Issues and Limitations on the Road to Fair and Inclusive AI Solutions for Biomedical Challenges.

Objective: In this paper, we explore the correlation between performance reporting and the development of inclusive AI solutions for biomedical problems. Our study examines the critical aspects of bias and noise in the context of medical decision support, aiming to provide actionable solutions. Contributions: A key contribution of our work is the recognition that measurement processes introduce noise and bias arising from human data interpretation and selection.

Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies.

Generative Adversarial Networks (GANs) have emerged as a powerful tool in artificial intelligence, particularly for unsupervised learning. This systematic review analyzes GAN applications in healthcare, focusing on image and signal-based studies across various clinical domains. Following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we reviewed 72 relevant journal articles.

A unique chaperoning mechanism in class A JDPs recognizes and stabilizes mutant p53.

J-domain proteins (JDPs) constitute a large family of molecular chaperones that bind a broad spectrum of substrates, targeting them to Hsp70, thus determining the specificity of and activating the entire chaperone functional cycle. The malfunction of JDPs is therefore inextricably linked to myriad human disorders. Here, we uncover a unique mechanism by which chaperones recognize misfolded clients, present in human class A JDPs.

Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space.

Positron emission tomography/computed tomography (PET/CT) is increasingly used in oncology, neurology, cardiology, and emerging medical fields. The success stems from the cohesive information that hybrid PET/CT imaging offers, surpassing the capabilities of individual modalities when used in isolation for different malignancies. However, manual image interpretation requires extensive disease-specific knowledge, and it is a time-consuming aspect of physicians' daily routines.

Automated detection of airflow obstructive diseases: A systematic review of the last decade (2013-2022).

Background And Objective: Obstructive airway diseases, including asthma and Chronic Obstructive Pulmonary Disease (COPD), are two of the most common chronic respiratory health problems. Both of these conditions require health professional expertise in making a diagnosis. Hence, this process is time intensive for healthcare providers and the diagnostic quality is subject to intra- and inter- operator variability.

Speech Emotion Recognition Using Attention Model.

Speech emotion recognition is an important research topic that can help to maintain and improve public health and contribute towards the ongoing progress of healthcare technology. There have been several advancements in the field of speech emotion recognition systems including the use of deep learning models and new acoustic and temporal features. This paper proposes a self-attention-based deep learning model that was created by combining a two-dimensional Convolutional Neural Network (CNN) and a long short-term memory (LSTM) network.

L-Tetrolet Pattern-Based Sleep Stage Classification Model Using Balanced EEG Datasets.

Background: Sleep stage classification is a crucial process for the diagnosis of sleep or sleep-related diseases. Currently, this process is based on manual electroencephalogram (EEG) analysis, which is resource-intensive and error-prone. Various machine learning models have been recommended to standardize and automate the analysis process to address these problems.

A review of automated sleep disorder detection.

Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating sleep disorder detection.

Heart rate variability for medical decision support systems: A review.

Heart Rate Variability (HRV) is a good predictor of human health because the heart rhythm is modulated by a wide range of physiological processes. This statement embodies both challenges to and opportunities for HRV analysis. Opportunities arise from the wide-ranging applicability of HRV analysis for disease detection.

Application of photoplethysmography signals for healthcare systems: An in-depth review.

Background And Objectives: Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, heart rate variability, blood pressure, etc. Hence, PPG signals can produce a wide variety of biological information that can be useful for the detection and diagnosis of various health problems. In this review, we are interested in the possible health disorders that can be detected using PPG signals.

NudC guides client transfer between the Hsp40/70 and Hsp90 chaperone systems.

In the eukaryotic cytosol, the Hsp70 and the Hsp90 chaperone machines work in tandem with the maturation of a diverse array of client proteins. The transfer of nonnative clients between these systems is essential to the chaperoning process, but how it is regulated is still not clear. We discovered that NudC is an essential transfer factor with an unprecedented mode of action: NudC interacts with Hsp40 in Hsp40-Hsp70-client complexes and displaces Hsp70.

Automated Arrhythmia Detection Based on RR Intervals.

Abnormal heart rhythms, also known as arrhythmias, can be life-threatening. AFIB and AFL are examples of arrhythmia that affect a growing number of patients. This paper describes a method that can support clinicians during arrhythmia diagnosis.

Hsp40s play complementary roles in the prevention of tau amyloid formation.

The microtubule-associated protein, tau, is the major subunit of neurofibrillary tangles associated with neurodegenerative conditions, such as Alzheimer's disease. In the cell, however, tau aggregation can be prevented by a class of proteins known as molecular chaperones. While numerous chaperones are known to interact with tau, though, little is known regarding the mechanisms by which these prevent tau aggregation.

Fusion of Higher Order Spectra and Texture Extraction Methods for Automated Stroke Severity Classification with MRI Images.

This paper presents a scientific foundation for automated stroke severity classification. We have constructed and assessed a system which extracts diagnostically relevant information from Magnetic Resonance Imaging (MRI) images. The design was based on 267 images that show the brain from individual subjects after stroke.

Automated ASD detection using hybrid deep lightweight features extracted from EEG signals.

Background: Autism spectrum disorder is a common group of conditions affecting about one in 54 children. Electroencephalogram (EEG) signals from children with autism have a common morphological pattern which makes them distinguishable from normal EEG. We have used this type of signal to design and implement an automated autism detection model.

Future IoT tools for COVID-19 contact tracing and prediction: A review of the state-of-the-science.

In 2020 the world is facing unprecedented challenges due to COVID-19. To address these challenges, many digital tools are being explored and developed to contain the spread of the disease. With the lack of availability of vaccines, there is an urgent need to avert resurgence of infections by putting some measures, such as contact tracing, in place.

Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention.

In this paper, we discuss hybrid decision support to monitor atrial fibrillation for stroke prevention. Hybrid decision support takes the form of human experts and machine algorithms working cooperatively on a diagnosis. The link to stroke prevention comes from the fact that patients with Atrial Fibrillation (AF) have a fivefold increased stroke risk.

HSP40 proteins use class-specific regulation to drive HSP70 functional diversity.

The ubiquitous heat shock protein 70 (HSP70) family consists of ATP-dependent molecular chaperones, which perform numerous cellular functions that affect almost all aspects of the protein life cycle from synthesis to degradation. Achieving this broad spectrum of functions requires precise regulation of HSP70 activity. Proteins of the HSP40 family, also known as J-domain proteins (JDPs), have a key role in this process by preselecting substrates for transfer to their HSP70 partners and by stimulating the ATP hydrolysis of HSP70, leading to stable substrate binding.