A multi-national collaboration of scientists, clinicians and industry led by DetectED-X established a novel intelligent educational platform to enhance radiologic detection of COVID-19 appearances on computed tomography images of the lung. Simply readers anywhere can log on and try and diagnose lung CT cases with known truth. The tool was developed in four weeks through redeploying technology used for breast cancer detection and is now available free-of-charge to clinicians world-wide via the URL link: detectedx.com.

The tool has currently been used by 1300 clinicians from 147 countries who have examined 105 lung CT cases, some with the appearances of COVID-19, some without. All radiologic-image interactions have been recorded resulting in the world’s largest database on clinician efficiencies interacting with lung CT demonstrating COVID-19 appearances. Each clinician (reader) was asked to comment on whether each case contained the appearances of ground glass opacity, crazy paving/mosaic attenuation and/or consolidation and on the location of any perceived appearance. Then the reader gave an overall confidence score from 0-5 on whether the case is COVID-19 positive or not. Each reader score was compared against an expert consensus of four senior respiratory radiologists who provided the truth rankings.

This white paper with its interactive diagrams offers early observations around reader efficiency at detecting COVID-19 using CT lung cases by summarising details on:

Truth data Distribution of the characteristics of all cases, and cases given a truth ranking of 3-5 indicating a positive COVID-19 case

Discrepancies between reader and expert observations

  • Overall confidence scores for positive and normal cases

  • Detection of individual image presentations typical of COVID-19

OBSERVATIONS

1a. Truth data. Distribution of the characteristics of all cases .

This figure demonstrates the truth data by allocating each patient case with an overall truth score of 0-5 (Right vertical column, where 0 = Definitely no COVID-19, 5 = patient with definite COVID-19) to each of the three main CovED appearances. For each of the three appearances, 0 = no appearance present; 1 = appearance present. The graph is interactive so by clicking anywhere, the number of cases meeting a specific appearance pattern is highlighted. For example in the below, the highlighted area (see cursor) indicates that 4 cases had an overall COVID-19 confidence rating of 3, and demonstrated the appearances of ground glass opacity (1), and consolidation (1) but no appearance of crazy paving as a 0 is shown for those 4 cases for that appearance.

Overall, this graph demonstrates that for the 105 cases used to date in the CovED software, a good distribution of truths is shown across the overall ratings and individual appearances.