AIMC Topic: Humans

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A novel speech signal feature extraction technique to detect speech impairment in children accurately.

Computers in biology and medicine
Speech signal processing and extracting useful information from speech signal is necessary for speech language impairment (SLI) detection in children. Although different features has been suggested for SLI detection, there is still a scope exist for ...

Exploring data augmentation methods to enhance EEG measures for epilepsy seizure detection.

Computers in biology and medicine
Automatic seizure detection using machine learning can reduce the workload of clinicians in epilepsy diagnosis. However, the class imbalance between seizure and non-seizure data limits model performance. Data augmentation offers a solution, yet few s...

Novel Artificial Intelligence-Driven Infant Meningitis Screening From High-Resolution Ultrasound Imaging.

Ultrasound in medicine & biology
BACKGROUND: Infant meningitis can be a life-threatening disease and requires prompt and accurate diagnosis to prevent severe outcomes or death. Gold-standard diagnosis requires lumbar puncture (LP) to obtain and analyze cerebrospinal fluid (CSF). Des...

Deep Learning-Based Automated Detection of the Middle Cerebral Artery in Transcranial Doppler Ultrasound Examinations.

Ultrasound in medicine & biology
OBJECTIVE: Transcranial Doppler (TCD) ultrasound has significant clinical value for assessing cerebral hemodynamics, but its reliance on operator expertise limits broader clinical adoption. In this work, we present a lightweight real-time deep learni...

Advances in intraoperative imaging technologies for complex biliary disease.

Seminars in pediatric surgery
Intraoperative imaging has enhanced the precision of biliary surgery in pediatric patients by improving visualization and reducing complications. This review examines intraoperative cholangiography (IOC), fluorescent cholangiography (FC) with indocya...

Identification of novel biomarkers linked to M1 macrophage infiltration in the diagnosis of inflammatory bowel diseases.

International immunopharmacology
Inflammatory bowel disease (IBD) often lacks a definitive diagnostic standard, leading to diagnoses through exclusion. This study aimed to create a predictive model for IBD using bioinformatics and deep learning while identifying potential biomarkers...

Comparative analysis of machine learning-derived nomogram and biomarkers in predicting side-specific extraprostatic extension: Preliminary findings.

Clinical imaging
AIM: This study aimed to assess and compare the performance of nomograms and machine learning (ML) techniques using preoperative biomarkers for predicting side-specific extraprostatic extension (EPE) in prostate cancer, which is linked to poor outcom...

Ionic liquids and lysosomotropic detergents as inhibitors of the SARS-CoV-2 main protease: QSAR modeling, synthesis and biological testing.

Biochemical and biophysical research communications
SARS-CoV-2 infection is highly contagious, prompting the World Health Organization to classify it as a global public health emergency. The virus has numerous potential hosts, which complicates efforts for effective prevention, diagnosis, and treatmen...

Evaluation of meibomian gland dysfunction with deep learning model considering different datasets and gland morphology.

Computers in biology and medicine
Meibomian gland dysfunction (MGD) is recognized as the primary cause of evaporative-type dry eye disease (DED). Diagnosis typically involves assessing meibomian gland (MG) morphology alongside symptom evaluation. Traditionally, experts manually grade...

Radio DINO: A foundation model for advanced radiomics and AI-driven medical imaging analysis.

Computers in biology and medicine
Radiomics is transforming medical imaging by extracting complex features that enhance disease diagnosis, prognosis, and treatment evaluation. However, traditional approaches face significant challenges, such as the need for manual feature engineering...