There have been several reports analyzing the radiological characteristics of COVID-19 pneumonia [1, 6–11]. There are challenges in the diagnosis of COVID-19 pneumonia using AI technology, including binary diagnosis (COVID-19 present or absent), segmentation and quantification of the abnormal lung opacities, and discriminating COVID-19 from non-COVID-19 pneumonias [12]. Usefullness of AI to detect COVID-19 pneumonia based on chest CT data has already been reported in a large patient cohorts [13]. However, there are no reports on the use of commercial AI program InferRead™ CT Pneumonia.
In this study, we demonstrated the efficacy of InferRead™ CT Pneumonia for detecting SARS-CoV-2 with sufficient sensitivity and specificity regardless of the CT slice thickness. Furthermore, InferRead™ CT Pneumonia showed a high sensitivity comparable with that of respiratory specialists, and when limited to SARS-CoV-2-positive cases, an extremely high agreement was observed in detecting COVID-19 pneumonia between respiratory specialists and AI, even on CT images taken at a slice thickness of 5 mm. The number of CT devices per 100,000 people in Japan in 2011 is reported to be 12,945, of which the number of multirow detector CT is 8,347 [14]. Therefore, InferRead™ CT pneumonia is expected to be useful in assisting the diagnosis of COVID-19. However, since there is a lack of respiratory specialists and radiologists in Japan, as well as a disparity in the number of CT scanners installed in different regions [14], it is difficult to say that CT scans are being used efficiently. The image analysis AI program can facilitate the detection of COVID-19 pneumonia of SARS-CoV-2-positive patients in institutions with only single detector-row CT or without respiratory specialists or diagnostic radiologists and is thought to be useful in eliminating the disparity in crisis management under the spread of coronavirus infections.
It might be considered that the sensitivity of chest CT for COVID-19 pneumonia is not very high immediately after the onset of the disease, which might have affected the chance of detecting it. In particular, it could be assumed that imaging with a slice thickness of 5 mm would have a lower sensitivity to detect COVID-19 pneumonia than with a slice thickness of 0.5 mm. However, no significant difference was observed in the number of days passed from onset to CT imaging between the groups of different slice thicknesses. Therefore, the timing when the CT scan was performed was not considered to be a factor affecting the difference in the results between 0.5 mm and 5 mm. In addition, comparing the cases where each specialist and AI diagnosed SARS-CoV-2 positive cases as COVID-19 pneumonia and those where they could not, significant differences were observed in the number of days since the disease onset. Therefore, it could be assumed that the diagnosis of COVID-19 pneumonia was more difficult when the duration of illness was shorter probably because the findings were milder or atypical. Nevertheless, the length of time since onset did not make any difference in the ability of each specialist or AI to detect COVID-19 pneumonia.
However, it must be emphasized that the diagnosis of COVID-19 must be based on clinical symptoms and detection of SARS-CoV-2 and not only on imaging and that this image analysis AI program should only be used as an adjunct to the diagnosis of COVID-19 pneumonia. In the SARS-CoV-2-negative cases, discrepancy between the diagnosis by AI and that by respiratory specialists was found. The SARS-CoV-2-negative cases used in this study were biased toward the elderly, and the results of multiple logistic regression analysis indicated that this may have contributed to the discrepancy in diagnosis. Older patients often have more complex background lung modifications than younger patients, which may have influenced the diagnostic discrepancy. The performance of InferRead™ CT pneumonia in diagnosing lung abnormalities, which are often seen in the elderly, as something other than COVID-19 pneumonia is important, and in this regard, there is a need for improvement in the performance of InferRead™ CT pneumonia.
On the other hand, we investigated the affected area of COVID-19 pneumonia based on the per-lobar VOI presented by InferRead™ CT Pneumonia and showed that the affected volume fraction of both lower lobes of the lung was significantly higher than that of the other lobe on the same side and that the concerned volume fraction of the middle lobe of the right lung was significantly lower than that of the other lobe on the same side. Regarding the chest CT images of COVID-19, findings that are relatively typical or atypical for COVID-19 have already been accumulated [1]. For example, Haseli et al. reviewed the distribution of lesions on CT images of 63 patients with COVID-19 pneumonia and showed that the lower lobes were mainly involved [8]. In addition, Salahshour et al. reported that the CT images of 439 cases of COVID-19 pneumonia were characterized by lower lobe distribution along with ground-glass opacities, subpleural distribution, multiple lobar distribution, and bilateral distribution [9]. The results of the present study were similar to those reported above, especially in the cases of bilateral lower lobe involvement with the absence of right middle lobe involvement, where the diagnosis of COVID-19 pneumonia was as reliable as that of the respiratory specialists. The distribution of affected lung lobes provided by InferRead™ CT Pneumonia also seemed to be useful in assisting the diagnosis of COVID-19 pneumonia.
There are several limitations in this study. First, this is a single-center, retrospective study. Therefore, there is potential for case selection bias and institutional bias. Second, the number of patients included in the study was limited. Third, the data were not adjusted for the timing of CT imaging or disease severity in the clinical course of the disease. These issues can be resolved by accumulating more cases, but new findings on COVID-19 should be shared as soon as possible, and this is an issue for the future. Fourth, background changes of the lungs before enrollment in this study were not collected. In the imaging diagnosis of COVID-19 pneumonia, it is required to take into consideration the background lung changes, especially in older patients.