Commercial AI solutions in detecting COVID-19 pneumonia in chest CT: not yet ready for clinical implementation?

OBJECTIVES: In response to the COVID-19 pandemic, many researchers have developed artificial intelligence (AI) tools to differentiate COVID-19 pneumonia from other conditions in chest CT. However, in many cases, performance has not been clinically validated. The aim of this study was to evaluate the performance of commercial AI solutions in differentiating COVID-19 pneumonia from other lung conditions. METHODS: Four commercial AI solutions were evaluated on a dual-center clinical dataset consisting of 500 CT studies; COVID-19 pneumonia was microbiologically proven in 50 of these. Sensitivity, specificity, positive and negative predictive values, and AUC were calculated. In a subgroup analysis, the performance of the AI solutions in differentiating COVID-19 pneumonia from other conditions was evaluated in CT studies with ground-glass opacities (GGOs). RESULTS: Sensitivity and specificity ranges were 62-96% and 31-80%, respectively. Negative and positive predictive values ranged between 82-99% and 19-25%, respectively. AUC was in the range 0.54-0.79. In CT studies with GGO, sensitivity remained unchanged. However, specificity was lower, and ranged between 15 and 53%. AUC for studies with GGO was in the range 0.54-0.69. CONCLUSIONS: This study highlights the variable specificity and low positive predictive value of AI solutions in diagnosing COVID-19 pneumonia in chest CT. However, one solution yielded acceptable values for sensitivity. Thus, with further improvement, commercial AI solutions currently under development have the potential to be integrated as alert tools in clinical routine workflow. Randomized trials are needed to assess the true benefits and also potential harms of the use of AI in image analysis. KEY POINTS: • Commercial AI solutions achieved a sensitivity and specificity ranging from 62 to 96% and from 31 to 80%, respectively, in identifying patients suspicious for COVID-19 in a clinical dataset. • Sensitivity remained within the same range, while specificity was even lower in subgroup analysis of CT studies with ground-glass opacities, and interrater agreement between the commercial AI solutions was minimal to nonexistent. • Thus, commercial AI solutions have the potential to be integrated as alert tools for the detection of patients with lung changes suspicious for COVID-19 pneumonia in a clinical routine workflow, if further improvement is made.

Online teaching in radiology as a pilot model for modernizing medical education: results of an international study in cooperation with the ESR.

BACKGROUND: Due to the outbreak of the coronavirus disease 2019 (COVID-19), it proved necessary to rapidly change medical education from on-site to online teaching. Thus, medical educators were forced to rethink the purpose of teaching and the best form of transmission of knowledge. In cooperation with the European Society of Radiology (ESR), we investigated the attitudes of radiologists in Europe and North America toward innovative online teaching concepts. METHODS: In total, 224 radiologists from 31 different countries participated in our cross-sectional, web-based survey study. On a 7-point Likert scale, participants had to answer 27 questions about the online teaching situation before/during the pandemic, technical and social aspects of online teaching and the future role of online teaching in radiology. RESULTS: An overwhelming majority stated that radiology is particularly well-suited for online teaching (91%), that online teaching should play a more prominent role after the pandemic (73%) and that lecturers should be familiar with online teaching techniques (89%). Difficulties include a higher workload in preparing online courses (59%), issues with motivating students to follow online courses (56%) and the risk of social isolation (71%). Before the pandemic, only 12% of teaching was provided online; for the future, our participants deemed a proportion of approximately 50% online teaching appropriate. CONCLUSION: Our participants are open-minded about online teaching in radiology. As the best way of transferring knowledge in medical education is still unclear, online teaching offers potential for innovation in radiology education. To support online teaching development, a structured, framework-based "online curriculum" should be established.