Primary Care

Disruptive innovations in the clinical laboratory: catching the wave of precision diagnostics.

Disruptive innovation is an invention that disrupts an existing market and creates a new one by providing a different set of values, which ultimately overtakes the existing market. Typically, when disruptive innovations are introduced, their performance is initially less than existing standard technologies, but because of their ability to bring the cost down, and with gradual improvement, they end...

DeepCervix: A deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques.

Cervical cancer, one of the most common fatal cancers among women, can be prevented by regular screening to detect any precancerous lesions at early stages and treat them. Pap smear test is a widely performed screening technique for early detection of cervical cancer, whereas this manual screening method suffers from high false-positive results because of human errors. To improve the manual screen...

Learning Carbohydrate Digestion and Insulin Absorption Curves Using Blood Glucose Level Prediction and Deep Learning Models.

Type 1 diabetes is a chronic disease caused by the inability of the pancreas to produce insulin. Patients suffering type 1 diabetes depend on the appr...

Using Wearables and Machine Learning to Enable Personalized Lifestyle Recommendations to Improve Blood Pressure.

Blood pressure (BP) is an essential indicator for human health and is known to be greatly influenced by lifestyle factors, like activity and sleep fa...

Prophylactic effect of . seed extract on inflammatory markers and histopathological changes in high-fat-fed ovariectomized rats.

BACKGROUND AND AIM: L. seeds (TFG) are used as spices in Indian cuisine. In Indian traditional medicine, TFG is used to treat diabetes, dyslipidemia,...

Development of Machine Learning Models for Prediction of Osteoporosis from Clinical Health Examination Data.

Osteoporosis is treatable but often overlooked in clinical practice. We aimed to construct prediction models with machine learning algorithms to serve...

Generalizability of heterogeneous treatment effects based on causal forests applied to two randomized clinical trials of intensive glycemic control.

Purpose Machine learning is an attractive tool for identifying heterogeneous treatment effects (HTE) of interventions but generalizability of machine ...

Role of Robotics and Artificial Intelligence in Oral Health and Preventive Dentistry - Knowledge, Perception and Attitude of Dentists.

PURPOSE: To assess the knowledge, attitude and perception of dentists (dental students, dental school graduates/interns, postgraduate dentists) of the...

PyPEF-An Integrated Framework for Data-Driven Protein Engineering.

Data-driven strategies are gaining increased attention in protein engineering due to recent advances in access to large experimental databanks of prot...

Machine Learning to Identify Metabolic Subtypes of Obesity: A Multi-Center Study.

BACKGROUND AND OBJECTIVE: Clinical characteristics of obesity are heterogenous, but current classification for diagnosis is simply based on BMI or met...

Economic Evaluations of Artificial Intelligence in Ophthalmology.

Artificial intelligence (AI) is expected to cause significant medical quality enhancements and cost-saving improvements in ophthalmology. Although the...

Health Recognition Algorithm for Sports Training Based on Bi-GRU Neural Networks.

The healthcare benefits associated with regular physical activity recognition and monitoring have been considered in several research studies. Regular...

Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning.

Retinopathy of prematurity (ROP) is a potentially blinding disorder seen in low birth weight preterm infants. In India, the burden of ROP is high, wit...

Solvent Screening for Solubility Enhancement of Theophylline in Neat, Binary and Ternary NADES Solvents: New Measurements and Ensemble Machine Learning.

Theophylline, a typical representative of active pharmaceutical ingredients, was selected to study the characteristics of experimental and theoretical...

The effect of cardiac rhythm on artificial intelligence-enabled ECG evaluation of left ventricular ejection fraction prediction in cardiac intensive care unit patients.

The presence of left ventricular systolic dysfunction (LVSD) alters clinical management and prognosis in most acute and chronic cardiovascular conditi...

Impact of Artificial Intelligence Decision Support Using Deep Learning on Breast Cancer Screening Interpretation with Single-View Wide-Angle Digital Breast Tomosynthesis.

Background The high volume of data in digital breast tomosynthesis (DBT) and the lack of agreement on how to best implement it in screening programs m...

Evaluating the Efficacy of Povidone-Iodine Solution Infection Prophylaxis in Immediate Tissue Expander-Based Breast Reconstruction: A Controlled Retrospective Analysis.

There is currently no consensus among plastic surgeons regarding the optimal infection prophylaxis for immediate tissue expander placement following ...

Machine Learning for Predicting the 3-Year Risk of Incident Diabetes in Chinese Adults.

We aimed to establish and validate a risk assessment system that combines demographic and clinical variables to predict the 3-year risk of incident d...

Deep learning for diabetic retinopathy detection and classification based on fundus images: A review.

Diabetic Retinopathy is a retina disease caused by diabetes mellitus and it is the leading cause of blindness globally. Early detection and treatment ...

Discovery of novel DGAT1 inhibitors by combination of machine learning methods, pharmacophore model and 3D-QSAR model.

DGAT1 plays a crucial controlling role in triglyceride biosynthetic pathways, which makes it an attractive therapeutic target for obesity. Thus, devel...