Artificial Neural Network as Diagnostic Tools For Rifampicin-Resistant Tuberculosis In Indonesia: A Predictive Model Study and Economic Evaluation
Trial ID: NCT04208789
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-06-15
Completion date: 2020-10-02
Sponsor: Hasanuddin University
Collaborators: Chulalongkorn University
Conditions: MDR Tuberculosis; Resistance to Tuberculostatic Drugs
Interventions: Rapid Molecular Drug-Resistant Tuberculosis Test; Artificial Intelligent Model; Drug Susceptibility Test
Locations: Kanudjoso Djatiwibowo General Hospital; Tarakan General Hospital; Wahidin Sudirohusodo General Hospital; Labuang Baji General Hospital; Balai Besar Kesehatan Paru Masyarakat
Countries: Indonesia; Indonesia; Indonesia; Indonesia; Indonesia
Enrollment: 524
Age range: None - None
Sex: ALL

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)

Eligibility criteria:
Inclusion criteria:

1. Default cases under WHO criteria
2. Failure cases under WHO criteria
3. Physician-referred cases for presumptive drug-resistant TB as follows :

With or without immunocompromised condition, With or without any adverse reaction of anti TB drug, With or without any comorbidities (such as diabetes mellitus, heart disease)

Exclusion Criteria:

1. Incomplete Information on Rapid Molecular Test Results, and Culture Results
2. Participants or family are unable/unwilling to provide additional information obtained through questionnaire

Outcomes:
Accuracy of Artificial Intelligent Model to Drug Susceptibility Test Results