AIMC Topic: Humans

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SHIVA-CMB: a deep-learning-based robust cerebral microbleed segmentation tool trained on multi-source T2*GRE- and susceptibility-weighted MRI.

Scientific reports
Cerebral microbleeds (CMB) represent a feature of cerebral small vessel disease (cSVD), a prominent vascular contributor to age-related cognitive decline, dementia, and stroke. They are visible as spherical hypointense signals on T2*- or susceptibili...

Human motion similarity evaluation based on deep metric learning.

Scientific reports
In order to eliminate the impact of camera viewpoint factors and human skeleton differences on the action similarity evaluation and to address the issue of human action similarity evaluation under different viewpoints, a method based on deep metric l...

Fuzzy APPSS: A novel method for quantifying COVID-19 impact in India under triangular spherical fuzzy environment.

Scientific reports
In the current scenario, decision-making models are essential for analyzing real-world problems. To address the dynamic nature of these problems, fuzzy decision-making models have been proposed by various researchers. However, an advanced technique i...

Limitations of panoramic radiographs in predicting mandibular wisdom tooth extraction and the potential of deep learning models to overcome them.

Scientific reports
Surgeons routinely interpret preoperative radiographic images for estimating the shape and position of the tooth prior to performing tooth extraction. In this study, we aimed to predict the difficulty of lower wisdom tooth extraction using only panor...

A proficient approach for the classification of Alzheimer's disease using a hybridization of machine learning and deep learning.

Scientific reports
Alzheimer's disease (AD) is a neurodegenerative disorder. It causes progressive degeneration of the nervous system, affecting the cognitive ability of the human brain. Over the past two decades, neuroimaging data from Magnetic Resonance Imaging (MRI)...

Innovative modified-net architecture: enhanced segmentation of deep vein thrombosis.

Scientific reports
A new era for diagnosing and treating Deep Vein Thrombosis (DVT) relies on precise segmentation from medical images. Our research introduces a novel algorithm, the Modified-Net architecture, which integrates a broad spectrum of architectural componen...

A machine learning-based immune response signature to facilitate prognosis prediction in patients with endometrial cancer.

Scientific reports
Endometrial cancer is the most prevalent form of gynecologic malignancy, with a significant surge in incidence among youngsters. Although the advent of the immunotherapy era has profoundly improved patient outcomes, not all patients benefit from immu...

Clinically validated classification of chronic wounds method with memristor-based cellular neural network.

Scientific reports
Chronic wounds are a syndrome that affects around 4% of the world population due to several pathologies. The COV-19 pandemic has enforced the need of developing new techniques and technologies that can help clinicians to monitor the affected patients...

A machine learning tool for early identification of celiac disease autoimmunity.

Scientific reports
Identifying which patients should undergo serologic screening for celiac disease (CD) may help diagnose patients who otherwise often experience diagnostic delays or remain undiagnosed. Using anonymized outpatient data from the electronic medical reco...

Interpretable machine learning-driven biomarker identification and validation for Alzheimer's disease.

Scientific reports
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by limited effective treatments, underscoring the critical need for early detection and diagnosis to improve intervention outcomes. This study integrates various bioinformatics me...