AI Medical Compendium Journal:
Frontiers in endocrinology

Showing 111 to 120 of 141 articles

Ultrasound images-based deep learning radiomics nomogram for preoperative prediction of rearrangement in papillary thyroid carcinoma.

Frontiers in endocrinology
PURPOSE: To create an ultrasound -based deep learning radiomics nomogram (DLRN) for preoperatively predicting the presence of rearrangement among patients with papillary thyroid carcinoma (PTC).

Identification of glycolysis genes signature for predicting prognosis in malignant pleural mesothelioma by bioinformatics and machine learning.

Frontiers in endocrinology
BACKGROUND: Glycolysis-related genes as prognostic markers in malignant pleural mesothelioma (MPM) is still unclear. We hope to explore the relationship between glycolytic pathway genes and MPM prognosis by constructing prognostic risk models through...

Development and validation of a machine learning-augmented algorithm for diabetes screening in community and primary care settings: A population-based study.

Frontiers in endocrinology
BACKGROUND: Opportunely screening for diabetes is crucial to reduce its related morbidity, mortality, and socioeconomic burden. Machine learning (ML) has excellent capability to maximize predictive accuracy. We aim to develop ML-augmented models for ...

An overview of deep learning applications in precocious puberty and thyroid dysfunction.

Frontiers in endocrinology
In the last decade, deep learning methods have garnered a great deal of attention in endocrinology research. In this article, we provide a summary of current deep learning applications in endocrine disorders caused by either precocious onset of adult...

Deep learning for screening primary osteopenia and osteoporosis using spine radiographs and patient clinical covariates in a Chinese population.

Frontiers in endocrinology
PURPOSE: Many high-risk osteopenia and osteoporosis patients remain undiagnosed. We proposed to construct a convolutional neural network model for screening primary osteopenia and osteoporosis based on the lumbar radiographs, and to compare the diagn...

Deep learning reveals cuproptosis features assist in predict prognosis and guide immunotherapy in lung adenocarcinoma.

Frontiers in endocrinology
BACKGROUND: Cuproptosis is a recently found non-apoptotic cell death type that holds promise as an emerging therapeutic modality in lung adenocarcinoma (LUAD) patients who develop resistance to radiotherapy and chemotherapy. However, the Cuproptosis'...

Deep-learning image reconstruction for image quality evaluation and accurate bone mineral density measurement on quantitative CT: A phantom-patient study.

Frontiers in endocrinology
BACKGROUND AND PURPOSE: To investigate the image quality and accurate bone mineral density (BMD) on quantitative CT (QCT) for osteoporosis screening by deep-learning image reconstruction (DLIR) based on a multi-phantom and patient study.

A comprehensive review of methods based on deep learning for diabetes-related foot ulcers.

Frontiers in endocrinology
BACKGROUND: Diabetes mellitus (DM) is a chronic disease with hyperglycemia. If not treated in time, it may lead to lower limb amputation. At the initial stage, the detection of diabetes-related foot ulcer (DFU) is very difficult. Deep learning has de...

Identification and Optimization of Contributing Factors for Precocious Puberty by Machine/Deep Learning Methods in Chinese Girls.

Frontiers in endocrinology
BACKGROUND AND OBJECTIVES: As the worldwide secular trends are toward earlier puberty, identification of contributing factors for precocious puberty is critical. We aimed to identify and optimize contributing factors responsible for onset of precocio...

Associations Between Different Dietary Vitamins and the Risk of Obesity in Children and Adolescents: A Machine Learning Approach.

Frontiers in endocrinology
BACKGROUNDS: Simultaneous dietary intake of vitamins is considered as a common and real scenario in daily life. However, limited prospective studies have evaluated the association between multivitamins intake and obesity in children and adolescents.