Acquaintance to Artificial Neural Networks and use of artificial intelligence as a diagnostic tool for tuberculosis: A review

• Published in: Tuberculosis (Edinburgh, Scotland) Vol. 108; pp. 1 - 9
• Main Authors: Dande, Payal, Samant, Purva
• Format: Journal Article
• Language: English
• Published: Scotland Elsevier Ltd 01.01.2018; Elsevier Science Ltd
• Subjects: Artificial intelligence; Artificial Neural Networks; Biomarkers - blood; Computer simulation; Decision Support Techniques; Diagnosis; Diagnosis, Computer-Assisted - methods; Diagnostic software; Diagnostic systems; Disease detection; Drug resistance; Early Diagnosis; Fatalities; Fuzzy Logic; Genetic algorithms; HIV; Host-Pathogen Interactions; Human immunodeficiency virus; Humans; Medical diagnosis; Mycobacterium tuberculosis - pathogenicity; Neural networks; Neural Networks (Computer); Neuro fuzzy logic; Predictive Value of Tests; Reproducibility of Results; Sputum; Studies; Tuberculosis; Tuberculosis - blood; Tuberculosis - diagnosis; Tuberculosis - microbiology; Tuberculosis - physiopathology; Tuberculosis; Diagnosis; Artificial Neural Networks; Artificial intelligence; Neuro fuzzy logic
• ISSN: 1472-9792; 1873-281X; 1873-281X
• DOI: 10.1016/j.tube.2017.09.006

Tuberculosis [TB] has afflicted numerous nations in the world. As per a report by the World Health Organization [WHO], an estimated 1.4 million TB deaths in 2015 and an additional 0.4 million deaths resulting from TB disease among people living with HIV, were observed. Most of the TB deaths can be prevented if it is detected at an early stage. The existing processes of diagnosis like blood tests or sputum tests are not only tedious but also take a long time for analysis and cannot differentiate between different drug resistant stages of TB. The need to find newer prompt methods for disease detection has been aided by the latest Artificial Intelligence [AI] tools. Artificial Neural Network [ANN] is one of the important tools that is being used widely in diagnosis and evaluation of medical conditions. This review aims at providing brief introduction to various AI tools that are used in TB detection and gives a detailed description about the utilization of ANN as an efficient diagnostic technique. The paper also provides a critical assessment of ANN and the existing techniques for their diagnosis of TB. Researchers and Practitioners in the field are looking forward to use ANN and other upcoming AI tools such as Fuzzy-logic, genetic algorithms and artificial intelligence simulation as a promising current and future technology tools towards tackling the global menace of Tuberculosis. Latest advancements in the diagnostic field include the combined use of ANN with various other AI tools like the Fuzzy-logic, which has led to an increase in the efficacy and specificity of the diagnostic techniques. [Display omitted]