Sensor-Based Fuzzy Inference of COVID-19 Transmission Risk in Cruise Ships.

Cruise ships are densely populated ecosystems where infectious diseases can spread rapidly. Hence, early detection of infected individuals and risk assessment (RA) of the disease transmissibility are critical. Recent studies have investigated the long-term assessment of transmission risk on cruise ships; however, short-term approaches are limited by data unavailability.

E pluribus unum interpretable convolutional neural networks.

The adoption of convolutional neural network (CNN) models in high-stake domains is hindered by their inability to meet society's demand for transparency in decision-making. So far, a growing number of methodologies have emerged for developing CNN models that are interpretable by design. However, such models are not capable of providing interpretations in accordance with human perception, while maintaining competent performance.

Interpretable EEG-Based Emotion Recognition Using Fuzzy Cognitive Maps.

The brain is one of the most complex parts of the human body, consisting of billions of neurons and it is involved in almost all vital functions. To study the brain functionality, Electroencephalography (EEG) is used to record the electrical activity generated by the brain through electrodes placed on the scalp surface. In this paper, an auto-constructed Fuzzy Cognitive Map (FCM) model is used for interpretable emotion recognition, based on EEG signals.

Key research questions for implementation of artificial intelligence in capsule endoscopy.

Background: Artificial intelligence (AI) is rapidly infiltrating multiple areas in medicine, with gastrointestinal endoscopy paving the way in both research and clinical applications. Multiple challenges associated with the incorporation of AI in endoscopy are being addressed in recent consensus documents.

Objectives: In the current paper, we aimed to map future challenges and areas of research for the incorporation of AI in capsule endoscopy (CE) practice.

Artificial Intelligence in Cardiology-A Narrative Review of Current Status.

Artificial intelligence (AI) is an integral part of clinical decision support systems (CDSS), offering methods to approximate human reasoning and computationally infer decisions. Such methods are generally based on medical knowledge, either directly encoded with rules or automatically extracted from medical data using machine learning (ML). ML techniques, such as Artificial Neural Networks (ANNs) and support vector machines (SVMs), are based on mathematical models with parameters that can be optimally tuned using appropriate algorithms.

Constructive Fuzzy Cognitive Map for Depression Severity Estimation.

Depression is a common and serious medical disorder that negatively affects the mood and the emotions of people, especially adolescents. In this paper, a novel framework for automatically creating Fuzzy Cognitive Maps (FCMs) is proposed. It is applied for the estimation of the severity of depression among adolescents, based on their electroencephalogram (EEG).

A Constructive Fuzzy Representation Model for Heart Data Classification.

The early detection of Heart Disease (HD) and the prediction of Heart Failure (HF) via telemonitoring and can contribute to the reduction of patients' mortality and morbidity as well as to the reduction of respective treatment costs. In this study we propose a novel classification model based on fuzzy logic applied in the context of HD detection and HF prediction. The proposed model considers that data can be represented by fuzzy phrases constructed from fuzzy words, which are fuzzy sets derived from data.

Artificial intelligence in small bowel capsule endoscopy - current status, challenges and future promise.

Neural network-based solutions are under development to alleviate physicians from the tedious task of small-bowel capsule endoscopy reviewing. Computer-assisted detection is a critical step, aiming to reduce reading times while maintaining accuracy. Weakly supervised solutions have shown promising results; however, video-level evaluations are scarce, and no prospective studies have been conducted yet.

The future of capsule endoscopy in clinical practice: from diagnostic to therapeutic experimental prototype capsules.

Capsule endoscopy (CE) is indicated as a first-line clinical examination for the detection of small-bowel pathology, and there is an ever-growing drive for it to become a method for the screening of the entire gastrointestinal tract (GI). Although CE's main function is diagnosis, the research for therapeutic capabilities has intensified to make therapeutic capsule endoscopy (TCE) a target within reach. This manuscript presents the research evolution of CE and TCE through the last 5 years and describes notable problems, as well as clinical and technological challenges to overcome.

Sensors, Signal and Image Processing in Biomedicine and Assisted Living.

Sensor technologies are crucial in biomedicine, as the biomedical systems and devices used for screening and diagnosis rely on their efficiency and effectiveness [...

Uncertainty-Aware Visual Perception System for Outdoor Navigation of the Visually Challenged.

Every day, visually challenged people (VCP) face mobility restrictions and accessibility limitations. A short walk to a nearby destination, which for other individuals is taken for granted, becomes a challenge. To tackle this problem, we propose a novel visual perception system for outdoor navigation that can be evolved into an everyday visual aid for VCP.

Follow-up on: optimizing lesion detection in small bowel capsule endoscopy and beyond: from present problems to future solutions.

This review presents noteworthy advances in clinical and experimental Capsule Endoscopy (CE), focusing on the progress that has been reported over the last 5 years since our previous review on the subject. Areas covered: This study presents the commercially available CE platforms, as well as the advances made in optimizing the diagnostic capabilities of CE. The latter includes recent concept and prototype capsule endoscopes, medical approaches to improve diagnostic yield, and progress in software for enhancing visualization, abnormality detection, and lesion localization.

Use of Hyperspectral/Multispectral Imaging in Gastroenterology. Shedding Some⁻Different⁻Light into the Dark.

Hyperspectral/Multispectral imaging (HSI/MSI) technologies are able to sample from tens to hundreds of spectral channels within the electromagnetic spectrum, exceeding the capabilities of human vision. These spectral techniques are based on the principle that every material has a different response (reflection and absorption) to different wavelengths. Thereby, this technology facilitates the discrimination between different materials.

DINOSARC: Color Features Based on Selective Aggregation of Chromatic Image Components for Wireless Capsule Endoscopy.

Wireless Capsule Endoscopy (WCE) is a noninvasive diagnostic technique enabling the inspection of the whole gastrointestinal (GI) tract by capturing and wirelessly transmitting thousands of color images. Proprietary software "stitches" the images into videos for examination by accredited readers. However, the videos produced are of large length and consequently the reading task becomes harder and more prone to human errors.

Detecting and Locating Gastrointestinal Anomalies Using Deep Learning and Iterative Cluster Unification.

This paper proposes a novel methodology for automatic detection and localization of gastrointestinal (GI) anomalies in endoscopic video frame sequences. Training is performed with weakly annotated images, using only image-level, semantic labels instead of detailed, and pixel-level annotations. This makes it a cost-effective approach for the analysis of large videoendoscopy repositories.

Deep Endoscopic Visual Measurements.

Robotic endoscopic systems offer a minimally invasive approach to the examination of internal body structures, and their application is rapidly extending to cover the increasing needs for accurate therapeutic interventions. In this context, it is essential for such systems to be able to perform measurements, such as measuring the distance traveled by a wireless capsule endoscope, so as to determine the location of a lesion in the gastrointestinal tract, or to measure the size of lesions for diagnostic purposes. In this paper, we investigate the feasibility of performing contactless measurements using a computer vision approach based on neural networks.

Novel experimental and software methods for image reconstruction and localization in capsule endoscopy.

Background And Study Aims: Capsule endoscopy (CE) is invaluable for minimally invasive endoscopy of the gastrointestinal tract; however, several technological limitations remain including lack of reliable lesion localization. We present an approach to 3D reconstruction and localization using visual information from 2D CE images.

Patients And Methods: Colored thumbtacks were secured in rows to the internal wall of a LifeLike bowel model.

Intelligent visual localization of wireless capsule endoscopes enhanced by color information.

Wireless capsule endoscopy (WCE) is performed with a miniature swallowable endoscope enabling the visualization of the whole gastrointestinal (GI) tract. One of the most challenging problems in WCE is the localization of the capsule endoscope (CE) within the GI lumen. Contemporary, radiation-free localization approaches are mainly based on the use of external sensors and transit time estimation techniques, with practically low localization accuracy.

KID Project: an internet-based digital video atlas of capsule endoscopy for research purposes.

Background And Aims:  Capsule endoscopy (CE) has revolutionized small-bowel (SB) investigation. Computational methods can enhance diagnostic yield (DY); however, incorporating machine learning algorithms (MLAs) into CE reading is difficult as large amounts of image annotations are required for training. Current databases lack graphic annotations of pathologies and cannot be used.

Wavelet-based signal analysis for heart failure hospitalization prediction.

Heart failure (HF) is commonly a chronic condition associated with frequent hospital admissions. Early knowledge about a possible deterioration of this condition would enable early treatment for the prevention of adverse events and related hospital admissions. In this paper we present a computational method for predictive information extraction from daily physiological signals, which can be obtained by a telemonitoring system with wearable sensors.

Telemonitoring predicts in advance heart failure admissions.

Background: Heart failure (HF) is increasingly common and characterised by frequent admissions to hospital. To reduce the risk of HF hospitalisation (HFH), approaches as telemonitoring (TM) have been introduced. This study aimed to develop an algorithm for detecting patients at high risk of HFH, using daily collected physiological data (blood pressure, heart rate, weight) by non-invasive TM.

Blood detection in wireless capsule endoscope images based on salient superpixels.

Wireless capsule endoscopy (WCE) enables screening of the gastrointestinal (GI) tract with a miniature, optical endoscope packed within a small swallowable capsule, wirelessly transmitting color images. In this paper we propose a novel method for automatic blood detection in contemporary WCE images. Blood is an alarming indication for the presence of pathologies requiring further treatment.

Comparative assessment of feature extraction methods for visual odometry in wireless capsule endoscopy.

Wireless capsule endoscopy (WCE) enables the non-invasive examination of the gastrointestinal (GI) tract by a swallowable device equipped with a miniature camera. Accurate localization of the capsule in the GI tract enables accurate localization of abnormalities for medical interventions such as biopsy and polyp resection; therefore, the optimization of the localization outcome is important. Current approaches to endoscopic capsule localization are mainly based on external sensors and transit time estimations.

Wireless endoscopy in 2020: Will it still be a capsule?

Currently, the major problem of all existing commercial capsule devices is the lack of control of movement. In the future, with an interface application, the clinician will be able to stop and direct the device into points of interest for detailed inspection/diagnosis, and therapy delivery. This editorial presents current commercially-available new designs, European projects and delivery capsule and gives an overview of the progress required and progress that will be achieved -according to the opinion of the authors- in the next 5 year leading to 2020.