Endocrinology

Temporal machine learning framework for diabetic foot ulcer healing trajectory prediction.

OBJECTIVES: Diabetic foot ulcer management relies predominantly on reactive treatment adjustments based on current wound status. This study developed an accessible machine learning framework using routinely collected clinical metadata (no imaging required) to predict healing phase transitions at the next clinical appointment, enabling proactive treatment planning with an integrated recommendation ...

Development and validation of a machine learning model based on interpretable clinical characteristics for preoperative prediction of Ki-67 expression in pituitary adenomas.

Factors and determinants of primary care to tertiary care referrals in Singapore: A multi-centre analysis using artificial intelligence-powered large language models.

BACKGROUND: As Singapore adopts a population health approach under Healthier Singapore (Healthier SG), optimizing healthcare resources is crucial. We ...

Interpretable Machine Learning Model Using Digitized US Features for Classifying Complex Thyroid Nodules.

Purpose To develop a digitized integrated feature-based interpretable machine learning classification model to accurately recognize complex thyroid no...

Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application.

BACKGROUND: Diabetic kidney disease (DKD) represents the leading cause of end-stage renal disease (ESRD) worldwide, characterized by a complex pathoph...

Improving Diagnostic Precision in Thyroid Pathology by Synergistic Use of AI and Molecular Markers.

OBJECTIVES: For indeterminate thyroid nodules, molecular tests offer high negative predictive value (NPV), reducing missed malignancies, but have limi...

Artificial intelligence approaches for non-invasive diabetes prediction using ECG signals: A systematic review.

Diabetes is a major global health challenge, with many individuals remaining undiagnosed due to the limitations of traditional screening methods. Arti...

Predictive value of tear lipidomics biomarkers for TAO activity and relationship with clinical characteristics.

The objective of this study was to explore the lipid metabolic changes in the active thyroid-associated ophthalmopathy (TAO) through tear lipidomics a...

Pathophysiological mechanisms of fatigue and multidisciplinary management strategies (Review).

Fatigue is a common clinical symptom, and its complex pathophysiological mechanisms markedly affect the quality of life and social function of patient...

Comment on "Deep learning habitat radiomics based on ultrasound for predicting preoperative locally progression and postoperative recurrence risk of thyroid cancer: a multicenter study".

Integrating body composition analysis and machine learning for non-invasive identification of metabolic dysfunction-associated fatty liver disease: a large-scale health examination-based study.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a highly prevalent liver condition closely linked to obesity, insulin resistance, and ...

Prediction of recurrent ischemic stroke using machine learning from real-world data.

BACKGROUND: Recurrent ischemic stroke (RIS) is a significant challenge in Malaysia, affecting approximately 33% of patients. However, studies using ar...

Machine learning-based classification of papillary thyroid carcinoma versus multinodular goiter using preoperative laboratory and cytology data.

An enhanced diabetes prediction using an improved hybrid deep learning algorithm with mountain gazelle optimizer.

Diabetes is one of the global health challenges and requires early detection and an accurate diagnosis for the prevention of serious complications. Tr...

[Efficacy prediction of acupuncture and moxibustion combined with conventional treatment for PCOS-IR based on real-world data: machine learning and interpretability analysis].

OBJECTIVE: To construct an efficacy prediction model for polycystic ovary syndrome with insulin resistance (PCOS-IR) treated with acupuncture and moxi...

RenoTrue: A diabetes-specific machine learning model to estimate glomerular filtration rate for people with diabetes.

BACKGROUND: Existing methods for estimating GFR in people with diabetes have shown inaccuracies when compared to mGFR measurements. We developed and v...

Contrasting land-use sources of microplastic and endocrine-disrupting compound pollution in a major coastal river network.

Environmental monitoring in coastal megacities prioritizes land-based microplastics (MPs) and endocrine-disrupting compounds (EDCs) as key threats to ...

Real-world Study of Three-gas Breath Testing Nationwide and the Association With Symptoms.

GOALS AND BACKGROUND: Hydrogen (H 2 ) breath testing (BT) is used to diagnose small intestinal bacterial overgrowth (SIBO), and methane (CH 4 ) to dia...

Identification of severe hypoglycemia in adults with type 1 diabetes using CGM-based machine learning: evidence from the FGM-Japan study.

BACKGROUND: Severe hypoglycemia (SH) in adults with type 1 diabetes mellitus (T1DM) is associated with significant morbidity and mortality; however, i...

Integrated network pharmacology and bioinformatics analysis reveals MME as key target of Notoginsenoside R1 in diabetic nephropathy.

BACKGROUND: Diabetic nephropathy (DN) is a major diabetes complication and a primary cause of end-stage renal failure. Notoginsenoside R1 (NGR1) is kn...