Latest AI and machine learning research in endocrinology for healthcare professionals.
When approaching thyroid gland tumor classification, the differentiation between samples with and without "papillary thyroid carcinoma-like" nuclei is a daunting task with high inter-observer variability among pathologists. Thus, there is increasing interest in the use of machine learning approaches to provide pathologists real-time decision support. In this paper, we optimize and quantitatively c...
A growing number of smart wearable biosensors are operating in the medical IoT environment and those that capture physiological signals have received special attention. Electrocardiogram (ECG) is one of the physiological signals used in the cardiovascular and medical fields that has encouraged researchers to discover new non-invasive methods to diagnose hyperglycemia as a personal variable. Over t...
Oral administration is the most convenient and commonly used approach for drug delivery, while it is still a challenge to overcome the complicated gas...
Applying deep learning in population genomics is challenging because of computational issues and lack of interpretable models. Here, we propose GenNet...
A 34-year-old woman with a history of congenital hypothyroidism and 15 years of obstructive sleep apnoea was admitted with a left submandibular swelli...
PURPOSE: Polycystic Ovary Syndrome (PCOS) is the most frequent endocrinopathy in women of reproductive age. Machine learning (ML) is the area of artif...
Deep neural networks (DNNs) that predict mutational status from H&E slides of cancers can enable inexpensive and timely precision oncology. Although e...
PURPOSE: A self-defined convolutional neural network is developed to automatically classify whole-body scintigraphic images of concern (i.e., the norm...
The time series of blood glucose concentration in diabetic patients are time-varying, nonlinear, and non-stationary. In order to improve the accuracy ...
BACKGROUND AND AIMS: Artificial Intelligence (AI) methods have recently become critical for research in diabetes in the era of big-data science.
PURPOSE: The objective of this study was to establish diagnostic technology to automatically grade the severity of diabetic retinopathy (DR) according...
OBJECTIVES: Circulating insulin concentrations mediate vascular-inflammatory and prothrombotic factors. However, it is unknown whether interindividual...
People with diabetes require lifelong access to healthcare services to delay the onset of complications. Their disease management processes generate g...
Diabetic retinopathy (DR) is a common chronic fundus disease, which has four different kinds of microvessel structure and microvascular lesions: micro...
Hammour fish (grouper fish) are known to be of great nutritional value for human consumption, as their protein has a high biological value and contain...
The study aims to explore the application of international classification of diseases (ICD) coding technology and embedded electronic medical record (...
AIMS: This research work presented a comparative study of machine learning (ML), including two objectives: (i) determination of the risk factors of di...
The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes i...
BACKGROUND: Sellar masses (SM) frequently present with insidious hormonal dysfunction. We previously showed that, by utilizing a combined reflex/refle...
BACKGROUND: Deep-learning algorithms (DLAs) have been used in artificial intelligence aided ultrasonography diagnosis of thyroid and breast lesions. H...