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Use of Artificial Intelligence in Research and Clinical Decision Making for Combating Mycobacterial Diseases

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Artificial Intelligence and Machine Learning in Healthcare

Abstract

Tuberculosis (TB) and leprosy (caused by mycobacterial pathogens) are two age-old infections, which we are facing even today. India is a major contributor to the global burden of leprosy and tuberculosis, which adversely affects the diverse communities as well as having a prevalence in different parts of the country. Timely diagnostics and effective treatment are very challenging, and the emergence of drug resistance has further complicated the management of these mycobacterial diseases. Various lineages of these mycobacterial pathogens show varying phenotypes in terms of clinical presentations and treatment outcomes. Altogether these factors make it further difficult to understand the full genetic diversity and pathogenicity of these pathogens using the conventional genomic and proteomic approaches. However, thanks to the recent technological advances in the genomics and proteomics field, many of these constraints have been suitably addressed. While it is relatively simpler to produce the omics data in a high-throughput manner, the bottleneck now is the pace to assimilate this large data into some useful information to reach a relevant, meaningful conclusion in a timely manner to assist the clinician in making a judgment.

In India, genetic diversity of different strains has been widely studied using approaches based on Next-generation sequencing (NGS), metagenomics, spoligotyping, and PCR. But there are still gaps in predicting phenotypes accurately from genotypic data, in particular for certain drugs. Recently, Machine learning (ML) methods were successfully used to develop predictive classification models and to identify compounds based on their biological activities. Artificial Intelligence- (AI) based ML learns from known data characteristics and makes predictions. Machine learning approaches can find statistical dependencies in the data and also take into account the non-linear and feature-interaction effects. In this way, new knowledge can be unleashed and data has been proven to be useful that can provide clinically actionable recommendations and high priority features like mutation/variant/polymorphism profile and its association with the drug as well as drug resistance profile, genotype information regarding clustering and molecular epidemiology of mycobacteria. Moreover, the data utilized by the model for prediction can also be implied in rapid diagnostics and transmission dynamics studies. In this chapter, we gathered the current information about the use of Genome-wide Association Study (GWAS) and NGS in mycobacterial disease and a machine learning literature supporting applications for identification and antimicrobial susceptibility testing in mycobacteria. We have attempted to provide a comprehensive introduction about the technological advancements in high throughput data and explain how NGS with ML can be used in clinical decision-making, genomics, proteomics, docking, simulations, drug screening, and drug-repurposing.

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Acknowledgments

The authors thank Dr. Aparup Das, Director, ICMR-National Institute of Research in Tribal Health, Jabalpur for the encouragement and kind support. The manuscript has been approved by the Publication Screening Committee of ICMR-NIRTH, Jabalpur and assigned with the number ICMR-NIRTH/PSC/51/2020.

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  1. Indian Council of Medical Research-National Institute of Research in Tribal Health, Jabalpur, Madhya Pradesh, India

    Mukul Sharma & Pushpendra Singh

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  1. Mukul Sharma
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  2. Pushpendra Singh
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Correspondence to Pushpendra Singh .

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  1. Amity University, Noida, Uttar Pradesh, India

    Ankur Saxena

  2. Amity University, Noida, Uttar Pradesh, India

    Shivani Chandra

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Sharma, M., Singh, P. (2021). Use of Artificial Intelligence in Research and Clinical Decision Making for Combating Mycobacterial Diseases. In: Saxena, A., Chandra, S. (eds) Artificial Intelligence and Machine Learning in Healthcare . Springer, Singapore. https://doi.org/10.1007/978-981-16-0811-7_9

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