Endocrinology

Machine learning applications in imaging analysis for patients with pituitary tumors: a review of the current literature and future directions.

PURPOSE: To provide an overview of fundamental concepts in machine learning (ML), review the literature on ML applications in imaging analysis of pituitary tumors for the last 10 years, and highlight the future directions on potential applications of ML for pituitary tumor patients.

Diagnostic classification of solitary pulmonary nodules using support vector machine model based on 2-[18F]fluoro-2-deoxy-D-glucose PET/computed tomography texture features.

PURPOSE: This study aimed to evaluate the diagnostic value of a support vector machine (SVM) model built with texture features based on standard 2-[F]fluoro-2-deoxy-D-glucose (F-FDG) PET in patients with solitary pulmonary nodules (SPNs) at a volume larger than 5 mL.

Machine learning-based prediction of outcomes of the endoscopic endonasal approach in Cushing disease: is the future coming?

OBJECTIVE: Machine learning (ML) is an innovative method to analyze large and complex data sets. The aim of this study was to evaluate the use of ML t...

Editorial. Machine learning and artificial intelligence applied to the diagnosis and management of Cushing disease.

Performance of deep neural network-based artificial intelligence method in diabetic retinopathy screening: a systematic review and meta-analysis of diagnostic test accuracy.

OBJECTIVE: Automatic diabetic retinopathy screening system based on neural networks has been used to detect diabetic retinopathy (DR). However, there ...

Thyroid Nodule Malignancy Risk Stratification Using a Convolutional Neural Network.

This study evaluates the performance of convolutional neural networks (CNNs) in risk stratifying the malignant potential of thyroid nodules alongside ...

[Efficacy of Neoadjuvant Endocrine Therapy for Prostate Ductal Carcinoma with Large Multiple Cysts Prior to Robot-Assisted Laparoscopic Radical Prostatectomy].

A 71-year-old man with gross hematuria and urinary retention showed a 7×8 cm polycystic mass compressing the prostate on the right ventral side on pel...

Machine learning reveals serum sphingolipids as cholesterol-independent biomarkers of coronary artery disease.

BACKGROUNDCeramides are sphingolipids that play causative roles in diabetes and heart disease, with their serum levels measured clinically as biomarke...

Dynamic changes of views on the brain changes of Cushing's syndrome using different computer-assisted tool.

Cushing's syndrome (CS) provides a unique model for assessing the neurotoxic effect of chronic hypercortisolism on human brains. With the ongoing deve...

Machine Learning Applications in Endocrinology and Metabolism Research: An Overview.

Machine learning (ML) applications have received extensive attention in endocrinology research during the last decade. This review summarizes the basi...

Chatteringfree hybrid adaptive neuro-fuzzy inference system-particle swarm optimisation data fusion-based BG-level control.

In this study, a closed-loop control scheme is proposed for the glucose-insulin regulatory system in type-1 diabetic mellitus (T1DM) patients. Some in...

Medios- An offline, smartphone-based artificial intelligence algorithm for the diagnosis of diabetic retinopathy.

PURPOSE: An observational study to assess the sensitivity and specificity of the Medios smartphone-based offline deep learning artificial intelligence...

Validation of Deep Convolutional Neural Network-based algorithm for detection of diabetic retinopathy - Artificial intelligence versus clinician for screening.

PURPOSE: Deep learning is a newer and advanced subfield in artificial intelligence (AI). The aim of our study is to validate a machine-based algorithm...

Application of Machine Learning Methods in Predicting Nuclear Receptors and their Families.

Nuclear receptors (NRs) are a superfamily of ligand-dependent transcription factors that are closely related to cell development, differentiation, rep...

Machine Learning by Ultrasonography for Genetic Risk Stratification of Thyroid Nodules.

IMPORTANCE: Thyroid nodules are common incidental findings. Ultrasonography and molecular testing can be used to assess risk of malignant neoplasm.

Detection of Thyroid Nodules with Ultrasound Images Based on Deep Learning.

BACKGROUND: Thyroid nodules are a common clinical entity with high incidence. Ultrasound is often employed to detect and evaluate thyroid nodules. The...

Retinopathy Analysis Based on Deep Convolution Neural Network.

At medical checkups or mass screenings, the fundus examination is effective for early detection of systemic hypertension, arteriosclerosis, diabetic r...

Deep Learning Techniques for Diabetic Retinopathy Detection.

Diabetes occurs due to the excess of glucose in the blood that may affect many organs of the body. Elevated blood sugar in the body causes many proble...

Developing a Prototype Knowledge-Based System for Diagnosis and Treatment of Diabetes Using Data Mining Techniques.

BACKGROUND: Diabetes is a disease that affects the body's ability to produce or use insulin. A total of 425 million people are suffering from diabetes...

A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms.

BACKGROUND: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep ...