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Indian Journal of Microbiology

, Volume 58, Issue 2, pp 252–255 | Cite as

Integrated Artificial Intelligence Approaches for Disease Diagnostics

  • Rajat Vashistha
  • Deepak Chhabra
  • Pratyoosh Shukla
Opinion Article
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Abstract

Mechanocomputational techniques in conjunction with artificial intelligence (AI) are revolutionizing the interpretations of the crucial information from the medical data and converting it into optimized and organized information for diagnostics. It is possible due to valuable perfection in artificial intelligence, computer aided diagnostics, virtual assistant, robotic surgery, augmented reality and genome editing (based on AI) technologies. Such techniques are serving as the products for diagnosing emerging microbial or non microbial diseases. This article represents a combinatory approach of using such approaches and providing therapeutic solutions towards utilizing these techniques in disease diagnostics.

Keywords

Artificial intelligence Computer aided diagnostics Mechanobiology Robotic surgery 
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References

  1. 1.
    Liu C, Zhang L, Liu H, Cheng K (2017) Delivery strategies of the CRISPR-Cas9 gene- editing system for therapeutic applications. J Control Release 266:17–26.  https://doi.org/10.1016/j.jconrel.2017.09.012 CrossRefPubMedGoogle Scholar
  2. 2.
    Liu J, Wen J, Zhang Z, Liu H, Sun Y (2015) Voyage inside the cell: microsystems and nanoengineering for intracellular measurement and manipulation. Microsyst Nanoeng 1:15020.  https://doi.org/10.1038/micronano.2015.20 CrossRefGoogle Scholar
  3. 3.
    Siuly S, Zhang Y (2016) Medical big data: neurological diseases diagnosis through medical data analysis. Data Sci Eng 1:54–64.  https://doi.org/10.1007/s41019-016-0011-3 CrossRefGoogle Scholar
  4. 4.
    Zarei M (2017) Advances in point-of-care technologies for molecular diagnostics. Biosens Bioelectron 98:494–506.  https://doi.org/10.1016/j.bios.2017.07.024 CrossRefPubMedGoogle Scholar
  5. 5.
    Karsten SL, Tarhan MC, Kudo LC, Collard D, Fujita H (2015) Point-of-care (POC) devices by means of advanced MEMS. Talanta 145:55–59.  https://doi.org/10.1016/j.talanta.2015.04.032 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Dos Santos FLC, Paci M, Nanni L, Brahnam S, Hyttinen J (2015) Computer vision for virus image classification. Biosystems Eng 138:11–22.  https://doi.org/10.1016/j.biosystemseng.2015.01.005 CrossRefGoogle Scholar
  7. 7.
    Qiu X, Ge S, Gao P, Li K, Yang S, Zhang S, Ye X, Xia N, Qian S (2017) A smartphone-based point-of-care diagnosis of H1N1 with microfluidic convection PCR. Microsyst Technol 23:2951–2956.  https://doi.org/10.1007/s00542-016-2979-z CrossRefGoogle Scholar
  8. 8.
    Chakradhar S (2017) Predictable response: finding optimal drugs and doses using artificial intelligence. Nat Med 23:1244–1247.  https://doi.org/10.1038/nm1117-1244 CrossRefPubMedGoogle Scholar
  9. 9.
    Sareen S, Gupta SK, Sood SK (2017) An intelligent and secure system for predicting and preventing Zika virus outbreak using Fog computing. Enterp Inf Syst 11:1436–1456.  https://doi.org/10.1080/17517575.2016.1277558 Google Scholar
  10. 10.
    Cao Y, Liu C, Liu B, Brunette MJ, Zhang N, Sun T, Zhang P, Peinado J, Garavito ES, Garcia LL, Curioso WH (2017) Improving tuberculosis diagnostics using deep learning and mobile health technologies among resource-poor communities in Peru. Smart Health 1:66–76.  https://doi.org/10.1109/chase.2016.18 Google Scholar
  11. 11.
    Li B, Li B, Guo T, Sun Z, Li X, Li X, Chen L, Zhao J, Mao Y (2017) Artificial neural network models for early diagnosis of hepatocellular carcinoma using serum levels of α-fetoprotein, α-fetoprotein-L3, des-γ-carboxy prothrombin, and golgi protein. Oncotarget 8:80521–80530.  https://doi.org/10.18632/oncotarget.19298 PubMedPubMedCentralGoogle Scholar
  12. 12.
    Cheng Y, Liu C, Zhang N, Wang S, Zhang Z (2014) Proteomics analysis for finding serum markers of ovarian cancer. BioMed Res 2014:1–9.  https://doi.org/10.1155/2014/179040 CrossRefGoogle Scholar
  13. 13.
    Kumar V, Chhabra D, Shukla P (2017) Xylanase production from Thermomyces lanuginosus VAPS-24 using low cost agro-industrial residues via hybrid optimization tools and its potential use for saccharification. Bioresour Technol 243:1009–1019.  https://doi.org/10.1016/j.biortech.2017.07.094 CrossRefPubMedGoogle Scholar
  14. 14.
    Baweja M, Singh PK, Shukla P (2016) Enzyme technology, functional proteomics and systems biology toward unraveling molecular basis for functionality and interactions in biotechnological processes. In: Shukla P (ed) Frontier discoveries and innovations in interdisciplinary microbiology. Springer, India, pp 207–212.  https://doi.org/10.1007/978-81-322-2610-9_13 CrossRefGoogle Scholar
  15. 15.
    Vijayvargiya S, Shukla P (2013) Regulatory motif identification in biological sequences: an overview of computational methodologies. In: Shukla P, Pletschke B (eds) Advances in enzyme biotechnology. Springer, India, pp 111–124.  https://doi.org/10.1007/978-81-322-1094-8_8 CrossRefGoogle Scholar
  16. 16.
    Kurzrock R, Stewart DJ (2016) Click chemistry, 3D-printing, and omics: the future of drug development. Oncotarget 7:2155.  https://doi.org/10.18632/oncotarget.6787 CrossRefPubMedGoogle Scholar
  17. 17.
    Daneshmand M, Bilici O, Bolotnikova A, Anbarjafari G (2017) Medical robots with potential applications in participatory and opportunistic remote sensing: a review. Robot Auton Syst 95:160–180.  https://doi.org/10.1016/j.robot.2017.06.009 CrossRefGoogle Scholar
  18. 18.
    Tonutti M, Gras G, Yang GZ (2017) A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery. Artif Intell Med 80:39–47.  https://doi.org/10.1016/j.artmed.2017.07.004 CrossRefPubMedGoogle Scholar
  19. 19.
    Kalia VC (2013) Quorum sensing inhibitors: an overview. Biotechnol Adv 31:224–245.  https://doi.org/10.1016/j.biotechadv.2012.10.004 CrossRefPubMedGoogle Scholar
  20. 20.
    Kumar R, Koul S, Kumar P, Kalia VC (2016) Searching biomarkers in the sequenced genomes of Staphylococcus for their rapid identification. Indian J Microbiol 56:64–71.  https://doi.org/10.1007/s12088-016-0565-9 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Koul S, Kumar P, Kalia VC (2015) A unique genome wide approach to search novel markers for rapid identification of bacterial pathogens. J Mol Genet Med 9:194.  https://doi.org/10.4172/1747-0862.1000194 CrossRefGoogle Scholar

Copyright information

© Association of Microbiologists of India 2018

Authors and Affiliations

  1. 1.Optimization and Mechatronics Laboratory, Department of Mechanical Engineering, University Institute of Engineering and TechnologyMaharshi Dayanand UniversityRohtakIndia
  2. 2.Enzyme Technology and Protein Bioinformatics Laboratory, Department of MicrobiologyMaharshi Dayanand UniversityRohtakIndia

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