ArticleScientific reports2020
Transfer learning with chest X-rays for ER patient classification.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature.Journal of medical Internet research · 2020Pooled it
- Challenges of AI driven diagnosis of chest X-rays transmitted through smart phones: a case study in COVID-19.Scientific reports · 2023Article
- Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images.medRxiv : the preprint server for health sciences · 2022Article
- COVID19 Diagnosis Using Chest X-rays and Transfer Learning.medRxiv : the preprint server for health sciences · 2022Article
- Deep transfer learning to quantify pleural effusion severity in chest X-rays.BMC medical imaging · 2022Article
- Application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment images.Frontiers in neuroscience · 2022Article
- Validating deep learning inference during chest X-ray classification for COVID-19 screening.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 1 country.
Funding
Abstract
One of the challenges with urgent evaluation of patients with acute respiratory distress syndrome (ARDS) in the emergency room (ER) is distinguishing between cardiac vs infectious etiologies for their pulmonary findings. We conducted a retrospective study with the collected data of 171 ER patients. ER patient classification for cardiac and infection causes was evaluated with clinical data and chest X-ray image data. We show that a deep-learning model trained with an external image data set can be used to extract image features and improve the classification accuracy of a data set that does not contain enough image data to train a deep-learning model. An analysis of clinical feature importance was performed to identify the most important clinical features for ER patient classification. The current model is publicly available with an interface at the web link: http://nbttranslationalresearch.org/ .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.