The impact of radiologists’ characteristics on the detection of COVID-19 in chest CT scans

Abstract

Purpose

To retrospectively assess the relationships between radiologists’ characteristics and their performance in interpreting chest CTs for COVID-19 detection.

Methods

Using DetectedX software, performance data were collected online from 74 radiologists from 29 countries, who each interpreted 30 anonymized digital chest CTs (15 positive and 15 negative scans for COVID-19). Radiologist-specific information was also collected: radiology experience, radiological specialty, number of lung CTs read weekly, familiarity with and education about COVID-19 presentation, and suspected COVID-19 cases seen weekly. The influence of this radiologist-specific information on the radiologists’ sensitivity, specificity, and ROC AUC was determined using regression analysis.

Results

Radiologists without respiratory imaging specialization had greater ROC AUCs (0.83 vs. 0.70, p = .006) and sensitivities (74.0% vs. 47.7%, p = .002) than their specialized peers. Radiologists without COVID-19 case encounters had greater sensitivity (80.6% vs. 63.1%, p = .017) but lower specificity (71.1% vs. 83.4%, p = .014) than those who encountered at least one case weekly.

Conclusion

Specialization and prior experience with COVID-19 may impact the interpretation of suspected COVID-19 chest CTs.

Clinical significance

These results show the importance of diverse expertise and continuous training in managing novel diseases like COVID-19. Incorporating varied perspectives and experiences could enhance diagnostic accuracy and ensure a more comprehensive approach to disease management. In the case of novel diseases, specialization may not provide the same advantage as with more familiar disorders. The diagnostic value of specific experience with a novel disorder may help to compensate for a lack of specialization in the field, particularly in emergent situations. While specialization is important, generalization also holds value and utility in appropriate contexts.