Machine intelligence and medical cyber-physical system architectures for smart healthcare: Taxonomy, challenges, opportunities, and possible solutions.

Hospitals use medical cyber-physical systems (MCPS) more often to give patients quality continuous care. MCPS isa life-critical, context-aware, networked system of medical equipment. It has been challenging to achieve high assurance in system software, interoperability, context-aware intelligence, autonomy, security and privacy, and device certifiability due to the necessity to create complicated MCPS that are safe and efficient. The MCPS system is shown in the paper as a newly developed application case study of artificial intelligence in healthcare. Applications for various CPS-based healthcare systems are discussed, such as telehealthcare systems for managing chronic diseases (cardiovascular diseases, epilepsy, hearing loss, and respiratory diseases), supporting medication intake management, and tele-homecare systems. The goal of this study is to provide a thorough overview of the essential components of the MCPS from several angles, including design, methodology, and important enabling technologies, including sensor networks, the Internet of Things (IoT), cloud computing, and multi-agent systems. Additionally, some significant applications are investigated, such as smart cities, which are regarded as one of the key applications that will offer new services for industrial systems, transportation networks, energy distribution, monitoring of environmental changes, business and commerce applications, emergency response, and other social and recreational activities.The four levels of an MCPS's general architecture-data collecting, data aggregation, cloud processing, and action-are shown in this study. Different encryption techniques must be employed to ensure data privacy inside each layer due to the variations in hardware and communication capabilities of each layer. We compare established and new encryption techniques based on how well they support safe data exchange, secure computing, and secure storage. Our thorough experimental study of each method reveals that, although enabling innovative new features like secure sharing and safe computing, developing encryption approaches significantly increases computational and storage overhead. To increase the usability of newly developed encryption schemes in an MCPS and to provide a comprehensive list of tools and databases to assist other researchers, we provide a list of opportunities and challenges for incorporating machine intelligence-based MCPS in healthcare applications in our paper's conclusion.

An automated machine learning tool for breast cancer diagnosis for healthcare professionals.

The paper proposes a hybrid metaheuristic algorithm known as harmony search and simulated annealing (HS-SA) for accurate and precise breast malignancy disclosure by integrating harmony search (HS) and simulated annealing (SA) optimisation methods. An enhanced wavelet-based contourlet transform (WBCT) procedure for mining the highlights of the region of interest (ROI) is explored, that allows execution upgradation over other standard procedures. The anticipated HS-SA algorithm aims to reduce the feature dimensions and assemble at the unparalleled optimal feature subset. The SVM classifier fed with the picke.d feature subsets and assisted by varied kernel functions upheld its classification capacities in contrast with the conformist machine learning classification and optimisation methods. The portrayed computer-aided diagnosis (CAD) model is confronted by evaluating its learning capability on two different breast mammographic datasets i) benchmark BCDR-F03 dataset and ii) local mammographic dataset. Preliminary propagations, experimental outcomes, and quantifiable assessments likewise demonstrate that the proposed model is pragmatic and favourable for the automated breast malignancy findings with optimal performance and fewer overheads. The discoveries show that the proposed CAD system (HS-SA+Kernel SVM) is superior to various characterisation accuracy techniques with an accuracy of 99.89% for the local mammographic dataset and 99.76% for benchmark BCDR-F03 dataset, AUC of 99.41% for the local mammographic dataset and 99.21% for reference BCDR-F03 dataset while keeping the element space restricted to only seven feature subsets and computational prerequisites as low as is judicious.

A data-centric artificial intelligent and extended reality technology in smart healthcare systems.

Extended reality (XR) solutions are quietly maturing, and their novel use cases are already being investigated, particularly in the healthcare industry. By 2022, the extended reality market is anticipated to be worth $209 billion. Certain diseases, such as Alzheimer's, Schizophrenia, Stroke rehabilitation stimulating specific areas of the patient's brain, healing brain injuries, surgeon training, realistic 3D visualization, touch-free interfaces, and teaching social skills to children with autism, have shown promising results with XR-assisted treatments. Similar effects have been used in video game therapies like Akili Interactive's EndeavorRx, which has previously been approved by the Food and Drug Administration (FDA) as a treatment regimen for children with attention deficit hyperactivity disorder (ADHD). However, while these improvements have received positive feedback, the field of XR-assisted patient treatment is in its infancy. The growth of XR in the healthcare sphere has the potential to transform the delivery of medical services. Imagine an elderly patient in a remote setting having a consultation with a world-renowned expert without ever having to leave their house. Rather than operating on cadavers in a medical facility, a surgical resident does surgery in a virtual setting at home. On the first try, a nurse uses a vein finder to implant an IV. Through cognitive treatment in a virtual world, a war veteran recovers from post-traumatic stress disorder (PTSD). The paper discusses the potential impact of XR in transforming the healthcare industry, as well as its use cases, challenges, XR tools and techniques for intelligent health care, recent developments of XR in intelligent healthcare services, and the potential benefits and future aspects of XR techniques in the medical domain.

Automated atrophy assessment for Alzheimer's disease diagnosis from brain MRI images.

An inventive scheme for automated tissue segmentation and classification is offered in this paper using Fast Discrete Wavelet Transform (DWT)/Band Expansion Process (BEP), Kernel-based least squares Support Vector Machine (KLS-SVM) and F-score, backed by Principal Component Analysis (PCA). Using input as T1, T2 and Proton Density (PD) scans of patients, CSF (Cerebrospinal Fluid), WM (White matter) and GM (Gray matter) are afforded as output, which act as hallmark for brain atrophy and thus sustaining in diagnosis of Alzheimer's disease (AD) from Mild Cognitive Impairment (MCI) and Healthy Controls (HC). The blending of BEP features from DWT and texture features from Gray Level Co-occurrence Matrix (GLC) promises to be a savior in atrophy revelation of the segmented tissues. Data used for evaluation of this study is taken from the ADNI database that encloses T1-weighted s-MRI (Structural Magnetic Imaging Resonance) scans of 158 patients with AD and 145 HC. Preprocessing steps unearthed five parameters for classification (i.e. cortical thickness, curvature, gray matter volume, surface area, and sulcal depth), in the preliminary step. For challenging the classifier performance, ROC (Receiver operating characteristics) curves are painted and the SVM classifiers of two-dimensional spaces took the top two important features as classification features for separating HC and AD to the maximum extent. The final results revealed that Fast DWT + F-Score + PCA + KLS-SVM + Poly Kernel is giving 100% tissue classification accuracy for test samples under consideration with only 7 input features.