Smart diagnostics devices through artificial intelligence and mechanobiological approaches

被引：0
作者：
Dinesh Yadav
Ramesh Kumar Garg
Deepak Chhabra
Rajkumar Yadav
Ashwani Kumar
Pratyoosh Shukla
机构：
[1] Deenbandhu Chhotu Ram University of Science and Technology,Department of Mechanical Engineering
[2] Maharshi Dayanand University,Optimization and Mechatronics Laboratory, Department of Mechanical Engineering, University Institute of Engineering and Technology
[3] Indira Gandhi University,Department of Computer Science & Engineering
[4] Maharshi Dayanand University,Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology
来源：
3 Biotech | 2020年 / 10卷
关键词：
Mechanobiology; Diagnostics; Biological; Mechanical; Fluidic; Artificial intelligence;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The present work illustrates the promising intervention of smart diagnostics devices through artificial intelligence (AI) and mechanobiological approaches in health care practices. The artificial intelligence and mechanobiological approaches in diagnostics widen the scope for point of care techniques for the timely revealing of diseases by understanding the biomechanical properties of the tissue of interest. Smart diagnostic device senses the physical parameters due to change in mechanical, biological, and luidic properties of the cells and to control these changes, supply the necessary drugs immediately using AI techniques. The latest techniques like sweat diagnostics to measure the overall health, Photoplethysmography (PPG) for real-time monitoring of pulse waveform by capturing the reflected signal due to blood pulsation), Micro-electromechanical systems (MEMS) and Nano-electromechanical systems (NEMS) smart devices to detect disease at its early stage, lab-on-chip and organ-on-chip technologies, Ambulatory Circadian Monitoring device (ACM), a wrist-worn device for Parkinson’s disease have been discussed. The recent and futuristic smart diagnostics tool/techniques like emotion recognition by applying machine learning algorithms, atomic force microscopy that measures the fibrinogen and erythrocytes binding force, smartphone-based retinal image analyser system, image-based computational modeling for various neurological disorders, cardiovascular diseases, tuberculosis, predicting and preventing of Zika virus, optimal drugs and doses for HIV using AI, etc. have been reviewed. The objective of this review is to examine smart diagnostics devices based on artificial intelligence and mechanobiological approaches, with their medical applications in healthcare. This review determines that smart diagnostics devices have potential applications in healthcare, but more research work will be essential for prospective accomplishments of this technology.
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