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By using multi-center chest CT data, an intelligent assessment model for the severity of ARDS was constructed. Based on CT quantitative features and clinical characteristics, a prediction model for short-term critical events (such as mechanical ventilation decisions, prone position strategies, death, ECMO use, etc.) was established. The disease was staged and quantified, and a diagnosis and risk stratification model for ARDS was developed to assist in guiding the diagnosis and treatment strategies for ARDS.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Training group, testing group, validation group | The study adopts a stratified random sampling strategy with an 8:2 split to construct training and internal validation datasets, together with an independent external test cohort from a separate center. No randomization of clinical interventions or treatments is involved. The model will be developed and evaluated using observational data derived from real-world clinical pathways and outcomes, with the objectives of assessing performance in disease severity stratification, treatment recommendation, and mortality prediction. Model performance will be compared with established ICU severity scores and existing AI-based approaches according to a prespecified statistical analysis plan. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| CT scan | Diagnostic Test | CT scan |
|
| Measure | Description | Time Frame |
|---|---|---|
| Accuracy of ARDS severity classification | Accuracy of the artificial intelligence-based model in classifying ARDS severity (mild, moderate, or severe), using the reference clinical classification defined by the 2023 global ARDS criteria as the ground truth. | Baseline, defined as within 24 hours of index chest CT acquisition during ICU admission. |
| Treatment plan matching rate between model-recommended and actual clinical management. | Concordance rate between model-recommended treatment strategies and actual clinical management decisions across five predefined intervention modalities: mechanical ventilation, high-flow nasal oxygen therapy, non-invasive ventilation, prone positioning, and neuromuscular blockade. | Baseline, defined as within 24 hours of index chest CT acquisition during ICU admission. |
| Accuracy of 28-day in-hospital mortality prediction. | Accuracy of the model in predicting all-cause in-hospital mortality within 28 days, based on integrated chest CT imaging features and clinical variables. | Up to 28 days from ICU admission, or until hospital discharge, whichever occurs first. |
| Measure | Description | Time Frame |
|---|---|---|
| Comparative performance improvement over baseline AI models. | Absolute performance improvement of the proposed model compared with three commonly used baseline artificial intelligence models across ARDS severity classification, treatment plan matching, and 28-day mortality prediction. | Baseline for severity classification and treatment plan matching; up to 28 days from ICU admission for mortality prediction |
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Inclusion Criteria:
Exclusion Criteria:
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The study population consists of adult patients diagnosed with acute respiratory distress syndrome (ARDS) who were admitted to the intensive care units (ICUs) of three tertiary comprehensive hospitals in China. Eligible participants are retrospectively identified from electronic medical records between January 2020 and December 2024. All included patients meet the predefined inclusion and exclusion criteria based on the 2023 Global ARDS Definition and have available chest CT imaging and corresponding clinical data. This cohort represents a real-world ICU population with diverse etiologies of ARDS and varying degrees of disease severity.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Department of critical care medicine, Zhongshan Hospital, Fudan University | Shanghai | Fengling Rd | 200032, P. R. | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 31570096 | Result | Ding XF, Li JB, Liang HY, Wang ZY, Jiao TT, Liu Z, Yi L, Bian WS, Wang SP, Zhu X, Sun TW. Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort study. J Transl Med. 2019 Oct 1;17(1):326. doi: 10.1186/s12967-019-2075-0. | |
| 31576250 |
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Individual participant data that underlie the results of the study, including de-identified demographic information, clinical variables, laboratory findings, ventilator or HFNC parameters, and AI-derived quantitative CT features, will be shared. All data will be fully de-identified in accordance with applicable regulations before sharing. Imaging data (CT scans) will also be provided in de-identified format when permitted by participating sites.
Individual participant data will be available starting 12 months after publication of the primary results and will remain accessible for at least 5 years.
Individual participant data will be shared with qualified researchers for scientifically sound proposals. Requests must include a methodologically appropriate analysis plan and an institutional review board (IRB) or ethics committee approval when required. Data will be shared only in de-identified form. All requests will be reviewed by the principal investigator and the study steering committee, who will evaluate the scientific rationale, feasibility, and compliance with data-protection regulations. Upon approval, data will be accessed through a secure, password-protected data-sharing platform.
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Dec 12, 2025 |
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| Calibration performance of 28-day mortality prediction. | Calibration of the mortality prediction model assessed using calibration curves and calibration statistics to evaluate agreement between predicted and observed 28-day in-hospital mortality. | Up to 28 days from ICU admission, or until hospital discharge, whichever occurs first. |
| Model interpretability based on imaging and clinical feature contributions. | Quantification of the relative contributions of imaging-derived features and clinical variables to mortality prediction using Shapley Additive Explanations (SHAP). Feature importance will be analyzed overall and stratified by ARDS severity. | Baseline for feature extraction; up to 28 days from ICU admission for outcome association analysis. |
| Association between treatment concordance and 28-day in-hospital mortality. | Association between concordance of model-recommended interventions and actual clinical treatments and 28-day in-hospital mortality, evaluated using multivariable logistic regression adjusted for key imaging-derived structural metrics. | Up to 28 days from ICU admission, or until hospital discharge, whichever occurs first. |
| Zhang Z. Prediction model for patients with acute respiratory distress syndrome: use of a genetic algorithm to develop a neural network model. PeerJ. 2019 Sep 16;7:e7719. doi: 10.7717/peerj.7719. eCollection 2019. |
| 30921400 | Result | Zeiberg D, Prahlad T, Nallamothu BK, Iwashyna TJ, Wiens J, Sjoding MW. Machine learning for patient risk stratification for acute respiratory distress syndrome. PLoS One. 2019 Mar 28;14(3):e0214465. doi: 10.1371/journal.pone.0214465. eCollection 2019. |
| 37564041 | Result | Zhou Y, Feng J, Mei S, Tang R, Xing S, Qin S, Zhang Z, Xu Q, Gao Y, He Z. A deep learning model for predicting COVID-19 ARDS in critically ill patients. Front Med (Lausanne). 2023 Jul 25;10:1221711. doi: 10.3389/fmed.2023.1221711. eCollection 2023. |
| 38256439 | Result | Chiumello D, Coppola S, Catozzi G, Danzo F, Santus P, Radovanovic D. Lung Imaging and Artificial Intelligence in ARDS. J Clin Med. 2024 Jan 5;13(2):305. doi: 10.3390/jcm13020305. |
| 32646771 | Result | Albahri OS, Zaidan AA, Albahri AS, Zaidan BB, Abdulkareem KH, Al-Qaysi ZT, Alamoodi AH, Aleesa AM, Chyad MA, Alesa RM, Kem LC, Lakulu MM, Ibrahim AB, Rashid NA. Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects. J Infect Public Health. 2020 Oct;13(10):1381-1396. doi: 10.1016/j.jiph.2020.06.028. Epub 2020 Jul 1. |
| 31463251 | Result | Amisha, Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Prim Care. 2019 Jul;8(7):2328-2331. doi: 10.4103/jfmpc.jfmpc_440_19. |
| 32204716 | Result | Gutierrez G. Artificial Intelligence in the Intensive Care Unit. Crit Care. 2020 Mar 24;24(1):101. doi: 10.1186/s13054-020-2785-y. |
| 31592346 | Result | Ahuja AS. The impact of artificial intelligence in medicine on the future role of the physician. PeerJ. 2019 Oct 4;7:e7702. doi: 10.7717/peerj.7702. eCollection 2019. |
| 35771774 | Result | Shenoy S, Rajan AK, Rashid M, Chandran VP, Poojari PG, Kunhikatta V, Acharya D, Nair S, Varma M, Thunga G. Artificial intelligence in differentiating tropical infections: A step ahead. PLoS Negl Trop Dis. 2022 Jun 30;16(6):e0010455. doi: 10.1371/journal.pntd.0010455. eCollection 2022 Jun. |
| 22990871 | Result | Chiumello D, Marino A, Brioni M, Menga F, Cigada I, Lazzerini M, Andrisani MC, Biondetti P, Cesana B, Gattinoni L. Visual anatomical lung CT scan assessment of lung recruitability. Intensive Care Med. 2013 Jan;39(1):66-73. doi: 10.1007/s00134-012-2707-9. Epub 2012 Sep 19. |
| 16044083 | Result | Raghavendran K, Davidson BA, Woytash JA, Helinski JD, Marschke CJ, Manderscheid PA, Notter RH, Knight PR. The evolution of isolated bilateral lung contusion from blunt chest trauma in rats: cellular and cytokine responses. Shock. 2005 Aug;24(2):132-8. doi: 10.1097/01.shk.0000169725.80068.4a. |
| 11719313 | Result | Gattinoni L, Caironi P, Pelosi P, Goodman LR. What has computed tomography taught us about the acute respiratory distress syndrome? Am J Respir Crit Care Med. 2001 Nov 1;164(9):1701-11. doi: 10.1164/ajrccm.164.9.2103121. No abstract available. |
| 15812622 | Result | Gattinoni L, Pesenti A. The concept of "baby lung". Intensive Care Med. 2005 Jun;31(6):776-84. doi: 10.1007/s00134-005-2627-z. Epub 2005 Apr 6. |
| 21809107 | Result | Xirouchaki N, Magkanas E, Vaporidi K, Kondili E, Plataki M, Patrianakos A, Akoumianaki E, Georgopoulos D. Lung ultrasound in critically ill patients: comparison with bedside chest radiography. Intensive Care Med. 2011 Sep;37(9):1488-93. doi: 10.1007/s00134-011-2317-y. Epub 2011 Aug 2. |
| 28040987 | Result | Yadav H, Thompson BT, Gajic O. Fifty Years of Research in ARDS. Is Acute Respiratory Distress Syndrome a Preventable Disease? Am J Respir Crit Care Med. 2017 Mar 15;195(6):725-736. doi: 10.1164/rccm.201609-1767CI. |
| 34957747 | Result | Yildirim F, Karaman I, Kaya A. Current situation in ARDS in the light of recent studies: Classification, epidemiology and pharmacotherapeutics. Tuberk Toraks. 2021 Dec;69(4):535-546. doi: 10.5578/tt.20219611. |
| 38432698 | Result | Yang P, Sjoding MW. Acute Respiratory Distress Syndrome: Definition, Diagnosis, and Routine Management. Crit Care Clin. 2024 Apr;40(2):309-327. doi: 10.1016/j.ccc.2023.12.003. Epub 2024 Jan 4. |
| 32825837 | Result | Tzotzos SJ, Fischer B, Fischer H, Zeitlinger M. Incidence of ARDS and outcomes in hospitalized patients with COVID-19: a global literature survey. Crit Care. 2020 Aug 21;24(1):516. doi: 10.1186/s13054-020-03240-7. No abstract available. |
| 27875408 | Result | Riviello ED, Buregeya E, Twagirumugabe T. Diagnosing acute respiratory distress syndrome in resource limited settings: the Kigali modification of the Berlin definition. Curr Opin Crit Care. 2017 Feb;23(1):18-23. doi: 10.1097/MCC.0000000000000372. |
| 28252536 | Result | Villar J, Martin-Rodriguez C, Dominguez-Berrot AM, Fernandez L, Ferrando C, Soler JA, Diaz-Lamas AM, Gonzalez-Higueras E, Nogales L, Ambros A, Carriedo D, Hernandez M, Martinez D, Blanco J, Belda J, Parrilla D, Suarez-Sipmann F, Tarancon C, Mora-Ordonez JM, Blanch L, Perez-Mendez L, Fernandez RL, Kacmarek RM; Spanish Initiative for Epidemiology, Stratification and Therapies for ARDS (SIESTA) Investigators Network. A Quantile Analysis of Plateau and Driving Pressures: Effects on Mortality in Patients With Acute Respiratory Distress Syndrome Receiving Lung-Protective Ventilation. Crit Care Med. 2017 May;45(5):843-850. doi: 10.1097/CCM.0000000000002330. |
| 28828358 | Result | Garcia-Laorden MI, Lorente JA, Flores C, Slutsky AS, Villar J. Biomarkers for the acute respiratory distress syndrome: how to make the diagnosis more precise. Ann Transl Med. 2017 Jul;5(14):283. doi: 10.21037/atm.2017.06.49. |
| 29135617 | Result | McNicholas BA, Rooney GM, Laffey JG. Lessons to learn from epidemiologic studies in ARDS. Curr Opin Crit Care. 2018 Feb;24(1):41-48. doi: 10.1097/MCC.0000000000000473. |
| 26903337 | Result | Bellani G, Laffey JG, Pham T, Fan E, Brochard L, Esteban A, Gattinoni L, van Haren F, Larsson A, McAuley DF, Ranieri M, Rubenfeld G, Thompson BT, Wrigge H, Slutsky AS, Pesenti A; LUNG SAFE Investigators; ESICM Trials Group. Epidemiology, Patterns of Care, and Mortality for Patients With Acute Respiratory Distress Syndrome in Intensive Care Units in 50 Countries. JAMA. 2016 Feb 23;315(8):788-800. doi: 10.1001/jama.2016.0291. |
| 36070788 | Result | Gorman EA, O'Kane CM, McAuley DF. Acute respiratory distress syndrome in adults: diagnosis, outcomes, long-term sequelae, and management. Lancet. 2022 Oct 1;400(10358):1157-1170. doi: 10.1016/S0140-6736(22)01439-8. Epub 2022 Sep 4. |
| 38035100 | Result | Xu H, Sheng S, Luo W, Xu X, Zhang Z. Acute respiratory distress syndrome heterogeneity and the septic ARDS subgroup. Front Immunol. 2023 Nov 14;14:1277161. doi: 10.3389/fimmu.2023.1277161. eCollection 2023. |
| 34090669 | Result | Banavasi H, Nguyen P, Osman H, Soubani AO. Management of ARDS - What Works and What Does Not. Am J Med Sci. 2021 Jul;362(1):13-23. doi: 10.1016/j.amjms.2020.12.019. Epub 2020 Dec 26. |
| 37487152 | Result | Matthay MA, Arabi Y, Arroliga AC, Bernard G, Bersten AD, Brochard LJ, Calfee CS, Combes A, Daniel BM, Ferguson ND, Gong MN, Gotts JE, Herridge MS, Laffey JG, Liu KD, Machado FR, Martin TR, McAuley DF, Mercat A, Moss M, Mularski RA, Pesenti A, Qiu H, Ramakrishnan N, Ranieri VM, Riviello ED, Rubin E, Slutsky AS, Thompson BT, Twagirumugabe T, Ware LB, Wick KD. A New Global Definition of Acute Respiratory Distress Syndrome. Am J Respir Crit Care Med. 2024 Jan 1;209(1):37-47. doi: 10.1164/rccm.202303-0558WS. |
| 788524 | Result | Katzenstein AL, Bloor CM, Leibow AA. Diffuse alveolar damage--the role of oxygen, shock, and related factors. A review. Am J Pathol. 1976 Oct;85(1):209-28. No abstract available. |
| 37907946 | Result | Villar J, Szakmany T, Grasselli G, Camporota L. Redefining ARDS: a paradigm shift. Crit Care. 2023 Oct 31;27(1):416. doi: 10.1186/s13054-023-04699-w. |
| Dec 27, 2025 |
| Prot_SAP_000.pdf |
| ICF | No | No | Yes | Informed Consent Form | Dec 12, 2025 | Dec 27, 2025 | ICF_001.pdf |
| ID | Term |
|---|---|
| D055371 | Acute Lung Injury |
| ID | Term |
|---|---|
| D055370 | Lung Injury |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
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| ID | Term |
|---|---|
| D014057 | Tomography, X-Ray Computed |
| ID | Term |
|---|---|
| D007090 | Image Interpretation, Computer-Assisted |
| D003952 | Diagnostic Imaging |
| D019937 | Diagnostic Techniques and Procedures |
| D003933 | Diagnosis |
| D011856 | Radiographic Image Enhancement |
| D007089 | Image Enhancement |
| D010781 | Photography |
| D011859 | Radiography |
| D014056 | Tomography, X-Ray |
| D014054 | Tomography |
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