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Public Health Genomics and Precision Health Knowledge Base (v9.0)
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Precision Health Database|Search|Public Health Genomics and Precision Health Knowledge Base (PHGKB)
Heart, Lung, Blood and Sleep Disorders
Last data update: Apr 25, 2024
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Risk of Bias in Chest Radiography Deep Learning Foundation Models.
Ben Glocker et al. Radiol Artif Intell 2023 5(6) e230060
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The diagnostic value of chest X-ray scanning by the help of Artificial Intelligence in Heart Failure (ART-IN-HF).
Ahmet Celik et al. Clin Cardiol 2023
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A Multiclass Radiomics Method-Based WHO Severity Scale for Improving COVID-19 Patient Assessment and Disease Characterization From CT Scans.
John Anderson Garcia Henao et al. Invest Radiol 2023
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Diagnosis of malignant pleural effusion with combinations of multiple tumor markers: A comparison study of five machine learning models.
Yixi Zhang et al. The International journal of biological markers 2023 3936155231158125
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Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available?
Giovanni Irmici et al. Diagnostics (Basel, Switzerland) 2023 13(2)
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Differentiation of malignant from benign pleural effusions based on artificial intelligence.
Wang Sufei et al. Thorax 2022
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Diagnostic accuracy of a commercially-available, deep learning-based chest-Xray interpretation software for detecting culture-confirmed pulmonary tuberculosis.
Tavaziva Gamuchirai et al. International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases 2022
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Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning.
Gourdeau Daniel et al. Scientific reports 2022 12(1) 5616
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[A preliminary investigation on a deep learning convolutional neural networks based pulmonary tuberculosis CT diagnostic model].
Wu S C et al. Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases 2021 44(5) 450-455
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Diagnostic accuracy of adenosine deaminase for pleural tuberculosis in a low prevalence setting: A machine learning approach within a 7-year prospective multi-center study.
Garcia-Zamalloa Alberto et al. PloS one 2021 16(11) e0259203
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Recalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph.
Kuo Po-Chih et al. NPJ digital medicine 2021 Feb 4(1) 25
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Analysis of clinical features and imaging signs of COVID-19 with the assistance of artificial intelligence.
Ren H-W et al. European review for medical and pharmacological sciences 2020 Aug 24(15) 8210-8218
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Deep learning-based segmentation of malignant pleural mesothelioma tumor on computed tomography scans: application to scans demonstrating pleural effusion.
Gudmundsson Eyjolfur et al. Journal of medical imaging (Bellingham, Wash.) 2020 Jan 7(1) 012705
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Deep learning, computer-aided radiography reading for tuberculosis: a diagnostic accuracy study from a tertiary hospital in India.
Nash Madlen et al. Scientific reports 2020 Jan 10(1) 210
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Identifying tuberculous pleural effusion using artificial intelligence machine learning algorithms.
Ren Zenghua et al. Respiratory research 2019 Oct 20(1) 220
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 25, 2024
Content source:
Public Health Genomics Branch in the Division of Blood Disorders and Public Health Genomics
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National Center on Birth Defects and Developmental Disabilities
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