Objective: The artificial intelligence competition in healthcare was organized for the first time at the annual aviation, space, and technology festival (TEKNOFEST), Istanbul/Türkiye, in September 2021. In this article, the data set preparation and competition processes were explained in detail; the anonymized and annotated data set is also provided via official website for further research. Materials and Methods: Data set recorded over the period covering 2019 and 2020 were centrally screened from the e-Pulse and Teleradiology System of the Republic of Türkiye, Ministry of Health using various codes and filtering criteria. The data set was anonymized. The data set was prepared, pooled, curated, and annotated by 7 radiologists. The training data set was shared with the teams via a dedicated file transfer protocol server, which could be accessed using private usernames and passwords given to the teams under a nondisclosure agreement signed by the representative of each team. Results: The competition consisted of 2 stages. In the first stage, teams were given 192 digital imaging and communications in medicine images that belong to 1 of 3 possible categories namely, hemorrhage, ischemic, or non-stroke. Teams were asked to classify each image as either stroke present or absent. In the second stage of the competition, qualifying 36 teams were given 97 digital imaging and communications in medicine images that contained hemorrhage, ischemia, or both lesions. Among the employed methods, Unet and DeepLabv3 were the most frequently observed ones. Conclusion: Artificial intelligence competitions in healthcare offer good opportunities to collect data reflecting various cases and problems. Especially, annotated data set by domain experts is more valuable. Cite this article as: Koç U, Akçapınar Sezer E, Alper Özkaya Y, et al. Artificial intelligence in healthcare competition (TEKNOFEST-2021): Stroke data set. Eurasian J Med., 2022;54(3):248-258.

Background: The objective of our study was to investigate the location, extension and type of novel coronavirus-induced disease 2019 (COVID-19) infection involvement and hepatic steatosis on initial chest computed tomography (CT). The relationship between fatty liver and severity of the disease was also investigated by measuring the liver attenuation index (LAI). Methods: This study evaluated the chest CT images of 343 patients (201 male, mean age 48.43 years) who were confirmed to have COVID-19, using nasopharyngeal swab. The chest CTs were analyzed for laterality, number of involved lobes, diffuseness, number of lesions, and lesion types. The CT attenuation values of liver and spleen were measured, and LAI was calculated for the detection of hepatic steatosis. Univariate and multivariate logistic regression analysis were used to identify the independent early predictors for severe COVID-19. Results: There was no significant difference between genders in terms of clinical course. Liver density and LAI were significantly lower in the intensive care unit (ICU) patients. The prevalence of severe disease was higher in the patients with hepatic steatosis than in the non-steatotic group (odds ratio [OR] 3.815, 95% confidence interval [CI] 1.97–7.37, P < 0.001). After adjusting for age and comorbidities including hypertension, diabetes mellitus, coronary artery disease, chronic obstructive pulmonary disease, and chronic kidney disease, multivariate logistic regression analysis showed that non-alcoholic fatty liver disease (NAFLD) was an independent risk factor for COVID-19 severity (OR 3.935, 95% CI 1.77-8.70, P = 0.001). The optimal cut-off value for LAI was calculated as 0.5 for predicting patients who required ICU treatment. Conclusions: On the initial chest CT images of COVID-19 patients, presence of fatty liver is a strong predictor for severe disease.