Endocrinology

Screening of a novel free fatty acid receptor 1 (FFAR1) agonist peptide by phage display and machine learning based-amino acid substitution.

Free fatty acid receptor 1 (FFAR1 or GPR40) has attracted attention for the treatment of type 2 diabetes mellitus, and various small-molecule agonists have been developed. However, most FFAR1 agonists as well as endogenous ligands, such as linoleic acids, have high lipophilicity, and their high lipophilicity is related to off-target toxicity. Therefore, we need to focus on new ligand candidates wi...

The effect of diabetes on major robotic hepatectomy.

Studies regarding the influence of diabetes on perioperative outcomes after major hepatectomy are conflicting. The objective of this study is to analyze the effects of diabetes on patients undergoing robotic major hepatectomy. With Institutional Review Board (IRB) approval, 94 patients undergoing major hepatectomy were prospectively followed. Demographic data and postoperative outcomes were analyz...

Deep Learning-Based Diabetic Retinopathy Severity Grading System Employing Quadrant Ensemble Model.

The diabetic retinopathy accounts in the deterioration of retinal blood vessels leading to a serious compilation affecting the eyes. The automated DR ...

GestAltNet: aggregation and attention to improve deep learning of gestational age from placental whole-slide images.

The placenta is the first organ to form and performs the functions of the lung, gut, kidney, and endocrine systems. Abnormalities in the placenta caus...

Adequacy and Effectiveness of Watson For Oncology in the Treatment of Thyroid Carcinoma.

BACKGROUND: IBM's Watson for Oncology (WFO) is an artificial intelligence tool that trains by acquiring data from the Memorial Sloan Kettering Cancer ...

Machine learning-based prediction model using clinico-pathologic factors for papillary thyroid carcinoma recurrence.

This study analyzed the prognostic significance of clinico-pathologic factors, including the number of metastatic lymph nodes (LNs) and lymph node rat...

Machine Learning in Preoperative Prediction of Postoperative Immediate Remission of Histology-Positive Cushing's Disease.

BACKGROUND: There are no established accurate models that use machine learning (ML) methods to preoperatively predict immediate remission after transs...

Artificial intelligence in health data analysis: The Darwinian evolution theory suggests an extremely simple and zero-cost large-scale screening tool for prediabetes and type 2 diabetes.

AIMS: The effective identification of individuals with early dysglycemia status is key to reduce the incidence of type 2 diabetes. We develop and vali...

Deep learning differentiates between healthy and diabetic mouse ears from optical coherence tomography angiography images.

We trained a deep learning algorithm to use skin optical coherence tomography (OCT) angiograms to differentiate between healthy and type 2 diabetic mi...

A review on current advances in machine learning based diabetes prediction.

Diabetes is a metabolic disorder comprising of high glucose level in blood over a prolonged period in the body as it is not capable of using it proper...

Protein glycation - biomarkers of metabolic dysfunction and early-stage decline in health in the era of precision medicine.

Protein glycation provides a biomarker in widespread clinical use, glycated hemoglobin HbA (A1C). It is a biomarker for diagnosis of diabetes and pred...

Development and Validation of a Deep Learning Based Diabetes Prediction System Using a Nationwide Population-Based Cohort.

BACKGROUND: Previously developed prediction models for type 2 diabetes mellitus (T2DM) have limited performance. We developed a deep learning (DL) bas...

Assessment of Thoracic Pain Using Machine Learning: A Case Study from Baja California, Mexico.

Thoracic pain is a shared symptom among gastrointestinal diseases, muscle pain, emotional disorders, and the most deadly: Cardiovascular diseases. Due...

A comparison between deep learning convolutional neural networks and radiologists in the differentiation of benign and malignant thyroid nodules on CT images.

INTRODUCTION: We designed 5 convolutional neural network (CNN) models and ensemble models to differentiate malignant and benign thyroid nodules on CT,...

Establishment of noninvasive diabetes risk prediction model based on tongue features and machine learning techniques.

BACKGROUND: Diabetes is a chronic noncommunicable disease with high incidence rate. Diabetics without early diagnosis or standard treatment may contri...

Combinatorial K-Means Clustering as a Machine Learning Tool Applied to Diabetes Mellitus Type 2.

A new original procedure based on k-means clustering is designed to find the most appropriate clinical variables able to efficiently separate into gro...

The Use of Machine Learning Techniques to Determine the Predictive Value of Inflammatory Biomarkers in the Development of Type 2 Diabetes Mellitus.

Certain inflammatory biomarkers, such as interleukin-6, interleukin-1, C-reactive protein (CRP), and fibrinogen, are prototypical acute-phase paramet...

Thyroid gland delineation in noncontrast-enhanced CTs using deep convolutional neural networks.

The purpose of this study is to develop a deep learning method for thyroid delineation with high accuracy, efficiency, and robustness in noncontrast-e...

Diabetes detection using deep learning techniques with oversampling and feature augmentation.

BACKGROUND AND OBJECTIVE: Diabetes is a chronic pathology which is affecting more and more people over the years. It gives rise to a large number of d...

Discrimination of malignant from benign thyroid lesions through neural networks using FTIR signals obtained from tissues.

The current gold standard in cancer diagnosis-the microscopic examination of hematoxylin and eosin (H&E)-stained biopsies-is prone to bias since it gr...