ArticleEuropean radiology2023
AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Artificial intelligence for diagnostics in radiology practice: a rapid systematic scoping review.EClinicalMedicine · 2025Article
- The diagnostic and prognostic capability of artificial intelligence in spinal cord injury: A systematic review.Brain & spine · 2025Review
- Optimising strategies for artificial intelligence-assisted classification of viral pneumonias on CT imaging: a comparative study of selective and default approaches.Polish journal of radiology · 2025Article
- Impact of human and artificial intelligence collaboration on workload reduction in medical image interpretation.NPJ digital medicine · 2024Article
- A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections.Innovation (Cambridge (Mass.)) · 2024Article
- Assessing the Capability of ChatGPT, Google Bard, and Microsoft Bing in Solving Radiology Case Vignettes.The Indian journal of radiology & imaging · 2024Article
- Robust Medical Diagnosis: A Novel Two-Phase Deep Learning Framework for Adversarial Proof Disease Detection in Radiology Images.Journal of imaging informatics in medicine · 2024Article
- Plaque features of the middle cerebral artery are associated with periprocedural complications of intracranial angioplasty and stenting.Neuroradiology · 2024Article
- Algorithms for predicting COVID outcome using ready-to-use laboratorial and clinical data.Frontiers in public health · 2024Article
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25 authors.
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Abstract
objectivesDifferentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence.
methodsWe included n = 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (n = 991 CT scans; n = 462 COVID-19, and n = 529 CAP) from three centers in China. An independent Chinese and German test dataset of n = 600 CT scans from six centers (COVID-19 / CAP; n = 300 each) was used to test the performance of eight blinded radiologists and the AI algorithm. A subtest dataset (180 CT scans; n = 90 each) was used to evaluate the radiologists' performance without and with AI assistance to quantify changes in diagnostic accuracy, reporting time, and diagnostic confidence.
resultsThe diagnostic accuracy of the AI algorithm in the Chinese-German test dataset was 76.5%. Without AI assistance, the eight radiologists' diagnostic accuracy was 79.1% and increased with AI assistance to 81.5%, going along with significantly shorter decision times and higher confidence scores.
conclusionThis large multicenter study demonstrates that AI assistance in CT-based differentiation of COVID-19 and CAP increases radiological performance with higher accuracy and specificity, faster diagnostic time, and improved diagnostic confidence. KEY POINTS: • AI can help radiologists to get higher diagnostic accuracy, make faster decisions, and improve diagnostic confidence. • The China-German multicenter study demonstrates the advantages of a human-machine interaction using AI in clinical radiology for diagnostic differentiation between COVID-19 and CAP in CT scans.
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