Child face detection on front passenger seat through deep learning.

Objective: One of the main causes of death worldwide among young people are car crashes, and most of these fatalities occur to children who are seated in the front passenger seat and who, at the time of an accident, receive a direct impact from the airbags, which is lethal for children under 13 years of age. The present study seeks to raise awareness of this risk by interior monitoring with a child face detection system that serves to alert the driver that the child should not be sitting in the front passenger seat.

Methods: The system incorporates processing of data collected, elements of deep learning such as transfer learning, fine-tunning and facial detection to identify the presence of children in a robust way, which was achieved by training with a dataset generated from scratch for this specific purpose.

A Bibliometric Review on of the First Fifteen Years of Research (2009-2023).

Introduction: is a relatively novel pathogen first described in 2009 in Japan. It has increased its presence worldwide, becoming a public health concern due to its innate resistance to antifungals and outbreak potential.

Methods: We performed a query using the word " from the Scopus database, further performing a bibliometric analysis with the open-source R package Bibliometrix.

A Bibliometric Review on Gut Microbiome and Alzheimer's Disease Between 2012 and 2021.

Research on the microbiome has drawn an increasing amount of attention over the past decade. Even more so for its association with disease. Neurodegenerative diseases, such as Alzheimer's disease (AD) have been a subject of study for a long time with slow success in improving diagnostic accuracy or identifying a possibility for treatment.

Measuring Heart Rate Variability Using Facial Video.

Heart Rate Variability (HRV) has become an important risk assessment tool when diagnosing illnesses related to heart health. HRV is typically measured with an electrocardiogram; however, there are multiple studies that use Photoplethysmography (PPG) instead. Measuring HRV with video is beneficial as a non-invasive, hands-free alternative and represents a more accessible approach.

In-Vehicle Alcohol Detection Using Low-Cost Sensors and Genetic Algorithms to Aid in the Drinking and Driving Detection.

Worldwide, motor vehicle accidents are one of the leading causes of death, with alcohol-related accidents playing a significant role, particularly in child death. Aiming to aid in the prevention of this type of accidents, a novel non-invasive method capable of detecting the presence of alcohol inside a motor vehicle is presented. The proposed methodology uses a series of low-cost alcohol MQ3 sensors located inside the vehicle, whose signals are stored, standardized, time-adjusted, and transformed into 5 s window samples.

Robust Discovery of Mild Cognitive Impairment Subtypes and Their Risk of Alzheimer's Disease Conversion Using Unsupervised Machine Learning and Gaussian Mixture Modeling.

Background: Alzheimer's Disease (AD) is an irreversible, progressive brain disorder that slowly destroys memory and thinking skills. The ability to correctly predict the diagnosis of Alzheimer's disease in its earliest stages can help physicians make more informed clinical decisions on therapy plans.

Objective: This study aimed to determine whether the unsupervised discovering of latent classes of subjects with Mild Cognitive Impairment (MCI) may be useful in finding different prodromal AD stages and/or subjects with a low MCI to AD conversion risk.

Signal and Texture Features from T2 Maps for the Prediction of Mild Cognitive Impairment to Alzheimer's Disease Progression.

Early detection of Alzheimer's disease (AD) is crucial to preserve cognitive functions and provide the opportunity for patients to enter clinical trials. In recent years, some studies have reported that features related to the signal and texture of MRI images can be an effective biomarker of AD. To test these claims, a study was conducted using T2 maps, a sequence not previously studied, of 40 patients with mild cognitive impairment (MCI) from the Alzheimer's Disease Neuroimaging Initiative database, who either progressed to AD (18) or remained stable (22).

Identification and Temporal Characterization of Features Associated with the Conversion from Mild Cognitive Impairment to Alzheimer's Disease.

Background: Diagnosing Alzheimer's disease (AD) in its earliest stages is important for therapeutic and support planning. Similarly, being able to predict who will convert from mild cognitive impairment (MCI) to AD would have clinical implications.

Objectives: The goals of this study were to identify features from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database associated with the conversion from MCI to AD, and to characterize the temporal evolution of that conversion.

Speed Bump Detection Using Accelerometric Features: A Genetic Algorithm Approach.

Among the current challenges of the Smart City, traffic management and maintenance are of utmost importance. Road surface monitoring is currently performed by humans, but the road surface condition is one of the main indicators of road quality, and it may drastically affect fuel consumption and the safety of both drivers and pedestrians. Abnormalities in the road, such as manholes and potholes, can cause accidents when not identified by the drivers.

Scan-rescan precision of subchondral bone curvature maps from routine 3D DESS water excitation sequences: Data from the Osteoarthritis Initiative.

Background: Subchondral bone (SCB) undergoes changes in the shape of the articulating bone surfaces and is currently recognized as a key target in osteoarthritis (OA) treatment. The aim of this study was to present an automated system that determines the curvature of the SCB regions of the knee and to evaluate its cross-sectional and longitudinal scan-rescan precision

Methods: Six subjects with OA and six control subjects were selected from the Osteoarthritis Initiative (OAI) pilot study database. As per OAI protocol, these subjects underwent 3T MRI at baseline and every twelve months thereafter, including a 3D DESS WE sequence.

Integration and comparison of different genomic data for outcome prediction in cancer.

Background: In cancer, large-scale technologies such as next-generation sequencing and microarrays have produced a wide number of genomic features such as DNA copy number alterations (CNA), mRNA expression (EXPR), microRNA expression (MIRNA), and DNA somatic mutations (MUT), among others. Several analyses of a specific type of these genomic data have generated many prognostic biomarkers in cancer. However, it is uncertain which of these data is more powerful and whether the best data-type is cancer-type dependent.

Bilateral Image Subtraction and Multivariate Models for the Automated Triaging of Screening Mammograms.

Mammography is the most common and effective breast cancer screening test. However, the rate of positive findings is very low, making the radiologic interpretation monotonous and biased toward errors. This work presents a computer-aided diagnosis (CADx) method aimed to automatically triage mammogram sets.

Magnetization-prepared rapid acquisition with gradient echo magnetic resonance imaging signal and texture features for the prediction of mild cognitive impairment to Alzheimer's disease progression.

Early diagnoses of Alzheimer's disease (AD) would confer many benefits. Several biomarkers have been proposed to achieve such a task, where features extracted from magnetic resonance imaging (MRI) have played an important role. However, studies have focused exclusively on morphological characteristics.

Improved Diagnostic Multimodal Biomarkers for Alzheimer's Disease and Mild Cognitive Impairment.

The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) is very important for treatment research and patient care purposes. Few biomarkers are currently considered in clinical settings, and their use is still optional. The objective of this work was to determine whether multimodal and nonpreviously AD associated features could improve the classification accuracy between AD, MCI, and healthy controls, which may impact future AD biomarkers.

SurvExpress: an online biomarker validation tool and database for cancer gene expression data using survival analysis.

Validation of multi-gene biomarkers for clinical outcomes is one of the most important issues for cancer prognosis. An important source of information for virtual validation is the high number of available cancer datasets. Nevertheless, assessing the prognostic performance of a gene expression signature along datasets is a difficult task for Biologists and Physicians and also time-consuming for Statisticians and Bioinformaticians.