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A survey of artificial intelligence models for wireless capsule endoscopy videos for superior automatic diagnosis: problems and solutions

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Multimedia Tools and Applications 2025年 1-35页

作者： El-Gammal, Eman M. El-Shafai, Walid Taha, Taha E. El-Fishawy, Adel S. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Center of Advanced Software and Biomedical Clinical Engineering Consultations Faculty of Engineering Cairo University Giza Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia

Wireless Capsule Endoscopy (WCE) emerged as an innovative and patient-centric approach for non-invasive and painless examination of the gastrointestinal (GI) tract. It serves as a pivotal tool in helping medical practitioners in the early detection of anomalies within the intricate domain of the human GI tract. The automated identification of aberrations assumes paramount significance, contributing not only to temporal efficiency but also to timely diagnosis. Within the realm of academic literature, a plethora of Artificial Intelligence (AI) methodologies have been proposed to effectuate the automatic classification, segmentation, and synthesis of anomalies inherent in WCE images. This scholarly endeavor undertakes a comprehensive and meticulous survey, elucidating the spectrum of anomaly classification, summarization, and detection techniques harnessed for computer-Aided Diagnosis (CAD) in the context of WCE images. The survey begins by delineating the methodologies underpinning WCE image classification and video processing. Subsequently, an exhaustive evaluation of techniques for analyzing WCE images is furnished, accompanied by an in-depth exploration of pertinent surveys, in addition to a judicious assessment of their merits and demerits. Furthermore, this study undertakes a rigorous evaluation of prevailing datasets utilized to benchmark the efficacy of various WCE techniques. In summary, this survey serves as a vanguard, delineating promising avenues for future investigations in the realm of WCE image analysis models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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Implementation of a Multipath Fully Differential OTA in 0.18-μm CMOS Process

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IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2023年第1期31卷 147-151页

作者： Akbari, Meysam Hussein, Safwan Mawlood Hashim, Yasir Khateb, Fabian Kulej, Tomasz Tang, Kea-Tiong University of Kurdistan Department of Electrical Engineering Sanandaj66177 Iran Tishk International University Faculty of Engineering Department of Computer Engineering Erbil44001 Iraq Brno University of Defence Department of Electrical Engineering Brno66210 Czech Republic Czech Technical University in Prague Faculty of Biomedical Engineering Kladno16636 Czech Republic Czestochowa University of Technology Department of Electrical Engineering Czestochowa42201 Poland National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

This brief implements a highly efficient fully differential transconductance amplifier, based on several input-to-output paths. Some traditional techniques, such as positive feedback, nonlinear tail current sources, and current mirror-based paths, are combined to increase the transconductance, thus leading to larger dc gain and higher gain bandwidth (GBW) product. Two flipped voltage-follower (FVF) cells are employed as variable current sources to provide class-AB operation and adaptive biasing of all other drivers. The proposed structure includes several input-to-output paths that play the role of dynamic current boosters during the slewing phase, thus improving the slew rate (SR) performance. The circuit was fabricated in a TSMC 0.18-μm CMOS process with a silicon area of 54.5×30.1μm. Experimental results show a GBW of 173.3 MHz, a dc gain of 72.7 dB, and an SR of 139.4 V/μs for a capacitive load of 2×5 pF. The proposed circuit consumes 619 μW of power, under a supply voltage of 1.8 V. © 1993-2012 IEEE.

关键词： Operational amplifiers

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A Scattered Wireless EEG Recording System

A Scattered Wireless EEG Recording System

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2023 IEEE biomedical Circuits and Systems Conference, BioCAS 2023

作者： Han, Yiming Zhao, Linran Stephany, Raymond G Hsieh, Ju-Chun Wang, Huiliang Jia, Yaoyao The University of Texas at Austin Department of Electrical and Computer Engineering United States The University of Texas at Austin Department of Biomedical Engineering United States

ISBN: (纸本)9798350300260

This paper presents a scattered wirelessly powered Electroencephalographic (EEG) recording system with 32 standalone devices to enable untethered, continuous, long-term EEG recording. Each device incorporates a wireless low-power EEG recording ASIC, a receiver (Rx) coil, and a hydrogel electrode in a compact cube (1.2 cm × 1.2 cm × 0.5 cm). The 32 EEG devices are wirelessly powered by a transmitter (Tx) coil array, under the control of a central control unit, through an inductive link operating at 60 MHz. The 60 MHz carrier also serves as the data link, enabling forward data transmission of user commands from the central control unit to EEG devices and back data transmission from EEG devices to the central control unit. In each EEG device, the ASIC will decode the user commands that are loaded onto the inductive link carrier by a novel synchronized pulse width demodulator at a data rate of 4 Mbps to control the ASIC digitizing the EEG signal or transmitting the EEG data. The EEG analog frontend (AFE) with a gain of 45 dB, bandpass of 0.03 Hz to 800 Hz, and input-referred noise (IRN) of 4.5 μVrms amplifies and filters EEG signals. Then, the 10-bit successive approximation register analog-to-digital converter (SAR-ADC) with an effective number of bits (ENOB) of 9.6 bits digitizes the amplified EEG signals. The backscatter-based data Tx transfers the EEG data to the central control unit via load-shift-keying (LSK) modulation at a data rate of 3.75 Mbps. The overall ASIC and the prototype of the scattered wirelessly powered EEG recording system have been successfully evaluated on human subjects. © 2023 IEEE.

关键词： EEG recording inductive link scattered structure synchronized pulse width modulation

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An Explainable Artificial Intelligence model in the assessment of Brain MRI Lesions in Multiple Sclerosis using Amplitude Modulation - Frequency Modulation multi-scale feature sets 24

An Explainable Artificial Intelligence model in the assessme...

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24th International Conference on Digital Signal Processing, DSP 2023

作者： Nicolaou, Andria Kakas, Antonis Pattichis, Constantinos S. Pattichis, Marios S. Fotso, Kevin Loizou, Christos P. Pantzaris, Marios Department of Computer Science University of Cyprus Nicosia Cyprus Department of Electrical Engineering and Computer Engineering University of New Mexico United States Department of Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Cyprus Institute of Neurology and Genetics Nicosia Cyprus

ISBN: (纸本)9798350339598

The objective of this study was to implement an explainable artificial intelligence (AI) model with embedded rules to assess Multiple Sclerosis (MS) disease evolution based on brain Magnetic Resonance Imaging (MRI) multi-scale lesion evaluation. Amplitude Modulation-Frequency Modulation (AM-FM) features were extracted from manually segmented brain MS lesions obtained using MRI and were labeled with the Expanded Disability Status Scale (EDSS). Machine learning models were used to classify the MS subjects with a benign course of the disease and subjects with advanced accumulating disability. Rules were extracted from the selected model with high accuracy and then were modified to perform argumentation-based reasoning. It is demonstrated that the proposed explainable AI modeling can distinguish MS subjects and give meaningful information to track the progression of the disease. Future research will examine more subjects and add new feature sets and models. © 2023 IEEE.

关键词： Frequency modulation

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Detecting Cardiovascular Disease From PPG Signals using Machine Learning 4

Detecting Cardiovascular Disease From PPG Signals using Mach...

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4th IEEE International Multidisciplinary Conference on engineering Technology, IMCET 2023

作者： Sadek, Tamara Ahmad, Julia Khoury, Fadi Badawe, Heba Khraiche, Massoud American University of Beirut Department of Electrical and Computer Engineering Beirut Lebanon American University of Beirut Biomedical Engineering Department Beirut Lebanon

ISBN: (纸本)9798350313826

Cardiovascular diseases (CVD) are the leading cause of death worldwide. Early detection of CVD allows for the use of effective and inexpensive interventions. CVD prediction using machine learning has achieved high accuracy using multiple data types such as Photoplethysmography (PPG), Electrocardiogram (ECG), and Blood Pressure (BP). However, these biological signals cannot all be reliably collected in real-time from a single wearable device and necessitate patients to be equipped with several devices. In this work, we trained a machine learning model to predict CVD using only PPG data collected from wearable devices, achieving an accuracy of 88% along with an F1-score of 90%. The information that this system can provide the patients with regarding their health status would enable the early detection and management of CVD, eventually decreasing the risk of morbidity. © 2023 IEEE.

关键词： Photoplethysmography

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Improving Information Freshness in Edge-Assisted Smart Grids: An AoI-Aware Routing Strategy for Neighborhood Area Networks 22

Improving Information Freshness in Edge-Assisted Smart Grids...

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22nd IEEE International Conference on Industrial informatics, INDIN 2024

作者： Farag, Hossam Kotb, Mostafa Stefanović, Čedomir Gidlund, Mikael Department of Electronic Systems Aalborg University Denmark Department of Informatics Universität Hamburg Germany Department of Electrical Engineering Aswan University Egypt Mathematics Department Aswan University Egypt Department of Computer and Electrical Engineering Mid Sweden University Sweden

ISBN: (纸本)9798331527471

The integration of edge controllers into smart grid infrastructures facilitates advanced functionalities and high responsiveness, thereby bolstering the overall efficiency of the energy grid. The freshness of the sensing information received at the edge controller, captured by the Age of Information (AoI) metric, is vital to maintain system stability, where outdated information may lead to incorrect responses to grid conditions, potentially causing inefficiencies or system disruptions. However, the timeliness of the transmitted updates is mainly compromised by the delay and congestion within the routing links in the Neighborhood Area Network (NAN). In this work, we develop an intelligent routing strategy to improve the AoI of the Routing Protocol for Low-power and lossy networks (RPL), which is the common routing protocol for smart grids. Our proposed method is based on an AoI-aware parent selection mechanism, by which a node becomes attached to the parent with the highest probability of delivering a packet within a predefined AoI threshold. The prediction is made based on a supervised machine learning model, trained using the collection of heterogeneous routing metrics. The performance of the proposed method is evaluated via extensive discrete-event simulations and the results show its potency to improve the peak AoI and the AoI violation probability compared to the standard RPL. © 2024 IEEE.

关键词： Edge computing

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Anomaly Detection Employing a 5G Core Data Analytics Framework 13

Anomaly Detection Employing a 5G Core Data Analytics Framewo...

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13th IEEE International Conference on Cloud Networking, CloudNet 2024

作者： Oliveira, Junia Maisa Almeida, Jonatan De Britto E Silva, Erik Rodrigues Moreira, Larissa Ferreira Moreira, Rodrigo Silva, Flavio O. MacEdo, Daniel F. Nogueira, Jose Marcos Universidade Federal de Minas Computer Science Department Gerais Brazil University of Bologna Department of Electrical Electronic and Information Engineering Italy Universidade Federal de Minas Electrical Engineering Department Gerais Brazil Brazil Department of Informatics Portugal

ISBN: (纸本)9798350376562

The Network Data Analytics Function (NWDAF) within the 5G core is not an inherent feature of open-source 5G cores, making its implementation necessary based on provider demands. However, for those seeking to integrate NWDAF into the core but lacking expertise in core architecture or application programming interface (API) integration, this task can be daunting, potentially resulting in the abandonment of data analytics implementation efforts. While the current version of NWDAF specifies certain use cases, it does not provide alternatives for developing new analytics scenarios. To fill this gap, this paper proposes a framework for 5G network data analytics designed to facilitate algorithmic modifications and the addition of new analytical contexts. The framework was developed considering the 3GPP technical specifications related to data analytics. Its evaluation was performed through a use case focused on cybersecurity anomaly detection applied to a 5G network core. The results demonstrated that the framework simplifies installation, increases flexibility for new algorithms, and integrates seamlessly into the 5G network core, effectively addressing previously identified shortcomings. © 2024 IEEE.

关键词： 3GPP 5G core 5G network anomaly detection data analytics network data analytics NWDAF

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AtheroRisk: An Integrated computer Software System for Stroke Risk Stratification Utilizing Carotid Plaque Analysis in Ultrasound Videos

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SN computer Science 2025年第2期6卷 1-16页

作者： Gemenaris, Michalis Liapi, Georgia D. Markides, Christos Constantinou, Kyriacos Loizou, Christos P. Neophytou, Michalis Kardoulas, Dimitrios Pattichis, Marios S. Griffin, Maura Nicolaides, Andrew Pattichis, Constantinos S. Kyriacou, Efthyvoulos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Science and Engineering Frederick University Nicosia Cyprus Department of Computer Science University of Cyprus Nicosia Cyprus American Medical Center Nicosia Cyprus Vascular Surgery Clinic Attikon General University Hospital Athens Greece Department of Electrical and Comp. Engineering Univ. of New Mexico Albuquerque NM United States Vascular Screening and Diagnostic Center Nicosia Cyprus

In this study we propose and evaluate AtheroRisk, a standalone integrated computer software system for the analysis of carotid B-mode ultrasound (U/S) images and videos. Our goal was to provide a tool to help physicians stratify stroke risk. The presented system is based on outcomes from different research studies, as well as European guidelines for the evaluation and treatment of carotid artery disease. The goal is to enable reproducible analysis of atherosclerotic plaques in real time. AtheroRisk brings together analysis of U/S image and/or video of the carotid arteries facilitating anonymization, standardization, noise filtering and segmentation of plaques;followed by image and motion feature extraction to derive plaque-composition and stability. All the above steps are combined to determine the annual stroke risk rate and the 5-year stroke-free survival rate. The system is powered by an SQLite local database, in which the end user can save and manage data extracted from processing and analysis in an anonymized way. The first version of AtheroRisk was developed following an incremental software development process. The developed system was verified using 54 U/S videos (27 Asymptomatic, AS;27 Symptomatic, SY cases) while the clinician’s satisfaction and comments were collected through a questionnaire-based validation process. The results show that the integrated system proposed in this study can be successfully used for the automated image and video analysis of the CCA plaque in ultrasound videos. © The Author(s) 2025.

关键词： Carotid plaque analysis Motion analysis Plaque features Stroke risk stratification Ultrasound video

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Customizable Graphite-On-Paper-Based Keypads: Toward Disposable and Recyclable Wireless Human-Machine Interfaces

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2023年第1期2卷 25-33页

作者： Zulfiqar, Muhammad Hamza Hassan, Mahmood Ul Maqbool, Khawaja Qasim Zubair, Muhammad Mehmood, Muhammad Qasim Riaz, Kashif Massoud, Yehia MicroNano Laboratory Department of Electrical Engineering Lahore54600 Pakistan University of Engineering and Technology Lahore Department of Biomedical Engineering Lahore54890 Pakistan Bahria University Department of Computer Science Lahore Campus Islamabad44000 Pakistan Thuwal23955 Saudi Arabia

There is a rapid increase in the use of affordable electronic devices and human-machine interfaces (HMIs) with short serviceable life in almost every aspect of our lives. It is estimated that the waste electrical and electronic equipment (WEEE) and electronic waste (e-waste) generated in year 2021 was 57.5 million metric tons (Mt) and it is expected that the production of e-waste will increase to 110 Mt by the end of 2050. To mitigate these wastes, green HMIs are required which can be customized for multiple applications and can be recycled or disposed of with minimal environmental impact. This work presents customizable graphite-on-paper (GOP)-based keypad consists of interdigitated capacitive (IDC) touch sensors is demonstrated as HMIs to interact with different electronic and media applications wirelessly. The GOP keypads are fabricated through facile and green fabrication process by direct writing of graphite on flexible paper substrate. The GOP keypads can be fabricated in a home setting as the required materials are readily available, i.e., paper, pencils, and Arduino. The GOP keypads can be easily disposed of or recycled at the end of its life or requirement due to the employment of biodegradable materials like paper and graphite. The IDC touch sensors are optimized by analyzing the number of electrode fingers, finger's width, finger's overlap length, and spacing between the electrode fingers. The same GOP keypad is customized to interact with different electronic and media applications wirelessly, i.e., laptop cursor navigation, calculator app on mobile, numeric keypad, etc. The customizable GOP keypads have potential to be used as green wireless HMIs to enforce a circular economy by mitigating electronic and plastic waste, which leads to the vision of a sustainable and green world. © 2022 IEEE.

关键词： Substrates

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A Stretchable Leaky Integrate-and-Fire Neuron Enables Uncertainty Handling in Spiking Neural Networks

IEEE Electron Device Letters

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IEEE Electron Device Letters 2025年第6期46卷 999-1002页

作者： Mao, Linna Ke, Yizhen Su, Hengjie Zhang, Xinyang Sun, Biao Ma, Kaixue Li, Ting Institute of Biomedical Engineering Chinese Academy of Medical Sciences Peking Union Medical College Tianjin300192 China University of Houston Department of Electrical and Computer Engineering HoustonTX77005 United States Tianjin University School of Electrical and Information Engineering Tianjin300072 China Tianjin University School of Microelectronics Tianjin300072 China

Flexible neuromorphic computing systems have emerged as a promising candidate for next-generation edge computing systems, especially for the development of lightweight, low-power spiking neural network (SNN) systems. However, the mechanical coupling noise caused by flexible components poses challenges in uncertainty handling during the inference process in SNNs. Herein, we developed a stretchable leaky integrate-and-fire neuron device with enhanced uncertainty handling capabilities for stochastic SNN hardware implementation. These neuron devices utilize strain-induced uncertainty to model neuronal function as a conditional random variable. In addition, the SNN was incorporated in a Bayesian approximation model when the firing patterns exhibit mean stationarity over time steps. Simulations on classification and regression tasks on various datasets performed improved confidence and high generalization capability. The results demonstrate that the stretchable LIF neurons have significant potential for constructing robust and accurate SNNs for flexible neuromorphic computing. © 1980-2012 IEEE.

关键词： Edge computing

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