Predicting Semen Analysis Parameters from Testicular Ultrasonography Images Using Deep Learning Algorithms: An Innovative Approach to Male Infertility Diagnosis.

Semen analysis is universally regarded as the gold standard for diagnosing male infertility, while ultrasonography plays a vital role as a complementary diagnostic tool. This study aims to assess the effectiveness of artificial intelligence (AI)-driven deep learning algorithms in predicting semen analysis parameters based on testicular ultrasonography images. This study included male patients aged 18-54 who sought evaluation for infertility at the Urology Outpatient Clinic of our hospital between February 2022 and April 2023.

Generative Adversarial Network Based Contrast Enhancement: Synthetic Contrast Brain Magnetic Resonance Imaging.

Rationale And Objectives: Magnetic resonance imaging (MRI) is a vital tool for diagnosing neurological disorders, frequently utilising gadolinium-based contrast agents (GBCAs) to enhance resolution and specificity. However, GBCAs present certain risks, including side effects, increased costs, and repeated exposure. This study proposes an innovative approach using generative adversarial networks (GANs) for virtual contrast enhancement in brain MRI, with the aim of reducing or eliminating GBCAs, minimising associated risks, and enhancing imaging efficiency while preserving diagnostic quality.

Long term outcomes from the early days of minimally invasive thymoma surgery for Myasthenia Gravis patients: a follow-up of 19 years.

Background: Long-term survival outcomes are crucial for accurately determining the effectiveness of treatment in an indolent disease like thymoma. We aimed to analyze the clinical findings in terms of survival and relapse patterns with a median follow up of 105 months (8.7 years) in patients with thymoma and myasthenia gravis who underwent minimally invasive surgery between 2002 and 2015.

Accuracy and Readability of ChatGPT on Potential Complications of Interventional Radiology Procedures: AI-Powered Patient Interviewing.

Rationale And Objectives: It is crucial to inform the patient about potential complications and obtain consent before interventional radiology procedures. In this study, we investigated the accuracy, reliability, and readability of the information provided by ChatGPT-4 about potential complications of interventional radiology procedures.

Materials And Methods: Potential major and minor complications of 25 different interventional radiology procedures (8 non-vascular, 17 vascular) were asked to ChatGPT-4 chatbot.

Assessing ChatGPT's summarization of Ga PSMA PET/CT reports for patients.

Purpose: ChatGPT has recently been the subject of many studies, and its responses to medical questions have been successful. We examined ChatGPT-4's evaluation of structured Ga prostate-specific membrane antigen (PSMA) PET/CT reports of newly diagnosed prostate cancer patients.

Methods: Ga PSMA PET/CT reports of 164 patients were entered to ChatGPT-4.

MR Image Fusion-Based Parotid Gland Tumor Detection.

The differentiation of benign and malignant parotid gland tumors is of major significance as it directly affects the treatment process. In addition, it is also a vital task in terms of early and accurate diagnosis of parotid gland tumors and the determination of treatment planning accordingly. As in other diseases, the differentiation of tumor types involves several challenging, time-consuming, and laborious processes.

The impact of deep learning on diagnostic performance in the differentiation of benign and malignant thyroid nodules.

Aims: This study aims to use deep learning (DL) to classify thyroid nodules as benign and malignant with ultrasonography (US). In addition, this study investigates the impact of DL on the diagnostic success of radiologists with different experiences. Material and methods: This study included 576 US images of thyroid nodules.

Radiomics-based machine learning in the differentiation of benign and malignant bowel wall thickening radiomics in bowel wall thickening.

Purpose: To distinguish malignant and benign bowel wall thickening (BWT) by using computed tomography (CT) texture features based on machine learning (ML) models and to compare its success with the clinical model and combined model.

Methods: One hundred twenty-two patients with BWT identified on contrast-enhanced abdominal CT and underwent colonoscopy were included in this retrospective study. Texture features were extracted from CT images using LifeX software.

Predicting COVID-19 Outcomes: Machine Learning Predictions Across Diverse Datasets.

Background The COVID-19 infection has spread rapidly since its emergence and has affected a large part of the global population. With the increasing number of cases, researchers are trying to predict the prognosis of patients by using different data with artificial intelligence methods such as machine learning (ML). In this study, we aimed to predict mortality risk in COVID-19 patients using ML algorithms with different datasets.

Minimally invasive approaches for en-bloc anatomical lung and chest wall resection.

Background: The aim of this study was to evaluate the feasibility of anatomical lung and chest wall resection via minimally invasive surgery.

Methods: Between January 2013 and December 2021, a total of 22 patients (18 males, 4 females; mean age: 63±6.9 years; range, 48 to 78 years) who underwent anatomical lung and chest wall resection using minimally invasive surgery for non-small cell lung cancer were retrospectively analyzed.

Nurses' Knowledge Concerning Prevention and Treatment of Pressure Ulcers.

Pressure ulcers have high prevalence in patients and can be prevented with proper nursing interventions. The aim of this study was to evaluate nurses' knowledge about prevention and treatment of pressure ulcers. The present study was conducted with 111 nurses working in a General hospital in Greece.