sets, and intravenous pump lines, respiratory sets in neonatal intensive care units (NICUs). Studies have shown that phthalates, including DEHP, can cross the placenta and blood-brain barrier, possibly leading to neurodevelopmental impairment in vitro and in vivo.

Radiomics Boosts Deep Learning Model for IPMN Classification.

Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant pancreas lesions, and they can progress into pancreatic cancer. Therefore, detecting and stratifying their risk level is of ultimate importance for effective treatment planning and disease control. However, this is a highly challenging task because of the diverse and irregular shape, texture, and size of the IPMN cysts as well as the pancreas.

Comparison of Two Different Positions for Ultrasound-Guided Intervertebral Distance Evaluation.

Objective: During neuraxial anaesthesia, correct patient positioning is key for increased block success and (patient) comfort. The aim of this prospective study was to compare the lateral fetal decubitus (LFD) position with the sitting fetal lotus (SFL) regarding interspinous distance, transverse diameters of paravertebral muscles measured with ultrasonography, and patient comfort.

Methods: Fifty adult participants who could sit cross-legged and had no lumbar anomalies were included in our prospective study.

An Efficient Multi-Scale Fusion Network for 3D Organs at Risk (OARs) Segmentation.

Accurate segmentation of organs-at-risks (OARs) is a precursor for optimizing radiation therapy planning. Existing deep learning-based multi-scale fusion architectures have demonstrated a tremendous capacity for 2D medical image segmentation. The key to their success is aggregating global context and maintaining high resolution representations.

A Population Model of Time-Dependent Changes in Serum Creatinine in (Near)term Neonates with Hypoxic-Ischemic Encephalopathy During and After Therapeutic Hypothermia.

The objective was to apply a population model to describe the time course and variability of serum creatinine (sCr) in (near)term neonates with moderate to severe encephalopathy during and after therapeutic hypothermia (TH). The data consisted of sCr observations up to 10 days of postnatal age in neonates who underwent TH during the first 3 days after birth. Available covariates were birth weight (BWT), gestational age (GA), survival, and acute kidney injury (AKI).

The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review.

Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly.

Selecting the best optimizers for deep learning-based medical image segmentation.

Purpose: The goal of this work is to explore the best optimizers for deep learning in the context of medical image segmentation and to provide guidance on how to design segmentation networks with effective optimization strategies.

Approach: Most successful deep learning networks are trained using two types of stochastic gradient descent (SGD) algorithms: adaptive learning and accelerated schemes. Adaptive learning helps with fast convergence by starting with a larger learning rate (LR) and gradually decreasing it.

A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging.

Purpose Of Review: Early and accurate diagnosis of pancreatic cancer is crucial for improving patient outcomes, and artificial intelligence (AI) algorithms have the potential to play a vital role in computer-aided diagnosis of pancreatic cancer. In this review, we aim to provide the latest and relevant advances in AI, specifically deep learning (DL) and radiomics approaches, for pancreatic cancer diagnosis using cross-sectional imaging examinations such as computed tomography (CT) and magnetic resonance imaging (MRI).

Recent Findings: This review highlights the recent developments in DL techniques applied to medical imaging, including convolutional neural networks (CNNs), transformer-based models, and novel deep learning architectures that focus on multitype pancreatic lesions, multiorgan and multitumor segmentation, as well as incorporating auxiliary information.

Transformer based Generative Adversarial Network for Liver Segmentation.

Automated liver segmentation from radiology scans (CT, MRI) can improve surgery and therapy planning and follow-up assessment in addition to conventional use for diagnosis and prognosis. Although convolutional neural networks (CNNs) have became the standard image segmentation tasks, more recently this has started to change towards Transformers based architectures because Transformers are taking advantage of capturing long range dependence modeling capability in signals, so called attention mechanism. In this study, we propose a new segmentation approach using a hybrid approach combining the Transformer(s) with the Generative Adversarial Network (GAN) approach.

Microglial iron trafficking: new player in brain injury.

Neonatal brain injury is a significant reason of neurodevelopmental abnormalities and long-term neurological impairments. Hypoxic-ischemic encephalopathy and preterm brain injury, including intraventricular hemorrhage are the most common grounds of brain injury for full-term and preterm neonates. The prevalence of hypoxic ischemic encephalopathy varies globally, ranging from 1 to 3.