The chest x-ray (CXR) was the gold standard in the diagnosis of pneumonia in children. However, CXR has limitations and cannot discriminate in etiology. Current guidelines recommend against routine use of CXR in children with uncomplicated lower respiratory tract infections (LRTI). We used routine care data from a multicentre RCT including 597 children with LRTI symptoms, to evaluate the influence of CXR on antibiotic prescription in the emergency department (ED). CXR remains frequently performed in non-complex children suspected of LRTI in the ED (18%). Children who underwent CXR were more likely to receive antibiotics, even when adjusted for symptoms, hospital and CXR results. Our study highlights the inferior role of CXR in treatment decisions for children with LRTI as CXR, regardless of its results, is independently associated with more antibiotic prescriptions.
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NEJM AI
October 2024
Google, Mountain View, CA, USA.
Background: Using artificial intelligence (AI) to interpret chest X-rays (CXRs) could support accessible triage tests for active pulmonary tuberculosis (TB) in resource-constrained settings.
Methods: The performance of two cloud-based CXR AI systems - one to detect TB and the other to detect CXR abnormalities - in a population with a high TB and human immunodeficiency virus (HIV) burden was evaluated. We recruited 1978 adults who had TB symptoms, were close contacts of known TB patients, or were newly diagnosed with HIV at three clinical sites.
Eur Radiol
January 2025
Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Objective: This study aimed to develop an open-source multimodal large language model (CXR-LLaVA) for interpreting chest X-ray images (CXRs), leveraging recent advances in large language models (LLMs) to potentially replicate the image interpretation skills of human radiologists.
Materials And Methods: For training, we collected 592,580 publicly available CXRs, of which 374,881 had labels for certain radiographic abnormalities (Dataset 1) and 217,699 provided free-text radiology reports (Dataset 2). After pre-training a vision transformer with Dataset 1, we integrated it with an LLM influenced by the LLaVA network.
Introduction: A chest X-ray (CXR) is the most common imaging investigation performed worldwide. Advances in machine learning and computer vision technologies have led to the development of several artificial intelligence (AI) tools to detect abnormalities on CXRs, which may expand diagnostic support to a wider field of health professionals. There is a paucity of evidence on the impact of AI algorithms in assisting healthcare professionals (other than radiologists) who regularly review CXR images in their daily practice.
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January 2025
Division of Respirology, Department of Child Health, Faculty of Medicine, Universitas Padjadjaran/Dr Hasan Sadikin General Hospital, Bandung, Indonesia.
Objective: To compare the persistent clinical symptoms, chest X-ray (CXR), spirometry and echocardiography results in adolescent survivors of drug-susceptible (DS) and drug-resistant (DR) pulmonary TB (PTB).
Methods: This retrospective cohort study was conducted in 52 adolescent PTB survivors. We compared persistent clinical symptoms, CXR, spirometry and echocardiography in DS-TB and DR-TB survivors.
IJTLD Open
January 2025
Respiratory Epidemiology & Clinical Research Unit, Centre for Outcomes Research & Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada.
Background: Pulmonary TB (PTB) predominantly affects individuals of working age. We sought to characterise the occupations of people newly diagnosed with PTB in Karachi, Pakistan, by type and physical intensity.
Design/methods: We did a secondary analysis of data from a study evaluating the diagnostic accuracy of artificial intelligence-based chest X-ray (CXR) analysis software, where individuals had been evaluated for active PTB using sputum cultures and had provided information on occupation.
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