Artificial intelligence–aided diagnosis model for acute respiratory distress syndrome combining clinical data and chest radiographs

Abstract

Objective The aim of this study was to develop an artificial intelligence–based model to detect the presence of acute respiratory distress syndrome (ARDS) using clinical data and chest X-ray (CXR) data. Method The transfer learning method was used to train a convolutional neural network (CNN) model with an external image dataset to extract the image features. Then, the last layer of the model was fine-tuned to determine the probability of ARDS. The clinical data were trained using three machine learning algorithms—eXtreme Gradient Boosting (XGB), random forest (RF), and logistic regression (LR)—to estimate the probability of ARDS. Finally, ensemble-weighted methods were proposed that combined the image model and the clinical data model to estimate the probability of ARDS. An analysis of the importance of clinical features was performed to explore the most important features in detecting ARDS. A gradient-weighted class activation mapping (Grad-CAM) model was used to explain what our CNN sees and understands when making a decision. Results The proposed ensemble-weighted methods improved the performances of the ARDS classifiers (XGB + CNN, area under the curve [AUC] = 0.916; RF + CNN, AUC = 0.920; LR + CNN, AUC = 0.920; XGB + RF + LR + CNN, AUC = 0.925). In addition, the ML model using clinical data to present the top 15 important features to identify the risk factors of ARDS. Conclusion This study developed combined machine learning models with clinical data and CXR images to detect ARDS. According to the results of the Shapley Additive exPlanations values and the Grad-CAM techniques, an explicable ARDS diagnosis model is suitable for a real-life scenario.

Yemeni university students public perceptions toward the use of artificial intelligence in healthcare: A cross-sectional study

“…The continuous discussion in medical care about how to strike the right balance between human judgment and AI suggestions is reflected in the differences in trust between AI and doctors’ assessments. It emphasizes the importance of properly weighing the benefits and drawbacks of using AI and human doctors to make well-informed, patient-centered judgments [1,3,6,17]. Achieving ideal patient results and preserving patient confidence in the healthcare system requires carefully balancing AI technology and human skills.…”

Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome

“…Research efforts have also been made to specifically diagnose ARDS [ 10 , 12 , 59 ]. In the work of Reamaroon et al [ 10 ], an image processing-based feature engineering was used in conjunction with deep learning for ARDS detection.…”

“…In the work of Reamaroon et al [ 10 ], an image processing-based feature engineering was used in conjunction with deep learning for ARDS detection. Sjolding et al [ 12 ] used a CNN to achieve high performance in identifying ARDS from chest X-rays, and Pai et al [ 59 ] used a multi-modal ensemble framework combining clinical data with chest X-ray imaging to predict ARDS in the first 48 h of admission. A method for dealing with ARDS label uncertainty is proposed by Reamaroon et al [ 60 ], where labels used to train the machine learning model have a confidence score reflecting expert uncertainty in ARDS diagnosis.…”

Student perspectives on the integration of artificial intelligence into healthcare services

“… 1 AI has the capacity to analyze and interpret vast volumes of data and make more accurate intelligent decisions than humans, making AI a likely future gold standard for health services. 2 , 4 AI takes several forms such as expert systems, machine learning (ML), deep learning (DL), and artificial neural networks (ANNs). Advances in affordable storage, faster networks, and increased computer power have enabled the integration of AI into different healthcare services.…”