AIMC Topic: Deep Learning

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Artificial intelligence-based model for predicting pulmonary arterial hypertension on chest x-ray images.

BMC pulmonary medicine
BACKGROUND: Pulmonary arterial hypertension is a serious medical condition. However, the condition is often misdiagnosed or a rather long delay occurs from symptom onset to diagnosis, associated with decreased 5-year survival. In this study, we devel...

Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images.

BMC medical imaging
BACKGROUND: Asymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This retrospective study aimed to use automated machine learning (AutoML) to develop a prediction model...

GADNN: a revolutionary hybrid deep learning neural network for age and sex determination utilizing cone beam computed tomography images of maxillary and frontal sinuses.

BMC medical research methodology
INTRODUCTION: The determination of identity factors such as age and sex has gained significance in both criminal and civil cases. Paranasal sinuses like frontal and maxillary sinuses, are resistant to trauma and can aid profiling. We developed a deep...

Deep-learning image analysis for high-throughput screening of opsono-phagocytosis-promoting monoclonal antibodies against Neisseria gonorrhoeae.

Scientific reports
Antimicrobial resistance (AMR) is nowadays a global health concern as bacterial pathogens are increasingly developing resistance to antibiotics. Monoclonal antibodies (mAbs) represent a powerful tool for addressing AMR thanks to their high specificit...

SlumberNet: deep learning classification of sleep stages using residual neural networks.

Scientific reports
Sleep research is fundamental to understanding health and well-being, as proper sleep is essential for maintaining optimal physiological function. Here we present SlumberNet, a novel deep learning model based on residual network (ResNet) architecture...

A novel deep learning-based perspective for tooth numbering and caries detection.

Clinical oral investigations
OBJECTIVES: The aim of this study was automatically detecting and numbering teeth in digital bitewing radiographs obtained from patients, and evaluating the diagnostic efficiency of decayed teeth in real time, using deep learning algorithms.

Development of a deep learning model that predicts critical events of pediatric patients admitted to general wards.

Scientific reports
Early detection of deteriorating patients is important to prevent life-threatening events and improve clinical outcomes. Efforts have been made to detect or prevent major events such as cardiopulmonary resuscitation, but previously developed tools ar...

filoVision - using deep learning and tip markers to automate filopodia analysis.

Journal of cell science
Filopodia are slender, actin-filled membrane projections used by various cell types for environment exploration. Analyzing filopodia often involves visualizing them using actin, filopodia tip or membrane markers. Due to the diversity of cell types th...

Research Progress of Artificial Intelligence in the Grading and Classification of Meningiomas.

Academic radiology
A meningioma is a common primary central nervous system tumor. The histological features of meningiomas vary significantly depending on the grade and subtype, leading to differences in treatment and prognosis. Therefore, early diagnosis, grading, and...

LAMA: Lesion-Aware Mixup Augmentation for Skin Lesion Segmentation.

Journal of imaging informatics in medicine
Deep learning can exceed dermatologists' diagnostic accuracy in experimental image environments. However, inaccurate segmentation of images with multiple skin lesions can be seen with current methods. Thus, information present in multiple-lesion imag...