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Artificial Intelligence and Robotics in Healthcare

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 29243

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Special Issue Editors

Dr. Nouman Ali

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Guest Editor

Department of Software Engineering, Mirpur University of Science and Technology, Mirpur 10250, Pakistan
Interests: deep learning; image classification; artificial intelligence

Dr. Ihsan Rabbi

E-Mail Website
Guest Editor

Department of Computer Sciences, University of Science and Technology Bannu, Khyber Pakhtunkhwa 28100, Pakistan
Interests: artificial intelligence; virtual and augmented reality

Special Issue Information

Dear Colleagues,

The recent advancement in the field of artificial intelligence (AI) and robotics has assisted humans in different healthcare applications. Due to artificial intelligence and robotics, good results have been obtained in research and medical applications relevant to cancer detection, automation of different surgical process, genetic testing and early detection of various diseases. AI is mainly used for predictions that can assist humans in different health care applications such as automatic detection of diseases, accurate diagnosis, decision support systems that can assist patients and medical practitioners, applications based on Internet of Medical Things, E-Health management, early disease detection and clinical decision support systems. The advancement in robotics is assisting humans through remote treatment, auxiliary robots and accurate diagnostics using robotics. The aim of this Special Issue is to peer review and publish the original research and review articles that are relevant to AI and robotics in healthcare. This Special Issue will explore a variety of interconnected topics, including but not limited to:

Applications of machine learning in health care.
Disease detection and diagnosis.
Applications of deep learning in health care.
Remote treatment.
Performance of tasks that can assist health care systems.
Decision support systems that can assisting patients and medical practitioners.
Application of AI with the Internet of Medical Things.
E-Health data management using AI.
Early detection of dieses through intelligent systems.
AI-based support for clinical decision making.
Auxiliary robots.
Application of robotics in healthcare systems.
Medical image classification.
Accurate diagnosis using robotics and AI.

Dr. Nouman Ali
Dr. Ihsan Rabbi
Guest Editors

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Keywords

artificial intelligence in healthcare
robotics in healthcare, smart healthcare systems
disease detection using AI, decision support systems for healthcare

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Published Papers (6 papers)

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Research

18 pages, 14493 KiB

Open AccessArticle

The Development of a Secure Internet Protocol (IP) Network Based on Asterisk Private Branch Exchange (PBX)

by Mubarak Yakubova, Olga Manankova, Assel Mukasheva, Alimzhan Baikenov and Tansaule Serikov

Appl. Sci. 2023, 13(19), 10712; https://doi.org/10.3390/app131910712 - 26 Sep 2023

Cited by 1 | Viewed by 1291

Abstract

The research problem described in this article is related to the security of an IP network that is set up between two cities using hosting. The network is used for transmitting telephone traffic between servers located in Germany and the Netherlands. The concern is that with the increasing adoption of IP telephony worldwide, the network might be vulnerable to hacking and unauthorized access, posing a threat to the privacy and security of the transmitted information. This article proposes a solution to address the security concerns of the IP network. After conducting an experiment and establishing a connection between the two servers using the WireShark sniffer, a dump of real traffic between the servers was obtained. Upon analysis, a vulnerability in the network was identified, which could potentially be exploited by malicious actors. To enhance the security of the network, this article suggests the implementation of the Transport Layer Security (TLS) protocol. TLS is a cryptographic protocol that provides secure communication over a computer network, ensuring data confidentiality and integrity during transmission. Integrating TLS into the network infrastructure, will protect the telephone traffic and prevent unauthorized access and eavesdropping. Full article

(This article belongs to the Special Issue Artificial Intelligence and Robotics in Healthcare)

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23 pages, 5456 KiB

Open AccessArticle

Orientation Control Design of a Telepresence Robot: An Experimental Verification in Healthcare System

by Ali Altalbe, Muhammad Nasir Khan, Muhammad Tahir and Aamir Shahzad

Appl. Sci. 2023, 13(11), 6827; https://doi.org/10.3390/app13116827 - 4 Jun 2023

Cited by 2 | Viewed by 2002

Abstract

Automation in the modern world has become a necessity for humans. Intelligent mobile robots have become necessary to perform various complex tasks in healthcare and industry environments. Mobile robots have gained attention during the pandemic; human–robot interaction has become vibrant. However, there are many challenges in obtaining human–robot interactions regarding maneuverability, controllability, stability, drive layout and autonomy. In this paper, we proposed a stability and control design for a telepresence robot called auto-MERLIN. The proposed design simulated and experimentally verified self-localization and maneuverability in a hazardous environment. A model from Rieckert and Schunck was initially considered to design the control system parameters. The system identification approach was then used to derive the mathematical relationship between the manipulated variable of robot orientation control. The theoretical model of the robot mechanics and associated control were developed. A design model was successfully implemented, analyzed mathematically, used to build the hardware and tested experimentally. Each level takes on excellent tasks for the development of auto-MERLIN. A higher level always uses the services of lower levels to carry out its functions. The proposed approach is comparatively simple, less expensive and easily deployable compared to previous methods. The experimental results showed that the robot is functionally complete in all aspects. A test drive was performed over a given path to evaluate the hardware, and the results were presented. Simulation and experimental results showed that the target path is maintained quite well. Full article

(This article belongs to the Special Issue Artificial Intelligence and Robotics in Healthcare)

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14 pages, 455 KiB

Open AccessArticle

A Hybrid CNN-LSTM Random Forest Model for Dysgraphia Classification from Hand-Written Characters with Uniform/Normal Distribution

by Fahad Masood, Wajid Ullah Khan, Khalil Ullah, Ahmad Khan, Fatemah H. Alghamedy and Hanan Aljuaid

Appl. Sci. 2023, 13(7), 4275; https://doi.org/10.3390/app13074275 - 28 Mar 2023

Cited by 6 | Viewed by 2354

Abstract

Parkinson’s disease (PD) Dysgraphia is a disorder that affects most PD patients and is characterized by handwriting anomalies caused mostly by motor dysfunctions. Several effective ways to quantify PD dysgraphia analysis have been used, including online handwriting processing. In this research, an integrated approach, using a combination of a convolutional neural network (CNN) and long short-term memory (LSTM) layers along with a Random Forest (RF) classifier, is proposed for dysgraphia classification. The proposed approach uses uniform and normal distributions to randomly initialize the weights and biases of the CNN and LSTM layers. The CNN-LSTM model predictions are paired with the RF classifier to enhance the model’s accuracy and endurance. The suggested method shows promise in identifying handwriting symbols for those with dysgraphia, with the CNN-LSTM model’s accuracy being improved by the RF classifier. The suggested strategy may assist people with dysgraphia in writing duties and enhance their general writing skills. The experimental results indicate that the suggested approach achieves higher accuracy. Full article

(This article belongs to the Special Issue Artificial Intelligence and Robotics in Healthcare)

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26 pages, 2735 KiB

Open AccessArticle

Modeling of Multi-Level Planning of Shifting Bottleneck Resources Integrated with Downstream Wards in a Hospital

by Aisha Tayyab, Saif Ullah, Toqeer Mahmood, Yazeed Yasin Ghadi, Bushra Latif and Hanan Aljuaid

Appl. Sci. 2023, 13(6), 3616; https://doi.org/10.3390/app13063616 - 12 Mar 2023

Cited by 1 | Viewed by 1413

Abstract

Planning and scheduling critical resources in hospitals is significant for better service and profit generation. The current research investigates an integrated planning and scheduling problem at different levels of operating rooms, intensive care units, and wards. The theory of constraints is applied to make plans and schedules for operating rooms based on the capacity constraints of the operating room itself and downstream wards. A mixed integer linear programming model is developed considering shifting bottleneck resources among the operating room, intensive care unit, and hospital wards to maximize the utilization of resources at all levels of planning. Different sizes of planning and scheduling problems of the hospital, including small, medium, and large sizes, are created with variable arrivals and surgery durations and solved using a CPLEX solver for validating the developed models. Later, the application of the proposed models in the real world to develop planning systems for hospitals is discussed, and future extensions are suggested. Full article

(This article belongs to the Special Issue Artificial Intelligence and Robotics in Healthcare)

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19 pages, 2027 KiB

Open AccessArticle

DeepBreastCancerNet: A Novel Deep Learning Model for Breast Cancer Detection Using Ultrasound Images

by Asaf Raza, Naeem Ullah, Javed Ali Khan, Muhammad Assam, Antonella Guzzo and Hanan Aljuaid

Appl. Sci. 2023, 13(4), 2082; https://doi.org/10.3390/app13042082 - 6 Feb 2023

Cited by 33 | Viewed by 11865

Abstract

Breast cancer causes hundreds of women’s deaths each year. The manual detection of breast cancer is time-consuming, complicated, and prone to inaccuracy. For Breast Cancer (BC) detection, several imaging methods are explored. However, sometimes misidentification leads to unnecessary treatment and diagnosis. Therefore, accurate detection of BC can save many people from unnecessary surgery and biopsy. Due to recent developments in the industry, deep learning’s (DL) performance in processing medical images has significantly improved. Deep Learning techniques successfully identify BC from ultrasound images due to their superior prediction ability. Transfer learning reuses knowledge representations from public models built on large-scale datasets. However, sometimes Transfer Learning leads to the problem of overfitting. The key idea of this research is to propose an efficient and robust deep-learning model for breast cancer detection and classification. Therefore, this paper presents a novel DeepBraestCancerNet DL model for breast cancer detection and classification. The proposed framework has 24 layers, including six convolutional layers, nine inception modules, and one fully connected layer. Also, the architecture uses the clipped ReLu activation function, the leaky ReLu activation function, batch normalization and cross-channel normalization as its two normalization operations. We observed that the proposed model reached the highest classification accuracy of 99.35%. We also compared the performance of the proposed DeepBraestCancerNet approach with several existing DL models, and the experiment results showed that the proposed model outperformed the state-of-the-art. Furthermore, we validated the proposed model using another standard, publicaly available dataset. The proposed DeepBraestCancerNet model reached the highest accuracy of 99.63%. Full article

(This article belongs to the Special Issue Artificial Intelligence and Robotics in Healthcare)

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18 pages, 377 KiB

Open AccessArticle

AI-Enabled Wearable Medical Internet of Things in Healthcare System: A Survey

by Fazli Subhan, Alina Mirza, Mazliham Bin Mohd Su’ud, Muhammad Mansoor Alam, Shibli Nisar, Usman Habib and Muhammad Zubair Iqbal

Appl. Sci. 2023, 13(3), 1394; https://doi.org/10.3390/app13031394 - 20 Jan 2023

Cited by 35 | Viewed by 9249

Abstract

Technology has played a vital part in improving quality of life, especially in healthcare. Artificial intelligence (AI) and the Internet of Things (IoT) are extensively employed to link accessible medical resources and deliver dependable and effective intelligent healthcare. Body wearable devices have garnered attention as powerful devices for healthcare applications, leading to various commercially available devices for multiple purposes, including individual healthcare, activity alerts, and fitness. The paper aims to cover all the advancements made in the wearable Medical Internet of Things (IoMT) for healthcare systems, which have been scrutinized from the perceptions of their efficacy in detecting, preventing, and monitoring diseases in healthcare. The latest healthcare issues are also included, such as COVID-19 and monkeypox. This paper thoroughly discusses all the directions proposed by the researchers to improve healthcare through wearable devices and artificial intelligence. The approaches adopted by the researchers to improve the overall accuracy, efficiency, and security of the healthcare system are discussed in detail. This paper also highlights all the constraints and opportunities of developing AI enabled IoT-based healthcare systems. Full article

(This article belongs to the Special Issue Artificial Intelligence and Robotics in Healthcare)

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