Endocrinology

Diagnostic Accuracy of Artificial Intelligence-Based Automated Diabetic Retinopathy Screening in Real-World Settings: A Systematic Review and Meta-Analysis.

PURPOSE: To evaluate the diagnostic accuracy of artificial intelligence (AI)-based automated diabetic retinopathy (DR) screening in real-world settings.

High cell density cultivation of Corynebacterium glutamicum by deep learning-assisted medium design and the subsequent feeding strategy.

To improve the cell productivity of Corynebacterium glutamicum, its initial specific growth rate was improved by medium improvement using deep neural network (DNN)-assisted design with Bayesian optimization (BO) and a genetic algorithm (GA). To obtain training data for the DNN, experimental design with an orthogonal array was set up using a chemically defined basal medium (GC XII). Based on the cu...

Gasless robot-assisted transaxillary hemithyroidectomy (RATH): learning curve and complications.

PURPOSE: Gasless robot-assisted transaxillary hemithyroidectomy (RATH) is regarded as an alternative surgical option for thyroid operations. However, ...

Robot therapy aids mental health in patients with hematological malignancy during hematopoietic stem cell transplantation in a protective isolation unit.

Patients with hematological malignancy experience physical and psychological pain, such as a sense of isolation and confinement due to intensive chemo...

Effects of Various Heavy Metal Exposures on Insulin Resistance in Non-diabetic Populations: Interpretability Analysis from Machine Learning Modeling Perspective.

Increasing and compelling evidence has been proved that heavy metal exposure is involved in the development of insulin resistance (IR). We trained an ...

ToxMPNN: A deep learning model for small molecule toxicity prediction.

Machine learning (ML) has shown a great promise in predicting toxicity of small molecules. However, the availability of data for such predictions is o...

A deep learning based holistic diagnosis system for immunohistochemistry interpretation and molecular subtyping.

BACKGROUND: Breast cancer in different molecular subtypes, which is determined by the overexpression rates of human epidermal growth factor receptor 2...

Artificial intelligence-aided 3D computed-tomography visualization technology for urological anatomy can significantly reduce the operation time of adrenal surgery.

Deep neural networks can differentiate thyroid pathologies on infrared hyperspectral images.

BACKGROUND AND OBJECTIVE: The thyroid is a gland responsible for producing important body hormones. Several pathologies can affect this gland, such as...

Integrated machine learning and deep learning for predicting diabetic nephropathy model construction, validation, and interpretability.

OBJECTIVE: To construct a risk prediction model for assisted diagnosis of Diabetic Nephropathy (DN) using machine learning algorithms, and to validate...

Diabetic Foot Screening Guidelines and the Role of Artificial Intelligence: Time to Turn the Tide!

Despite medical and technological advancements, foot amputations continue to rise. Thus, the effort of diabetic foot management should be toward preve...

Integrating network pharmacology, molecular docking and simulation approaches with machine learning reveals the multi-target pharmacological mechanism of against diabetic nephropathy.

Diabetic nephropathy (DN) is one of the most feared complications of diabetes and key cause of end-stage renal disease (ESRD). has been widely used t...

Initial Experience in Urological Surgery with a Novel Robotic Technology: Magnetic-Assisted Robotic Surgery in Urology.

Magnetic-assisted robotic surgery (MARS) has been developed to maximize patient benefits of minimally invasive surgery while enhancing surgeon contro...

Pediatric diabetes prediction using deep learning.

This study proposed a novel technique for early diabetes prediction with high accuracy. Recently, Deep Learning (DL) has been proven to be expeditious...

Usefulness of pituitary high-resolution 3D MRI with deep-learning-based reconstruction for perioperative evaluation of pituitary adenomas.

PURPOSE: To evaluate the diagnostic value of T1-weighted 3D fast spin-echo sequence (CUBE) with deep learning-based reconstruction (DLR) for depiction...

Machine Learning Method and Hyperspectral Imaging for Precise Determination of Glucose and Silicon Levels.

This article introduces an algorithm for detecting glucose and silicon levels in solution. The research focuses on addressing the critical need for ac...

Improved Glycemic Control through Robot-Assisted Remote Interview for Outpatients with Type 2 Diabetes: A Pilot Study.

: Our research group developed a robot-assisted diabetes self-management monitoring system to support Certified Diabetes Care and Education Specialist...

Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data.

The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare provi...

Haemorrhage diagnosis in colour fundus images using a fast-convolutional neural network based on a modified U-Net.

Retinal haemorrhage stands as an early indicator of diabetic retinopathy, necessitating accurate detection for timely diagnosis. Addressing this need,...

UC-stack: a deep learning computer automatic detection system for diabetic retinopathy classification.

. The existing diagnostic paradigm for diabetic retinopathy (DR) greatly relies on subjective assessments by medical practitioners utilizing optical i...