Endocrinology

Beyond the hot flashes: how machine learning is uncovering the complexity of menopause-related depression.

BACKGROUND: The transition into menopause marks a significant stage in a woman's life, indicating the end of reproductive capability. This period, encompassing perimenopause and menopause, is characterized by declining levels of estrogen and progesterone, leading to various symptoms such as hot flashes, sleep disturbances, sexual dysfunction, and mood irregularities. Moreover, cognitive functions,...

A novel RFE-GRU model for diabetes classification using PIMA Indian dataset.

Diabetes is a long-term condition characterized by elevated blood sugar levels. It can lead to a variety of complex disorders such as stroke, renal failure, and heart attack. Diabetes requires the most machine learning help to diagnose diabetes illness at an early stage, as it cannot be treated and adds significant complications to our health-care system. The diabetes PIMA Indian dataset (PIDD) wa...

Lesion classification and diabetic retinopathy grading by integrating softmax and pooling operators into vision transformer.

INTRODUCTION: Diabetic retinopathy grading plays a vital role in the diagnosis and treatment of patients. In practice, this task mainly relies on manu...

Curcumin nanocrystals ameliorate ferroptosis of diabetic nephropathy through glutathione peroxidase 4.

OBJECTIVE: The aim of this study was to investigate the effect of curcumin nanocrystals (Cur-NCs) on ferroptosis in high-glucose (HG)-induced HK-2 cel...

Neural Network and Logistic Regression Models Based on Ultrasound Radiomics and Clinical-Pathological Features to Predict Occult Level II Lymph Node Metastasis in Papillary Thyroid Carcinoma.

RATIONALE AND OBJECTIVES: Papillary thyroid carcinoma (PTC) often metastasizes to lateral cervical lymph nodes, especially in level II. This study aim...

mCNN-glucose: Identifying families of glucose transporters using a deep convolutional neural network based on multiple-scanning windows.

Glucose transporters are essential carrier proteins that function on the phospholipid bilayer to facilitate glucose diffusion across cell membranes. T...

Multi-institutional development and testing of attention-enhanced deep learning segmentation of thyroid nodules on ultrasound.

PURPOSE: Thyroid nodules are common, and ultrasound-based risk stratification using ACR's TIRADS classification is a key step in predicting nodule pat...

Transcriptomic profiling and machine learning reveal novel RNA signatures for enhanced molecular characterization of Hashimoto's thyroiditis.

While ultrasonography effectively diagnoses Hashimoto's thyroiditis (HT), exploring its transcriptomic landscape could reveal valuable insights into d...

Development of machine learning models for diagnostic biomarker identification and immune cell infiltration analysis in PCOS.

BACKGROUND: Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age. It is characterized by symptoms such ...

Predicting Gestational Diabetes Mellitus in the first trimester using machine learning algorithms: a cross-sectional study at a hospital fertility health center in Iran.

BACKGROUND: Gestational Diabetes Mellitus (GDM) is a common complication during pregnancy. Late diagnosis can have significant implications for both t...

Deep-Learning-Based Reconstruction of Single-Breath-Hold 3 mm HASTE Improves Abdominal Image Quality and Reduces Acquisition Time: A Quantitative Analysis.

Breath-hold T2-weighted half-Fourier acquisition single-shot turbo spin echo (HASTE) magnetic resonance imaging (MRI) of the upper abdomen with a sli...

Investigation of alpha-glucosidase inhibition activity of Artabotrys sumatranus leaf extract using metabolomics, machine learning and molecular docking analysis.

One way to treat diabetes mellitus type II is by using α-glucosidase inhibitor, that will slow down the postprandial glucose intake. Metabolomics anal...

Type 2 diabetes prediction method based on dual-teacher knowledge distillation and feature enhancement.

Diabetes prediction is an important topic in the field of medical health. Accurate prediction can help early intervention and reduce patients' health ...

Predicting autoimmune thyroiditis in primary Sjogren's syndrome patients using a random forest classifier: a retrospective study.

BACKGROUND: Primary Sjogren's syndrome (pSS) and autoimmune thyroiditis (AIT) share overlapping genetic and immunological profiles. This retrospective...

Non-invasive blood glucose monitoring using PPG signals with various deep learning models and implementation using TinyML.

Accurate and continuous blood glucose monitoring is essential for effective diabetes management, yet traditional finger pricking methods are often inc...

Artificial Intelligence and Whole Slide Imaging Assist in Thyroid Indeterminate Cytology: A Systematic Review.

INTRODUCTION: Thyroid cytopathology, particularly in cases of atypia of undetermined significance/follicular lesions of undetermined significance (AUS...

Artificial intelligence-based multi-modal multi-tasks analysis reveals tumor molecular heterogeneity, predicts preoperative lymph node metastasis and prognosis in papillary thyroid carcinoma: a retrospective study.

BACKGROUND: Papillary thyroid carcinoma (PTC) is the predominant form of thyroid cancer globally, especially when lymph node metastasis (LNM) occurs. ...

A machine learning model utilizing Delphian lymph node characteristics to predict contralateral central lymph node metastasis in papillary thyroid carcinoma: a prospective multicenter study.

BACKGROUND: This study aimed to use artificial intelligence (AI) to integrate various radiological and clinical pathological data to identify effectiv...

Prognostic Features for Overall Survival in Male Diabetic Patients Undergoing Hemodialysis Using Elastic Net Penalized Cox Regression; A Machine Learning Approach.

BACKGROUND: Diabetics constitute a significant percentage of hemodialysis (HD) patients with higher mortality, especially among male patients. A machi...

Imputation of Missing Continuous Glucose Monitor Data.

BACKGROUND: Continuous glucose monitors (CGMs) in research and clinical settings characterize glycemic profiles through repeated measurement of inters...