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A systematic review of artificial intelligence tools for chronic pulmonary embolism on CT pulmonary angiography.
Abdulaal, Lojain; Maiter, Ahmed; Salehi, Mahan; Sharkey, Michael; Alnasser, Turki; Garg, Pankaj; Rajaram, Smitha; Hill, Catherine; Johns, Christopher; Rothman, Alex Matthew Knox; Dwivedi, Krit; Kiely, David G; Alabed, Samer; Swift, Andrew James.
Afiliación
  • Abdulaal L; Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom.
  • Maiter A; Faculty of Applied Medical Science, King Abdulaziz University, Jeddah, Saudi Arabia.
  • Salehi M; Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom.
  • Sharkey M; Respiratory Physiology Department, Sheffield Pulmonary Vascular Disease Unit, Sheffield, United Kingdom.
  • Alnasser T; Department of Clinical Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom.
  • Garg P; Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom.
  • Rajaram S; Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom.
  • Hill C; Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom.
  • Johns C; Faculty of Medicine and Health Sciences, Norwich Medical School, University of East Anglia, Norwich, United Kingdom.
  • Rothman AMK; Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom.
  • Dwivedi K; Respiratory Physiology Department, Sheffield Pulmonary Vascular Disease Unit, Sheffield, United Kingdom.
  • Kiely DG; Department of Clinical Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom.
  • Alabed S; Respiratory Physiology Department, Sheffield Pulmonary Vascular Disease Unit, Sheffield, United Kingdom.
  • Swift AJ; Department of Clinical Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom.
Front Radiol ; 4: 1335349, 2024.
Article en En | MEDLINE | ID: mdl-38654762
ABSTRACT

Background:

Chronic pulmonary embolism (PE) may result in pulmonary hypertension (CTEPH). Automated CT pulmonary angiography (CTPA) interpretation using artificial intelligence (AI) tools has the potential for improving diagnostic accuracy, reducing delays to diagnosis and yielding novel information of clinical value in CTEPH. This systematic review aimed to identify and appraise existing studies presenting AI tools for CTPA in the context of chronic PE and CTEPH.

Methods:

MEDLINE and EMBASE databases were searched on 11 September 2023. Journal publications presenting AI tools for CTPA in patients with chronic PE or CTEPH were eligible for inclusion. Information about model design, training and testing was extracted. Study quality was assessed using compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM).

Results:

Five studies were eligible for inclusion, all of which presented deep learning AI models to evaluate PE. First study evaluated the lung parenchymal changes in chronic PE and two studies used an AI model to classify PE, with none directly assessing the pulmonary arteries. In addition, a separate study developed a CNN tool to distinguish chronic PE using 2D maximum intensity projection reconstructions. While another study assessed a novel automated approach to quantify hypoperfusion to help in the severity assessment of CTEPH. While descriptions of model design and training were reliable, descriptions of the datasets used in training and testing were more inconsistent.

Conclusion:

In contrast to AI tools for evaluation of acute PE, there has been limited investigation of AI-based approaches to characterising chronic PE and CTEPH on CTPA. Existing studies are limited by inconsistent reporting of the data used to train and test their models. This systematic review highlights an area of potential expansion for the field of AI in medical image interpretation.There is limited knowledge of A systematic review of artificial intelligence tools for chronic pulmonary embolism in CT. This systematic review provides an assessment on research that examined deep learning algorithms in detecting CTEPH on CTPA images, the number of studies assessing the utility of deep learning on CTPA in CTEPH was unclear and should be highlighted.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Front Radiol Año: 2024 Tipo del documento: Article País de afiliación: Reino Unido

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Front Radiol Año: 2024 Tipo del documento: Article País de afiliación: Reino Unido