ABSTRACT
Objectives: We evaluated the impact of the COVID-19 pandemic on the number of referrals for SPECT myocardial perfusion imaging (SPECT-MPI) as well as changes in the clinical and imaging characteristics. Methods: We respectively reviewed 1042 SPECT-MPI cases performed in a 4-month period during the COVID-19 pandemic (PAN; n=423) and compared their findings with those acquired in the same months before the pandemic (PRE; n=619). Results: The number of stress SPECT-MPI studies performed during the PAN period significantly dropped compared to the number of studies carried out in the PRE period (p = 0.014). In the PRE period, the rates of patients presenting with non-anginal, atypical and typical chest pain were 31%, 25% and 19%, respectively. The figures significantly changed in the PAN period to 19%, 42%, and 11%, respectively (all p-values <0.001). Regarding the pretest probability of coronary artery disease (CAD), a significant decrease and increase were noticed in patients with high and intermediate pretest probability, respectively (PRE: 18% and 55%, PAN: 6% and 65%, p <0.001 and 0.008, respectively). Neither the rates of myocardial ischemia nor infarction differed significantly in the PRE vs. PAN study periods. Conclusion: The number of referrals dropped significantly in the PAN era. While the proportion of patients with intermediate risk for CAD being referred for SPECT-MPI increased, those with high pretest probability were less frequently referred. Image parameters were mostly comparable between the study groups in the PRE and PAN periods.
ABSTRACT
OBJECTIVES: The ongoing Coronavirus disease 2019 (COVID-19) pandemic has drastically impacted the global health and economy. Computed tomography (CT) is the prime imaging modality for diagnosis of lung infections in COVID-19 patients. Data-driven and Artificial intelligence (AI)-powered solutions for automatic processing of CT images predominantly rely on large-scale, heterogeneous datasets. Owing to privacy and data availability issues, open-access and publicly available COVID-19 CT datasets are difficult to obtain, thus limiting the development of AI-enabled automatic diagnostic solutions. To tackle this problem, large CT image datasets encompassing diverse patterns of lung infections are in high demand. DATA DESCRIPTION: In the present study, we provide an open-source repository containing 1000+ CT images of COVID-19 lung infections established by a team of board-certified radiologists. CT images were acquired from two main general university hospitals in Mashhad, Iran from March 2020 until January 2021. COVID-19 infections were ratified with matching tests including Reverse transcription polymerase chain reaction (RT-PCR) and accompanying clinical symptoms. All data are 16-bit grayscale images composed of 512 × 512 pixels and are stored in DICOM standard. Patient privacy is preserved by removing all patient-specific information from image headers. Subsequently, all images corresponding to each patient are compressed and stored in RAR format.