Suchen

Filter löschen
Filtern nach: Type Work Entfernen Zwang Type: Work Stichwort Artificial Intelligence Entfernen Zwang Stichwort: Artificial Intelligence Stichwort Computer Vision Entfernen Zwang Stichwort: Computer Vision Fach Artificial Intelligence Entfernen Zwang Fach: Artificial Intelligence

Suchergebnisse

  • 1. Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine

    Beschreibung:
    The global healthcare sector continues to grow rapidly and is reflected as one of the fastestgrowing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. The overall insight in this paper will help researchers in academia and industry to understand and address the future research that needs to be discussed on SML in the healthcare and biomedical sectors.
    Stichwort:
    Healthcare, Precision Medicine, Artificial Intelligence, Computer Vision, Deep Learning, Medical Imaging, XAI, and Supervised Learning
    Fach:
    Artificial Intelligence and Data Science
    Schöpfer:
    Roy, Sudipta , Lim, Se-Jung , and Meena, Tanushree
    Mitwirkender:
    Jio Institute, CVMI-Computer Vision in Medical Imaging Project
    Owner:
    n.sakthivel@jioinstitute.edu.in
    Herausgeber:
    MDPI
    Ort:
    Switzerland and India
    Sprache:
    English
    Datum hochgeladen:
    10-02-2023
    Datum geändert:
    16-02-2023
    Datum erstellt:
    01-10-2022
    Rights Statement Tesim:
    In Copyright
    License Tesim:
    All rights reserved
    Resource Type:
    Article
    Identifikator:
    10.3390/diagnostics12102549

  • 2. Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift

    Beschreibung:
    Bone diseases are common and can result in various musculoskeletal conditions (MC). An estimated 1.71 billion patients suffer from musculoskeletal problems worldwide. Apart from musculoskeletal fractures, femoral neck injuries, knee osteoarthritis, and fractures are very common bone diseases, and the rate is expected to double in the next 30 years. Therefore, proper and timely diagnosis and treatment of a fractured patient are crucial. Contrastingly, missed fractures are a common prognosis failure in accidents and emergencies. This causes complications and delays in patients’ treatment and care. These days, artificial intelligence (AI) and, more specifically, deep learning (DL) are receiving significant attention to assist radiologists in bone fracture detection. DL can be widely used in medical image analysis. Some studies in traumatology and orthopaedics have shown the use and potential of DL in diagnosing fractures and diseases from radiographs. In this systematic review, we provide an overview of the use of DL in bone imaging to help radiologists to detect various abnormalities, particularly fractures. We have also discussed the challenges and problems faced in the DL-based method, and the future of DL in bone imaging.
    Stichwort:
    Deep Learning, Fractures, Bone Imaging, Artificial Intelligence, Radiology, and Computer Vision
    Fach:
    Data Science and Artificial Intelligence
    Schöpfer:
    Roy, Sudipta and Meena, Tanushree
    Mitwirkender:
    Jio Institute CVMI-Computer Vision in Medical Imaging Project
    Owner:
    n.sakthivel@jioinstitute.edu.in
    Herausgeber:
    MDPI
    Ort:
    Switzerland
    Sprache:
    English
    Datum hochgeladen:
    10-02-2023
    Datum geändert:
    16-02-2023
    Datum erstellt:
    01-10-2022
    Rights Statement Tesim:
    In Copyright
    License Tesim:
    All rights reserved
    Resource Type:
    Article
    Identifikator:
    10.3390/diagnostics12102420