Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study

Abstract

Background Chest x-ray is a relatively accessible, inexpensive, fast imaging modality that might be valuable in the prognostication of patients with COVID-19. We aimed to develop and evaluate an artificial intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19. Methods We did a retrospective study in multiple hospitals in the University of Pennsylvania Health System in Philadelphia, PA, USA, and Brown University affiliated hospitals in Providence, RI, USA. Patients who presented to a hospital in the University of Pennsylvania Health System via the emergency department, with a diagnosis of COVID-19 confirmed by RT-PCR and with an available chest x-ray from their initial presentation or admission, were retrospectively identified and randomly divided into training, validation, and test sets (7:1:2). Using the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical). The deep-learning features extracted from the model and clinical data were used to build time-to-event models to predict the risk of disease progression. The models were externally tested on patients who presented to an independent multicentre institution, Brown University affiliated hospitals, and compared with severity scores provided by radiologists. Findings 1834 patients who presented via the University of Pennsylvania Health System between March 9 and July 20, 2020, were identified and assigned to the model training (n=1285), validation (n=183), or testing (n=366) sets. 475 patients who presented via the Brown University affiliated hospitals between March 1 and July 18, 2020, were identified for external testing of the models. When chest x-rays were added to clinical data for severity prediction, area under the receiver operating characteristic curve (ROC-AUC) increased from 0·821 (95% CI 0·796–0·828) to 0·846 (0·815–0·852; p<0·0001) on internal testing and 0·731 (0·712–0·738) to 0·792 (0·780–0 ·803; p<0·0001) on external testing. When deep-learning features were added to clinical data for progression prediction, the concordance index (C-index) increased from 0·769 (0·755–0·786) to 0·805 (0·800–0·820; p<0·0001) on internal testing and 0·707 (0·695–0·729) to 0·752 (0·739–0·764; p<0·0001) on external testing. The image and clinical data combined model had significantly better prognostic performance than combined severity scores and clinical data on internal testing (C-index 0·805 vs 0·781; p=0·0002) and external testing (C-inde 0·752 vs 0·715; p<0·0001). Interpretation In patients with COVID-19, artificial intelligence based on chest x-rays had better prognostic performance than clinical data or radiologist-derived severity scores. Using artificial intelligence, chest x-rays can augment clinical data i...

Real-world evaluation of AI-driven COVID-19 triage for emergency admissions: External validation & operational assessment of lab-free and high-throughput screening solutions

“…Whereas many studies have investigated AI applications for diagnosis and prognosis during the pandemic [23][24][25][26] , key reviews highlight sector-wide methodological and reporting concerns that threaten generalisability, questioning the suitability of many models to-date for clinical use [27][28][29] . Reviewing the contribution of AI to the COVID-19 response, a recent editorial highlighted the promise of CURIAL-1.0 amongst other solutions to support patients during the pandemic, discussing the importance of highquality validation studies inclusive of diverse patient populations 30 .…”

COVID-19 diagnosis from chest x-rays: developing a simple, fast, and accurate neural network

“…It also achieved similar accuracy and sensitivity with the B1 and B3-X (6.6 and 12.3 million parameters respectively). Jiao et al [ 38 ] also used EfficientNet as the baseline ConvNet, but the prediction was different (COVID-19 severity: critical vs. non-critical), and the model was considerably more complex: it connected a convolutional layer (256 neurons) and a dense layer (32 neurons) on top of the baseline. Despite its complexity, the model did not perform as well on an external dataset: accuracy of 75% (95% CI 74, 77), sensitivity of 66% (95% CI 64, 68), and specificity of 70% (95% CI 69, 71).…”

“…The main limitation of our study is the lack of patient data. We appreciate that the model built by Jiao et al [ 38 ] included patient clinical data (e.g., age, sex, oxygen saturation, biomarkers, comorbidities), which slightly improved the accuracy of the image-based CNN. We recognize that this is an important avenue for future research.…”

Artificial intelligence: Potential tool to subside SARS-CoV-2 pandemic

“…The researchers had collected the X-ray samples from 1834 patients and used the artificial chest X-rays to predict the progression and severity of the COVID-19 disease. It was found that the AI based chest X-rays had better diagnosis in comparison to radiologist derived severity scores and clinical data [ 18 ]. Furthermore, convolutional neural network (CNN) was used by researchers to analyse chest X-rays in order to identify COVID-19 and its conditions.…”