Chest Radiograph AI Diagnostic Tool for COVID-19 (M Health Fairview)
2026-01-24https://doi.org/10.1148/atlas.1769275256266
141
Overview
Schema Version
https://atlas.rsna.org/schemas/2025-11/model.json
Name
Chest Radiograph AI Diagnostic Tool for COVID-19 (M Health Fairview)
Link
https://dx.doi.org/10.1148/ryai.210217
Indexing
Keywords: COVID-19, chest radiograph, clinical decision support, deep learning, prospective validation, external validation, model drift, equity analysis, sensitivity, specificity
Content: CH, ER, IN
RadLex: RID34696, RID5350, RID43261
SNOMED: 75570004, 840539006
Author(s)
Ju Sun
Le Peng
Taihui Li
Dyah Adila
Zach Zaiman
Genevieve B. Melton-Meaux
Nicholas E. Ingraham
Eric Murray
Daniel Boley
Sean Switzer
John L. Burns
Kun Huang
Tadashi Allen
Scott D. Steenburg
Judy Wawira Gichoya
Erich Kummerfeld
Christopher J. Tignanelli
Organization(s)
University of Minnesota
Emory University
M Health Fairview Informatics
Indiana University
North Memorial Health Hospital
Version
1.0
License
Text: © 2022 Radiological Society of North America, Inc. Article available via the PMC Open Access Subset for unrestricted re-use during the COVID-19 pandemic with acknowledgment of the original source; thereafter PMC has a perpetual license to host the article.
Contact
Corresponding author: Christopher J. Tignanelli, MD, MS (email: ude.nmu@enangitc)
Funding
Microsoft AI for Health COVID-19 grant (GPU support); AHRQ/PCORI K12HS026379; NIH NCATS KL2TR002492 and UL1TR002494; NIH NHLBI T32HL07741; NIBIB 75N92020D00018/75N92020F00001; NIBIB MIDRC 75N92020C00008 and 75N92020C00021; NSF #1928481; University of Minnesota OVPR COVID-19 Impact Grant.
Ethical review
Approved by the University of Minnesota IRB with waiver of consent (STUDY 00011158). External validation: Indiana University IRB exempt (STUDY 2010169012); Emory University IRB approved (STUDY 00000506).
Date
Published: 2022-06-01
References
[1] Sun J, Peng L, Li T, Adila D, Zaiman Z, Melton-Meaux GB, Ingraham NE, Murray E, Boley D, Switzer S, Burns JL, Huang K, Allen T, Steenburg SD, Gichoya JW, Kummerfeld E, Tignanelli CJ. "Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study". Radiology: Artificial Intelligence. 2022 Jul;4(4):e210217.. . doi:10.1148/ryai.210217. PMID: 35923381. PMCID: PMC9344211.
Model
Architecture
Deep learning pipeline comprising lung segmentation, outlier detection (GAN-based), and convolutional neural network feature extraction/classification; interpretable heatmaps generated from gradients.
Availability
Model not publicly available; can be made available upon request for investigative purposes.
Clinical benefit
Assists clinicians by providing an image-based COVID-19 likelihood score from chest radiographs to support diagnostic decision-making.
Clinical workflow phase
Clinical decision support systems in the emergency department and early inpatient care.
Decision threshold
Institution-specific thresholds selected to maximize the Youden index during validation.
Degree of automation
Decision support; provides scores and heatmaps to assist, not replace, clinician diagnosis.
Indications for use
Adult patients (≥18 years) in ED or inpatient settings undergoing chest radiography with unknown or negative COVID-19 status to assess likelihood of COVID-19 infection.
Input
Frontal chest radiographs (DICOM) from adult ED and inpatient encounters.
Instructions
Integrated into the Epic EHR; evaluates adult ED/inpatient chest radiographs in real time; provides a 0–1 COVID-19 diagnostic score and heatmap; institution-specific thresholding recommended.
Limitations
Underperforms compared with board-certified radiologists; reduced performance for mild disease; validation using public datasets yielded unrealistically high AUC; performance varies by site requiring institution-specific thresholds; segmentation may miss regions obscured by heart/diaphragm; controls not limited to suspected COVID-19 population; model evaluated only for unknown/negative COVID-19 status; models were trained/validated on fixed data and may need updating.
Output
CDEs: RDE339, RDE193
Description: COVID-19 diagnostic score (0–1 likelihood) with interpretable heatmap highlighting image regions contributing to the prediction.
Recommendation
Use as an adjunct to, not a replacement for, clinical decision-making and confirmatory testing.
Reproducibility
Performance reported across multiple sites with institution-specific thresholds; model not publicly released; real-time performance stable over 19 weeks without drift.
Use
Intended: Detection and diagnosis
Out-of-scope: Diagnosis
Excluded: Diagnosis
User
Intended: Physician, Radiologist, Referring provider
Out-of-scope: Patient
Excluded: Layperson