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Citation: Yahyatabar, M.; Jouvet, P.;
Fily, D.; Rambaud, J; Levy, M;
Khemani, R.G.; Cheriet, F.,
on behalf of the Pediatric Acute
Respiratory Distress Syndrome
Incidence and Epidemiology
(PARDIE) V3 Investigators and
PALISI Network. A Web-Based
Platform for the Automatic
Stratification of ARDS Severity.
Diagnostics 2023,13, 933.
https://doi.org/10.3390/
diagnostics13050933
Academic Editors: Chiara Romei
and Emanuele Neri
Received: 15 January 2023
Revised: 23 February 2023
Accepted: 24 February 2023
Published: 1 March 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
diagnostics
Article
A Web-Based Platform for the Automatic Stratification
of ARDS Severity
Mohammad Yahyatabar 1, Philippe Jouvet 2,∗, Donatien Fily 2, Jérome Rambaud 2, Michaël Levy 2,
Robinder G. Khemani 3, Farida Cheriet 1,† on behalf of the Pediatric Acute Respiratory Distress Syndrome
Incidence and Epidemiology (PARDIE) V3 Investigators and PALISI Network
1
Department of Computer and Software Engineering, Polytechnique Montréal, Montréal, QC H3T 1J4, Canada;
2Department of Pediatrics, Faculty of Medicine, University of Montréal, Montréal, QC H3C 3J7, Canada
3Department of Anesthesiology and Critical Care Medicine, Children’s Hospital of Los Angeles,
Los Angeles, CA 90027, USA
*Correspondence: philippe.jouvet@umontreal.ca
†
Membership of the Pediatric Acute Respiratory Distress Syndrome Incidence and Epidemiology (PARDIE) V3
Investigators and PALISI Network is provided in the Acknowledgments.
Abstract:
Acute respiratory distress syndrome (ARDS), including severe pulmonary COVID infection,
is associated with a high mortality rate. It is crucial to detect ARDS early, as a late diagnosis may
lead to serious complications in treatment. One of the challenges in ARDS diagnosis is chest X-ray
(CXR) interpretation. ARDS causes diffuse infiltrates through the lungs that must be identified using
chest radiography. In this paper, we present a web-based platform leveraging artificial intelligence
(AI) to automatically assess pediatric ARDS (PARDS) using CXR images. Our system computes a
severity score to identify and grade ARDS in CXR images. Moreover, the platform provides an image
highlighting the lung fields, which can be utilized for prospective AI-based systems. A deep learning
(DL) approach is employed to analyze the input data. A novel DL model, named Dense-Ynet, is
trained using a CXR dataset in which clinical specialists previously labelled the two halves (upper
and lower) of each lung. The assessment results show that our platform achieves a recall rate of
95.25% and a precision of 88.02%. The web platform, named PARDS-CxR, assigns severity scores
to input CXR images that are compatible with current definitions of ARDS and PARDS. Once it has
undergone external validation, PARDS-CxR will serve as an essential component in a clinical AI
framework for diagnosing ARDS.
Keywords:
chest X-ray; machine learning; acute respiratory distress syndrome; pediatric acute
respiratory distress syndrome; web-based platform
1. Introduction
Acute respiratory distress syndrome (ARDS) is a severe, even life-threatening condi-
tion, associated with respiratory failure, i.e., the inability of the lungs to fulfill their basic
function of exchanging gases in the body. ARDS occurs in children and adults; its main
causes include respiratory infection, aspiration, or trauma. The first description of ARDS as
a separate disease was provided in 1967. Variability in the ability to identify ARDS causes
difficulty in clinical trials. The Berlin definition introduced diagnostic criteria, such as acute
onset, severe hypoxemia (lack of oxygen in the blood), bilateral diffuse infiltrates visible
in chest radiography, and absence of any evidence of cardiac failure or fluid overload [
1
].
Despite intensive studies investigating ARDS (60,000+ articles found in PubMed), its mor-
tality rate is still as high as 43% [
2
]. Among the survivors of ARDS, a significant portion
experienced lasting damage to the lungs, especially in older patients. The Berlin definition
grades the severity of ARDS as being mild, moderate, or severe. Table 1illustrates the
oxygenation criteria and mortality rates associated with these severity levels.
Diagnostics 2023,13, 933. https://doi.org/10.3390/diagnostics13050933 https://www.mdpi.com/journal/diagnostics
Diagnostics 2023,13, 933 2 of 16
Table 1.
ARDS severities in the Berlin definition and associated oxygenation levels and mortality
rates [1].
Severity PaO2/FiO2Mortality
Mild 200–300 27%
Moderate 100–200 32%
Severe ≤100 45%
As seen in Table 1, considering the high mortality rate of ARDS and its rapid progres-
sion, early diagnosis of ARDS is vital. Furthermore, the mortality rate is directly associated
with the severity of the syndrome. The risk benefit profile of therapies depends on ARDS
severity, making early stratification of ARDS severity crucial for management. The Pediatric
Acute Lung Injury Consensus Conferences (PALICC) [
3
–
5
] were organized to address pedi-
atric ARDS (PARDS) specifications and give treatment and diagnosis recommendations.
According to the most recent definition of PARDS, PALICC-2 [
5
], the criteria allow for
new infiltrates in chest radiography, even if only a region within a single lung is affected.
One of the main reasons for this change in diagnostic criteria was the lack of agreement
in the interpretation of chest images between radiologists or between radiologists and
intensive care practitioners on the presence of bilateral infiltrates, which are required in the
Berlin standard. López-Fernández et al. showed that interobserver agreement for bilateral
infiltrates and quadrants of consolidation in PARDS was “slight”
(kappa 0.31 and 0.33) [6]
.
Sjoding et al. reported similar results, with interobserver reliability of ARDS diagnosis
being “moderate” (kappa = 0.50; 95CI, 0.40–0.59). Hence, there is an urgent need to improve
the reliability of Chest X-ray (CXR) intepretation in ARDS and PARDS to allow earlier
diagnosis of the syndrome [7].
Several studies have applied machine learning (ML) and artificial intelligence (AI)
approaches to analyze CXR images. One of the most common tasks reported in the literature
is diagnosing pulmonary pathologies using chest radiography. Thanks to massive publicly
available datasets, deep learning (DL) approaches have been broadly applied in medical
pathology detection. However, there is as yet no dataset annotated with ARDS labels. Thus,
few studies are found in the literature addressing the diagnosis of the syndrome.
To our knowledge, two papers present ML-based systems to identify ARDS in CXR
images. The first one [
8
] proposed a method for detecting ARDS using a traditional ML
approach based on hand-crafted features. The texture of intercostal image regions is
considered as a discriminative feature for classifying samples. To highlight intercostal areas,
a semi-automatic approach proposed by Plourde is utilized [
9
]. They succeed in reducing
the inter-observer variability between clinicians in diagnosing PARDS. However, their
approach is not automatic, and the rib segmentation step requires operator intervention.
In the second work, an automatic ARDS detection and grading approach was proposed
using a state-of-the-art DL model (Densenet) [
10
]. The authors first pretrained the model on
public datasets (not containing ARDS samples) and then refined the model with a custom
dataset consisting of ARDS-labeled images. Their approach performs well in diagnosing
ARDS, but the model provides no evidence for the support system’s decisions. Thus,
although it works well in analyzing ARDS cases, the model lacks interpretability, which is
essential for a ML system to be used in clinical settings.
Recently, due to the COVID-19 outbreak, the research community has gotten more
involved in computer-based analysis of chest X-ray images as one of the easiest and fastest
ways to check for signs of the disease. Mobile Chest X-ray Analysis [
11
] and Chester [
12
]
are prototype systems for CXR assessment developed using the aforementioned Densenet
model, trained on the public Chest-Xray14 dataset [
13
]. Both systems provide evidence
for the detected pathologies by means of saliency maps obtained using GradCam [
14
].
However, this can reveal areas that are irrelevant to the pathology being detected [
15
,
16
].
Thus, although these systems provide activation maps pointing out the references for the
decisions, they are not sufficiently reliable to be used in clinics.
Diagnostics 2023,13, 933 3 of 16
The main contributions of this paper are to create a tool for stratifying the severity
of ARDS in CXR images and to build a web-based platform for external validation. The
platform uses local information to classify X-rays based on the distribution of infiltrates
in the different lung quadrants, and it provides a global severity score for the image that
is applicable in both children and adults. The web-based platform, PARDS-CxR, can be
used as a standalone tool, or it can be integrated with other ARDS analysis tools to offer a
comprehensive approach for clinical use.
The following section first explains the details of the data collection used to train our
DL model. Then, we describe the proposed DL model and its evaluation process, and we
present the development of the Web platform. Section 3presents the results of testing the
ARDS assessment tool, and in Section 4, the strengths and drawbacks of our platform are
discussed. Section 5provides concluding remarks for this paper.
2. Materials and Methods
2.1. Methodology
This study contains four main phases, as illustrated in Figure 1. The end product is
PARDS-CxR, the web-based application to detect ARDS. First, a substantial set of data is
required to train the model. Existing public datasets do not include ARDS-labeled CXR
images, so we created a new one. This data collection process is summarized in Section 2.2.
Then, the proposed model must be trained on the CXR images. The model has two outputs
associated with lung segmentation and ARDS classification, as explained in Section 2.3.
The trained model is then tested on unseen data to be evaluated.
Sections 2.4–2.6
detail the
validation process. Finally, the model is uploaded to a server, and an interface is designed
so the user can easily access it. The web application is addressed in Section 2.7.
2.2. ARDS Dataset
Collectively, the main publicly available CXR datasets provide around a million images
with pathology labels [
17
]. This data motivated many researchers to employ AI techniques
in this domain. However, no such datasets assign ARDS-specific labels to images. As our
first step, we collected and annotated a dataset at Sainte-Justine Hospital, Montreal, Canada
(CHUSJ), to address the lack of appropriate data. Our dataset comprises three data sources
containing 373 CXR images. Ninety images and their corresponding labels came from a
previous study by our team [
8
]. A further 100 images were taken from the Chest X-ray14
dataset [
13
] and relabeled by clinical experts (JR, ML) in the hospital. Another 183 images
were provided by the PARDIE study, a multi-national study that prospectively gathered
chest X-ray images of children with ARDS [
6
]. For each image, labels were associated with
the four lung quadrants obtained by splitting each lung into upper and lower portions. We
refer to each quadrant by its position: left upper (LR), left lower (LL), right upper (RU),
and right lower (RL). According to the Berlin definition [
1
], visible bilateral infiltrates are a
mandatory criterion for a case to be categorized as ARDS. Two intensivists from CHUSJ
assessed the presence of infiltrates in each quadrant. A sample was included in the dataset
only if the clinical observers reached a consensus on the labels. In addition, 138 CXR images
were taken from the Montgomery dataset to represent the normal class [
18
]. These samples
were labeled as non-ARDS if agreed by the clinical experts. By dropping images with
disagreements in their labeling, our final ARDS dataset consisted of 356 images, of which
134 meet the bilateral infiltrates criteria in the Berlin definition [1].
Diagnostics 2023,13, 933 4 of 16
RL LL
RU
Model Training
L RU
L LU
L RL
L LL
ARDS DATASET
Joint Segmentation-Classification
Dense Y-net
L RU
L LU
L RL
L LL
+
LossClassification
+
LossSegmentation
Loss =
Trained Dense Y-net
1
0
1
1
Bi-lateral infiltrates? ARDS (YES / NO)
Training Data
Testing Data
Target
Data collection
Evaluation
Trained Dense Y-net UI Presentation
Back-End
Web Application
Server Side User Side
Front-End
Clinical Review
PARDS-CxR
LU
Figure 1. Organization of our study into four main phases. The data are collected from several data
sources and annotated at Saint-Justine Hospital, Montreal, Canada. The DL model is trained using
quadrant-level labels and lung segmentation maps. It is then evaluated on a set of previously unseen
images; both the classification and segmentation performances are assessed. Finally, a web-based
platform is designed and made available through the internet.
Diagnostics 2023,13, 933 5 of 16
2.3. Joint Segmentation and Classification Model
In computer-based diagnosis approaches, it is common to use segmentation ahead of
classification to determine the region of interest. Lung segmentation separates the lung
areas from the thoracic tissues surrounding them and is the primary image analysis step
in many clinical decision support systems. Generalization to new datasets is a difficult
challenge in the analysis of chest radiography. In that respect, segmentation is considered a
strategy to limit the impact of specific imaging devices and settings, since it restricts the
feature extraction to the lung fields and removes the effect of the image background [
19
,
20
].
However, serial usage of segmentation and classification propagates the segmentation
error into the classification network. Dense-Ynet is a convolutional network that takes
advantage of Densenet, Y-net, and U-net models to do both tasks simultaneously in a joint
segmentation–classification model. The backbone of the network used in this study is our
previously developed Dense-Unet [
21
]. Dense-Unet is a segmentation model in which
dense connections between the feature maps in various layers facilitate the information
flow throughout the model, letting designers choose a configuration with a small number
of training parameters. Our proposed Dense-Ynet takes advantage of automatic feature
extraction from both the original and segmented images (Figure 2). The model has two
outputs and is trained using two loss functions: the lung segmentation loss and quadrant
classification loss. The model works based on the convolution operation. A convolution
is a mathematical operation that filters the information of its input and creates feature
maps. An inevitable effect of the convolution operation is to change the dimensions of the
feature maps. To tackle this issue, upsampling and strided convolution operations are used
to ensure that feature maps coming from different layers can be concatenated. Squeeze
and excitation (SE) blocks [
22
] are also used after each convolution layer to improve the
representational power of the blocks by recalibrating the features. The key strengths of
Dense-Ynet are use of lung segmentation in its architecture, specialized connectivity, which
enable better generalization, and prediction of local labels for each image.
To reach the final decision based on the Berlin definition, we must test for existing
bilateral infiltrates. To that end, a simple logical operation in Equation (1) combines the
predictions of each quadrant to check this condition:
PARDS = (PRL ∨PRU )∧(PLL ∨PLU)(1)
PRL
,
PRU
,
PLL
, and
PLU
are the prediction labels for the right lower, right upper, left
lower, and left upper quadrants, respectively.
PARDS
is the inferred ARDS label, and
∨
and
∧
are logical or and and operations. The equation states that, if at least one quadrant is
involved on each side, the case is recognized as (P)ARDS.
Dense-Ynet
Network LABELRU
LABELLU
LABELLL
LABELRL
ARDS(Y/N)
Input
Deep model Lung Segmentation
ARDS Classification
Figure 2.
The Dense-Ynet model takes advantage of the interaction between the segmentation
and classification tasks by performing them simultaneously. The features from the original and
lung-segmented images are concatenated and utilized to classify ARDS cases.
Diagnostics 2023,13, 933 6 of 16
2.4. Experimental Design
In this work, 267 images of the ARDS dataset are used to train the Dense-Ynet model.
In addition, 35 images are used to validate the training process. For the testing stage,
54 images previously unseen by the network are used. The algorithm is evaluated with the
five-fold cross-validation strategy. Cross-validation is a method that tries various training
and testing data combinations to confirm the reported results’ reliability. Data augmentation
is a technique to enrich the training data by generating new images from the current training
set. For this purpose, we use basic image processing techniques, such as random rotation,
cropping, shifting, horizontal flipping, and intensity changing. The rectified linear unit
(
ReLu
) activation function introduces non-linearity to network blocks. The
Sigmoid
function
provides valid labels between zero and one in both the segmentation and classification
output layers.
Adam
is the optimizer used for updating the model weights during training.
To reach the optimal configuration, a set of hyperparameters must be explored to find the
best model structure and training policy. The Web platform
(see Section 2.7)
employs six
Dense-Ynet instances, corresponding to the best hyper-parameters sets. Using an ensemble
approach, the final result presented to the user combines the values received from the
individual models.
The PARDS-CxR application detects lung quadrants consolidation, and the final ARDS
label is derived from the quadrant predictions using Equation (1).
2.5. Scoring Scheme
To analyze the severity of ARDS in CXR images, a scoring scheme is proposed based
on the number and the position of affected lung quadrants (see Table 2). The scheme is
compatible with the Berlin definition, in which existing bilateral infiltrates are an essential
criterion for ARDS diagnosis in chest radiography.
Table 2. Severity scoring scheme based on affected lung quadrants.
Affected Quadrants Score Severity
4 quadrants 5 Severe
3 quadrants 4
2 quadrants (Different sides) 3 Mild
2 quadrants (Same side) 2
Non-ARDS
1 quadrant 1
No affected quadrant 0
Giving scores is important from two points of view. First, the score represents the
severity of the diffused infiltrates throughout the lungs. Second, reporting disease severity
helps clinicians follow appropriate treatment protocols or triaging. This type of system has
been proposed for the Murray Lung Injury Score, as well as as part of the recently proposed
RALE score in adult patients with ARDS.
2.6. Evaluation Metrics
Evaluation metrics are measured from the algorithm’s performance on unseen test data
to assess the approach. There is no metric representing the total capacity of the PARDS-CxR
platform. However, we use a set of performance metrics to provide a complete overview of
the model’s operation. A confusion matrix quantifies the ability of the classifier to detect
each class separately. It gives detailed measures comparing the actual and predicted labels,
as shown in Figure 3.
Diagnostics 2023,13, 933 7 of 16
True Negative
False Negative True Positive
False Positive
Class 0
Class 0 Class 1
Class 1
Actual
Predicted
Figure 3.
Confusion matrix for a binary classification problem. The matrix contains four elements
that, together, evaluate the system’s predictions versus the real labels.
The elements of the confusion matrix, namely, the true positive (TP), true negative (TN),
false positive (FP), and false negative (FN) values, serve to calculate several assessment
metrics as follows:
Accuracy =TP +TN
FP +FN +TP +TN (2)
Precision =TP
FP +TP (3)
Recall =T P
FN +T P (4)
F1=2×Precision ×Recall
Precision +Recall . (5)
The
Accuracy
metric represents the overall correctness of a classification algorithm. It
cannot fully express the model performance, however, especially in the case of unbalanced
testing data.
Precision
and
Recall
reveal the model’s performance in discriminating between
the different classes.
Precision
represents how precise the model is in identifying the target
(positive) class. Specifically, it points out what portion of cases predicted as positive are
really ARDS cases. On the other hand, the
Recall
value shows what proportion of predicted
ARDS cases are actually labeled as ARDS. These two metrics have a complementary role in
describing the model’s behavior. The
F
1 score, derived from
Precision
and
Recall
values, is
a single metric to quantify the algorithm’s performance.
The receiver operating characteristic (ROC) curve illustrates the diagnostic capacity of
a system by comparing true positive and false positive rates as the discrimination threshold
(applied at the network’s output layer to decide between the two classes) varies. The area
under the ROC curve (AUROC) represents the discriminatory power of the classifier.
2.7. Web-Based Platform
We designed a web-based platform to facilitate the diagnosis of ARDS in CXR images
by medical professionals. The platform is intended as a tool to provide a second opinion
to clinicians, but no direct medical use is recommended until medical professionals validate
the tool using external data. The PARDS-CxR platform takes advantage of six Dense-Ynet
instances to provide scores for each input image. The scores are given based on the number and
combination of affected lung quadrants as explained in Section 2.5. A global score is assigned by
combining the outputs from the model instances. In addition, the application provides accurate
lung segmentation maps, which are helpful in AI-based analysis of CXR images.
The web application utilizes the React library to create a user-friendly and interactive
user interface (UI) for delivering the specified services. The library enables efficient code
writing and makes it easier to manage, refine, and integrate the application with other tools.
The platform supports both English and French languages and has two main modes for
ARDS definitions for adults (Berlin) and children (PALICC-2). The difference between the
modes is that, when using PALICC-2 mode, the platform requires two input images. The
application response includes segmentation maps, severity scores (local and global), and
an interpretation based on the definition.
Diagnostics 2023,13, 933 8 of 16
Although the deep models are trained using graphical processing units (GPUs), the
evaluation model does not require a GPU and can process the results in 2-3 s. Thus, the
running bottleneck could be the network connection speed. The application is capable
of storing data and providing log files, but this feature is currently disabled and will be
activated when the validation protocol is approved. The PARDS-CxR platform is detailed
further in Section 3.3.
3. Results
3.1. Quadrant-Based Classification
The PARDS-CxR web-based platform uses Dense-Ynet as the joint segmentation-
classification model. In classification, the model predicts four labels associated with lung
quadrants, as explained in Section 2.3. The platform uses an ensemble of six Dense-Ynet
model instances with different training and model structure configurations. Regarding
model structures, we experimented with different channel depths in convolution blocks,
loss functions, weights for merging loss functions, activation functions, and initial network
weights. For the training configurations, we varied several hyperparameters, namely, the
learning rate, training batch size, augmentation probability, and stopping criterion.
Figure 4shows the confusion matrix of the ensemble of models. To merge the results
from the model instances, a hard voting strategy is employed based on the labels predicted
independently by the models. To be precise, each model is trained separately with its
specific configuration. The testing is also done independently, and if at least three models
decide that an image is an ARDS case, the combined result is positive. By combining
models with various configurations, the intrinsic biases of each one to accept or reject an
image as ARDS are balanced in the ensemble output. Thus, the final performance improves
compared to any individual model.
Non-ARDS ARDS
Actual
Non-ARDS
ARDS
Predicted
64.1% 1.5%
4.1% 30.3%
Figure 4.
Final confusion matrix obtained from the combination of network instances using hard
voting. The numbers (percentages) are obtained by taking the average of several tests (five-fold
cross validation).
Table 3compares the classification performances of the Dense-Ynet instances in terms of
the four assessment metrics seen previously. Some of the listed models achieve higher precision,
while others reach better recall values. By combining the predicted labels provided by these
models, the ensemble algorithm achieves the highest
F
1 score, representing the best compromise
between precision and recall. Indeed, ensembling the models does not outperform every one in
terms of Precision and Recall, but the final F1 and accuracy values improve.
Diagnostics 2023,13, 933 9 of 16
Table 3.
Evaluation of the six models and the result of their combination (ensemble model) for classification.
Model Accuracy Recall Precision F1
Network 1 92.95% 88.45% 91.99% 90.19%
Network 2 93.54% 96.41% 84.37% 89.99%
Network 3 92.04% 94.42% 87.89% 91.03%
Network 4 92.96% 100.0% 83.33% 90.91%
Network 5 87.32% 100.0% 74.29% 85.25%
Network 6 88.74% 80.01% 80.01% 80.02%
Ensemble model 94.35% 95.25% 88.02% 91.49%
In this paper, the problem of ARDS diagnosis is based on the classification of lung
quadrants. Thus, the task can also be considered as a multi-label classification problem.
Figure 5
shows the ROC curves of all quadrants’ predictions for the Dense-Ynet instances,
i.e., the ROC curves associated with the binary classification of the lung quadrants, re-
gardless of their positions. The AUROC metric is not directly related to the system’s
performance in ARDS diagnosis, but the misclassification of one lung quadrant may cause
an error in classifying the image as a whole.
Figure 5. ROC curves for classification of lung quadrants regardless of their position in the lungs.
3.2. ARDS Severity Prediction
As seen in Table 2, the application determines the severity of ARDS in CXR images
based on the number and combination of affected lung quadrants. The platform provides
a global score for each input image by taking the average of the scores from each model.
CXR images are then categorized into one of three severity grades based on the predicted
scores: non-ARDS, mild ARDS, and severe ARDS. The platform’s effectiveness in deter-
mining ARDS severity is illustrated in Figure 6. The three-class confusion matrix shows
that the approach can detect ARDS and discriminate between mild and severe states of
the syndrome.
Diagnostics 2023,13, 933 10 of 16
Non-ARDS Mild ARDS Severe ARDS
Actual
Non-ARDS
Mild ARDS
Severe ARDS
Predicted
64.1% 0.5% 0.2%
1.5% 11.5% 0.7%
1.8% 0.9% 18.8%
Figure 6.
Confusion matrix for classification of ARDS severity with three levels (none, mild, severe).
3.3. PARDS-CxR, the Web-Based Platform
Our web application is currently loaded on a web server at CHUSJ and is accessible
at the address (https://chestxray-reader.chusj-sip-ia.ca, accessed on 15 January 2023).
The process of training and testing the deep model was programmed in Python using the
PyTorch library [
23
]. The training process and hyperparameter search were executed on GPU,
as they required intensive parallel computing. The trained model was then transferred to CPU
to evaluate new images; thus, no graphical processor is necessary on the server to run the
application. The graphical user interface was written in JavaScript and is compatible with
various internet browsers on the client side. No data are kept on the server side, and the
application output image is available to store in the user’s local storage. The user interface
works in English and French, and CXR images can be uploaded using the menu option or
drag-and-drop (see Figure 7).
The application bases itself on the most accepted definitions for ARDS and PARDS.
Based on the Berlin definition, the presence of bilateral infiltrates in chest radiography is
a criterion manifesting the existence of ARDS [
1
]. The platform processes the image and
displays its decision by providing a percentage associated with the level of infiltration
in each quadrant (Figure 7). A global percentage is also given based on infiltrate levels
of infiltrates in quadrants and their combination as in Table 2. This value represents the
severity of ARDS in the input image. An image with a global percentage above 60%
is interpreted as an ARDS case, since, based on the proposed severity scoring system,
infiltrates should be diffused through both lungs. Reporting each quadrant’s involvement
is necessary, since it gives the rationale behind the global severity measure. As seen in
Figure 7, a segmentation map highlighting the lung segments is also provided.
Identifying progression of ARDS is also possible, as two images taken at different times can
be compared by the system. Additionally, an example of CXR image comparison is displayed
in Figure 8.
Diagnostics 2023,13, 933 11 of 16
PARDS-CxR
Dashboard
Profile
chusj-research
Mode
ARDS
PARDS
Documentation
Help
References
About
Quadrant UR
83.3 %
Quadrant UL
33.3 %
Quadrant LR
83.3 %
Quadrant LL
16.7 %
Lung Mask Regional Severity
The CXR image does not meet ARDS Criterion (Berlin definition)
50 %
Download Result Upload New Image Upload second Image
(Switch Mode)
Figure 7.
Main interface of the PARDS-CxR web application. In the standard mode, a single CXR
image is analyzed according to the Berlin definition.
PARDS-CxR
Dashboard
Profile
chusj-research
Mode
ARDS
PARDS
Documentation
Help
References
About Lung Mask
Progressive infiltrates detected (50% to 100%)
CXR images meet PARDS Criterion (PALICC-2 definition)
Download Result Upload New Image
Regional Severity
Quadrant UR
83.3 %
Quadrant UL
33.3 %
Quadrant LR
83.3 %
Quadrant LL
16.7 %
Quadrant UR
100 %
Quadrant UL
100 %
Quadrant LR
100 %
Quadrant LL
100 %
Figure 8.
PARDS-CxR interface in image comparison mode. The platform can analyze two CXR
images to detect ARDS progression based on the PALICC-2 definition.
Diagnostics 2023,13, 933 12 of 16
4. Discussion
The proposed DenseY-net is a joint segmentation–classification model that diagnoses
(P)ARDS based on lung quadrant-level classification. The results show that the model can
accurately classify quadrants and, consequently, the entire input image. This labeling strat-
egy offers a reasoning framework for decision-making and incorporates an interpretability
feature into the platform. Ensemble modeling is used to combine the outcomes from six
model instances. PARDS-CxR can also do lung field segmentation, which is a necessary
element in many decision support systems. Our approach performs well in detecting the
severity of ARDS by giving a score to each input determined by the number and posi-
tion of affected lung quadrants. This makes the model compatible with both ARDS and
PARDS definitions.
A few large chest radiography datasets are publicly available for the research commu-
nity [
13
,
24
,
25
]. A key benefit of deep learning is its capacity to analyze and learn features
from a substantial amount of data. Therefore, it is unsurprising that several ML researchers
have investigated CXR image analysis in various contexts. However, important limitations
of these datasets make them unsuitable for developing dependable systems for the hospital
setting. Indeed, most of the data are annotated using clinicians’ notes processed by natural
language processing (NLP) techniques [
26
]. This leads to erroneous labeling of a portion of
the images. For example, a 10% error is reported for Chest X-ray 14 [13], even though it is
one of the most frequently used CXR datasets. The clinical review in [
27
] reveals an even
higher rate of data labeling errors in that dataset.
Although adding some level of noise to the training inputs can improve a deep model’s
performance, biases and extensive labeling errors will decrease the model’s accuracy. This
could be a reason for the relatively poor generalization ability of deep models when
confronting new samples from other data sources. Furthermore, available samples are
annotated for a limited number of pathologies. Public CXR datasets cover between 14 and
18 chest pathologies, but these do not include ARDS or PARDS. To address this constraint,
we collected our own CXR dataset from three different sources and annotated it for PARDS
at CHUSJ. This dataset was labeled at the lung quadrant level, and the lung fields were
manually identified in each image to establish a segmentation ground truth. The resulting
dataset contains 356 CXR images, including 134 that meet the bilateral criteria for ARDS.
Annotating data is costly in the clinical field, even more so considering that the DenseY-
net model needs lung maps and quadrant-level ground-truth labels. Consequently, our
ARDS dataset is relatively small. Nonetheless, our model is designed in such a way as to
train adequately on small datasets. The specialized connectivity within the model allows
for the creation of a lighter model with shallower intermediate feature maps, resulting
in a smaller number of training parameters. A model with fewer parameters is more
appropriate for training with small datasets. The algorithm was assessed on our own
dataset, as explained in Section 2.4. A bigger dataset could increase the generalization
capacity of the model ensemble. Moreover, external validation of the platform using data
from various health centers will make it more reliable as a tool for prospective clinical
research. Thus, as next steps in the web application’s development, external validation
and improving interpretability are two major points, since both are necessary to turn the
platform into a practical tool in clinics.
Moreover, according to the (P)ARDS definition, co-occurrence of detectable infiltrates
in CXR and hypoxemia is necessary when no evidence of cardiogenic pulmonary edema
is observed. Thus, although the presence of infiltrates in chest radiography is known as
the most limiting factor for diagnosing ARDS, meeting other criteria is a requisite. The
Clinical Decision Support System (CDSS) lab at CHUSJ has the capacity to investigate other
ARDS diagnosis criteria, including cardiac failure and hypoxemia. Le et al. have employed
NLP techniques and ML algorithms to detect cardiac failure in children [
28
]. Sauthier et al.
have developed a method to accurately estimate Pao2 levels using noninvasive data [
29
].
Integrating the tool proposed in this study with other works will lead to a system giving
comprehensive ARDS diagnoses. Sufficient electronic medical infrastructure is available in
Diagnostics 2023,13, 933 13 of 16
the PICU of CHUSJ to facilitate the flow of data from various sources [
30
]. By accessing data
from clinical narrative analysis, measuring oxygenation indices, and detecting infiltrates
in CXR images, it will be possible to make clinical decisions in real time. Therefore, an
important objective for our team will be to implement an ARDS diagnosis package at
CHUSJ, integrating all these criteria and data sources.
The strength of this study lies in the development of an algorithm that, in comparison
to existing approaches, is more interpretable and automated and is compatible with existing
ARDS definitions. Unlike an earlier ARDS diagnosis method proposed by our research
team [
8
], the DL-based approach used in this application does not need any interaction from
clinicians or operators to guide the algorithm. The novel model provides an end-to-end
process that is simple for the user and provides the diagnotic outputs instantaneously.
Recently, Sjoding et al. [
10
] proposed annother automatic algorithm for detecting ARDS in
CXR images. However, their approach lacks explainability, i.e., the system’s decisions are
not supported by further information. By contract, since PARDS-CxR detects infiltrates in
each lung quadrant, the basis for the decision is integral to our method. This strengthens
the platform’s reliability, since the user can reject or accept the decision by observing the
delivered explanation. In addition, the proposed approach is compatible with both PARDS
and ARDS definitions [
1
,
3
], as the scoring scheme used translates to a disease severity level.
At present, the main limitation of our algorithm is its lack of external validation. Indeed,
its development relied on a limited number of CXR images with a single team annotating
them. For this reason, we have implemented the algorithm on a web platform to allow
researchers to conduct validation studies.
5. Conclusions
This work has described a deep learning method and web-based platform for diag-
nosing acute respiratory distress syndrome (ARDS) from chest X-ray (CXR) images. The
platform uses an ensemble of novel Dense-Ynet networks that can accurately detect lung
infiltrates in different quadrants and combine this information to detect ARDS and grade
its severity. This approach ensures that our tool is compatible with various ARDS defini-
tions in both adults and children. Following feedback from clinical researchers during a
validation phase, the platform will be integrated into a complete clinical decision system
for ARDS. The tool presented here will serve as the CXR analysis component within an
AI-based framework that will monitor other factors, such as hypoxemia and occurence of
cardiac arrest.
Author Contributions:
P.J., F.C., M.Y. and R.G.K. conceptualized and designed the study. M.Y., P.J., F.C.,
D.F., M.L. and J.R. developed the study protocol. M.Y., D.F., M.L. and J.R. conducted the algorithm
development. M.Y., F.C. and P.J. drafted the initial manuscript. All authors approved the final manuscript
as submitted. All authors have read and agreed to the published version of the manuscript.
Funding:
This study was supported by grants from IVADO (Artificial Intelligence Research and
Transfer Institute), the Quebec Ministry of Health and Sainte-Justine Hospital. M.Y. is financed by
an award from the Fonds de Recherche en Santé du Québec (FRQS) Chair in Artificial Intelligence and
Health. P.J. earns a research salary from FRQS.
Institutional Review Board Statement:
The study was approved by the Institutional Review Board
of Sainte-Justine Hospital (approval number: 2023-5124).
Informed Consent Statement:
The study was carried out on a research database and the Institutional
Review Board did not require informed consent.
Data Availability Statement:
Access to data can be requested from Philippe Jouvet. Specific institu-
tional review board rules will apply.
Acknowledgments:
The authors gratefully acknowledge Philippe Debanné for his assistance in
reviewing the manuscript. Furthermore, the authors thank all the investigators (pediatric inten-
sivists/Radiologists) who participated in the PARDIE V3 study (Country, site and investigator
list): Argentina. Hospital De Ninos Ricardo Gutierrez: Rossana Poterala; Hospital de Ninos sor
Diagnostics 2023,13, 933 14 of 16
Maria Ludovica: Pablo Castellani/Martin Giampieri/Claudia Pedraza; Hospital Nacional Alejan-
dro Posadas: Nilda Agueda Vidal/Deheza Rosemary/Gonzalo Turon/Cecilia Monjes; Hospital
Pediatrico Juan Pablo II: Segundo F. Espanol; Hospital Universitario Austral: Alejandro Siaba Ser-
rate/Thomas Iolster/Silvio Torres; Sanatorio de Ninos de Rosario: Fernando Paziencia. Australia.
Princess Margaret Hospital for Children: Simon Erickson/Samantha Barr/Sara Shea. Bolivia. Hospi-
tal del Nino Manuel Ascencio Villaroel: Alejandro F. Martinez Leon/Gustavo A. Guzman Rivera.
Canada. CHU Sainte-Justine: Philippe Jouvet/Guillaume Emeriaud/Mariana Dumitrascu/Mary
Ellen French. Chile. Hospital Base de Valdivia: Daniel Caro I/Andrés A Retamal Caro; Hospital
El Carmen de Maipu: Pablo Cruces Romero/Tania Medina; Hospital Luis Calvo Mackenna: Car-
los Acuna; Hospital Padre Hurtado: Franco Diaz/Maria Jose Nunez. China. Children’s Hospital
of Fudan Univ: Yang Chen. Colombia. Clinica Infantil de Colsubsidio: Rosalba Pardo Carrero;
Hospital General de Medellin: Yurika P. Lopez Alarcon; Hospital Militar Central: Ledys María
Izquierdo; Hospital Pablo Tobon Uribe (HPTU): Byron E. Piñeres Olave. France. CHU de Nantes:
Pierre Bourgoin; Hopital d’enfants de Brabois–CHU de Nancy: Matthieu Maria. Greece. University
of Crete, University Hospital PICU: George Briassoulis/Stavroula Ilia. Italy. Children’s Hospital
Bambino Gesu: Matteo Di Nardo/Fabrizio Chiusolo/Ilaria Erba/Orsola Gawronski; Children’s
Hospital Vittore Buzzi: Anna Camporesi. Japan. Hiroshima University: Nobuaki Shime/Shinichiro
Ohshimo/Yoshiko Kida/Michihito Kyo. Malaysia. Universiti Kebangsaan Malaysia: Swee Fong
Tang/Chian Wern Tai; University Malaya Medical Center: Lucy Chai See Lum/Ismail Elghuwael.
Mexico. Hospital Espanol De Mexico: Nestor J. Jimenez Rivera. Peru. Hospital de Emergencias
Pediatricas: Daniel Vasquez Miranda/Grimaldo Ramirez Cortez; Instituto Nacional de Salud del
Nino: Jose Tantalean. Portugal. Hospital Santa Maria–Centro Hospitalar Lisboa Norte: Cristina
Camilo. Saudi Arabia. King Abdullah Specialist Children’s Hospital, King Abdulaziz Medical City:
Tarek Hazwani/Nedaa Aldairi/Ahmed Al Amoudi/Ahmad Alahmadti. Spain. Cruces University
Hospital: Yolanda Lopez Fernandez/Juan Ramon Valle/Lidia Martinez/Javier Pilar Orive; Hospi-
tal Regional Universitario de Malaga: Jose Manuel Gonzalez Gomez/Antonio Morales Martinez;
Hospital Universitari I Politecnic La Fe: Vicent Modesto I Alapont; Sant Joan de Deu University
Hospital: Marti Pons Odena; Hospital Universitario Central De Asturias: Alberto Medina; Virgen
de la Arrixaca University Hospital: Susana Reyes Dominguez. Turkey. Akdeniz University School
of Medicine: Oguz Dursun/Ebru Atike Ongun; Izmir Katip Celebi University Medical School and
Tepecik Research and Training Hospital: Fulya Kamit Can/Ayse Berna Anil. UK. Evelina London
Children’s Hospital: Jon Lillie/Shane Tibby/Paul Wellman/Holly Belfield/Claire Lloyd; Great Or-
mond St. Children’s Hospital: Joe Brierley/Troy E. Dominguez/Eugenia Abaleke/Yael Feinstein;
Noah’s Ark Children’s Hospital for Wales: Siva Oruganti/Sara Harrison; Nottingham University
Hospitals: Catarina Silvestre; Oxford Radcliffe Hospitals NHS Foundation Trust: James Weitz; Royal
Manchester Children’s Hospital: Peter-Marc Fortune/Gayathri Subramanian/Claire Jennings; St.
Mary’s Hospital: David Inwald/Calandra Feather/May-Ai Seah/Joanna Danin. USA. Arkansas Chil-
dren’s Hospital: Ron Sanders/ Glenda Hefley/Katherine Irby/Lauren Edwards/Robert F Buchmann;
Children’s Hospital and Medical Center: Sidharth Mahapatra/Edward Truemper/Lucinda Kustka;
Children’s Hospital at Dartmouth: Sholeen T. Nett/Marcy Singleton/J. Dean Jarvis; Children’s
Hospital Colorado: Aline B. Maddux/Peter M. Mourani/Kimberly Ralston/Yamila Sierra/Jason
Weinman/Zach VanRheen/Christopher Newman; Children’s Hospital Los Angeles: Robinder Khe-
mani/Christopher Newth/Jeni Kwok/Rica Morzov/Natalie Mahieu; Children’s Hospital of Philadel-
phia: Nadir Yehya/Natalie Napolitano/Marie Murphy/Laurie Ronan/Ryan Morgan/Sherri Ku-
bis/Elizabeth Broden; Children’s Hospital of Wisconsin: Rainer Gedeit/Kathy Murkowski/Katherine
Woods/Mary Kasch; Children’s Mercy Hospital and Clinics: Yong Y. Han/Jeremy T. Affolter/Kelly S.
Tieves/Amber Hughes-Schalk; Cincinnati Children’s Hospital Medical Center: Ranjit S. Chima/Kelli
Krallman/Erin Stoneman/Laura Benken/Toni Yunger; Connecticut Children’s Medical Center:
Christopher L Carroll/James Santanelli; Inova Children’s Hospital: W. Keith Dockery/Shirin Jafari-
Namin/Dana Barry/Keary Jane’t; Joseph M Sanzari Children’s Hospital at Hackensack University
Medical Center: Shira Gertz; Nicklaus Children’s Hospital: Fernando Beltramo/Balagangadhar
Totapally/Beatriz Govantes; Northwestern University, Ann & Robert H Lurie Children’s Hospital
of Chicago: Bria Coates/Lawren Wellisch/Kiona Allen/Avani Shukla; Penn State Hershey Chil-
dren’s Hospital: Neal J. Thomas/Debbie Spear; Rainbow Babies and Children’s Hospital, Steven
L. Shein/Pauravi Vasavada; Saint Barnabas Medical Center: Shira Gertz; Stony Brook Children’s
Hospital: Margaret M. Parker/Daniel Sloniewsky; The Children’s Hospital of Oklahoma; Chris-
tine Allen/Amy Harrell; UCSF Benioff Children’s Hospital Oakland: Natalie Cvijanovich; Uni-
versity of Miami/Holtz Children’s Hospital: Asumthia S. Jeyapalan/Alvaro Coronado-Munoz;
Diagnostics 2023,13, 933 15 of 16
University of Michigan–C.S. Mott Children’s Hospital: Heidi Flori/Mary K. Dahmer/Chaandini Jay-
achandran/Joseph Kohne; University of Minnesota Masonic Children’s Hospital: Janet Hume/Dan
Nerheim/Kelly Dietz; University of WA/Seattle Children’s Hospital: Lincoln Smith/Silvia Hart-
mann/Erin Sullivan/Courtney Merritt; Weill Cornell Medical College: Deyin D. Hsing/Steve
Pon/Jim Brian Estil/Richa Gautam; Yale School of Medicine: John S. Giuliano Jr./Joana Ta.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
AI Artificial Intelligence
ARDS Acute Respiratory distress Syndrome
AUROC Area Under the ROC Curve
CDSS Clinical Decision Support System
CHUSJ Centre Hospitalier Universitaire Sainte-Justine (Sainte-Justine Hospital)
CXR Chest X-ray
CPU Central Processing Unit
DL Deep Learning
GPU Graphical Processing Unit
LL Left Lower
LU Left Upper
ML Machine Learning
NLP Natural Language Processing
PALICC Pediatric Acute Lung Injury Consensus Conference
PARDS Pediatric Acute Respiratory Distress Syndrome
PICU Pediatric Intensive Care Unit
ReLU Rectified Linear Unit
RL Right Lower
ROC Receiver Operating Characteristic
RU Right Upper
UI User Interface
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