Potenciális COVID-19 fertőzés automatikus felismerésé hagyományos véranalízis alapján: Automatic detection of potential COVID-19 infection based on conventional blood analysis

To control the spread of the COVID-19 it is very important to identify those who have been already infected by this new type of virus. The rRT-PCR (reverse transcription polymerase chain reaction) testing is the golden standard for COVID-19 detection, but it is time consuming, laborious manual process and it is very short in supply. To reduce the number of tests, in this article we will present a possible solution for COVID-19 preliminary patient filtering based on regular blood tests, using artificial intelligence (AI) models. The most appropriate AI model will be selected using our auto-adaptive AI platform, AutomaticAI. The hyperparameters of the selected algorithm will also be adjusted automatically by this platform to match the context of the problem. Kivonat A COVID-19 terjedésének megfékezése érdekében nagyon fontos azonosítani azokat a személyeket, akiket már megfertőzött ezen új típusú vírus. Az rRT-PCR (reverse transcription polymerase chain reaction) teszt a COVID-19 detektálásának leghatékonyabb eszköze, ám időigényes, fárasztó kézi folyamat, és nagyon szűk a készlet belőle. A tesztek számának csökkentése érdekében, ebben a cikkben a COVID-19 előzetes betegszűrésének lehetséges megoldását mutatjuk be hagyományos vérvizsgálatok alapján, mesterséges intelligencia (AI) modellek felhasználásával. A leghatékonyabb AI-modellt automatikusan alkalmazkodó AI-platformunk, az AutomaticAI segítségével választjuk ki. A kiválasztott algoritmus hiperparamétereit platformunk képes automatikusan beállítani, ezáltal megfelelve a probléma kontextusának. &nbsp

Intrasession and Between-Visit Variability of Retinal Vessel Density Values Measured with OCT Angiography in Diabetic Patients

In clinical practice the measurement error of an instrument has special importance in analyzing and interpreting data, and acknowledging limitations. The purpose of this study was to evaluate intrasession and between-visit reproducibility of OCT angiography measurements in diabetic patients. A total of 54 eyes of 27 diabetic patients underwent OCT angiography imaging. Foveal avascular zone (FAZ) area and superficial retinal vessel density (VD) at 3 mm were calculated using the AngioAnalytics software. Three consecutive images were acquired at first visit and one image 1 month later. Intrasession and between-visit reproducibility of parameters were characterized by intraclass correlation coefficient (ICC), coefficient of variation (CV), and coefficient of repeatability (CR) values. We measured excellent (>0.90) ICC values both in intrasession and between-visit comparisons. CV was higher for the FAZ area compared to VD both in intrasession (7.79% vs. 2.87%) and in between-visit (12.33% vs. 2.95%) comparisons. Between-visit CR value for VD was 4.53% (95% CI: 3.72-5.79%). These data suggest that OCT angiography shows excellent repeatability in diabetic patients, indicating that this non-invasive technology might be suitable for longitudinal assessment of microvascular complications