Artificial Inteligent for Diagnosing Drug-Resistant Tuberculosis

Summary

Title: Artificial Neural Network as Diagnostic Tools For Rifampicin-Resistant Tuberculosis In Indonesia. A Predictive Model Study and Economic Evaluation.

Background: Drug-resistant tuberculosis has become a global threat particularly in Indonesia. The need to increase detection, followed by appropriate treatment is a concern in dealing with these cases. The rapid molecular test (specifically for detecting rifampicin-resistant) is now being utilized in health care service, particularly at primary care level with some challenges including the lack of quality control (including how to obtained and treat the specimen properly prior to the examination) which then, affect the reliability of the results. Drug-Susceptibility Test (DST) is still, the gold standard in diagnosing drug-resistant tuberculosis but this procedure is time-consuming and costly. The artificial intelligent including data exploration and modeling is a promising method to classify potential drug-resistant cases based on the association of several factors.

Objective :

1. To develop a model using an artificial intelligence approach that is able to classify the possibility of rifampicin-resistant tuberculosis.
2. To assess the diagnostic ability and the accuracy of the model in comparison to existing rapid test and the gold standard
3. To evaluate the cost-effectiveness evaluation of Artificial Neural Network model in Web-Based Application in comparison with the standard diagnostic tools

Methodology

1. A cross-sectional study involving all suspected drug-resistant tuberculosis cases that being referred to the study center to undergo rapid molecular test and DST test over the past 5 years.
2. A comprehensive, retrospective medical records assessment and tuberculosis individual report will be performed to obtain a variable of interest.
3. Questionnaire assessment for confirmation of insufficient information.
4. Model Building through machine learning and deep learning procedure
5. Model Validation and testing using training data set and data from the different study center

Hypothesis :

Artificial Intelligent Model will yield a similar or superior result of diagnostic ability compare the Rapid Molecular Test according to the Drug-Susceptibility Test. (Superiority Trial)

Conditions

• MDR Tuberculosis
• Resistance to Tuberculostatic Drugs

Interventions

DIAGNOSTIC_TEST

Rapid Molecular Drug-Resistant Tuberculosis Test

GeneXpert MTB/RIF assay is a nucleic acid amplification (NAA) test which simultaneously detects DNA of Mycobacterium tuberculosis complex (MTBC) and resistance to rifampin (RIF) (i.e. mutation of the rpoB gene) in less than two hours. This system integrates and automates sample processing, nucleic acid amplification, and detection of the target sequences. The primers in the XpertMTB/RIF assay amplify a portion of the rpoB gene containing the 81 base pair "core" region. The probes are able to differentiate between the conserved wild-type sequence and mutations in the core region that is associated with rifampicin resistance. The output of this procedure is detected, undetected, or indeterminate.

OTHER

Artificial Intelligent Model

The artificial intelligent model is a model that developed from several associated factors with machine learning and deep learning method in order to classify the possibility of drug-resistant tuberculosis. The Artificial neural network will be built using deep learning software.

DIAGNOSTIC_TEST

Drug Susceptibility Test

This procedure uses Löwenstein-Jensen (LJ) medium to determine whether the isolates of M. tuberculosis are susceptible to anti-TB agents. Media containing the critical concentration of the anti-TB agent is inoculated with a dilution of a culture suspension (usually a 10-2 dilution of a MacFarland 1 suspension) and control media without the anti-TB agent is inoculated with usually a 10-4 dilution of a MacFarland 1 suspension. Growth (i.e. a number of colonies) on the agent-containing media is compared to the growth on the agent-free control media. The ratio of the number of colonies on the medium containing the anti-TB agent to the number of colonies (corrected for the dilution factor) on the medium without the anti-TB agent is calculated, and the proportion is expressed as a percentage. Provisional results for susceptible isolates may be read after 3-4 weeks of incubation; definitive results may be read after 6 weeks of incubation. Resistance may be reported within 3-4 weeks.

Sponsors & Collaborators

• Chulalongkorn University

  collaborator OTHER

• Hasanuddin University

  lead OTHER

Principal Investigators

• Sathirakorn Pongpanich, Prof · Chulalongkorn University

• Wandee Sirichokchatchawan, Ph.D · Chulalongkorn University

• Bumi Herman, MD · Hasanuddin University

Eligibility

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2020-06-15

Primary Completion

2020-09-30

Completion

2020-10-02

Countries

• Indonesia

Study Locations