Tuberculosis diagnostics and biomarkers: needs, challenges, recent advances, and opportunities

Tuberculosis is unique among the major infectious diseases in that it lacks accurate rapid point-of-care diagnostic tests. Failure to control the spread of tuberculosis is largely due to our inability to detect and treat all infectious cases of pulmonary tuberculosis in a timely fashion, allowing continued Mycobacterium tuberculosis transmission within communities. Currently recommended gold-standard diagnostic tests for tuberculosis are laboratory based, and multiple investigations may be necessary over a period of weeks or months before a diagnosis is made. Several new diagnostic tests have recently become available for detecting active tuberculosis disease, screening for latent M. tuberculosis infection, and identifying drug-resistant strains of M. tuberculosis. However, progress toward a robust point-of-care test has been limited, and novel biomarker discovery remains challenging. In the absence of effective prevention strategies, high rates of early case detection and subsequent cure are required for global tuberculosis control. Early case detection is dependent on test accuracy, accessibility, cost, and complexity, but also depends on the political will and funder investment to deliver optimal, sustainable care to those worst affected by the tuberculosis and human immunodeficiency virus epidemics. This review highlights unanswered questions, challenges, recent advances, unresolved operational and technical issues, needs, and opportunities related to tuberculosis diagnostics

Rapid Diagnostic Algorithms as a Screening Tool for Tuberculosis: An Assessor Blinded Cross-Sectional Study

Background: A major obstacle to effectively treat and control tuberculosis is the absence of an accurate, rapid, and low-cost diagnostic tool. A new approach for the screening of patients for tuberculosis is the use of rapid diagnostic classification algorithms. Methods: We tested a previously published diagnostic algorithm based on four biomarkers as a screening tool for tuberculosis in a Central European patient population using an assessor-blinded cross-sectional study design. In addition, we developed an improved diagnostic classification algorithm based on a study population at a tertiary hospital in Vienna, Austria, by supervised computational statistics. Results: The diagnostic accuracy of the previously published diagnostic algorithm for our patient population consisting of 206 patients was 54% (CI: 47%–61%). An improved model was constructed using inflammation parameters and clinical information. A diagnostic accuracy of 86% (CI: 80%–90%) was demonstrated by 10-fold cross validation. An alternative model relying solely on clinical parameters exhibited a diagnostic accuracy of 85% (CI: 79%–89%). Conclusion: Here we show that a rapid diagnostic algorithm based on clinical parameters is only slightly improved by inclusion of inflammation markers in our cohort. Our results also emphasize the need for validation of new diagnostic algorithms in different settings and patient populations

Expert System with an Embedded Imaging Module for Diagnosing Lung Diseases

Lung diseases are one of the major causes of suffering and death in the world. Improved survival rate could be obtained if the diseases can be detected at its early stage. Specialist doctors with the expertise and experience to interpret medical images and diagnose complex lung diseases are scarce. In this work, a rule-based expert system with an embedded imaging module is developed to assist the general physicians in hospitals and clinics to diagnose lung diseases whenever the services of specialist doctors are not available. The rule-based expert system contains a large knowledge base of data from various categories such as patient's personal and medical history, clinical symptoms, clinical test results and radiological information. An imaging module is integrated into the expert system for the enhancement of chest X-Ray images. The goal of this module is to enhance the chest X-Ray images so that it can provide details similar to more expensive methods such as MRl and CT scan. A new algorithm which is a modified morphological grayscale top hat transform is introduced to increase the visibility of lung nodules in chest X-Rays. Fuzzy inference technique is used to predict the probability of malignancy of the nodules. The output generated by the expert system was compared with the diagnosis made by the specialist doctors. The system is able to produce results\ud which are similar to the diagnosis made by the doctors and is acceptable by clinical standards

A survey on artificial intelligence based techniques for diagnosis of hepatitis variants

Hepatitis is a dreaded disease that has taken the lives of so many people over the recent past years. The research survey shows that hepatitis viral disease has five major variants referred to as Hepatitis A, B, C, D, and E. Scholars over the years have tried to find an alternative diagnostic means for hepatitis disease using artificial intelligence (AI) techniques in order to save lives. This study extensively reviewed 37 papers on AI based techniques for diagnosing core hepatitis viral disease. Results showed that Hepatitis B (30%) and C (3%) were the only types of hepatitis the AI-based techniques were used to diagnose and properly classified out of the five major types, while (67%) of the paper reviewed diagnosed hepatitis disease based on the different AI based approach but were not classified into any of the five major types. Results from the study also revealed that 18 out of the 37 papers reviewed used hybrid approach, while the remaining 19 used single AI based approach. This shows no significance in terms of technique usage in modeling intelligence into application. This study reveals furthermore a serious gap in knowledge in terms of single hepatitis type prediction or diagnosis in all the papers considered, and recommends that the future road map should be in the aspect of integrating the major hepatitis variants into a single predictive model using effective intelligent machine learning techniques in order to reduce cost of diagnosis and quick treatment of patients

Sensitive electrochemiluminescence (ECL) immunoassays for detecting lipoarabinomannan (LAM) and ESAT-6 in urine and serum from tuberculosis patients.

BackgroundTuberculosis (TB) infection was responsible for an estimated 1.3 million deaths in 2017. Better diagnostic tools are urgently needed. We sought to determine whether accurate TB antigen detection in blood or urine has the potential to meet the WHO target product profiles for detection of active TB.Materials and methodsWe developed Electrochemiluminescence (ECL) immunoassays for Lipoarabinomannan (LAM) and ESAT-6 detection with detection limits in the pg/ml range and used them to compare the concentrations of the two antigens in the urine and serum of 81 HIV-negative and -positive individuals with presumptive TB enrolled across diverse geographic sites.ResultsLAM and ESAT-6 overall sensitivities in urine were 93% and 65% respectively. LAM and ESAT-6 overall sensitivities in serum were 55% and 46% respectively. Overall specificity was ≥97% in all assays. Sensitivities were higher in HIV-positive compared to HIV-negative patients for both antigens and both sample types, with signals roughly 10-fold higher on average in urine than in serum. The two antigens showed similar concentration ranges within the same sample type and correlated.ConclusionsLAM and ESAT-6 can be detected in the urine and serum of TB patients, regardless of the HIV status and further gains in clinical sensitivity may be achievable through assay and reagent optimization. Accuracy in urine was higher with current methods and has the potential to meet the WHO accuracy target if the findings can be transferred to a point-of-care TB test

An application of ANFIS for Lung Diseases Early Detection System

Indonesian Basic Health Research in 2018 showed the prevalence of pneumonia, pulmonary tuberculosis (TB) and lung cancer in Indonesia 4.0% 0.4% and 0.18%, respectively. However, the number of lung specialists is small. According to the Indonesian Lung Specialist Association webpage, the number of doctors joined in the association up to 2008 were 452. This amount is very less when compared with existing lung disease cases. Thus, the handling of lung disease will be too late. The use of ANFIS for early detection of lung disease is growing. However, the systems designed are need preprocessing data to be executed and still applied for one type of disease. This paper will design a desktop application based on ANFIS expert system to detect lung disease early, i.e. for pneumonia, pulmonary TB and lung cancer. The system will work based on simple symptoms expressed by the patient. Subtractive clustering is used for clustering process. The results of the training showed that the models were able to give better performance compared to the model built using conventional clustering methods. The test results show that those three models have comparable performance compared to their counterpart. Software validation shows that the it gives 94.00% succeed for training data and up to 100% for testing data. This application is not intended to replace the role of a doctor, but to help diagnose the patient's condition earlier

Clinical presentation and diagnostic work up of suspected pulmonary embolism in a district hospital emergency centre serving a high HIV/TB burden population

Introduction: The diagnosis of Pulmonary Embolism (PE) is challenging to make and is often missed in the Emergency Centre. The true incidence of PE in South Africa is unknown. The diagnostic work-up of PE has been improved by the use of Clinical decision rules (CDRs) and CT Pulmonary Angiography (CTPA) in high-income countries. Currently used CDRs have not been validated in the South African environment, where HIV and TB are highly prevalent. Both conditions are known to induce a hyper-coagulable state. Methods: This study was a retrospective chart review of patients with suspected PE that had CTPAs performed from October 2013 to October 2015 at Mitchell’s Plain Hospital in South Africa. Data was collected on demographics, presenting symptoms and signs, vitals, bedside investigations, HIV and TB status, use of CDRs and CTPA result. A Revised Geneva Score was calculated retrospectively and compared to the CTPA result. Results: The median age of patients with confirmed PE was 45 years and 68% were female. The CTPA yield for PE in our study population was 32%. The most common presenting complaint was dyspnoea (83%), followed by cough and chest pain. 29% of patients also had clinical features of DVT. No sign or symptom was seen to be markedly different in those with confirmed PE compared to those without. Among patients with confirmed PE, 37% were HIV positive and 52% had current TB. The retrospective revised Geneva Scores compared poorly with the CTPA results. Discussion: PE remains a diagnostic challenge. Worldwide, the use of CDRs has shown to improve the utilization of CTPA. In our study, the retrospectively calculated CDR was not predictive of PE in a population with a high prevalence of HIV and TB. Emergency physicians should be cautious when making a clinical probability assessment of PE in this setting. However, further studies are needed to determine whether HIV and TB could be independent risk factors for PE

Clinical presentation and diagnostic work up of suspected pulmonary embolism in a district hospital emergency centre serving a high HIV/TB burden population

Introduction: The diagnosis of Pulmonary Embolism (PE) is challenging to make and is often missed in the Emergency Centre. The true incidence of PE in South Africa is unknown. The diagnostic work-up of PE has been improved by the use of Clinical decision rules (CDRs) and CT Pulmonary Angiography (CTPA) in high-income countries. Currently used CDRs have not been validated in the South African environment, where HIV and TB are highly prevalent. Both conditions are known to induce a hyper-coagulable state. Methods: This study was a retrospective chart review of patients with suspected PE that had CTPAs performed from October 2013 to October 2015 at Mitchell’s Plain Hospital in South Africa. Data was collected on demographics, presenting symptoms and signs, vitals, bedside investigations, HIV and TB status, use of CDRs and CTPA result. A Revised Geneva Score was calculated retrospectively and compared to the CTPA result. Results: The median age of patients with confirmed PE was 45 years and 68% were female. The CTPA yield for PE in our study population was 32%. The most common presenting complaint was dyspnoea (83%), followed by cough and chest pain. 29% of patients also had clinical features of DVT. No sign or symptom was seen to be markedly different in those with confirmed PE compared to those without. Among patients with confirmed PE, 37% were HIV positive and 52% had current TB. The retrospective revised Geneva Scores compared poorly with the CTPA results. Discussion: PE remains a diagnostic challenge. Worldwide, the use of CDRs has shown to improve the utilization of CTPA. In our study, the retrospectively calculated CDR was not predictive of PE in a population with a high prevalence of HIV and TB. Emergency physicians should be cautious when making a clinical probability assessment of PE in this setting. However, further studies are needed to determine whether HIV and TB could be independent risk factors for PE

Automatic Chest X-rays Analysis using Statistical Machine Learning Strategies

Tuberculosis (TB) is a disease responsible for the deaths of more than one million people worldwide every year. Even though it is preventable and curable, it remains a major threat to humanity that needs to be taken care of. It is often diagnosed in developed countries using approaches such as sputum smear microscopy and culture methods. However, since these approaches are rather expensive, they are not commonly used in poor regions of the globe such as India, Africa, and Bangladesh. Instead, the well known and affordable chest x-ray (CXR) interpretation by radiologists is the technique employed in those places. Nevertheless, if this method is obsolete in other parts of the world nowadays it is because of its many flaws including: i) it is a tedious task that requires experienced medical personnel --which is scarce given the high demand for it--, ii) it is manual and difficult when executed for a large population, and iii) it is prone to human error depending on the proficiency and aptitude of the interpreter. Researchers have thus been trying to overcome these challenges over the years by proposing software solutions that mainly involve computer vision, artificial intelligence, and machine learning. The problems with these existing solutions are that they are either complex or not reliable enough. The need for better solutions in this specific domain as well as my desire to bring my contribution to something meaningful are what led us to investigate in this direction. In this manuscript, I propose a simple fully automatic software solution that uses only machine learning and image processing to analyze and detect anomalies related to TB in CXR scans. My system starts by extracting the region of interest from the incoming images, then performs a computationally inexpensive yet efficient feature extraction that involves edge detection using Laplacian of Gaussian and positional information retention. The extracted features are then fed to a regular random forest classifier for discrimination. I tested the system on two benchmark data collections --Montgomery and Shenzhen-- and obtained state-of-the-art results that reach up to 97% classification accuracy