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Automated engineered-stone silicosis screening and staging using Deep Learning with X-rays

computers in Biology and Medicine 2025年 191卷 110153-110153页

作者： Priego-Torres, Blanca Sanchez-Morillo, Daniel Khalili, Ebrahim Conde-Sánchez, Miguel Ángel García-Gámez, Andrés León-Jiménez, Antonio Bioengineering Automation and Robotics Research Group Department of Automation Engineering Electronics and Computer Architecture and Networks School of Engineering University of Cadiz Cádiz Puerto Real11519 Spain Puerta del Mar University Hospital Cádiz11009 Spain Pulmonology Department Puerta del Mar University Hospital Cádiz11009 Spain Radiology Department Puerta del Mar University Hospital Cádiz11009 Spain Radiology Department Puerto Real University Hospital Cádiz Puerto Real11510 Spain

Silicosis, a debilitating occupational lung disease caused by inhaling crystalline silica, continues to be a significant global health issue, especially with the increasing use of engineered stone (ES) surfaces containing high silica content. Traditional diagnostic methods, dependent on radiological interpretation, have low sensitivity, especially, in the early stages of the disease, and present variability between evaluators. This study explores the efficacy of deep learning techniques in automating the screening and staging of silicosis using chest X-ray images. Utilizing a comprehensive dataset, obtained from the medical records of a cohort of workers exposed to artificial quartz conglomerates, we implemented a preprocessing stage for rib-cage segmentation, followed by classification using state-of-the-art deep learning models. The segmentation model exhibited high precision, ensuring accurate identification of thoracic structures. In the screening phase, our models achieved near-perfect accuracy, with ROC AUC values reaching 1.0, effectively distinguishing between healthy individuals and those with silicosis. The models demonstrated remarkable precision in the staging of the disease. Nevertheless, differentiating between simple silicosis and progressive massive fibrosis, the evolved and complicated form of the disease, presented certain difficulties, especially during the transitional period, when assessment can be significantly subjective. Notwithstanding these difficulties, the models achieved an accuracy of around 81% and ROC AUC scores nearing 0.93. This study highlights the potential of deep learning to generate clinical decision support tools to increase the accuracy and effectiveness in the diagnosis and staging of silicosis, whose early detection would allow the patient to be moved away from all sources of occupational exposure, therefore constituting a substantial advancement in occupational health diagnostics. © 2025 The Authors

关键词： Occupational diseases

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Retinal Disorder Diagnosis Based on Hybrid Deep Learning Models

SSRN

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SSRN 2022年

作者： Sedik, Ahmed El-Hag, Noha A. El-Hoseny, Heba M. El-Banby, Ghada M. Khalaf, Ashraf A.M. Abd El-Samie, Fathi E. El-Shafai, Walid Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Egypt Menoufia University Egypt Department of Electronics and Electrical Communication Engineering Al-Obour High Institute for Engineering and Technology Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Deptartment of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh84428 Saudi Arabia Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtainedwith several modalities such as magnetic resonance (MR) and confocal microscopy need to be classified for anomaly *** MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, braintumors are represented as areas composed of abnormal cells that need to be detected. In addition, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify © 2022, The Authors. All rights reserved.

关键词： Ophthalmology

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Design and Implementation of IoT Enabled Smart Assistive Systems for Healthcare Applications

Design and Implementation of IoT Enabled Smart Assistive Sys...

引用

Communications and Information Technologies (GCCIT), Global Conference on

作者： Dankan Gowda V Srinivas D Rachakonda Srinivas Balaji Shesharao Ingole Priya Dongare Jadhav KDV Prasad Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India School of Business SR University Warangal Telangana India Department of Computer Science & Engineering Aditya Institute of Technology and Management Srikakulam Andhra Pradesh India Department of Robotics and Automation Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India (Research) Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

The use of IoT technology in healthcare systems has been adopted in patient monitoring because IoT technology has been adopted rapidly in the systems. This paper discusses on design and implementation of an IoT integrated smart assistive health care system to enhance the precision and speed of patient health assessment. In the proposed system, wearable sensors are used to capture respiratory rate, pulse rate, blood pressure, oxygen saturation, and body temperature with data to be sent to the cloud server as and when it is received. Some of the features are, however, small data transmission delays, high accuracy in the physiological signals and long durations of monitoring due to the low power consumption. The simulations showed that the response time falls below 500ms, and physicians retain up to a 2 % variance to clinical grade devices, which actualized robust and effective patient care. The incorporation of predicting capacities, thus providing ways of early identification of health complications, makes the system applicable in chronic disease treatments and telemedicine.

关键词： Temperature sensors Temperature measurement Patient monitoring Accuracy Power demand Medical services Blood pressure Time factors Biomedical monitoring Wearable sensors

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Implementing Genetic Algorithms for Optimization in Neuro-Cognitive Rehabilitation robotics

Implementing Genetic Algorithms for Optimization in Neuro-Co...

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Cognitive robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： L. B. Abhang Ravindra Changala Anudeb Ghosh Prabhakar S Manage Vuda Sreenivasa Rao B Kiran Bala Automation and Robotics Dept Pravara Rural Engineering College Loni Under. SPPU University Pune Department of Information Technology Guru Nanak Institutions Technical Campus Hyderabad India Department of Environmental Studies Siksha - Bhavana Visva-Bharati Santiniketan Department of Electronics and Communication KLE Technological University's MSSCET Belagavi Campus Belagavi Karnataka Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram A.P the Department Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Trichy

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

The study explores the intersection of robotics and neuro-cognitive rehabilitation to offer personalized assistance for individuals with neurological disorders, emphasizing autism spectrum disorder (ASD). The proposed methodology integrates Genetic Algorithms (GAs) to optimize the performance of robotic interventions. The data collection phase involves compiling a comprehensive image dataset capturing facial expressions, gestures, and relevant visual cues associated with ASD. Leveraging robotic platforms designed for therapeutic interventions, feature extraction techniques identify intricate patterns within the data. Advanced algorithms, including GAs, classify the dataset into positive (ASD) and negative (non-ASD) categories. The framework introduces a Diagnosis Matrix for enhanced diagnostic precision, correlating observed robotic interactions with clinical assessments. An Ontology Knowledge base adapts responses based on evolving patient needs. The proposed method surpasses all others, achieving an accuracy of 95.08% and demonstrating superior precision, recall, and F1-score metrics. This indicates the efficacy of the proposed approach in achieving a well-balanced performance with high accuracy and robustness in correctly identifying positive instances. The results underscore the potential of the proposed method for classification tasks, showcasing its superiority in comparison to traditional SVM, CNN, and even a well-established deep learning architecture like VGG-16. The Receiver Operating Characteristic curve validates the model's discriminatory power.

关键词： Support vector machines Neurological diseases Visualization Knowledge based systems Receivers Ontologies Robustness

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A Learning Assisted Approach for Electronic and Optical Characterization of Tin-Doped Titanium Oxide 2

A Learning Assisted Approach for Electronic and Optical Char...

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2nd IEEE International Conference on Artificial Intelligence in Everything, AIE 2022

作者： Vubangsi, Mercel Nforna, Edwin Akongnwi Kamsu, Brigitte Fotso Tchoffo, M. Al-Turjman, Fadi Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey Htttc Bambili University of Bamenda Nwr Computational Materials Science Lab Department of Computer Science Cameroon Htttc Bambili University of Bamenda Materials Science Research Unit Department of Fundamental Sciences Cameroon Faculty of Science University of Dschang Condensed Matter Electronics and Signal Processing Research Unit Department of Physics Cameroon University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Mersin Kyrenia10 Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

In this work, we evaluate the effect of variation of mole fraction of Tin in doped titanium oxide on band gap. To achieve this, we resort to Matminer's "dielectric_constant"dataset and apply an algorithm to featurize the chemical formulae of the compounds in the dataset. Using k-fold cross-validation of random forest, we obtain a Machine learning model with R2 value of 0.81 MAE=0.45. Applying our model to predict the band gap of doped Titanium oxide over a range of 6 compositions, we find that the 50% doping level has a remarkably lower band gap than all other compositions which makes it a more suitable candidate for optical applications such as in solar cells. © 2022 IEEE.

关键词： Smart contract

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Evaluating the Effect of Speed and Acceleration on Human Factors During an Assembly Task in Human-Robot Collaboration

SSRN

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SSRN 2023年

作者： Apraiz Iriarte, Ainhoa Lasa, Ganix Mazmela, Maitane Arana-Arexolaleiba, Nestor Serrano, Antonio Elguea, Íñigo Etxabe, Amaia Design Innovation Centre Faculty of Engineering Mechanical and Industrial Production Mondragon Unibertsitatea Arrasate20500 Spain Robotics and Automation Electronics and Computer Science Department Mondragon Unibertsitatea Arrasate20500 Spain Research & Development Department Electrotécnica Alavesa S.L. Vitoria-Gasteiz1010 Spain

In Industry 5.0, the workers' well-being is the central pillar. Therefore, research on methods and technics to improve the workers' user experience in a human-robot collaborative environment is necessary. While the effects of dynamic variables, such as speed and acceleration, on human safety have been extensively studied, their impact on human perception has not been fully explored. This study investigates the effects of the robot's speed and acceleration on humans. An experimental research approach was used, where 20 participants (10 women and 10 men) performed an assembly task collaborating with a robot. Two experiments were defined, and the participants were evenly distributed: in the first experiment, the participants started by performing the task at a slow robot speed and then performed the same task at a faster speed. In the second experiment, the other half followed the opposite procedure. Key Performance Indicators (KPIs), physiological values (via EEG and GSR), and perceptual values (using the standardised UEQ-S questionnaire) were collected. The results showed that the robot's speed and acceleration impact the task's completion time and participants' emotional responses. Our results lead to a new concept, "HRI speed bell", which indicates that it is necessary to investigate the optimal speed and acceleration to ensure good trust and perceived safety. Furthermore, the task sequence also influences participants' expectations and performance. Finally, gender-differentiated results were also observed. © 2023, The Authors. All rights reserved.

关键词： Robots

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Development of an Efficient Charging Control System for Pico-Hydro Power Generation Utilizing Multiple Impact Water Wheel (MIWW) with MPPT Control

Development of an Efficient Charging Control System for Pico...

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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Yusuke Asami Hiroshi Takami Fuminori Ishibashi Shota Kawate Hidemi Kurita Masaki Shimizu Graduate School of Electrical Eng. and Computer Science Shibaura Institute of Technology Tokyo Japan Dept. of Electrical Eng. and Robotics Course College of Engineering Shibaura Institute of Technology Tokyo Japan Dept. of Electrical Eng. Faculty of Engineering Shibaura Institute of Technology Tokyo Japan Koa Electronics Co. Ltd. Nagano Japan Harmony with Nature Technology Laboratory Nagano Japan Souken Group Nagano Japan

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

This paper introduces an advanced charging control system for a Multiple Impact Water Wheel (MIWW) used in pico-hydropower generation. The control system maximizes energy conversion efficiency by employing a Maximum Power Point Tracking (MPPT) algorithm, which dynamically adjusts the system's operation based on real-time voltage and current measurements. Unlike traditional methods that rely on monitoring rotational speed, this approach simplifies the system by eliminating the need for speed sensors, thereby reducing costs and enhancing reliability. The control system is structured into four main operational phases: the water turbine acceleration phase, optimal control (MPPT) phase, drooping characteristic suppression phase, and overvoltage protection phase. Each phase is designed to optimize distinct aspects of system performance, ensuring stable and efficient power generation under varying water flow conditions. Experimental validation was conducted by subjecting the system to three different water pressure levels (0.036 MPa, 0.048 MPa, and 0.042 MPa). The results demonstrate that the system successfully maintained stable voltage and current, even during rapid fluctuations in water pressure, confirming the effectiveness of the proposed MPPT algorithm. By addressing the challenges of droop characteristics and overvoltage conditions, the control system ensures the reliability and efficiency of pico-hydropower systems. This research contributes to the development of cost-effective, efficient, and sustainable energy solutions, especially in regions lacking conventional power infrastructure.

关键词： Maximum power point trackers Water Fluctuations Heuristic algorithms Velocity control Wheels Sensor systems Voltage control Power generation Turbines

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AirLoc: Object-based Indoor Relocalization

arXiv

引用

arXiv 2023年

作者： Aryan Li, Bowen Scherer, Sebastian Lin, Yun-Jou Wang, Chen The Department of Electronics and Communication Delhi Technological University Delhi India The Robotics Institute Carnegie Mellon University PittsburghPA15213 United States OPPO Palo AltoCA United States The Department of Computer Science and Engineering State University of New York BuffaloNY14260 United States

Indoor relocalization is vital for both robotic tasks like autonomous exploration and civil applications such as navigation with a cell phone in a shopping mall. Some previous approaches adopt geometrical information such as key-point features or local textures to carry out indoor relocalization, but they either easily fail in environment with visually similar scenes or require many database images. Inspired by the fact that humans often remember places by recognizing unique landmarks, we resort to objects, which are more informative than geometry elements. In this work, we propose a simple yet effective object-based indoor relocalization approach, dubbed AirLoc. To overcome the critical challenges of the object reidentification and remembering object relationships, we extract object-wise appearance embedding and inter-object geometric relationship. The geometry and appearance features are integrated to generate cumulative scene features. This results in a robust, accurate, and portable indoor relocalization system, which outperforms the state-of-the-art methods in room-level relocalization by 9.5% of PR-AUC and 7% of accuracy. In addition to exhaustive evaluation, we also carry out real-world tests, where AirLoc shows robustness in challenges like severe occlusion, perceptual aliasing, viewpoint shift, and deformation. Copyright © 2023, The Authors. All rights reserved.

关键词： Geometry

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Dual-mode power reduction technique for real-time image and video processing board

Dual-mode power reduction technique for real-time image and ...

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作者： Jacob, Sunil Menon, Varun G. Joseph, Saira Sehdev, Paramjit Center for Robotics SCMS School of Engineering and Technology Cochin683576 India Department of Computer Science and Engineering SCMS School of Engineering and Technology Cochin683576 India Department of Electronics and Communication Engineering SCMS School of Engineering and Technology Cochin683576 India Department of Mathematics and Computer Science Coppin State University BaltimoreMD21216 United States

In real-time image and video processing boards, power, speed, and area are the most often used measures for determining the performance of motion imagery applications. Due to technological advancement, power consumption has gained major attention in real-time image processing ability compared to speed. The increase in on-chip temperature due to larger power consumption has resulted in reduced operating life of chip and battery-driven devices. In this work, a new logic family has been introduced i.e., dual-mode logic (DML), which provides flexibility between the optimization of energy and delay (E-D optimization). This gate can be switched between two modes of operation that is a static mode (CMOS-like mode), which provides low power consumption and dynamic mode, which provides high speed. Recently, power leakage has become a dominant problem due to continuous data transfer among a large number of connected devices. Thus, to reduce power leakage, a self-controllable voltage level (SVL) power reduction technique is used along with DML logic. In the SVL technique, a maximum dc voltage is provided to the active load circuit on-demand or decrease the dc supplied to the load circuit in the standby mode. Integrating DML with the SVL technique reduces power consumption as well as leakage power. A 4-bit RCA, 8-bit RCA, and 16-bit RCA are used for verifying the proposed method and comparison of performance parameters is done with a conventional circuit. Complete circuit implementation and simulation are carried out in TANNER EDA version 13 tools with operating voltage of 1 V. The proposed system is further applied to real-time image, and we obtain the finest resolution level with minimum power consumption. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Electric power utilization

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Autonomous Power Supply Development for Hydrometeorological Monitoring Station

Autonomous Power Supply Development for Hydrometeorological ...

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IEEE International Energy Conference and Exhibition (EnergyCon)

作者： Anatoliy Prystupa Volodymyr Kazymyr Anatolijs Zabaš ta Anatoliy Revko Serhii Stepenko Kateryna Novyk Electrical Engineering Information and Measurement Technologies Department Chernihiv Polytechnic National University Chernihiv Ukraine Information and Computer system Department Chernihiv Polytechnic National University Chernihiv Ukraine Institute of Industrial Electronics and Electrical Engineering Riga Technical University Riga Latvia Electronics Automation Robotics and Mechatronics Department Chernihiv Polytechnic National University Chernihiv Ukraine

This paper presents the results of the investigation of the power supply system of the autonomous monitoring system. In particular, calculations of the system’s daily balance of energy consumption were performed. Also, the question of the influence of the angle of inclination of solar converters on energy generation is considered and the necessity of collecting solar radiation during the whole daylight, not focusing on the maximum amount of energy, is substantiated. This research aimed to develop an algorithm for calculating the power supply system for autonomous monitoring systems. The proposed algorithm takes into account the coordination of diagrams of generation and consumption of energy within the autonomous monitoring station and statistical analysis of information from the NASA website. Experiments have shown the effectiveness of this method. It can be used in future design of power supply systems for autonomous monitoring stations.

关键词： Power measurement Power supplies Statistical analysis NASA Energy measurement Reliability theory Battery charge measurement

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