AIMC Topic: Algorithms

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A Review of Recent Developments in Artificial Intelligence and Big Data Technologies for Ophthalmology Referrals and Clinical Practice.

Medical science monitor : international medical journal of experimental and clinical research
Ophthalmology is undergoing rapid transformation through the integration of smart technologies such as artificial intelligence (AI), big data analytics, and clinical decision support systems (CDSS). With increasing pressure to improve clinical effici...

Explainable AI for pain perception: subject-independent EEG decoding using DeepSHAP and CNNs.

Biomedical physics & engineering express
Objective.Accurate classification of pain levels is essential for clinical monitoring, particularly in clinical populations with limited verbal communication. This study explores the feasibility of decoding pain from EEG using explainable deep learni...

ACFSENet: an adaptive cross-frequency global sparse encoding network for end-to-end EEG emotion recognition.

Biomedical physics & engineering express
End-to-end EEG-based emotion recognition is attracting increasing attention due to its potential in human-computer interaction, mental health, and affective brain-computer interfaces (aBCIs). However, most existing methods overlook cross-frequency in...

Harnessing AI in critical care: opportunities, challenges and key steps for success.

Thorax
BACKGROUND: The integration of artificial intelligence (AI) into critical care offers significant potential to enhance early diagnosis, predict patient deterioration, personalise treatment and inform clinical decision-making. Despite this promise, AI...

Dynamic Ensemble Selection for Early Detection of Deep Vein Thrombosis in Fracture Patients.

Journal of medical systems
Deep vein thrombosis (DVT) in fracture patients is often clinically silent, with a high incidence of thrombosis and associated mortality. Static machine learning methods struggle to address the challenge of early DVT diagnosis due to their inability ...

Low-complexity fetal heart rate monitoring from carbon-based single-channel dry electrodes maternal electrocardiogram.

Physiological measurement
. Fetal and maternal health during pregnancy can be monitored with sensors such as Doppler or scalp fetal ECG. This study focuses on single-channel dry electrode maternal abdominal ECG () to extract fetal heart rate () using a low-complexity algorith...

Interpretable deep learning for enhanced multi-class classification of gastrointestinal endoscopic images.

Biomedical physics & engineering express
Gastrointestinal (GI) endoscopy serves as a vital tool for assessing the GI tract and diagnosing related disorders. Recent progress in deep learning has shown significant improvements in identifying anomalies using sophisticated models and data augme...

Workflow improvements from automated large vessel occlusion detection algorithms are dependent on care team engagement.

Journal of neurointerventional surgery
BACKGROUND: Automated machine learning (ML)-based large vessel occlusion (LVO) detection algorithms have been shown to improve in-hospital workflow metrics including door-to-groin time (DTG). The degree to which care team engagement and interaction a...

Research on missing value prediction of measured ERT data for coal mine based on a GRNN algorithm.

PloS one
In the process of long-term monitoring of the coal seam floor of a coal mining face using electrical resistivity tomography (ERT), the data loss caused by electrode disconnection adversely affects early warning of water inrush and prevents the identi...

A physics-inspired memory-augmented deep learning framework for magnetic core loss prediction.

PloS one
Accurate prediction of magnetic core loss is a key challenge for improving the efficiency and reliability of power electronic systems. Traditional empirical models such as the Steinmetz equation are only applicable to sinusoidal steady-state conditio...