LAUSR

Artificial intelligent of things (AIoT) has become a potential tool to be implemented in a wide range of fields and expanding with interdisciplinary sciences. On the other hand, in clinical scenario human blood clotting disease (Royal disease) detection has been considered as an urgent issue to be solved. This study uses AIoT with deep long short-term memory (LSTM) networks for biosensing application. Analysed the potent clinical target, human blood clotting factor IX by its aptamer/antibody as the probe on the micro-scaled fingers and gaps of interdigitated electrode. The obtained earlier results by the current-volt measurements have shown the changes with the surface modification. The limit of detection (LOD) was noticed as 1 pM with antibody as the probe, whereas aptamer behaved better with the LOD at 100 fM. The time series predictions from AIoT application supported the obtained results from the laboratory analyses using both probes. This application clearly supports the results obtained on the interdigitated electrode sensor as aptamer to be the better option for analysing the blood clotting defects. The current study supports a great implementation of AIoT in sensing application, can be followed for other clinical biomarkers. This article is protected by copyright. All rights reserved.