Endocrinology

Machine Learning Approach to Metabolomic Data Predicts Type 2 Diabetes Mellitus Incidence.

Metabolomics, with its wealth of data, offers a valuable avenue for enhancing predictions and decision-making in diabetes. This observational study aimed to leverage machine learning (ML) algorithms to predict the 4-year risk of developing type 2 diabetes mellitus (T2DM) using targeted quantitative metabolomics data. A cohort of 279 cardiovascular risk patients who underwent coronary angiography a...

Algorithms for predicting COVID outcome using ready-to-use laboratorial and clinical data.

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging crisis affecting the public health system. The clinical features of COVID-19 can range from an asymptomatic state to acute respiratory syndrome and multiple organ dysfunction. Although some hematological and biochemical parameters are altered during moderate and severe COVID-19, there is still a lack...

Comparison of 21 artificial intelligence algorithms in automated diabetic retinopathy screening using handheld fundus camera.

BACKGROUND: Diabetic retinopathy (DR) is a common complication of diabetes and may lead to irreversible visual loss. Efficient screening and improved ...

Digital twins and artificial intelligence in metabolic disease research.

Digital twin technology is emerging as a transformative paradigm for personalized medicine in the management of chronic conditions. In this article, w...

Research on disease diagnosis based on teacher-student network and Raman spectroscopy.

Diabetic nephropathy is a serious complication of diabetes, and primary Sjögren's syndrome is a disease that poses a major threat to women's health. T...

Hybrid deep learning assisted multi classification: Grading of malignant thyroid nodules.

Thyroid nodules are commonly diagnosed with ultrasonography, which includes internal characteristics, varying looks, and hazy boundaries, making it ch...

A fully autonomous robotic ultrasound system for thyroid scanning.

The current thyroid ultrasound relies heavily on the experience and skills of the sonographer and the expertise of the radiologist, and the process is...

Unveiling the molecular complexity of proliferative diabetic retinopathy through scRNA-seq, AlphaFold 2, and machine learning.

BACKGROUND: Proliferative diabetic retinopathy (PDR), a major cause of blindness, is characterized by complex pathogenesis. This study integrates sing...

Machine learning algorithms for identifying contralateral central lymph node metastasis in unilateral cN0 papillary thyroid cancer.

PURPOSE: The incidence of thyroid cancer is growing fast and surgery is the most significant treatment of it. For patients with unilateral cN0 papilla...

Risk prediction model of metabolic syndrome in perimenopausal women based on machine learning.

INTRODUCTION: Metabolic syndrome (MetS) is considered to be an important parameter of cardio-metabolic health and contributing to the development of a...

Association between the stress hyperglycemia ratio and 28-day all-cause mortality in critically ill patients with sepsis: a retrospective cohort study and predictive model establishment based on machine learning.

BACKGROUND: Sepsis is a severe form of systemic inflammatory response syndrome that is caused by infection. Sepsis is characterized by a marked state ...

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus.

Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that hea...

Machine learning to predict the occurrence of thyroid nodules: towards a quantitative approach for judicious utilization of thyroid ultrasonography.

INTRODUCTION: Ultrasound is instrumental in the early detection of thyroid nodules, which is crucial for appropriate management and favorable outcomes...

Model-Informed Precision Dosing Using Machine Learning for Levothyroxine in General Practice: Development, Validation and Clinical Simulation Trial.

Levothyroxine is one of the most prescribed drugs in the western world. Dosing is challenging due to high-interindividual differences in effective dos...

Microstrip isoelectric focusing with deep learning for simultaneous screening of diabetes, anemia, and thalassemia.

BACKGROUND: Hemoglobin (Hb) is an important protein in red blood cells and a crucial diagnostic indicator of diseases, e.g., diabetes, thalassemia, an...

Automated machine learning model for fundus image classification by health-care professionals with no coding experience.

To assess the feasibility of code-free deep learning (CFDL) platforms in the prediction of binary outcomes from fundus images in ophthalmology, evalua...

SSiT: Saliency-Guided Self-Supervised Image Transformer for Diabetic Retinopathy Grading.

Self-supervised Learning (SSL) has been widely applied to learn image representations through exploiting unlabeled images. However, it has not been fu...

Optimizing removal of antiretroviral drugs from tertiary wastewater using chlorination and AI-based prediction with response surface methodology.

Chemical and pharmaceutical chemicals found in water sources create substantial risks to human health and the environment. The presence of pharmaceuti...

Enhanced thyroid nodule segmentation through U-Net and VGG16 fusion with feature engineering: A comprehensive study.

BACKGROUND AND OBJECTIVE: The thyroid gland, a key component of the endocrine system, is pivotal in regulating bodily functions. Thermography, a non-i...

Pre-hospital glycemia as a biomarker for in-hospital all-cause mortality in diabetic patients - a pilot study.

BACKGROUND: Type 2 Diabetes Mellitus (T2DM) presents a significant healthcare challenge, with considerable economic ramifications. While blood glucose...