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Everything you need to know about Computed Tomography (CT) & CT Scanning

Kidney: Artificial Intelligence Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Kidney ❯ Artificial Intelligence

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  • OBJECTIVE. The objective of our study was to systematically review the literature about the application of artificial intelligence (AI) to renal mass characterization with a focus on the methodologic quality items.
    MATERIALS AND METHODS. A systematic literature search was conducted using PubMed to identify original research studies about the application of AI to renal mass characterization. Besides baseline study characteristics, a total of 15 methodologic quality items were extracted and evaluated on the basis of the following four main categories: modeling, performance evaluation, clinical utility, and transparency items. The qualitative synthesis was presented using descriptive statistics with an accompanying narrative.
    CONCLUSION. To bring AI-based renal mass characterization from research to practice, future studies need to improve modeling and performance evaluation strategies and pay attention to clinical utility and transparency issues.
    Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
    Kocak B et al.
    AJR 2020; 215:1113–1122
  • OBJECTIVE. The objective of our study was to systematically review the literature about the application of artificial intelligence (AI) to renal mass characterization with a focus on the methodologic quality items.
    CONCLUSION. To bring AI-based renal mass characterization from research to practice, future studies need to improve modeling and performance evaluation strategies and pay attention to clinical utility and transparency issues.
    Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
    Kocak B et al.
    AJR 2020; 215:1113–1122
  • “As a broad concept, artificial intelligence (AI) covers a wide variety of machine learning (ML) methods or algorithms that create models without strict rule-based programming beforehand. These algorithms can improve and correct themselves through experience. The goal of AI tools is to predict certain outcomes using multiple variables. In the field of medical imaging, there has been extensive interest in AI tools.”
    Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
    Kocak B et al.
    AJR 2020; 215:1113–1122
  • "In this study, we systematically reviewed 30 studies about the application of AI to re- nal mass characterization. Our focus was on the methodologic quality items related to modeling, performance evaluation, clinical utility, and transparency. The quality items were favorable for modeling and perfor- mance evaluation categories for most stud- ies. On the other hand, they were poor in terms of clinical utility evaluation and transparency for most studies.”
    Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
    Kocak B et al.
    AJR 2020; 215:1113–1122
© 1999-2021 Elliot K. Fishman, MD, FACR. All rights reserved.