Automatic coronavirus disease 2019 diagnosis based on chest radiography and deep learning - Success story or dataset bias?

Digital Determinants of Health: Health data poverty amplifies existing health disparities-A scoping review

Artificial intelligence (AI) and machine learning (ML) have an immense potential to transform healthcare as already demonstrated in various medical specialties. This scoping review focuses on the factors that influence health data poverty, by conducting a literature review, analysis, and appraisal of results. Health data poverty is often an unseen factor which leads to perpetuating or exacerbating health disparities. Improvements or failures in addressing health data poverty will directly impact the effectiveness of AI/ML systems. The potential causes are complex and may enter anywhere along the development process. The initial results highlighted studies with common themes of health disparities (72%), AL/ML bias (28%) and biases in input data (18%). To properly evaluate disparities that exist we recommend a strengthened effort to generate unbiased equitable data, improved understanding of the limitations of AI/ML tools, and rigorous regulation with continuous monitoring of the clinical outcomes of deployed tools.

New patch-based strategy for COVID-19 automatic identification using chest x-ray images

Purpose

The development of a robust model for automatic identification of COVID-19 based on chest x-rays has been a widely addressed topic over the last couple of years; however, the scarcity of good quality images sets, and their limited size, have proven to be an important obstacle to obtain reliable models. In fact, models proposed so far have suffered from over-fitting erroneous features instead of learning lung features, a phenomenon known as shortcut learning. In this research, a new image classification methodology is proposed that attempts to mitigate this problem.

Methods

To this end, annotation by expert radiologists of a set of images was performed. The lung region was then segmented and a new classification strategy based on a patch partitioning that improves the resolution of the convolution neural network is proposed. In addition, a set of native images, used as an external evaluation set, is released.

Results

The best results were obtained for the 6-patch splitting variant with 0.887 accuracy, 0.85 recall and 0.848 F1score on the external validation set.

Conclusion

The results show that the proposed new strategy maintains similar values between internal and external validation, which gives our model generalization power, making it available for use in hospital settings.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12553-022-00704-4.

Multi-center validation of an artificial intelligence system for detection of COVID-19 on chest radiographs in symptomatic patients

OBJECTIVES

While chest radiograph (CXR) is the first-line imaging investigation in patients with respiratory symptoms, differentiating COVID-19 from other respiratory infections on CXR remains challenging. We developed and validated an AI system for COVID-19 detection on presenting CXR.

METHODS

A deep learning model (RadGenX), trained on 168,850 CXRs, was validated on a large international test set of presenting CXRs of symptomatic patients from 9 study sites (US, Italy, and Hong Kong SAR) and 2 public datasets from the US and Europe. Performance was measured by area under the receiver operator characteristic curve (AUC). Bootstrapped simulations were performed to assess performance across a range of potential COVID-19 disease prevalence values (3.33 to 33.3%). Comparison against international radiologists was performed on an independent test set of 852 cases.

RESULTS

RadGenX achieved an AUC of 0.89 on 4-fold cross-validation and an AUC of 0.79 (95%CI 0.78-0.80) on an independent test cohort of 5,894 patients. Delong's test showed statistical differences in model performance across patients from different regions (p < 0.01), disease severity (p < 0.001), gender (p < 0.001), and age (p = 0.03). Prevalence simulations showed the negative predictive value increases from 86.1% at 33.3% prevalence, to greater than 98.5% at any prevalence below 4.5%. Compared with radiologists, McNemar's test showed the model has higher sensitivity (p < 0.001) but lower specificity (p < 0.001).

CONCLUSION

An AI model that predicts COVID-19 infection on CXR in symptomatic patients was validated on a large international cohort providing valuable context on testing and performance expectations for AI systems that perform COVID-19 prediction on CXR.

KEY POINTS

• An AI model developed using CXRs to detect COVID-19 was validated in a large multi-center cohort of 5,894 patients from 9 prospectively recruited sites and 2 public datasets. • Differences in AI model performance were seen across region, disease severity, gender, and age. • Prevalence simulations on the international test set demonstrate the model's NPV is greater than 98.5% at any prevalence below 4.5%.

Performance Analysis for COVID-19 Diagnosis Using Custom and State-of-the-Art Deep Learning Models

The modern scientific world continuously endeavors to battle and devise solutions for newly arising pandemics. One such pandemic which has turned the world’s accustomed routine upside down is COVID-19: it has devastated the world economy and destroyed around 45 million lives, globally. Governments and scientists have been on the front line, striving towards the diagnosis and engineering of a vaccination for the said virus. COVID-19 can be diagnosed using artificial intelligence more accurately than traditional methods using chest X-rays. This research involves an evaluation of the performance of deep learning models for COVID-19 diagnosis using chest X-ray images from a dataset containing the largest number of COVID-19 images ever used in the literature, according to the best of the authors’ knowledge. The size of the utilized dataset is about 4.25 times the maximum COVID-19 chest X-ray image dataset used in the explored literature. Further, a CNN model was developed, named the Custom-Model in this study, for evaluation against, and comparison to, the state-of-the-art deep learning models. The intention was not to develop a new high-performing deep learning model, but rather to evaluate the performance of deep learning models on a larger COVID-19 chest X-ray image dataset. Moreover, Xception- and MobilNetV2- based models were also used for evaluation purposes. The criteria for evaluation were based on accuracy, precision, recall, F1 score, ROC curves, AUC, confusion matrix, and macro and weighted averages. Among the deployed models, Xception was the top performer in terms of precision and accuracy, while the MobileNetV2-based model could detect slightly more COVID-19 cases than Xception, and showed slightly fewer false negatives, while giving far more false positives than the other models. Also, the custom CNN model exceeds the MobileNetV2 model in terms of precision. The best accuracy, precision, recall, and F1 score out of these three models were 94.2%, 99%, 95%, and 97%, respectively, as shown by the Xception model. Finally, it was found that the overall accuracy in the current evaluation was curtailed by approximately 2% compared with the average accuracy of previous work on multi-class classification, while a very high precision value was observed, which is of high scientific value.

Telemedicine and virtual respiratory care in the era of COVID-19

The World Health Organization defines telemedicine as “an interaction between a health care provider and a patient when the two are separated by distance”. The COVID-19 pandemic has forced a dramatic shift to telephone and video consulting for follow up and routine ambulatory care for reasons of infection control. Short Message Service (“text”) messaging has proved a useful adjunct to remote consulting allowing transfer of photographs and documents. Maintaining non-communicable diseases care is a core component of pandemic preparedness and telemedicine has developed to enable (for example) remote monitoring of sleep apnoea, telemonitoring of chronic obstructive pulmonary disease, digital support for asthma self-management, remote delivery of pulmonary rehabilitation. There are multiple exemplars of telehealth instigated rapidly to provide care for people with COVID-19, to manage the spread of the pandemic, or to maintain safe routine diagnostic or treatment services.

Despite many positive examples of equivalent functionality and safety, there remain questions about the impact of remote delivery of care on rapport and the longer-term impact on patient/professional relationships. Although telehealth has the potential to contribute to universal health coverage by providing cost-effective accessible care, there is a risk of increasing social health inequalities if the “digital divide” excludes those most in need of care. As we emerge from the pandemic, the balance of remote versus face-to-face consulting, and the specific role of digital health in different clinical and healthcare contexts will evolve. What is clear is that telemedicine in one form or another will be part of the “new norm”.

Towards smart diagnostic methods for COVID-19: Review of deep learning for medical imaging

The infectious disease known as COVID-19 has spread dramatically all over the world since December 2019. The fast diagnosis and isolation of infected patients are key factors in slowing down the spread of this virus and better management of the pandemic. Although the CT and X-ray modalities are commonly used for the diagnosis of COVID-19, identifying COVID-19 patients from medical images is a time-consuming and error-prone task. Artificial intelligence has shown to have great potential to speed up and optimize the prognosis and diagnosis process of COVID-19. Herein, we review publications on the application of deep learning (DL) techniques for diagnostics of patients with COVID-19 using CT and X-ray chest images for a period from January 2020 to October 2021. Our review focuses solely on peer-reviewed, well-documented articles. It provides a comprehensive summary of the technical details of models developed in these articles and discusses the challenges in the smart diagnosis of COVID-19 using DL techniques. Based on these challenges, it seems that the effectiveness of the developed models in clinical use needs to be further investigated. This review provides some recommendations to help researchers develop more accurate prediction models.