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Computer-aided Detection of Tuberculosis from Microbiological and Radiographic Images

Data Intelligence 2023年第4期5卷 1008-1032页

作者： Abdullahi Umar Ibrahim Ayse Gunnay Kibarer Fadi Al-Turjman Department of Biomedical Engineering Near East UniversityNicosiaMersin 10Turkey Research Center for Al and loT Faculty of EngineeringUniversity of KyreniaMersin 10Turkey Artificial Intelligence Engineering Dept. Al and Robotics InstituteNear East UniversityMersin 1Turkey

Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis *** can be detected from microscopic slides and chest X-ray but as a result of the high cases of tuberculosis,this method can be tedious for both Microbiologists and Radiologists and can lead to *** challenges can be solved by employing Computer-Aided Detection(CAD)via Al-driven models which learn features based on convolution and result in an output with high *** this paper,we described automated discrimination of X-ray and microscope slide images into tuberculosis and non-tuberculosis cases using pretrained AlexNet *** study employed Chest X-ray dataset made available on Kaggle repository and microscopic slide images from both Near East University Hospital and Kaggle *** classification of tuberculosis using microscopic slide images,the model achieved 90.56%accuracy,97.78%sensitivity and 83.33%specificity for 70:30 *** classification of tuberculosis using X-ray images,the model achieved 93.89%accuracy,96.67%sensitivity and 91.11%specificity for 70:30 *** result is in line with the notion that CNN models can be used for classifying medical images with higher accuracy and precision.

关键词： Tuberculosis Deep Learning Pretrained AlexNet Chest X-ray Microscopic slide

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BART LAB AI Elderly Support Robot for Enhanced Biomechanical Assistance in Sit-to-Stand and Walking Activities

BART LAB AI Elderly Support Robot for Enhanced Biomechanical...

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Rehabilitation engineering and Assistive Technology (i-CREATe), International Convention on

作者： Jackrit Suthakorn Branesh M. Pillai Bibhu Sharma Department of Biomedical Engineering Center for Biomedical and Robotics Technology (BART LAB) Faculty of Engineering Mahidol University Thailand

ISBN: (数字)9798350355154

ISBN: (纸本)9798350355161

The increasing elderly population underscores the pressing need for technological solutions that enhance Quality of Life (QoL) by facilitating independent living. Mobility, encompassing essential movements such as sit-to-stand, stand-to-sit, and walking, becomes increasingly challenging with age, making it a critical aspect of independence in the elderly. This research aims to develop a BART LAB AI-Elderly Supporting Robot, designed to assist elderly individuals in sit-to-stand and walking activities, thereby improving their mobility and independence. The design and control mechanisms of the robot are based on detailed biomechanical modeling and optimized to align with the natural movement patterns of elderly individuals. The iterative design process involved biomechanical analysis, system optimization, and the subsequent development and validation of the robot. The optimization process utilized biomechanical data to tailor the kinematic and kinetic features of the robot, ensuring that the support provided is both effective and intuitive for the user. The validation results demonstrated that the robot significantly improved the mobility of elderly users while respecting their physiological limits, thereby enhancing their ability to live independently and safely. This development has substantial implications for elderly care by offering a robotic assistant that improves mobility and promotes safer and more independent living for the elderly.

关键词： Legged locomotion Biomechanics Technological innovation Pressing Reliability engineering Physiology Iterative methods Older adults Robots Optimization

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Advancing Mobility in Stair Climbing with BART LAB's Intelligent Wheelchair: A Deep Learning Approach to Pose Estimation

Advancing Mobility in Stair Climbing with BART LAB's Intelli...

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Rehabilitation engineering and Assistive Technology (i-CREATe), International Convention on

作者： Branesh M. Pillai Dileep Sivaraman Songpol Ongwattanakul Jackrit Suthakorn Department of Biomedical Engineering Center for Biomedical and Robotics Technology (BART LAB) Faculty of Engineering Mahidol University Thailand

ISBN: (数字)9798350355154

ISBN: (纸本)9798350355161

This study addresses the critical urban mobility challenges faced by individuals with lower-limb impairments, particularly the limitations of conventional wheelchairs in navigating stairs of varying dimensions. Conventional wheelchairs struggle with these challenges, particularly those with non-uniform stair dimensions, which impede urban accessibility. To overcome these limitations, we introduce a semi-autonomous tracked stair-climbing wheelchair (SCW) that features an innovative kinematic mechanism that seamlessly switches locomotion modes and autonomously adjusts the wheelchair's pose to adapt to diverse terrains. The research focuses on enhancing pose estimation capabilities during stair climbing through a novel deep-learning approach. By integrating a depth camera with an advanced kinematic model, a sophisticated algorithm capable of precise stair width estimation and pose adjustment has been developed. Experimental results confirm the system's effectiveness, achieving a Mean Absolute Error (MAE) of 0.05 meters and a Root Mean Squared Error (RMSE) of 0.07 meters in height measurement, with high precision (0.92) and recall (0.89) rates in identifying stair components. These findings demonstrate the system's capability to overcome accessibility and safety challenges, offering a technical stride towards improved inclusivity and independence in urban environments.

关键词： Meters Navigation Wheelchairs Semantic segmentation Pose estimation Urban areas Kinematics Stairs Robustness Safety

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A Hybrid EEG-EMG Framework for Humanoid Control using Deep Learning Transformers

A Hybrid EEG-EMG Framework for Humanoid Control using Deep L...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Olikkal, Parthan Pillai, Branesh M. Suthakorn, Jackrit Ali, Habib Vinjamuri, Ramana University of Maryland Baltimore County Vinjamuri lab Department of Computer Science and Electrical Engineering BaltimoreMD21220 United States Mahidol University Center for Biomedical and Robotics Technology Nakorm Pathom Salaya Thailand

ISBN: (纸本)9781665481090

Brain-computer interfaces (BCIs) offer promising solutions for upper limb rehabilitation. Despite advancements in deep learning, traditional models for motor rehabilitation using electroencephalography (EEG) or electromyography (EMG) to control assistive devices require enhancement. This study aims to enhance motor control capabilities by integrating EEG and EMG signals using a Transformer-based deep learning model. Ten able-bodies subjects performed center-out tasks on a low-cost upper limb rehabilitation table, capturing 2D kinematic data, EEG, and EMG signals simultaneously. The tasks varied in complexity across four levels. Preprocessed EEG and EMG signals were fused and given as input to the proposed model, which was evaluated using three performance metrics. Results showed that the EEG-EMG combined model achieved 87.27% accuracy across all the four levels. Furthermore, the model's output successfully controlled a humanoid robot to replicate similar movements. These findings highlight the efficacy of combined EEG-EMG data in improving accuracy and performance in BCI applications, advancing assistive technologies and neurorehabilitation interventions. © 2024 IEEE.

关键词： Anthropomorphic robots

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Adaptive Polynomial Predictive Filter: Solving Inconsistent and Interrupted Sensor Data Challenges

Adaptive Polynomial Predictive Filter: Solving Inconsistent ...

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IEEE International Conference on robotics and Biomimetics

作者： Dileep Sivaraman Branesh M. Pillai Songpol Ongwattanakul Jackrit Suthakorn Department of Biomedical Engineering Center for Biomedical and Robotics Technology (BART LAB) Faculty of Engineering Mahidol University Thailand

This article discusses the challenges faced by sensor fusion systems owing to inconsistent and interrupted sensor data and proposes an adaptive polynomial predictive filter-based sensor fusion approach to address this problem. This approach incorporates an adaptive polynomial filter based on polynomial extrapolation that can effectively capture the temporal dynamics of the sensor data and provide a robust estimate of the underlying signal. This paper provides a detailed analysis of the proposed approach, including its theoretical foundation, implementation, and performance evaluation using simulated sensor data. The simulation results demonstrate that the adaptive polynomial sensor fusion approach outperforms several state-of-the-art sensor fusion techniques, particularly in scenarios in which the sensor data are inconsistent or interrupted. The article concludes that the proposed approach offers an effective solution to the problem of inconsistent and interrupted sensor data in sensor fusion, with potential applications in various fields including robotics, autonomous vehicles, and healthcare.

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Revised in-depth meta-analysis on the efficacy of robot-assisted versus traditional free-hand pedicle screw insertion

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Laparoscopic, Endoscopic and Robotic Surgery 2024年第4期7卷 155-165页

作者： Sorayouth Chumnanvej Branesh M.Pillai Jackrit Suthakorn Siriluk Chumnanvej Neurosurgery Division Department of SurgeryFaculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand Center for Biomedical and Robotics Technology(BART LAB) Faculty of EngineeringMahidol UniversityBangkokThailand Department of Anesthesiology and Operating Room Phramongkutklao HospitalBangkokThailand

Objective:Robot-assisted pedicle screw insertion has recently emerged as an alternative to the traditional free-hand ***,discrepancies in the accuracy of screw placement between the 2 methods have been highlighted by some comparative *** meta-analysis was conducted to synthesize evidence comparing these ***:Searches were conducted in 5 electronic databases adhering to specific eligibility criteria for randomized and observational *** data were analyzed using RevMan software and the results are presented as odds ratios(ORs),mean differences,or standard mean differences(SMDs)with 95%confidence intervals(CIs).Our analysis included 12 studies(7 randomized trials and 5 observational studies,involving 883 patients and 4903 screws).Results:The results demonstrated a higher rate of Grade A Gertzbein and Robbins pedicle placement score(OR:1.77;95%CI:1.10e2.87),a lower rate of revision surgeries(OR:0.21;95%CI:0.09e0.52),and a shorter radiation exposure duration(SMD?1.38,95%CI:2.32 to0.44)in the robot-assisted group compared with the free-hand ***,the length of hospital stay,volume of intraoperative blood loss,postoperative visual analogue scale scores for back pain,and rate of wound infection were similar between the 2 *** heterogeneity was observed in some ***:Compared with the free-hand method,the robot-assisted technique provides greater accuracy and reduced radiation *** efficacy of the robot-assisted technique is expected to improve further as experience with its use in surgery grows.

关键词： Robotic surgery Pedicle screw placement Free-hand Fluoroscopy Meta-analysis

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The role of artificial intelligence and IoT in prediction of earthquakes:Review

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Artificial Intelligence in Geosciences 2024年第1期5卷 154-172页

作者： Joshua Pwavodi Abdullahi Umar Ibrahim Pwadubashiyi Coston Pwavodi Fadi Al-Turjman Ali Mohand-Said Ecole et Observatoire des Sciences de La Terre ITES UMR7063-CNRS/Universit’e de Strasbourg5 rue Rene Descartes67084StrasbourgFrance Research Center for Science Technology and Engineering(BILTEM)Near East University99138NicosiaTRNCMersin 10Turkey Department of Biomedical Engineering Near East UniversityMersin 10Nicosia99010Turkey Department of Biomedical Engineering Faculty of EngineeringCyprus International UniversityHaspolatNorth Cyprus via Mersin 10Nicosia99258Turkey Artificial Intelligence Engineering Department AI and Robotics InstituteNear East UniversityMersin99138Turkey Research Center for AI and IoT Faculty of EngineeringUniversity of KyreniaMersin 99138Turkey

Earthquakes are classified as one of the most devastating natural disasters that can have catastrophic effects on the environment,lives,and *** has been an increasing interest in the prediction of earthquakes and in gaining a comprehensive understanding of the mechanisms that underlie their generation,yet earthquakes are the least predictable natural *** data,global positioning system,interferometry synthetic aperture radar(InSAR),and seismometers such as microelectromechanical system,seismometers,ocean bottom seismometers,and distributed acoustic sensing systems have all been used to predict earthquakes with a high degree of *** advances in seismic wave recording,storage,and analysis,earthquake time,location,and magnitude prediction remain *** the other hand,new developments in artificial intelligence(AI)and the Internet of Things(IoT)have shown promising potential to deliver more insights and ***,this article reviewed the use of AI-driven Models and IoT-based technologies for the prediction of earthquakes,the limitations of current approaches,and open research *** review discusses earthquake prediction setbacks due to insufficient data,inconsistencies,diversity of earthquake precursor signals,and the earth’s geophysical ***,this study examines potential approaches or solutions that scientists can employ to address the challenges they face in earthquake *** analysis is based on the successful application of AI and IoT in other fields.

关键词： Earthquakes Seismicity Artificial intelligence Internet of things Prediction

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Machine intelligence-accelerated discovery of all-natural plastic substitutes

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Nature Nanotechnology 2024年第6期19卷 782-791页

作者： Chen, Tianle Pang, Zhenqian He, Shuaiming Li, Yang Shrestha, Snehi Little, Joshua M. Yang, Haochen Chung, Tsai-Chun Sun, Jiayue Whitley, Hayden Christopher Lee, I-Chi Woehl, Taylor J. Li, Teng Hu, Liangbing Chen, Po-Yen Department of Chemical and Biomolecular Engineering University of Maryland College ParkMD United States Department of Mechanical Engineering University of Maryland College ParkMD United States Department of Chemistry and Biochemistry University of Maryland College ParkMD United States Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Materials Science and Engineering University of Maryland College ParkMD United States Maryland Robotics Center College ParkMD United States

One possible solution against the accumulation of petrochemical plastics in natural environments is to develop biodegradable plastic substitutes using natural components. However, discovering all-natural alternatives that meet specific properties, such as optical transparency, fire retardancy and mechanical resilience, which have made petrochemical plastics successful, remains challenging. Current approaches still rely on iterative optimization experiments. Here we show an integrated workflow that combines robotics and machine learning to accelerate the discovery of all-natural plastic substitutes with programmable optical, thermal and mechanical properties. First, an automated pipetting robot is commanded to prepare 286 nanocomposite films with various properties to train a support-vector machine classifier. Next, through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated stagewise, establishing an artificial neural network prediction model. We demonstrate that the prediction model can conduct a two-way design task: (1) predicting the physicochemical properties of an all-natural nanocomposite from its composition and (2) automating the inverse design of biodegradable plastic substitutes that fulfils various user-specific requirements. By harnessing the model’s prediction capabilities, we prepare several all-natural substitutes, that could replace non-biodegradable counterparts as exhibiting analogous properties. Our methodology integrates robot-assisted experiments, machine intelligence and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes starting from building blocks taken from the generally-recognized-as-safe database. © The Author(s) 2024.

关键词： Neural networks

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Intra-Operative Trajectory Realignment Algorithm for Minimizing Pedicle Screw Misplacement in Cervical Spinal Fusion

Intra-Operative Trajectory Realignment Algorithm for Minimiz...

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IEEE International Conference on robotics and Biomimetics

作者： Pasit Komonsuwan Shen Treratanakulchai Sorayouth Chumnanvej Jackrit Suthakorn Department of Biomedical Engineering Center for Biomedical and Robotics Technology (BART LAB) Faculty of Engineering Mahidol University Thailand Faculty of Medicine Ramathibodi Hospital Mahidol University Thailand

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Cervical spinal fusion is a surgical procedure that requires precise pedicle screw placement to ensure successful outcomes and avoid complications. Traditional manual methods rely on the surgeon's experience and anatomical landmark identification, which can lead to misalignment errors and complications. This study proposes an intra-operative trajectory re-alignment algorithm designed to address discrepancies between pre-operative and intra-operative images, particularly due to the inherent mobility of the cervical spine. The algorithm utilizes 3D CT scans for pre-operative imaging and 2D X-rays for intra-operative imaging. Principal Component Analysis (PCA) is applied to each 3D segmented spine to construct reference frames. These kinematic frames are manipulated to optimize the error between the simulated X-ray images and the reference 3D CT images. The CTspine1K data was used to evaluate the proposed algorithm by measuring geometric and clinical errors. The results show that the proposed algorithm reduces errors, and preventing screw perforation.

关键词： Three-dimensional displays Navigation Computed tomography Imaging X-rays Kinematics Fasteners Trajectory Testing Principal component analysis

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An Uncertain Optimal Factorization of Cooperative Manipulators for Robust Optimal Control Schemes 30

An Uncertain Optimal Factorization of Cooperative Manipulato...

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30th International Conference on Electrical engineering, ICEE 2022

作者： Nasiri, Neda Fakharian, Ahmad Menhaj, Mohammad Bagher Qazvin Branch Islamic Azad University Faculty of Electrical Biomedical and Mechatronics Engineering Qazvin Iran The Center of Excellence on Control and Robotics Tehran Iran

ISBN: (纸本)9781665480871

This paper presents two robust optimal controllers for Cooperative Manipulators (CoMans) holding an object on the basis of a modified factorization approach. Due to advantages such as: simplicity, flexibility, systematic procedure and not canceling nonlinearities, the State-Dependent Riccati Equation (SDRE) has been chosen as a basis for robust controllers design in the literature. For developing robust optimal controllers via SDRE, the first step is to obtain uncertain State-Dependent Coefficient (SDC) representation. To design robust optimal schemes for some systems such as CoMans, some terms such as gravity, friction and the external forces have been neglected in the uncertain SDC structure and added to the control input. This study, proposes two robust optimal controllers for CoMans carrying a load: Mixing SDRE-SMC (MiSS) and transformed robust to optimal controller where the pre-mentioned terms can be included in the uncertain SDC representation called uncertain optimal SDC. Simulation results are provided to easily verify the satisfactory performance of both schemes using uncertain optimal SDC and superiority of the transformed robust to optimal control versus MiSS in trajectory tracking of CoMans. © 2022 IEEE.

关键词： Sliding mode control

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