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  • 1.
    Abdelakram, Hafid
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Abdullah, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Estimating Physiological Parameters in Various Age Groups: Windkessel 4 Element Model and PPG Waveform Analysis Approach2023In: IEEE 4th International Multidisciplinary Conference on Engineering Technology, IMCET 2023, IEEE, 2023, p. 194-197Conference paper (Refereed)
    Abstract [en]

    Non-invasive monitoring of cardiovascular health through photoplethysmography (PPG) waveforms has emerged as a crucial area of research. The Windkessel 4-Element (WK4) model is a mathematical approach used to estimate key physiological parameters related to cardiovascular health, including arterial compliance, peripheral resistance, inertance, and total arterial resistance. This study aimed to evaluate key physiological parameters associated with cardiovascular health using the WK4 model, leveraging real-life PPG waveform data obtained from volunteers across three distinct age groups. To achieve this, an algorithm was developed to automatically determine optimal parameter values for each volunteer. The results revealed a mean correlation coefficient of 0.96 between the automatically generated waveforms by the algorithm and the actual real-life PPG waveforms, indicating robust agreement. Notably, only the total arterial resistance parameter exhibited significant differences among the age groups, suggesting that the algorithm holds promise for detecting agerelated changes in cardiovascular health. These findings emphasize the potential for the development of a non-invasive tool to assess cardiovascular health status and enhance healthcare outcomes. Furthermore, they underscore the capability of the developed algorithm as a non-invasive means to evaluate various aspects of cardiovascular physiology. Additionally, the versatility of this algorithm opens doors for its application in educational settings, promoting knowledge advancement, empowering research endeavors, and facilitating advancements in the field.

  • 2.
    Abdelakram, Hafid
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Abdullah, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kristoffersson, Annica
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Folke, Mia
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Impact of Activities in Daily Living on Electrical Bioimpedance Measurements for Bladder Monitoring2023Conference paper (Refereed)
    Abstract [en]

    Accurate bladder monitoring is critical in the management of conditions such as urinary incontinence, voiding dysfunction, and spinal cord injuries. Electrical bioimpedance (EBI) has emerged as a cost-effective and non-invasive approach to monitoring bladder activity in daily life, with particular relevance to patient groups who require measurement of bladder urine volume (BUV) to prevent urinary leakage. However, the impact of activities in daily living (ADLs) on EBI measurements remains incompletely characterized. In this study, we investigated the impact of normal ADLs such as sitting, standing, and walking on EBI measurements using the MAX30009evkit system with four electrodes placed on the lower abdominal area. We developed an algorithm to identify artifacts caused by the different activities from the EBI signals. Our findings demonstrate that various physical activities clearly affected the EBI measurements, indicating the necessity of considering them during bladder monitoring with EBI technology performed during physical activity (or normal ADLs). We also observed that several specific activities could be distinguished based on their impedance values and waveform shapes. Thus, our results provide a better understanding of the impact of physical activity on EBI measurements and highlight the importance of considering such physical activities during EBI measurements in order to enhance the reliability and effectiveness of EBI technology for bladder monitoring.

  • 3.
    Abdelakram, Hafid
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Difallah, Sabrina
    Laboratory of Instrumentation, University of Sciences and Technology Houari Boumediene, 16111 Algiers, Algeria.
    Alves, Camille
    Assistive Technology Lab (NTA), Faculty of Electrical Engineering, Federal University of Uberlandia, Uberlandia 38408-100, Brazil.
    Abdullah, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Folke, Mia
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kristoffersson, Annica
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    State of the Art of Non-Invasive Technologies for Bladder Monitoring: A Scoping Review2023In: Sensors, E-ISSN 1424-8220, Vol. 23, no 5, article id 2758Article, review/survey (Refereed)
    Abstract [en]

    Bladder monitoring, including urinary incontinence management and bladder urinary volume monitoring, is a vital part of urological care. Urinary incontinence is a common medical condition affecting the quality of life of more than 420 million people worldwide, and bladder urinary volume is an important indicator to evaluate the function and health of the bladder. Previous studies on non-invasive techniques for urinary incontinence management technology, bladder activity and bladder urine volume monitoring have been conducted. This scoping review outlines the prevalence of bladder monitoring with a focus on recent developments in smart incontinence care wearable devices and the latest technologies for non-invasive bladder urine volume monitoring using ultrasound, optical and electrical bioimpedance techniques. The results found are promising and their application will improve the well-being of the population suffering from neurogenic dysfunction of the bladder and the management of urinary incontinence. The latest research advances in bladder urinary volume monitoring and urinary incontinence management have significantly improved existing market products and solutions and will enable the development of more effective future solutions.

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  • 4.
    Abdelakram, Hafid
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kristoffersson, Annica
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Abdullah, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Edu-Mphy: A Low-Cost Multi-Physiological Recording System for Education and Research in Healthcare and Engineering2023In: Abstracts: Medicinteknikdagarna 2023, 2023, p. 117-117Conference paper (Other academic)
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  • 5.
    Abdullah, Saad
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Department of Biomedical Engineering, Riphah International University, Lahore, Pakistan.
    Abdelakram, Hafid
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kristoffersson, Annica
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Bilal Saeed, Muhammad
    Biomedical Engineering Department, NED University of Engineering and Technology, Karachi, Pakistan..
    Saad, Samreen
    Department of Biochemistry, Karachi University, Karachi, Pakistan.
    Real-Time Portable Raspberry Pi-Based System for Sickle Cell Anemia Detection2023In: Abstracts: Medicinteknikdagarna 2023, 2023, p. 118-118Conference paper (Other academic)
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  • 6.
    Abdullah, Saad
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Abdelakram, Hafid
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Folke, Mia
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kristoffersson, Annica
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Machine Learning-Based Classification of Hypertension using CnD Features from Acceleration Photoplethysmography and Clinical Parameters2023In: Proceedings - IEEE Symposium on Computer-Based Medical Systems, Institute of Electrical and Electronics Engineers Inc. , 2023, p. 923-924Conference paper (Refereed)
    Abstract [en]

    Cardiovascular diseases (CVDs) are a leading cause of death worldwide, and hypertension is a major risk factor for acquiring CVDs. Early detection and treatment of hypertension can significantly reduce the risk of developing CVDs and related complications. In this study, a linear SVM machine learning model was used to classify subjects as normal or at different stages of hypertension. The features combined statistical parameters derived from the acceleration plethysmography waveforms and clinical parameters extracted from a publicly available dataset. The model achieved an overall accuracy of 87.50% on the validation dataset and 95.35% on the test dataset. The model's true positive rate and positive predictivity was high in all classes, indicating a high accuracy, and precision. This study represents the first attempt to classify cardiovascular conditions using a combination of acceleration photoplethysmogram (APG) features and clinical parameters The study demonstrates the potential of APG analysis as a valuable tool for early detection of hypertension.

  • 7.
    Abdullah, Saad
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Hafid, Abdelakram
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Folke, Mia
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kristoffersson, Annica
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    A Novel Fiducial Point Extraction Algorithm to Detect C and D Points from the Acceleration Photoplethysmogram (CnD)2023In: Electronics, E-ISSN 2079-9292, Vol. 12, no 5, article id 1174Article in journal (Refereed)
    Abstract [en]

    The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task. Various feature extraction methods have been proposed in the literature. In this study, we present a novel fiducial point extraction algorithm to detect c and d points from the acceleration photoplethysmogram (APG), namely “CnD”. The algorithm allows for the application of various pre-processing techniques, such as filtering, smoothing, and removing baseline drift; the possibility of calculating first, second, and third photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting APG fiducial points. An evaluation of the CnD indicated a high level of accuracy in the algorithm’s ability to identify fiducial points. Out of 438 APG fiducial c and d points, the algorithm accurately identified 434 points, resulting in an accuracy rate of 99%. This level of accuracy was consistent across all the test cases, with low error rates. These findings indicate that the algorithm has a high potential for use in practical applications as a reliable method for detecting fiducial points. Thereby, it provides a valuable new resource for researchers and healthcare professionals working in the analysis of photoplethysmography signals.

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  • 8.
    Abdullah, Saad
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Hafid, Abdelakram
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Folke, Mia
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kristoffersson, Annica
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points2023In: Frontiers in Bioengineering and Biotechnology, E-ISSN 2296-4185, Vol. 11, article id 1199604Article in journal (Refereed)
    Abstract [en]

    Photoplethysmography is a non-invasive technique used for measuring several vital signs and for the identification of individuals with an increased disease risk. Its principle of work is based on detecting changes in blood volume in the microvasculature of the skin through the absorption of light. The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task, where various feature extraction methods have been proposed in the literature. In this work, we present PPGFeat, a novel MATLAB toolbox supporting the analysis of raw photoplethysmography waveform data. PPGFeat allows for the application of various preprocessing techniques, such as filtering, smoothing, and removal of baseline drift; the calculation of photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting photoplethysmography fiducial points. PPGFeat includes a graphical user interface allowing users to perform various operations on photoplethysmography signals and to identify, and if required also adjust, the fiducial points. Evaluating the PPGFeat’s performance in identifying the fiducial points present in the publicly available PPG-BP dataset, resulted in an overall accuracy of 99% and 3038/3066 fiducial points were correctly identified. PPGFeat significantly reduces the risk of errors in identifying inaccurate fiducial points. Thereby, it is providing a valuable new resource for researchers for the analysis of photoplethysmography signals.

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  • 9.
    Abdullah, Saad
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Hafid, Abdelakram
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Shahid, H.
    Coventry University, Research Centre for Intelligent Healthcare, Coventry, United Kingdom.
    Comparing the Effectiveness of EMG and Electrical Impedance myography Measurements for Controlling Prosthetics2023In: IEEE Int. Multidiscip. Conf. Eng. Technol., IMCET, Institute of Electrical and Electronics Engineers Inc. , 2023, p. 189-193Conference paper (Refereed)
    Abstract [en]

    In recent years, the field of prosthetics has made significant progress towards creating prosthetic devices that are more functional, comfortable, and user-friendly. However, achieving intuitive control over prosthetic hand movements remains a significant challenge, especially for individuals with limb loss who rely on prosthetics for independent daily activities. To address this challenge, researchers have explored the potential of non-invasive techniques as electromyography (EMG) for prosthetic control. This paper aims to investigate the potential of using EMG and the electrical impedance myography (EIMG) techniques jointly for the measurement of hand movements. The study involved recording and comparing EMG and EIMG signals from a cohort of healthy individuals. These signals were captured during four distinct hand gestures: opening and closing the hand, as well as extending and flexing it, under varying time conditions, allowing for categorization into low and high-intensity movements. Data collection employed the Open BCI and ZRPI devices. The analysis of these signal waveforms revealed compelling results. Brachioradialis activity in EMG 2 exhibited an increase during open hand (0.015mV) and extension hand (0.009mV in low and 0.013mV in high intensity) gestures, accompanied by increased EIMG activity (56mV and 52mV respectively). Additionally, close hand (0.0018mV in low and 0.05mV in high intensity) and flexion hand (0.0075 in low intensity and 0.002 in high intensity) gestures exhibited heightened flexor carpi ulnaris activity with raised EIMG activity (57mV and 45mV respectively). These results proved to be consistent, acceptable, and aligned with existing literature. The findings of this paper indicate that both EMG and EIMG techniques could be used together to control custom-made hand prosthetics, demonstrating a significant development that could lead to more intuitive and easier-to-control prosthetics. Also, the results obtained could be valuable to researchers and engineers working in the prosthetics field, as it provides insights into the potential of non-invasive techniques for prosthetic control.

  • 10.
    Abdullah, Saad
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kanwal, Kehkashan
    Mälardalen University, School of Innovation, Design and Engineering.
    Hafid, A.
    Ziauddin University, Department of Electrical Engineering, Karachi, Pakistan.
    Difallah, S.
    University of Science and Technology Houari Boumediene, Instrumentation Laboratory, Algiers, Algeria.
    Low-cost BLE based intravenous monitoring and control infusion system2023In: Int. Conf. Adv. Electron., Control Commun. Syst., ICAECCS, Institute of Electrical and Electronics Engineers Inc. , 2023Conference paper (Refereed)
    Abstract [en]

    Administering the medications and fluids intravenously is a frequent practice in modern medical procedures, which plays a vital role in the treatment of certain acute conditions which require immediate action by drugs or fluids. This paper covers the design of a low-cost, wireless drip monitoring system for use in the hospital environment. The device is equipped with the Bluetooth low energy based battery-operated microcontroller, an infrared based drops counting system and a digital servo motor to control the drip flow rate, and it is attached to an existing intravenous stand. A LabView graphical user interface has also been developed to provide sets of input to the system to calculate the desired drip rate and the amount of pressure that digital servo motor must apply to achieve it. The system shows an average accuracy of 96% when compared with the measured and calculated values. This allows accurate computation of the level of the drip.

  • 11.
    Abdullah, Saad
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kristoffersson, Annica
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Machine learning approaches for cardiovascular hypertension stage estimation using photoplethysmography and clinical features2023In: Frontiers in Cardiovascular Medicine, E-ISSN 2297-055X, Vol. 10, article id 1285066Article in journal (Refereed)
    Abstract [en]

    Cardiovascular diseases (CVDs) are a leading cause of death worldwide, with hypertension emerging as a significant risk factor. Early detection and treatment of hypertension can significantly reduce the risk of developing CVDs and related complications. This work proposes a novel approach employing features extracted from the acceleration photoplethysmography (APG) waveform, alongside clinical parameters, to estimate different stages of hypertension. The current study used a publicly available dataset and a novel feature extraction algorithm to extract APG waveform features. Three distinct supervised machine learning algorithms were employed in the classification task, namely: Decision Tree (DT), Linear Discriminant Analysis (LDA), and Linear Support Vector Machine (LSVM). Results indicate that the DT model achieved exceptional training accuracy of 100% during cross-validation and maintained a high accuracy of 96.87% on the test dataset. The LDA model demonstrated competitive performance, yielding 85.02% accuracy during cross-validation and 84.37% on the test dataset. Meanwhile, the LSVM model exhibited robust accuracy, achieving 88.77% during cross-validation and 93.75% on the test dataset. These findings underscore the potential of APG analysis as a valuable tool for clinicians in estimating hypertension stages, supporting the need for early detection and intervention. This investigation not only advances hypertension risk assessment but also advocates for enhanced cardiovascular healthcare outcomes.

  • 12.
    Kanwal, Kehkashan
    et al.
    Department of Electrical Engineering, Faculty of Engineering Science Technology and Management, Ziauddin University, Karachi 74700, Pakistan.
    Asif, Muhammad
    Faculty of Computing and Applied Sciences, Sir Syed University of Engineering and Technology, Karachi 75300, Pakistan.
    Khalid, Syed Ghufran
    Department of Engineering, Faculty of Science and Technology, Nottingham Trent University, Nottingham, NG118NS, United Kingdom.
    Wasi, Sarwar
    Data Acquisition, Processing, and Predictive Analytics Lab, NCBC, Ziauddin University, Karachi 74700, Pakistan.
    Zafar, Farhana
    Department of Pediatrics, Ziauddin University, Karachi 74700, Pakistan.
    Kiran, Iffat
    Department of Electrical Engineering, Faculty of Engineering Science Technology and Management, Ziauddin University, Karachi 74700, Pakistan.
    Abdullah, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Comparative Analysis of Photoplethysmography Signal Quality from Right and Left Index Fingers2023In: Traitement du signal, ISSN 0765-0019, E-ISSN 1958-5608, Vol. 40, no 5, p. 2214-2199Article in journal (Refereed)
    Abstract [en]

    Photoplethysmography (PPG) has emerged as an increasingly attractive signal for non-invasive physiological measurements, owing to its simplicity, cost-effectiveness, and broad applicability spanning cardiovascular to respiratory systems. The burgeoning interest in PPG signal processing has facilitated its extensive incorporation in wearable devices, thus stimulating active research in this field. The present study undertakes a comprehensive evaluation to discern the optimal index finger (right or left) for PPG data acquisition and subsequent filtration, appraised through the lens of the signal-to-noise ratio (SNR) of the filtered signal. An analysis conducted on signals contaminated with white Gaussian noise unveiled that the Savitzky-Golay filter (a polynomial filter) with a window size of three outperformed other window lengths, rendering the highest SNR. Among the Infinite Impulse Response (IIR) filters compared; the Chebyshev I filter emerged as superior. Interestingly, the right index finger consistently demonstrated a higher mean SNR across filters: 0.49% for the Savitzky-Golay filters, 4.32% for the Butterworth (order 6), 7.71% for the Chebyshev I (order 10), and 4.02% for the Chebyshev II (order 4), relative to the left index finger for PPG signals perturbed by white Gaussian noise. These findings provide an insightful perspective for future research and development in wearable devices, suggesting potential superiority of the right index finger for PPG signal acquisition and filtration.

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  • 13.
    Khan, B.
    et al.
    Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong.
    Abdullah, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Khan, S.
    Center for Eye & Vision Research, 17W Science Park, Hong Kong.
    Current Progress in Conductive Hydrogels and Their Applications in Wearable Bioelectronics and Therapeutics2023In: Micromachines, E-ISSN 2072-666X, Vol. 14, no 5, article id 1005Article in journal (Refereed)
    Abstract [en]

    Wearable bioelectronics and therapeutics are a rapidly evolving area of research, with researchers exploring new materials that offer greater flexibility and sophistication. Conductive hydrogels have emerged as a promising material due to their tunable electrical properties, flexible mechanical properties, high elasticity, stretchability, excellent biocompatibility, and responsiveness to stimuli. This review presents an overview of recent breakthroughs in conductive hydrogels, including their materials, classification, and applications. By providing a comprehensive review of current research, this paper aims to equip researchers with a deeper understanding of conductive hydrogels and inspire new design approaches for various healthcare applications.

  • 14.
    Khan, Bangul
    et al.
    Department of Biomedical Engineering City University Hong Kong Kowloon Hong Kong China;Hongkong Center of Cerebro‐Cardiovascular Health Engineering (COCHE) Shatin Hong Kong China.
    Arbab, Areesha
    Department of Biomedical Engineering Mehran University of Engineering and Technology Jamshoro Sindh Pakistan.
    Khan, Samiullah
    Center for Eye & Vision Research Shatin Hong Kong China.
    Fatima, Hajira
    Department of Biomedical Engineering Quaid‐e‐Awam University of Engineering and Technology Nawab shah Pakistan.
    Bibi, Isha
    Department of Biomedical Engineering Mehran University of Engineering and Technology Jamshoro Sindh Pakistan.
    Chowdhry, Narinder P.
    Department of Biomedical Engineering Mehran University of Engineering and Technology Jamshoro Sindh Pakistan.
    Ansari, Abdul Q.
    Department of Biomedical Engineering Mehran University of Engineering and Technology Jamshoro Sindh Pakistan.
    Ursani, Ahsan A.
    Department of Biomedical Engineering Mehran University of Engineering and Technology Jamshoro Sindh Pakistan.
    Kumar, Sanjay
    Department of Biomedical Engineering NED University of Engineering and Technology Karachi Pakistan.
    Hussain, Jawad
    Department of Biomedical Engineering Riphah International University Lahore Pakistan.
    Abdullah, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Recent progress in thermosensitive hydrogels and their applications in drug delivery area2023In: MedComm – Biomaterials and Applications, ISSN 2769-643X, Vol. 2, no 3Article in journal (Refereed)
    Abstract [en]

    The scientific community has widely recognized thermosensitive hydrogelsas highly biocompatible material withimmense potential in drug deliverysystems. When the temperature of these hydrogels approaches that ofhuman body, a phase change occurs, enhancing their usefulness in a rangeof medical scenarios. This review article highlighted the background ofthermosensitive hydrogels, their properties, and their applications intransdermal, oral, ophthalmic, intravaginal, nasal, rectal, cancer therapy,and cell‐loaded drug delivery systems. The literature suggests numerousadvantages of these hydrogels over conventional drug delivery systems andfind applications in various fields, such as therapeutic systems, fillingprocesses, and sustained drug delivery systems. One of their key benefits isthe ability to eliminate invasive procedures like surgery, providing anoninvasive alternative for drug administration. Moreover,theystreamlinethe formulation process for both hydrophilic and hydrophobic drugdelivery systems, simplifying the development of effective treatments.The thermosensitive hydrogels have been found to be green materials withnegligible side effects and desirable drug delivery properties. Thethermosensitive hydrogel's sustained‐release characteristics, immunogenic-ity, and biodegradability have also gained increased interest. Some of thedisadvantages of thermosensitive hydrogels include delayed temperatureresponse, weak mechanical characteristics, and poor biocompatibility,which limits their potential use in drug delivery applications.

  • 15.
    Khan, Bangul
    et al.
    Hong Kong Centre for Cerebro-Caradiovasular Health Engineering (COCHE), Shatin, Hong Kong.
    Fatima, Hajira
    Riphah International University, Lahore, Pakistan.
    Qureshi, Ayatullah
    Riphah International University, Lahore, Pakistan.
    Kumar, Sanjay
    NED University, Karachi, Pakistan.
    Hanan, Abdul
    Mehran University of Engineering and Technology, Jamshoro, Pakistan.
    Hussain, Jawad
    Riphah International University, Lahore, Pakistan.
    Abdullah, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector2023In: Biomedical Materials & Devices, ISSN 2731-4812Article in journal (Refereed)
    Abstract [en]

    Artificial intelligence (AI) has the potential to make substantial progress toward the goal of making healthcare more personalized, predictive, preventative, and interactive. We believe AI will continue its present path and ultimately become a mature and effective tool for the healthcare sector. Besides this AI-based systems raise concerns regarding data security and privacy. Because health records are important and vulnerable, hackers often target them during data breaches. The absence of standard guidelines for the moral use of AI and ML in healthcare has only served to worsen the situation. There is debate about how far artificial intelligence (AI) may be utilized ethically in healthcare settings since there are no universal guidelines for its use. Therefore, maintaining the confidentiality of medical records is crucial. This study enlightens the possible drawbacks of AI in the implementation of healthcare sector and their solutions to overcome these situations.

  • 16.
    Riaz, Z.
    et al.
    Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong SAR, China.
    Khan, B.
    Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong SAR, China.
    Abdullah, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Khan, S.
    Center for Eye & Vision Research, 17W Science Park, Hong Kong SAR, China.
    Islam, M. S.
    Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong SAR, China.
    Lung Tumor Image Segmentation from Computer Tomography Images Using MobileNetV2 and Transfer Learning2023In: Bioengineering, E-ISSN 2306-5354, Vol. 10, no 8, article id 981Article in journal (Refereed)
    Abstract [en]

    Background: Lung cancer is one of the most fatal cancers worldwide, and malignant tumors are characterized by the growth of abnormal cells in the tissues of lungs. Usually, symptoms of lung cancer do not appear until it is already at an advanced stage. The proper segmentation of cancerous lesions in CT images is the primary method of detection towards achieving a completely automated diagnostic system. Method: In this work, we developed an improved hybrid neural network via the fusion of two architectures, MobileNetV2 and UNET, for the semantic segmentation of malignant lung tumors from CT images. The transfer learning technique was employed and the pre-trained MobileNetV2 was utilized as an encoder of a conventional UNET model for feature extraction. The proposed network is an efficient segmentation approach that performs lightweight filtering to reduce computation and pointwise convolution for building more features. Skip connections were established with the Relu activation function for improving model convergence to connect the encoder layers of MobileNetv2 to decoder layers in UNET that allow the concatenation of feature maps with different resolutions from the encoder to decoder. Furthermore, the model was trained and fine-tuned on the training dataset acquired from the Medical Segmentation Decathlon (MSD) 2018 Challenge. Results: The proposed network was tested and evaluated on 25% of the dataset obtained from the MSD, and it achieved a dice score of 0.8793, recall of 0.8602 and precision of 0.93. It is pertinent to mention that our technique outperforms the current available networks, which have several phases of training and testing.

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