AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Electrochemical Techniques

Showing 1 to 10 of 101 articles

Clear Filters

Label-Free Classification of L-Histidine Vs Artificial Human Sweat Using Laser Scribed Electrodes and a Multi-Layer Perceptron Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
A challenge in wearable technology lies in the realtime monitoring of molecular biomarkers associated with human health. Electrochemical sensors are one of the most useful tools for this purpose and are commonly used in health monitoring devices. Ele...

Artificial-Intelligence Bio-Inspired Peptide for Salivary Detection of SARS-CoV-2 in Electrochemical Biosensor Integrated with Machine Learning Algorithms.

Biosensors
Developing affordable, rapid, and accurate biosensors is essential for SARS-CoV-2 surveillance and early detection. We created a bio-inspired peptide, using the SAGAPEP AI platform, for COVID-19 salivary diagnostics via a portable electrochemical dev...

Back Propagation Artificial Neural Network Enhanced Accuracy of Multi-Mode Sensors.

Biosensors
The detection of small molecules is critical in many fields, but traditional electrochemical detection methods often exhibit limited accuracy. The construction of multi-mode sensors is a common strategy to improve detection accuracy. However, most ex...

Application of Machine Learning for FOS/TAC Soft Sensing in Bio-Electrochemical Anaerobic Digestion.

Molecules (Basel, Switzerland)
This study explores the application of various machine learning (ML) models for the real-time prediction of the FOS/TAC ratio in microbial electrolysis cell anaerobic digestion (MEC-AD) systems using data collected during a 160-day trial treating bre...

Neural Network-Enhanced Electrochemical/SERS Dual-Mode Microfluidic Platform for Accurate Detection of Interleukin-6 in Diabetic Wound Exudates.

Analytical chemistry
Interleukin-6 (IL-6) plays a pivotal role in the inflammatory response of diabetic wounds, providing critical insights for clinicians in the development of personalized treatment strategies. However, the low concentration of IL-6 in biological sample...

MIP-based electrochemical sensor with machine learning for accurate ZIKV detection in protein- and glucose-rich urine.

Analytical biochemistry
Nowadays, a multitude of biosensors are being developed worldwide. However, a significant challenge arises when these biosensors are tested in real sample environments, as many of them fail to perform as expected. This can lead to ambiguous results a...

ZnO nanoflower-mediated paper-based electrochemical biosensor for perfect classification of cardiac biomarkers with physics-informed machine learning.

Mikrochimica acta
The widespread exposure of acute myocardial infarction globally demands an ultrasensitive, rapid, and cost-effective biosensor for troponin-I and T in a dynamic concentration range. Traditionally, the saturation of sensor response limits accurate pre...

Machine Learning-Assisted Portable Dual-Readout Biosensor for Visual Detection of Milk Allergen.

Nano letters
Beta-lactoglobulin (β-LG), the primary allergen in cow's milk, makes developing a rapid, sensitive, and convenient detection method essential for individuals with allergies. In this study, a graphdiyne-based self-powered electrochemical biosensor has...

Electrochemical activation of alum sludge for the adsorption of lead (Pb(II)) and arsenic (As): Mechanistic insights and machine learning (ML) analysis.

Bioresource technology
Alum sludge (AlS) has emerged as an effective adsorbent for anionic contaminants, with traditional activation methods like acid/base treatments and calcination employed to enhance its adsorption capacity. However, these approaches encounter significa...

Enhancing the Predictive Performance of Molecularly Imprinted Polymer-Based Electrochemical Sensors Using a Stacking Regressor Ensemble of Machine Learning Models.

ACS sensors
The performance of electrochemical sensors is influenced by various factors. To enhance the effectiveness of these sensors, it is crucial to find the right balance among these factors. Researchers and engineers continually explore innovative approach...