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Precision Health Database


Genomics & Health Impact Scan Database|Search|PHGKB

Last data update: Jul 03, 2022. (Total: 48007 Documents since 2012)
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Query Trace: Tuberculosis[original query]>>Original Studies[Product Type]
Prediction and Diagnosis of Respiratory Disease by Combining Convolutional Neural Network and Bi-directional Long Short-Term Memory Methods.
Li Li et al. Frontiers in public health 2022 10881234
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
Machine learning-based in-hospital mortality prediction of HIV/AIDS patients with Talaromyces marneffei infection in Guangxi, China.
Shi Minjuan et al. PLoS neglected tropical diseases 2022 16(5) e0010388
Artificial Intelligence Assisting the Early Detection of Active Pulmonary Tuberculosis From Chest X-Rays: A Population-Based Study.
Nijiati Mayidili et al. Frontiers in molecular biosciences 2022 9874475
TB-Net: A Tailored, Self-Attention Deep Convolutional Neural Network Design for Detection of Tuberculosis Cases From Chest X-Ray Images.
Wong Alexander et al. Frontiers in artificial intelligence 2022 5827299
A treatment recommender clinical decision support system for personalized medicine: method development and proof-of-concept for drug resistant tuberculosis.
Verboven Lennert et al. BMC medical informatics and decision making 2022 22(1) 56
AutoCellANLS: An Automated Analysis System for Mycobacteria-Infected Cells Based on Unstained Micrograph.
Zhuang Yan et al. Biomolecules 2022 12(2)
Prediction of Tuberculosis Using an Automated Machine Learning Platform for Models Trained on Synthetic Data.
Rashidi Hooman H et al. Journal of pathology informatics 2022 1310
Integrating landmark modeling framework and machine learning algorithms for dynamic prediction of tuberculosis treatment outcomes.
Kheirandish Maryam et al. Journal of the American Medical Informatics Association : JAMIA 2022
Implementation of Universal Newborn Screening for Severe Combined Immunodeficiency in Singapore While Continuing Routine Bacille-Calmette-Guerin Vaccination Given at Birth.
Chan Su-Wan Bianca et al. Frontiers in immunology 2022 12794221
Differentiating nontuberculous mycobacterium pulmonary disease from pulmonary tuberculosis through the analysis of the cavity features in CT images using radiomics.
Yan Qinghu et al. BMC pulmonary medicine 2022 22(1) 4
Differentiating between drug-sensitive and drug-resistant tuberculosis with machine learning for clinical and radiological features.
Yang Feng et al. Quantitative imaging in medicine and surgery 2022 12(1) 675-687
Machine learning based on routine laboratory indicators promoting the discrimination between active tuberculosis and latent tuberculosis infection.
Luo Ying et al. The Journal of infection 2022
Evaluation of an artificial intelligence (AI) system to detect tuberculosis on chest X-ray at a pilot active screening project in Guangdong, China in 2019.
Liao Qinghua et al. Journal of X-ray science and technology 2021
Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis.
Codlin Andrew J et al. Scientific reports 2021 11(1) 23895
Identifying Drug-Resistant Tuberculosis in Chest Radiographs: Evaluation of CNN Architectures and Training Strategies.
Karki Manohar et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2021 20212964-2967
[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
A fully automatic artificial intelligence-based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis.
Yan Chenggong et al. European radiology 2021
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
Early Evaluation of an Ultra-Portable X-ray System for Tuberculosis Active Case Finding.
Vo Luan Nguyen Quang et al. Tropical medicine and infectious disease 2021 6(3)
Prediction of Multidrug-Resistant Tuberculosis Using Machine Learning Algorithms in SWAT, Pakistan.
Ali Mian Haider et al. Journal of healthcare engineering 2021 20212567080
Automated machine learning for endemic active tuberculosis prediction from multiplex serological data.
Rashidi Hooman H et al. Scientific reports 2021 11(1) 17900
Derivation and external validation of a risk score for predicting HIV-associated tuberculosis to support case finding and preventive therapy scale-up: A cohort study
AF Aud et al, PLOS Medicine, September 7, 2021
Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms.
Qin Zhi Zhen et al. The Lancet. Digital health 2021 3(9) e543-e554
Chest X-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: an individual patient data meta-analysis of diagnostic accuracy.
Tavaziva Gamuchirai et al. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 2021
Computer-aided interpretation of chest radiography reveals the spectrum of tuberculosis in rural South Africa.
Fehr Jana et al. NPJ digital medicine 2021 4(1) 106
Early identification of individuals at risk for loss to follow-up of tuberculosis treatment: A generalised hierarchical analysis.
Lima Shirley Verônica Melo Almeida et al. Heliyon 2021 7(4) e06788
Diagnostic performance of artificial intelligence model for pneumonia from chest radiography.
Kwon TaeWoo et al. PloS one 2021 16(4) e0249399
Artificial neural network to predict the effect of obesity on the risk of tuberculosis infection.
Badawi Alaa et al. Journal of public health research 2021 10(1)
Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia.
Herman Bumi et al. PloS one 2021 16(3) e0249243
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