ArticleJournal of medical imaging (Bellingham, Wash.)2021
Role of standard and soft tissue chest radiography images in deep-learning-based early diagnosis of COVID-19.
Article in Journal of medical imaging (Bellingham, Wash.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Comparison of chest X-ray radiography AI model to comorbidities for predicting intensive care unit admission for COVID-19.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Article
- Enabling end-to-end secure federated learning in biomedical research on heterogeneous computing environments with APPFLx.Computational and structural biotechnology journal · 2025Article
- Impact of retraining and data partitions on the generalizability of a deep learning model in the task of COVID-19 classification on chest radiographs.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Sequestration of imaging studies in MIDRC: stratified sampling to balance demographic characteristics of patients in a multi-institutional data commons.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Assessment of a deep learning model for COVID-19 classification on chest radiographs: a comparison across image acquisition techniques and clinical factors.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Predicting intensive care need for COVID-19 patients using deep learning on chest radiography.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- A 178-clinical-center experiment of integrating AI solutions for lung pathology diagnosis.Scientific reports · 2023Article
- Impact of X-radiation in the management of COVID-19 disease.World journal of radiology · 2022Review
- SPIE Computer-Aided Diagnosis conference anniversary review.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Role of standard and soft tissue chest radiography images in deep-learning-based early diagnosis of COVID-19.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Lessons learned in transitioning to AI in the medical imaging of COVID-19.Journal of medical imaging (Bellingham, Wash.) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
PubMed holds no abstract for this paper.
Indexed as
Identifiers
What OpenQuestion holds
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.