ArticleBMC pulmonary medicine2021
Deep learning computer-aided detection system for pneumonia in febrile neutropenia patients: a diagnostic cohort study.
Article in BMC pulmonary medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05489471 (A Prospective Study to Assess the Impact of an Artificial Intelligence System on Reporting of Chest X-rays, Evaluate the Ability of AI Driven Worklists to Improve Reporting Times and Improve Same Day CT Pathway for Suspected Lung Cancer), which is not on this map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Prospective Study to Assess the Impact of an Artificial Intelligence System on Reporting of Chest X-rays, Evaluate the Ability of AI Driven Worklists to Improve Reporting Times and Improve Same Day CT Pathway for Suspected Lung Cancer
Who cites it
3 citing papers in PubMed, 8 citations in OpenAlex.
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- Application of Artificial Intelligence in Thoracic Radiology: A Narrative Review.Tuberculosis and respiratory diseases · 2025Article
- Detection of other pathologies when utilising computer-assisted digital solutions for TB screening.IJTLD open · 2024Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundDiagnosis of pneumonia is critical in managing patients with febrile neutropenia (FN), however, chest X-ray (CXR) has limited performance in the detection of pneumonia. We aimed to evaluate the performance of a deep learning-based computer-aided detection (CAD) system in pneumonia detection in the CXRs of consecutive FN patients and investigated whether CAD could improve radiologists' diagnostic performance when used as a second reader.
methodsCXRs of patients with FN (a body temperature ≥ 38.3 °C, or a sustained body temperature ≥ 38.0 °C for an hour; absolute neutrophil count < 500/mm
resultsAmong 525 CXRs from 413 patients (52.3% men; median age 59 years), pneumonia was diagnosed in 128 (24.4%) CXRs. In the interpretation with CAD, average sensitivity of radiologists was significantly improved (75.4% to 79.4%, P = 0.003) while their specificity remained similar (75.4% to 76.8%, P = 0.101), compared to radiologist-alone interpretation. The CAD exhibited AUC, sensitivity, and specificity of 0.895, 88.3%, and 68.3%, respectively. The standalone CAD exhibited higher sensitivity (86.6% vs. 75.2%, P < 0.001) and lower specificity (64.8% vs. 75.4%, P < 0.001) compared to radiologist-alone interpretation.
conclusionsIn patients with FN, the deep learning-based CAD system exhibited radiologist-level performance in detecting pneumonia on CXRs and enhanced radiologists' performance.
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