Exploration of Diagnosis and Treatment Strategies and Prognostic Prediction Models for Acute Respiratory Distress Syndrome Based on Radiographic Evaluations Assessed by Artificial Intelligence
Conditions: ARDS (Acute Respiratory Distress Syndrome), AI (Artificial Intelligence)
Sponsor: Shanghai Zhongshan Hospital

The observational clinical research protocol initiated by 
the researchers 
 
Project name：Exploration of diagnosis and treatment strategies and 
prognostic prediction models for acute respiratory distress syndrome based 
on radiographic evaluations assessed by artificial intelligence 
 
Research unit：Zhongshan Hospital Fudan University 
Date: 2025-12-12 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Introduction 
Acute respiratory distress syndrome (ARDS) is an acute hypoxemic respiratory 
failure syndrome caused by pulmonary inflammation rather than cardiogenic 
pulmonary edema, and it was first described in 1967[1]. Over time, the clinical definition 
of ARDS has been repeatedly revised, allowing for more consistent and accurate 
identification of patients with similar characteristics for clinical management and for 
epidemiologic, observational, and interventional research. The 2023 global definition 
of ARDS states that the syndrome is triggered by acute diffuse inflammatory lung injury 
resulting from risk factors such as pneumonia, nonpulmonary infections, trauma, 
transfusion, burns, aspiration, or shock[2–4]. This injury increases vascular and epithelial 
permeability, leading to pulmonary edema and gravity-dependent atelectasis, all of 
which reduce the volume of aerated lung tissue. Clinically, ARDS is characterized by 
arterial hypoxemia and diffuse radiographic opacities associated with increased shunt, 
elevated alveolar dead space, and reduced lung compliance, often accompanied by 
dysregulated 
immune 
responses both locally and systemically[5]. Clinical 
manifestations may also be influenced by aspects of medical management, including 
initial levels of positive end-expiratory pressure (PEEP), fluid strategies, sedation, 
neuromuscular blockade, and prone positioning. Histopathologic findings vary but 
commonly include alveolar edema, inflammation, hyaline membrane formation, and 
alveolar hemorrhage, collectively termed diffuse alveolar damage[3,6]. 
    ARDS is a highly heterogeneous syndrome, characterized by diverse etiologies, 
inflammatory phenotypes, and morphologic patterns. This heterogeneity is reflected in 
its physiologic features, imaging manifestations, underlying causes, timing of onset, 
biomarker profiles, and genetic variation. The causes of ARDS can be broadly 
categorized into two groups: pulmonary and extrapulmonary. Pulmonary causes—such 
as pneumonia and ventilator-associated injury—primarily damage the alveolar 
epithelium, while extrapulmonary causes—such as sepsis and severe acute 
pancreatitis—initially injure the vascular endothelium and subsequently lead to 
pulmonary edema[14]. Consequently, personalized therapeutic strategies must be 
grounded in a deep understanding of ARDS pathophysiology, a complexity that has 
contributed to the relatively slow progress in ARDS management. Moreover, early in 
the disease course, clinical manifestations may be subtle in a subset of patients, making 
it critical to identify which individuals are at higher risk of progressing to severe disease 
and experiencing major complications, including death[15,16]. 
Pulmonary imaging is a critical tool for assessing the morphologic and mechanical 
characteristics of ARDS. Compared with standard chest radiography or lung ultrasound, 
chest CT offers substantially higher sensitivity and specificity. Lung ultrasound, while 

widely accessible, may increase the false-positive rate in ARDS diagnosis due to its 
high sensitivity to interstitial infiltration and consolidation[17]. Quantitative analysis of 
ARDS-related CT scans allows measurement of non-aerated, poorly aerated, well-
aerated, and over-aerated lung tissue, leading to the concepts of the “baby lung” and 
the lung “sponge model”[18]. These quantitative approaches reveal the redistribution of 
lung density in the prone position and changes in the fraction of non-aerated lung under 
different airway pressures, representing the gold standard for assessing lung 
recruitability and guiding mechanical ventilation strategies[19]. Similarly, in certain 
conditions such as thoracic trauma, early CT quantification of parenchymal injury can 
help predict the temporal evolution from initial focal or diffuse alveolar hemorrhage to 
typical ARDS patterns of pulmonary edema and interstitial alteration[20]. However, 
since the mid-1980s, the clinical use of quantitative CT analysis has remained limited 
because it requires manual image processing by clinicians[21]. Moreover, evaluating 
lung recruitability can take 6–8 hours and is subject to measurement variability. These 
limitations highlight the urgent need for new or more intelligent technologies to 
facilitate accurate, efficient, and standardized quantitative assessment while reducing 
operator-dependent bias. 
With the advancement of artificial intelligence (AI) technology, its role in 
healthcare systems is increasingly being realized. Broadly defined, AI refers to a 
machine or computational platform capable of making intelligent decisions in a manner 
analogous to human reasoning. Currently, AI shows promising applications across 
various fields including radiology, pathology, ophthalmology, cardiology, and oncology 
[22–24]. It is widely utilized in drug discovery, disease diagnosis, healthcare planning, 
health monitoring, digital consultation, surgical intervention, clinical data management, 
personalized treatment, and decision support[25]. Compared with conventional 
technologies, AI offers several advantages, such as faster detection, reduced burden on 
healthcare professionals, enhanced productivity, efficiency, accuracy, and precision, 
lower medical costs, and improved quality of care [25, 26]. 
In recent years, AI techniques—particularly deep learning (DL) methods—have 
advanced rapidly, achieving diagnostic accuracy comparable to human experts in many 
medical image analysis tasks with high efficiency. Examples include multi-organ 
segmentation in CT data, lesion segmentation in dental scans, retinal vessel 
segmentation in fundus images, and disease classification based on pathological images. 
Several studies have already applied DL methods to the diagnosis and severity 
prediction of acute respiratory distress syndrome (ARDS), demonstrating preliminary 
progress [27, 28]. AI can automatically and effectively analyze and segment acutely 
injured lungs, predict the development of ARDS, assess alveolar recruitment, and 

quantify relationships between lung tissue characteristics and clinical outcomes. The 
application of these technologies is expected to assist healthcare professionals in 
delivering better patient care, thereby reducing the disease burden. 
However, two limitations persist in current approaches. First, existing methods 
lack the ability to process multimodal data. Models are typically restricted to partial 
datasets, using machine learning techniques such as logistic regression, support vector 
machines, or random forests for textual data—including age, sex, and T-cell counts—
while employing a separate deep learning model for CT imaging analysis. This 
fragmented view of patient information compromises predictive accuracy in certain 
cases. Second, current methods fail to balance performance with interpretability. 
Although deep learning models such as convolutional neural networks (CNNs) may 
outperform traditional machine learning approaches like multivariable Cox regression 
in predictive accuracy, they often lack transparency in their decision-making process. 
Model interpretability is crucial in clinical settings, as physicians need to understand 
the factors influencing predictions to avoid unexpected errors when incorporating AI-
based recommendations. 
Advances in AI-enabled deep learning for CT image analysis allow automated 
identification of parenchymal lung changes, quantitative segmentation of 
hypo-ventilated or high-density regions, and integration with clinical data to build 
predictive models. Incorporating multicentre data can improve model generalizability 
and address the performance decline often observed in external validation of 
single-centre studies, thereby supporting the development of robust and reliable 
intelligent auxiliary strategies for ARDS diagnosis and management. Indeed, the high 
mortality associated with ARDS stems not only from inadequate management strategies 
but also from challenges in early detection, risk stratification, and severity prediction. 
Consequently, developing AI-assisted diagnostic and management tools represents a 
pressing need, particularly for critical conditions such as ARDS [29–31]. 
Therefore, this study proposes a multicentre retrospective investigation integrating 
data from 400 ARDS cases across three tertiary comprehensive hospitals. Through 
standardized image preprocessing and rigorous modeling approaches, we aim to 
develop and validate a CT-imaging-driven model for ARDS grading and clinical 
decision support, assess its cross-centre stability, and ultimately provide a generalizable 
intelligent tool for clinical practice. 
Primary Objective 
To develop an intelligent assessment model for ARDS severity using multicentre 
chest CT data. By integrating quantitative CT features with clinical characteristics, the 

model aims to predict short-term major clinical events (such as decisions regarding 
mechanical ventilation, prone positioning strategy, mortality, and ECMO use), stage 
and quantify the disease, and establish a diagnostic and risk-stratification model for 
ARDS to assist in guiding therapeutic strategies. 
Secondary Objective 
To validate the generalizability of the model across external centres and evaluate 
its applicability in multicentre real-world settings; and to analyze the influence of 
different centres and CT scanner models on imaging features and model performance. 
The overall research design and plan 
This was a retrospective, multicenter, observational cohort study. 
Study population 
This study planned to enroll a total of 400 ARDS patients admitted to the intensive 
care units of three comprehensive tertiary hospitals. 
Inclusion Criteria 
1. Patients meeting the 2023 global updated definition of ARDS. 
2. Admission to the Intensive Care Unit (ICU) for more than 48 hours. 
3. Availability of chest CT imaging data. 
Exclusion Criteria 
1. Patients aged <18 years. 
2. Patients with incomplete medical records. 
3. Absence 
of 
chest 
CT 
imaging 
or 
presence 
of 
technically 
inadequate/uninterpretable chest CT images. 
Sample Size and Data Partitioning 
This study is designed as a multicenter, large-sample, retrospective observational 
study and plans to include a total of 400 patients with ARDS who meet the eligibility 
criteria. Of these, 300 cases will be contributed by the primary institution and Sub-
center 1, and 100 cases by Sub-center 2. 
The study does not involve conventional experimental or control groups. Instead, 
cases will be partitioned into three datasets according to the model development 
workflow: 

Training set: 276 cases (approximately 80% of the combined cases from the 
primary institution and Sub-center 2), obtained through stratified random sampling to 
preserve the distribution of mild, moderate, and severe ARDS; 
Internal validation set: 69 cases (approximately 20% of cases from the same two 
centers), used to monitor overfitting and optimize model hyperparameters; 
External test set: 55 cases, all derived from Sub-center 1 and entirely independent 
of the model development process, used to evaluate model generalizability. 
Primary Outcome Measures  
The primary outcomes of this study focus on the overall effectiveness of the 
proposed AI-based framework across three key clinical tasks: 
(1) Accuracy of ARDS severity classification, defined as the agreement between 
model-predicted ARDS severity (mild, moderate, or severe) and the reference clinical 
classification based on the 2023 global ARDS criteria, assessed using chest CT images 
obtained within 24 hours of ICU admission; 
(2)Treatment plan matching rate, defined as concordance between model-
recommended and actual clinical management across five intervention modalities 
(mechanical ventilation, high-flow oxygen therapy, non-invasive ventilation, prone 
positioning, and neuromuscular blockade), evaluated at the time of index chest CT 
acquisition; 
(3) Accuracy of 28-day in-hospital mortality prediction, defined as the ability of 
the model to predict all-cause in-hospital mortality within 28 days of ICU admission. 
These three outcomes collectively reflect the overall effectiveness of the model 
within a closed-loop framework encompassing disease stratification, treatment 
recommendation, and prognosis prediction. 
Secondary Outcome Measures 
Secondary outcomes include comparative performance improvement of the 
proposed model over commonly used baseline AI models; calibration performance of 
mortality prediction; interpretability analyses based on imaging-derived and clinical 
feature contributions; and assessment of the association between treatment concordance 
and 28-day in-hospital mortality using multivariable regression models. 
Research procedures 
1. Data collection 

(1) Demographic and Baseline Clinical Data  
Age, sex, body mass index (BMI), comorbidities (including chronic kidney 
disease, coronary artery disease, hypertension, diabetes, liver cirrhosis, HIV infection, 
etc.), primary etiology of ARDS, and receipt of mechanical ventilation, high-flow nasal 
oxygen therapy, or ECMO.BMI is calculated as weight (kg) divided by height squared 
(m²). 
(2) Physiologic Parameters 
 Body temperature (T), heart rate (HR), respiratory rate (RR), peripheral oxygen 
saturation (SpO₂), and mean arterial pressure (MAP). 
(3) Laboratory Parameters 
pH, partial pressure of arterial oxygen (PaO₂), fraction of inspired oxygen (FiO₂), 
partial pressure of arterial carbon dioxide (PCO₂), lactate (Lac), platelet count (PLT), 
white blood cell count (WBC), hemoglobin (Hb), serum creatinine, C-reactive protein 
(CRP), procalcitonin (PCT), international normalized ratio (INR), B-type natriuretic 
peptide (BNP), and interleukin-6 (IL-6). 
(4) Ventilator or High-Flow Oxygen Parameters 
Positive end-expiratory pressure (PEEP), tidal volume (TV), plateau pressure 
(Pplat), and flow rate (FLOW). 
(5) Other Clinical Scores 
Sequential Organ Failure Assessment (SOFA) score and Acute Physiology and 
Chronic Health Evaluation II (APACHE II) score. 
2. CT Equipment and Scanning Parameters 
Chest CT images included in this study were acquired from two CT scanners with 
the following parameters: 
CT1 (64-slice): Manufacturer: Philips; Model: Brilliance; Tube voltage: 120 kV; 
Tube current: 1000 mA; Slice thickness: 5 mm; Slice interval: 5 mm; Pitch: 0.789:1. 
CT2 (128-slice): Manufacturer: United Imaging; Model: uCTR760; Tube voltage: 
120 kV; Tube current: 1000 mA; Slice thickness: 5 mm; Slice interval: 5 mm; Pitch: 
0.984:1. 
3. Quantitative Assessment of ARDS 
Preliminary Image Processing 

Chest CT images for all enrolled cases were exported from the hospital Picture 
Archiving and Communication System (PACS) in DICOM format. To minimize inter-
scanner variability, all non-contrast CT images underwent standardized preprocessing. 
Using Python (version 3.9.13) and the open-source SimpleITK library (version 2.2.1), 
all images were converted to NIFTI format. Subsequently, each image was normalized 
to arrays with a uniform mean and standard deviation to reduce distributional 
differences across scanners. Following preprocessing, the images were fed into the deep 
learning (DL) model. 
For dataset partitioning, the “createDataPartition” function from the caret package 
was used to perform stratified random sampling, assigning 60% of the data to the 
training set, 20% to the validation set, and the remaining 20% to the test set. The 
training set was used to fit the machine learning model, the validation set to select the 
best-performing model, and the test set to evaluate the final model performance. 
Construction of a Deep-Learning Lung Segmentation Model 
This study focuses on chest CT images with the lung as the organ of interest; 
therefore, the lung will be defined as the region of interest (ROI) for the deep-learning 
(DL) model. We plan to develop a U-shaped Transformer-based model for lung 
segmentation, with target classes including lesion regions, the whole lung, individual 
lobes, and all bronchopulmonary segments. 
The U-Net architecture is widely used for medical image segmentation and 
comprises an encoder, a decoder, and skip connections. The encoder extracts local 
image features through successive convolutional operations and employs down-
sampling to progressively expand the network receptive field. Conversely, the decoder 
upsamples processed features back to the original image resolution to enable pixel-level 
semantic inference. Skip connections fuse features from corresponding encoder and 
decoder layers, ensuring comprehensive feature maps that preserve precise spatial 
information alongside high-level semantics, which is critical for accurate segmentation. 
A Transformer can serve as the backbone within the U-Net encoder/decoder blocks; by 
using global attention mechanisms, it captures long-range dependencies and, in 
principle, attains a global receptive field. 
The proposed lung-segmentation DL model will accept lung-masked CT images 
as input (images cropped, masked, and rescaled to include the whole lung and lesions). 
The model outputs segmentation masks for left and right lungs, lobes, 
bronchopulmonary segments, and lesion types including ground-glass opacities (GGO), 
consolidation, 
and 
atelectasis. 
Anatomically, 
there 
are 
typically 
18–20 
bronchopulmonary segments; because segment-level granularity can complicate 

downstream analysis, we will simplify segmental mapping into four bilateral segments 
and define 11 lung regions for analysis: right upper, right upper–middle, right lower–
middle, right lower, whole right, left upper–middle, left lower–middle, left lower, whole 
left, and whole lung. By computing the proportion of lesion volume in each lung region, 
we will derive a structured set of imaging features reflecting regional lung involvement. 
Interpretable Severity Assessment 
Because segmentation results provide the primary evidence for severity 
assessment, we propose a multi-task architecture with separate task heads for 
segmentation and for lesion-severity evaluation. In this way, the segmentation DL 
model becomes a multi-task model capable of both precise lung/lesion segmentation 
and quantitative severity scoring; moreover, the segmentation outputs improve model 
interpretability for clinicians. 
Using the above lung-segmentation model, we will compute the following 
quantitative metrics to characterize CT-detected disease burden: 
① Absolute lesion volumes for the whole lung, each lobe, and each defined 
bronchopulmonary region. 
② Percent lesion volumes for the whole lung, each lobe, and each 
bronchopulmonary region (i.e., lesion volume divided by region volume), used to 
quantify ARDS severity and lesion distribution. 
③ Histograms of CT attenuation values (in Hounsfield units, HU) for different 
lesion regions. 
The inference pipeline of the multi-task model is as follows: chest CT scans are 
input to the DL segmentation system, whose segmentation head(s) generate masks for 
lesion regions, whole lung, lobes, and bronchopulmonary regions. Quantitative metrics 
are then calculated from the segmentation masks to quantify each patient’s lesion 
burden and to form a feature vector of regional involvement. These imaging features 
are projected into the DL model’s latent feature space and used by the severity-
assessment head to predict disease severity. This design enables end-to-end prediction 
while preserving explainability through explicit regional segmentation outputs. 
4. Etiological and Respiratory Mechanics Classification 
As noted above, existing approaches are limited in their ability to process 
multimodal data simultaneously, which restricts the model’s capacity to integrate all 

patient-level features. The Transformer architecture employed in this study is inherently 
well suited for multimodal learning and has been widely applied across natural 
language processing, computer vision, and cross-modal tasks. Representative examples 
such as GPT-4, CLIP, and BLIP demonstrate the capability of Transformer-based 
models to learn a unified feature space for both text and images, enabling joint 
understanding of heterogeneous inputs. 
Building upon the ARDS severity-assessment DL model described earlier, we 
further extend the framework to incorporate patient etiology and respiratory mechanics 
parameters to enable a more comprehensive and accurate prediction. Specifically, the 
encoder of the DL model is used to extract high-level features from CT scans, while a 
tokenizer is applied to encode structured clinical variables. The tokenizer may be 
initialized using models trained on large text corpora, such as a BERT tokenizer or a 
CLIP tokenizer. 
Because imaging and clinical variables naturally reside in different feature spaces, 
the model cannot directly process them in their raw form. To address this, we introduce 
learnable linear projection layers for each feature type, mapping all inputs into a unified 
latent space. The multimodal features are then concatenated and fed into the prediction 
head to estimate ARDS severity and classify physiologic or etiologic subtypes based on 
the full spectrum of patient characteristics. 
 
5. The overall process of the U-Net model 
 
 
Sample size estimation 
The sample size was preliminarily estimated using the event-per-variable principle. 
The mortality prediction component is designed to include no more than 15 candidate 
predictors in the final multivariable logistic regression model. Following the commonly 
accepted criterion of at least 10 outcome events per predictor, a minimum of 150 death 

events is required to ensure model stability and interpretability. Based on historical data 
from the participating centers, the in-ICU mortality rate among patients with ARDS is 
approximately 37.5%. Accordingly, the total target sample size for this study is 
estimated to be approximately 400 patients. 
Statistical Analysis Plan 
All eligible cases will be stratified and randomly sampled at an 8:2 ratio within 
Shanghai General Hospital and its affiliated centers to form the model training set and 
internal validation set. Cases from an additional participating center will be reserved as 
an independent external test cohort. 
Model performance will be evaluated for three predefined tasks: ARDS severity 
classification, treatment strategy prediction, and mortality risk prediction. Performance 
metrics will include accuracy, balanced accuracy, area under the receiver operating 
characteristic curve (AUC), precision–recall curve analysis, and the Brier score. During 
model development, five independent repeated experiments will be conducted, and 
performance will be reported as mean values with standard deviations. 
For comparative performance assessment, results from the five independent 
experiments will be compared using two-sided Wilcoxon rank-sum tests, with a 
significance threshold of 0.05. Different levels of statistical significance will be denoted 
using conventional symbols (e.g., “*”, “**”) corresponding to predefined P-value 
ranges. 
To assess the association between model outputs and clinical outcomes, model-
recommended intervention sets will first be encoded for agreement with actual clinical 
management. These agreement variables will then be incorporated into multivariable 
logistic regression models together with six imaging-derived anatomical features (e.g., 
diaphragmatic height, lung volume). Adjusted odds ratios with 95% confidence 
intervals will be reported, and a two-sided P value <0.05 will be considered statistically 
significant. 
For mortality risk prediction, ARDS severity classification and treatment strategy 
information will be sequentially added to the input features to evaluate their incremental 
impact on balanced accuracy and Brier score. Model calibration and discrimination will 
be assessed using calibration curves and precision–recall curves. In addition, least 
absolute shrinkage and selection operator (LASSO) regression will be applied to all 
structural, pathological, and intervention-related variables, and standardized 
coefficients will be extracted to facilitate interpretation of key risk-driving factors 
across different ARDS severity strata. 

Ethics and Informed Consent 
This study is a multicenter, retrospective, observational study involving the 
secondary use of previously collected clinical and imaging data. The study protocol, 
informed consent materials (where applicable), and all subject-related documents were 
submitted to the institutional ethics committee and approved prior to study initiation. 
The study will be conducted in accordance with the Declaration of Helsinki and relevant 
national and institutional regulations. 
The investigators will submit periodic progress reports to the ethics committee as 
required. Any protocol amendments or changes related to informed consent procedures 
will be reported to and approved by the ethics committee before implementation, unless 
such changes are necessary to eliminate an immediate and direct risk to participants, in 
which case the ethics committee will be notified promptly. The ethics committee will 
also be informed in writing upon study suspension or completion. 
Given the retrospective nature of the study and the use of existing identifiable data 
without commercial interest, a partial waiver of informed consent was approved by the 
ethics committee. For participants who can be successfully contacted, investigators will 
first provide oral information about the study and document the communication. If 
participants or their legal representatives subsequently return for clinical follow-up, 
written informed consent will be obtained. For participants who cannot be contacted, 
the requirement for informed consent is waived in accordance with ethical and 
regulatory guidelines. 
When applicable, participants will be provided with updated versions of the 
informed consent form and relevant written information during the study period. Signed 
informed consent documents will be retained as essential study records in compliance 
with institutional policies. 
Confidentiality and Data Protection 
All patient data will be handled in accordance with applicable laws and regulations 
on privacy protection. Prior to analysis, all data will be de-identified and used 
exclusively for scientific research purposes. Study results may be published in scientific 
journals; however, no personally identifiable information will be disclosed. 
Authorized representatives of regulatory authorities and institutional ethics 
committees may review study records as required for oversight purposes, in accordance 
with applicable regulations. Appropriate technical and organizational measures will be 
implemented to ensure data confidentiality and security throughout the study. 
 

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