Endocrinology

Stratifying High-Risk Thyroid Nodules Using a Novel Deep Learning System.

INTRODUCTION: The current ultrasound scan classification system for thyroid nodules is time-consuming, labor-intensive, and subjective. Artificial intelligence (AI) has been shown to increase the accuracy of predicting the malignancy rate of thyroid nodules. This study aims to demonstrate the state-of-the-art Swin Transformer to classify thyroid nodules.

Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits.

Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal remains challenging. In this study, we describe a deep learning (DL) method to analyze the impact of sequence variants on enhancers. Focusing on pancreati...

Screening adequacy of unstained thyroid fine needle aspiration samples using a deep learning-based classifier.

Fine needle aspiration (FNA) biopsy of thyroid nodules is a safe, cost-effective, and accurate diagnostic method for detecting thyroid cancer. However...

Artificial Intelligence (AI) in pediatric endocrinology.

Artificial Intelligence (AI) is integrating itself throughout the medical community. AI's ability to analyze complex patterns and interpret large amou...

A Comparative Analysis of Various Machine Learning Algorithms to Improve the Accuracy of HbA1c Estimation Using Wrist PPG Data.

Due to the inconvenience of drawing blood and the possibility of infection associated with invasive methods, research on non-invasive glycated hemoglo...

Improving the Efficacy of ACR TI-RADS Through Deep Learning-Based Descriptor Augmentation.

Thyroid nodules occur in up to 68% of people, 95% of which are benign. Of the 5% of malignant nodules, many would not result in symptoms or death, yet...

The auxiliary diagnosis of thyroid echogenic foci based on a deep learning segmentation model: A two-center study.

OBJECTIVE: The aim of this study is to develop AI-assisted software incorporating a deep learning (DL) model based on static ultrasound images. The so...

Unlocking the Potential of Zebrafish Research with Artificial Intelligence: Advancements in Tracking, Processing, and Visualization.

Zebrafish have become a widely accepted model organism for biomedical research due to their strong cortisol stress response, behavioral strain differe...

Can dexamethasone improve postoperative sleep and postoperative delirium in elderly patients undergoing robot-assisted laparoscopic radical prostatectomy? Protocol for a prospective, randomized, double-blind, controlled study.

BACKGROUND: Perioperative sleep disorders (PSD) are an independent risk factor for postoperative delirium (POD), which is a common complication after ...

Non-invasive Characterization of Glycosuria and Identification of Biomarkers in Diabetic Urine Using Fluorescence Spectroscopy and Machine Learning Algorithm.

The current study presents a steadfast, simple, and efficient approach for the non-invasive determination of glycosuria of diabetes mellitus using flu...

MATH: A Deep Learning Approach in QSAR for Estrogen Receptor Alpha Inhibitors.

Breast cancer ranks as the second leading cause of death among women, but early screening and self-awareness can help prevent it. Hormone therapy drug...

The learning curve on robot-assisted transaxillary thyroidectomy performed by a single endocrine surgeon in a third-level institution in Europe: a cumulative sum (CUSUM) analysis.

Robot-assisted transaxillary thyroidectomy is widely performed in Asian countries, although it is still under discussion in the Western World. However...

Evaluation of sexual function after robot-assisted radical prostatectomy: A farewell to IIEF questionnaire.

Longevity with localized prostate cancer (PCa) has been achieved, and the contribution of robot-assisted radical prostatectomy (RARP) to cancer contro...

Artificial Intelligence in Minimally Invasive Adrenalectomy: Using Deep Learning to Identify the Left Adrenal Vein.

BACKGROUND: Minimally invasive adrenalectomy is the main surgical treatment option for the resection of adrenal masses. Recognition and ligation of ad...

Noninvasive blood glucose sensing by secondary speckle pattern artificial intelligence analyses.

SIGNIFICANCE: Diabetes is a prevalent disease worldwide that can cause severe health problems. Accurate blood glucose detection is crucial for diabete...

Deep Learning-Based Differential Diagnosis of Follicular Thyroid Tumors Using Histopathological Images.

Deep learning systems (DLSs) have been developed for the histopathological assessment of various types of tumors, but none are suitable for differenti...

One-pot multicomponent synthesis of novel pyridine derivatives for antidiabetic and antiproliferative activities.

Due to the close relationship of diabetes with hypertension reported in various research, a set of pyridine derivatives with US FDA-approved drug cor...

iPADD: A Computational Tool for Predicting Potential Antidiabetic Drugs Using Machine Learning Algorithms.

Diabetes mellitus is a chronic metabolic disease, which causes an imbalance in blood glucose homeostasis and further leads to severe complications. Wi...

Frequency constraint-based adversarial attack on deep neural networks for medical image classification.

The security of AI systems has gained significant attention in recent years, particularly in the medical diagnosis field. To develop a secure medical ...

Elastic Deformation of Optical Coherence Tomography Images of Diabetic Macular Edema for Deep-Learning Models Training: How Far to Go?

UNLABELLED: - Objective: To explore the clinical validity of elastic deformation of optical coherence tomography (OCT) images for data augmentation in...