Exploring Radiologists' Burnout in the COVID-19 Era: A Narrative Review

A teleradiology network for the improvement of healthcare and patient management in the developing countries of the African continent

INTRODUCTION

Africa is the second-largest continent on Earth in terms of both size and population. However, inaccessibility and shortfall of trained radiologists impede the delivery of adequate healthcare to such a large population. Teleradiology holds considerable potential in improving patient outcomes and healthcare delivery in African nations by furnishing timely interpretation of radiological examinations, particularly in those areas where there is a particular scarcity of radiologists. The aim of the present study was to assess the impact of teleradiology in the improvement of healthcare and patient management in the developing countries of the African continent.

METHODS

In this retrospective study, from January 2017 and December 2022, the scans of a cohort of patients from eight African countries were uploaded to the teleradiology cloud server and interpreted by board certified radiologists empanelled by a teleradiology service provider.

RESULTS

The telehealth model proposed in the study was seen to provide timely and quality reporting of 58,223 scans of 39,513 patients with a mean turn-around-time (TAT) of 2.46 h 95 % CI (2.44-2.48).

DISCUSSION

A dedicated teleradiology model designed in this study allowed the interpretation and analysis of the scans of the cohort of patients from hospitals in African countries by teleradiologists via high quality DICOM-image transfer over a cloud-based platform. The outcomes of our investigation reflect that teleradiology provides an effective solution for early diagnosis/interpretation of examinations performed in Africa. Further, the currently proposed teleradiology model may be used for other developing countries across the world to improve quality of care.

Diagnostic performance of deep learning models versus radiologists in COVID-19 pneumonia: A systematic review and meta-analysis

PURPOSE

Although several studies have compared the performance of deep learning (DL) models and radiologists for the diagnosis of COVID-19 pneumonia on CT of the chest, these results have not been collectively evaluated. We performed a meta-analysis of original articles comparing the performance of DL models versus radiologists in detecting COVID-19 pneumonia.

METHODS

A systematic search was conducted on the three main medical literature databases, Scopus, Web of Science, and PubMed, for articles published as of February 1st, 2023. We included original scientific articles that compared DL models trained to detect COVID-19 pneumonia on CT to radiologists. Meta-analysis was performed to determine DL versus radiologist performance in terms of model sensitivity and specificity, taking into account inter and intra-study heterogeneity.

RESULTS

Twenty-two articles met the inclusion criteria. Based on the meta-analytic calculations, DL models had significantly higher pooled sensitivity (0.933 vs. 0.829, p < 0.001) compared to radiologists with similar pooled specificity (0.905 vs. 0.897, p = 0.746). In the differentiation of COVID-19 versus community-acquired pneumonia, the DL models had significantly higher sensitivity compared to radiologists (0.915 vs. 0.836, p = 0.001).

CONCLUSIONS

DL models have high performance for screening of COVID-19 pneumonia on chest CT, offering the possibility of these models for augmenting radiologists in clinical practice.