Introduction
CAD
CAD, PULMONARY INVOLVEMENT, SECONDARY
The COVID-19 pandemic has created new ways in which existing and developing technologies are used in radiology. Although definitive diagnosis relies on real-time reverse-transcriptase-polymerase chain reaction (RT–PCR), CT still plays an essential role in the screening and monitoring of COVID-19 evolution, setting patient discharge criteriaRadiologists measure pulmonary involvement in COVID-19 using either a quantitative assessment of the overall involvementCOVID-19 quantification judgement process (left) compared to the circle size comparison problem (right). In both cases, the apparent proportion of COVID-19 involvement (red) in relation to total lung area (blue) appears greater in the bottom examples. This is due to the overinfluence of a primary linear dimension (1D segment ratio = 80%) that is often insufficiently adjusted to the 2D context (2D circle ratio = 60%). This leads to an overestimation of the perceived percentages.Area judgement is a century-old field of study in cognitive psychology, as human beings exhibit an acute lack of precision in visual geometric comparisonsDespite the wide adoption of lung involvement scores, the area judgement cognitive bias remains unaddressed in radiology. For the first time, radiologists are required to geometrically compare such irregular and disparate shapes, on such a large scale and all the available methods have one common denominator: the reliance on the reader's volume perception. This study analyses the perception risks of these measures and clinically tests an AI-based mitigation strategy.To statistically confirm if this problem exists, we first conducted two experiments that analyse the two-step thinking process of a radiologist analysing CTs for COVID-19. The first experiment isolated and investigated the estimation step in simulated data, then the second examined the bias’s effect over the whole process. The primary hypothesis (H1) is that this geometric ratio assessment is prone to an overestimation bias. Next, a randomised clinical trial was conducted to study whether the bias could be mitigated by using a commercial AI clinical support system. The secondary hypothesis (H2) is that the reader’s objectiveness can be improved with the use of computer-aided diagnosis (CAD). As the study does not propose to address the development process of a new AI CAD system, it employs Rayscape