ChatGPT as an effective tool for quality evaluation of radiomics research.

Objectives: This study aimed to evaluate the effectiveness of ChatGPT-4o in assessing the methodological quality of radiomics research using the radiomics quality score (RQS) compared to human experts.

Methods: Published in European Radiology, European Radiology Experimental, and Insights into Imaging between 2023 and 2024, open-access and peer-reviewed radiomics research articles with creative commons attribution license (CC-BY) were included in this study. Pre-prints from MedRxiv were also included to evaluate potential peer-review bias.

MRI radiomics based machine learning model of the periaqueductal gray matter in migraine patients.

Background And Purpose:

A Novel Fusion of Radiomics and Semantic Features: MRI-Based Machine Learning in Distinguishing Pituitary Cystic Adenomas from Rathke's Cleft Cysts.

Objectives: To evaluate the performances of machine learning using semantic and radiomic features from magnetic resonance imaging data to distinguish cystic pituitary adenomas (CPA) from Rathke's cleft cysts (RCCs).

Materials And Methods: The study involved 65 patients diagnosed with either CPA or RCCs. Multiple observers independently assessed the semantic features of the tumors on the magnetic resonance images.

ChatGPT-assisted deep learning model for thyroid nodule analysis: beyond artifical intelligence.

Aims: To develop a deep learning model, with the aid of ChatGPT, for thyroid nodules, utilizing ultrasound images. The cytopathology of the fine needle aspiration biopsy (FNAB) serves as the baseline.

Material And Methods: After securing IRB approval, a retrospective study was conducted, analyzing thyroid ultrasound images and FNAB results from 1,061 patients between January 2017 and January 2022.

Synergizing photon-counting CT with deep learning: potential enhancements in medical imaging.

This review article highlights the potential of integrating photon-counting computed tomography (CT) and deep learning algorithms in medical imaging to enhance diagnostic accuracy, improve image quality, and reduce radiation exposure. The use of photon-counting CT provides superior image quality, reduced radiation dose, and material decomposition capabilities, while deep learning algorithms excel in automating image analysis and improving diagnostic accuracy. The integration of these technologies can lead to enhanced material decomposition and classification, spectral image analysis, predictive modeling for individualized medicine, workflow optimization, and radiation dose management.

Educating the next generation of radiologists: a comparative report of ChatGPT and e-learning resources.

Rapid technological advances have transformed medical education, particularly in radiology, which depends on advanced imaging and visual data. Traditional electronic learning (e-learning) platforms have long served as a cornerstone in radiology education, offering rich visual content, interactive sessions, and peer-reviewed materials. They excel in teaching intricate concepts and techniques that necessitate visual aids, such as image interpretation and procedural demonstrations.

Innovative advances in pediatric radiology: computed tomography reconstruction techniques, photon-counting detector computed tomography, and beyond.

In pediatric radiology, balancing diagnostic accuracy with reduced radiation exposure is paramount due to the heightened vulnerability of younger patients to radiation. Technological advancements in computed tomography (CT) reconstruction techniques, especially model-based iterative reconstruction and deep learning image reconstruction, have enabled significant reductions in radiation doses without compromising image quality. Deep learning image reconstruction, powered by deep learning algorithms, has demonstrated superiority over traditional techniques like filtered back projection, providing enhanced image quality, especially in pediatric head and cardiac CT scans.

Multi-class classification of thyroid nodules from automatic segmented ultrasound images: Hybrid ResNet based UNet convolutional neural network approach.

Background And Objectives: Early detection and diagnosis of thyroid nodule types are important because they can be treated more effectively in their early stages. The types of thyroid nodules are generally stated as atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS), benign follicular, and papillary follicular. The risk of malignancy for AUS/FLUS is typically stated to be between 5 and 15 %, while some studies indicate a risk as high as 25 %.

Improving radiology workflow using ChatGPT and artificial intelligence.

Artificial Intelligence is a branch of computer science that aims to create intelligent machines capable of performing tasks that typically require human intelligence. One of the branches of artificial intelligence is natural language processing, which is dedicated to studying the interaction between computers and human language. ChatGPT is a sophisticated natural language processing tool that can understand and respond to complex questions and commands in natural language.

Attitudes toward COVID-19 pandemic measures and clinical symptom severity in schizophrenia patients: a preliminary cross-sectional study.

Objectives: The study aim was to compare clinical symptom severity and quality of life (QoL) among schizophrenia patients according to their attitudes toward pandemic measures and reported lockdown-related disruption.

Methods: Patients with schizophrenia attending follow-up sessions at two community mental health centers were included in this cross-sectional study. Sociodemographic and clinical characteristics of patients were assessed using a standardized form and the following psychometric instruments: the Positive and Negative Syndrome Scale, Brief Psychiatric Rating Scale, State-Trait Anxiety Inventory, Heinrich-Carpenter Quality of Life Scale and Clinical Global Impressions Ratings-Severity scale.

Autistic features and executive functions in first episode psychosis: Associations with functionality and quality of life.

Aim: The present study aimed at investigating the relationships between autistic features and cognitive deficits, functionality and quality of life in first episode psychosis (FEP) patients.

Methods: Sixty FEP patients [mean age (SD) = 32.53 (10.

Neuropsychological and Neurophysiological Assessment in Different Phases of Bipolar Disorder.

Background: Recent studies have shown that cognitive deficits are significant and pervasive even among remitted bipolar disorder patients. The aim of the current controlled study was to investigate the relationships between cognitive performances, symptom severity, and event-related potentials with regard to different episodes in bipolar patients.

Methods: This study was conducted on a total of 60 patients diagnosed with bipolar disorder (20 depressive, 20 manic, and 20 in remission).

Mentalizing self and others: A controlled study investigating the relationship between alexithymia and theory of mind in major depressive disorder.

Background: Theory of mind (ToM) and alexithymia have been reported to relate with depression in recent studies. However, data regarding the role of alexithymia and ToM in depression remain uncertain.

Aim: The aim of the current study was to determine the levels of alexithymia and ToM abilities as well as their relationship with each other and clinical features in major depressive disorder (MDD).

Evaluating the effectiveness of anti-tuberculosis treatment by detecting Mycobacterium tuberculosis 85B messenger RNA expression in sputum.

Background: The antigen 85 complex (85B) is secreted in large quantities from growing mycobacteria and the presence of bacterial mRNA is an indicator of cell viability. The quantitative detection of 85B mRNA expression levels can be used to assess the success of anti-tuberculosis treatment outcomes to detect viable mycobacteria cells. Therefore, we evaluated the levels of 85B mRNA of Mycobacterium tuberculosis strains in patients with pulmonary tuberculosis.

Serum vitamin D status does not correlate with the severity of obstructive sleep apnea in male adults: A controlled study design with minimized factors influencing serum vitamin D levels.

Observational studies performed in homogeneous groups to objectively investigate the cause and effect relationship between vitamin D deficiency and sleep disorders are scarce. In this study, it was aimed to analyze the relationship between the severity of OSAS and vitamin-D levels among the participants whose features affecting serum vit-D levels were minimised. Serum 25-OH vitamin-D levels in 121 OSAS Male patients diagnosed by polysomnography without any systemic disease or vitamin-D supplement that may effect the vitamin-D metabolism were measured.

Investigating the relationship between plasma neuropeptide-S levels and clinical depression.

Purpose: Neuropeptide-S (NPS) is a novel 20-amino acid peptide, mainly expressed in the central nervous system and endocrine tissues. NPS has been linked to anxiety and fear-related behaviors. The association of NPS with depression in a human population has not been previously examined.

Quantitative analysis of the amygdala, thalamus and hippocampus on magnetic resonance images in paediatric bipolar disorders and compared with the children of bipolar parents and healthy control.

MR imaging studies in paediatric bipolar disorder have particularly focused on the amygdala and hippocampus, subcortical structures, and to a lesser extent on the thalamus. The purpose of this study was to perform structural analysis of the regions of interest (ROI) associated with mood regulation. In this study 18 children (between the ages of 12-18) were matched according to their age and sex and were divided into three groups.

The relationship of Chlamydophila pneumoniae with schizophrenia: The role of brain-derived neurotrophic factor (BDNF) and neurotrophin-3 (NT-3) in this relationship.

Several pathogens have been suspected of playing a role in the pathogenesis of schizophrenia. Chronic inflammation has been proposed to occur as a result of persistent infection caused by Chlamydophila pneumoniae cells that reside in brain endothelial cells for many years. It was recently hypothesized that brain-derived neurotrophic factor (BDNF) and neurotrophin-3 (NT-3) may play prominent roles in the development of schizophrenia.