Prospects of Using Machine Learning and Diamond Nanosensing for High Sensitivity SARS-CoV-2 Diagnosis

Shahzad Ahmad Qureshi*, Haroon Aman, Romana Schirhagl

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
26 Downloads (Pure)


The worldwide death toll claimed by Acute Respiratory Syndrome Coronavirus Disease 2019 (SARS-CoV), including its prevailed variants, is 6,812,785 ( accessed on 14 March 2023). Rapid, reliable, cost-effective, and accurate diagnostic procedures are required to manage pandemics. In this regard, we bring attention to quantum spin magnetic resonance detection using fluorescent nanodiamonds for biosensing, ensuring the benefits of artificial intelligence-based biosensor design on an individual patient level for disease prediction and data interpretation. We compile the relevant literature regarding fluorescent nanodiamonds-based SARS-CoV-2 detection along with a short description of viral proliferation and incubation in the cells. We also propose a potentially effective strategy for artificial intelligence-enhanced SARS-CoV-2 biosensing. A concise overview of the implementation of artificial intelligence algorithms with diamond magnetic nanosensing is included, covering this roadmap’s benefits, challenges, and prospects. Some mutations are alpha, beta, gamma, delta, and Omicron with possible symptoms, viz. runny nose, fever, sore throat, diarrhea, and difficulty breathing accompanied by severe body pain. The recommended strategy would deliver reliable and improved diagnostics against possible threats due to SARS-CoV mutations, including possible pathogens in the future.

Original languageEnglish
Article number171
Number of pages15
Issue number7
Publication statusPublished - Jul-2023


  • artificial intelligence
  • COVID-19 biosensing
  • deep learning
  • fluorescent nanodiamonds
  • machine learning
  • spin relaxometry


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