Use of Digital Technology to Enhance Tuberculosis Control: Scoping Review

Background Tuberculosis (TB) is the leading cause of death from a single infectious agent, with around 1.5 million deaths reported in 2018, and is a major contributor to suffering worldwide, with an estimated 10 million new cases every year. In the context of the World Health Organization’s End TB strategy and the quest for digital innovations, there is a need to understand what is happening around the world regarding research into the use of digital technology for better TB care and control. Objective The purpose of this scoping review was to summarize the state of research on the use of digital technology to enhance TB care and control. This study provides an overview of publications covering this subject and answers 3 main questions: (1) to what extent has the issue been addressed in the scientific literature between January 2016 and March 2019, (2) which countries have been investing in research in this field, and (3) what digital technologies were used? Methods A Web-based search was conducted on PubMed and Web of Science. Studies that describe the use of digital technology with specific reference to keywords such as TB, digital health, eHealth, and mHealth were included. Data from selected studies were synthesized into 4 functions using narrative and graphical methods. Such digital health interventions were categorized based on 2 classifications, one by function and the other by targeted user. Results A total of 145 relevant studies were identified out of the 1005 published between January 2016 and March 2019. Overall, 72.4% (105/145) of the research focused on patient care and 20.7% (30/145) on surveillance and monitoring. Other programmatic functions 4.8% (7/145) and electronic learning 2.1% (3/145) were less frequently studied. Most digital health technologies used for patient care included primarily diagnostic 59.4% (63/106) and treatment adherence tools 40.6% (43/106). On the basis of the second type of classification, 107 studies targeted health care providers (107/145, 73.8%), 20 studies targeted clients (20/145, 13.8%), 17 dealt with data services (17/145, 11.7%), and 1 study was on the health system or resource management. The first authors’ affiliations were mainly from 3 countries: the United States (30/145 studies, 20.7%), China (20/145 studies, 13.8%), and India (17/145 studies, 11.7%). The researchers from the United States conducted their research both domestically and abroad, whereas researchers from China and India conducted all studies domestically. Conclusions The majority of research conducted between January 2016 and March 2019 on digital interventions for TB focused on diagnostic tools and treatment adherence technologies, such as video-observed therapy and SMS. Only a few studies addressed interventions for data services and health system or resource management.

[1]  From the Centers for Disease Control and Prevention. Initial therapy for tuberculosis in the era of multidrug resistance: recommendations of the Advisory Council for the Elimination of Tuberculosis. , 1993, JAMA.

[2]  R. Stott,et al.  The World Bank , 2008, Annals of tropical medicine and parasitology.

[3]  H. Arksey,et al.  Scoping studies: towards a methodological framework , 2005 .

[4]  R. Upshur,et al.  Tuberculosis and poverty: what could (and should) be done? , 2010, The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.

[5]  T. Hsiao,et al.  Application of ultrasound-guided core biopsy to minimize the non-diagnostic results and the requirement of diagnostic surgery in extrapulmonary tuberculosis of the head and neck , 2016, European Radiology.

[6]  L. Chen,et al.  The development and application of digital PCR , 2015 .

[7]  G. Kibiki,et al.  Feasibility of Real Time Medication Monitoring Among HIV Infected and TB Patients in a Resource-Limited Setting , 2016, AIDS and Behavior.

[8]  B. Bai,et al.  [Establishment and preliminary application of detection of Mycobacterium tuberculosis in sputum based on variable number tandem repeat]. , 2016, Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences.

[9]  D. Jiao,et al.  Application of digital tomosynthesis in diagnosing spinal tuberculosis. , 2016, Clinical imaging.

[10]  L. Fukasawa,et al.  Performance of an in-house real-time polymerase chain reaction for identification of Mycobacterium tuberculosis isolates in laboratory routine diagnosis from a high burden setting , 2016, Memorias do Instituto Oswaldo Cruz.

[11]  Martha A. Tesfalul,et al.  Evaluation of a Mobile Health Approach to Tuberculosis Contact Tracing in Botswana , 2016, Journal of health communication.

[12]  Q. Guan,et al.  Generation and application of ssDNA aptamers against glycolipid antigen ManLAM of Mycobacterium tuberculosis for TB diagnosis. , 2016, The Journal of infection.

[13]  N. Horita,et al.  Digital PCR assay detection of circulating Mycobacterium tuberculosis DNA in pulmonary tuberculosis patient plasma. , 2016, Tuberculosis.

[14]  M. Egger,et al.  Developing a point-of-care electronic medical record system for TB/HIV co-infected patients: experiences from Lighthouse Trust, Lilongwe, Malawi , 2016, BMC Research Notes.

[15]  X. Liu,et al.  [Application of Gene Xpert Mycobacterium tuberculosis DNA and resistance to rifampicin assay in the rapid detection of tuberculosis in children]. , 2016, Zhonghua er ke za zhi = Chinese journal of pediatrics.

[16]  B. van Ginneken,et al.  An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information , 2016, Scientific Reports.

[17]  J. Burzynski,et al.  Enhancing management of tuberculosis treatment with video directly observed therapy in New York City. , 2016, The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.

[18]  D. Falzon,et al.  The Internet of Things to come: digital technologies and the End TB Strategy , 2016, BMJ Global Health.

[19]  E. Mazzola,et al.  Performance of real-time PCR Xpert ®MTB/RIF in diagnosing extrapulmonary tuberculosis. , 2016, Le infezioni in medicina : rivista periodica di eziologia, epidemiologia, diagnostica, clinica e terapia delle patologie infettive.

[20]  D. Chin,et al.  Multicenter evaluation of a real-time loop-mediated isothermal amplification (RealAmp) test for rapid diagnosis of Mycobacterium tuberculosis. , 2016, Journal of microbiological methods.

[21]  Yi-Sheng Liu,et al.  The Role of Video-Assisted Thoracoscopic Therapeutic Resection for Medically Failed Pulmonary Tuberculosis , 2016, Medicine.

[22]  M. DiStefano,et al.  mHealth for Tuberculosis Treatment Adherence: A Framework to Guide Ethical Planning, Implementation, and Evaluation , 2016, Global Health: Science and Practice.

[23]  R. Garfein,et al.  Digital health for the End TB Strategy: developing priority products and making them work , 2016, European Respiratory Journal.

[24]  S. Andronikou,et al.  Digital platform for improving non-radiologists’ and radiologists’ interpretation of chest radiographs for suspected tuberculosis — a method for supporting task-shifting in developing countries , 2016, Pediatric Radiology.

[25]  M. Pai,et al.  Computer-aided detection of pulmonary tuberculosis on digital chest radiographs: a systematic review. , 2016, The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.

[26]  A. Yassi,et al.  Learning from a cluster randomized controlled trial to improve healthcare workers' access to prevention and care for tuberculosis and HIV in Free State, South Africa: the pivotal role of information systems. , 2016 .

[27]  C. Wattal,et al.  Utility of multiplex real-time PCR in the diagnosis of extrapulmonary tuberculosis , 2016, The Brazilian journal of infectious diseases : an official publication of the Brazilian Society of Infectious Diseases.

[28]  Q. Shi,et al.  [Big data analysis of flow of tuberculosis cases in China, 2014]. , 2016, Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi.

[29]  F. Sarfo,et al.  Factors associated with performing tuberculosis screening of HIV-positive patients in Ghana: LASSO-based predictor selection in a large public health data set , 2016, BMC Public Health.

[30]  Bram van Ginneken,et al.  On Combining Multiple-Instance Learning and Active Learning for Computer-Aided Detection of Tuberculosis , 2016, IEEE Transactions on Medical Imaging.

[31]  Fran Van Heuverswyn,et al.  The use of digital PCR to improve the application of quantitative molecular diagnostic methods for tuberculosis , 2016, BMC Infectious Diseases.

[32]  A. Carballo-Diéguez,et al.  Smartphone Applications to Support Tuberculosis Prevention and Treatment: Review and Evaluation , 2016, JMIR mHealth and uHealth.

[33]  R. Garfein,et al.  Monitoring Therapy Adherence of Tuberculosis Patients by using Video-Enabled Electronic Devices , 2016, Emerging infectious diseases.

[34]  Sarah Iribarren,et al.  Call for Increased Patient Support Focus: Review and Evaluation of Mobile Apps for Tuberculosis Prevention and Treatment , 2016, Nursing Informatics.

[35]  Petter Holme,et al.  Connectivity of diagnostic technologies: improving surveillance and accelerating tuberculosis elimination. , 2016, The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.

[36]  Seymour G. Williams,et al.  Evaluating the electronic tuberculosis register surveillance system in Eden District, Western Cape, South Africa, 2015 , 2017, Global health action.

[37]  José António Nhavoto,et al.  Mobile health treatment support intervention for HIV and tuberculosis in Mozambique: Perspectives of patients and healthcare workers , 2017, PloS one.

[38]  D. Falzon,et al.  Tuberculosis control, and the where and why of artificial intelligence , 2017, ERJ Open Research.

[39]  L. Banting,et al.  Effectiveness of real-time polymerase chain reaction assay for the detection of Mycobacterium tuberculosis in pathological samples: a systematic review and meta-analysis , 2017, Systematic Reviews.

[40]  H. Kim,et al.  Bringing state-of-the-art diagnostics to vulnerable populations: The use of a mobile screening unit in active case finding for tuberculosis in Palawan, the Philippines , 2017, PloS one.

[41]  An evaluation study on phenotypical methods and real-time PCR for detection of Mycobacterium tuberculosis in sputa of two health centers in Iran , 2017, Iranian journal of microbiology.

[42]  R. Zachariah,et al.  Using mobile phones to ensure that referred tuberculosis patients reach their treatment facilities: a call that makes a difference , 2017, BMC Health Services Research.

[43]  Colin D. Furness,et al.  Technology and tuberculosis control: the OUT-TB Web experience , 2017, J. Am. Medical Informatics Assoc..

[44]  S. Chadha,et al.  Using mHealth to enhance TB referrals in a tribal district of India. , 2017, Public health action.

[45]  Y. Manabe,et al.  Text messaging to decrease tuberculosis treatment attrition in TB-HIV coinfection in Uganda , 2017, Patient preference and adherence.

[46]  C. Denkinger,et al.  Feasibility of the TBDx automated digital microscopy system for the diagnosis of pulmonary tuberculosis , 2017, PloS one.

[47]  Yang-hui Jin,et al.  [Application value of Xpert MTB/RIF in diagnosis of spinal tuberculosis and detection of rifampin resistance]. , 2017, Zhongguo gu shang = China journal of orthopaedics and traumatology.

[48]  Z. Lv,et al.  Utility of Real-Time Quantitative Polymerase Chain Reaction in Detecting Mycobacterium tuberculosis , 2017, BioMed research international.

[49]  Eliabe Rodrigues de Medeiros,et al.  Sistemas de información clínica para el manejo de la tuberculosis en la atención primaria de salud , 2017 .

[50]  E. Oren,et al.  Promoting adherence to treatment for latent TB infection through mobile phone text messaging: study protocol for a pilot randomized controlled trial , 2017, Pilot and Feasibility Studies.

[51]  T. Chorba,et al.  Notes from the Field: Use of Asynchronous Video Directly Observed Therapy for Treatment of Tuberculosis and Latent Tuberculosis Infection in a Long-Term–Care Facility ― Puerto Rico, 2016–2017 , 2017, MMWR. Morbidity and mortality weekly report.

[52]  W. Wiegerinck,et al.  The potential of a portable, point-of-care electronic nose to diagnose tuberculosis. , 2017, The Journal of infection.

[53]  S. Oommen,et al.  Laboratory diagnosis of tuberculosis: Advances in technology and drug susceptibility testing , 2017, Indian journal of medical microbiology.

[54]  T. Buchman,et al.  A New Method to Directly Observe Tuberculosis Treatment: Skype Observed Therapy, a Patient-Centered Approach , 2017, Journal of public health management and practice : JPHMP.

[55]  V. Kovalev,et al.  The TB Portals: an Open-Access, Web-Based Platform for Global Drug-Resistant-Tuberculosis Data Sharing and Analysis , 2017, Journal of Clinical Microbiology.

[56]  User experience analysis of e-TB Manager, a nationwide electronic tuberculosis recording and reporting system in Ukraine , 2017, ERJ Open Research.

[57]  H. Kimaro,et al.  Challenges and Strategies for Standardizing Information Systems for Integrated TB/HIV Services in Tanzania: A Case Study of Kinondoni Municipality , 2017, Electron. J. Inf. Syst. Dev. Ctries..

[58]  C. Mulder,et al.  Tuberculosis diagnostic technology: an African solution … think rats , 2017, African journal of laboratory medicine.

[59]  Brenden A. Bedard,et al.  Using Telemedicine for Tuberculosis Care Management: a Three County Inter-Municipal Approach , 2017, Journal of Medical Systems.

[60]  Implementation of the Xpert MTB/RIF assay for tuberculosis in Mongolia: a qualitative exploration of barriers and enablers , 2017, PeerJ.

[61]  K. Ruxrungtham,et al.  Utility of urine lipoarabinomannan (LAM) in diagnosing tuberculosis and predicting mortality with and without HIV: prospective TB cohort from the Thailand Big City TB Research Network. , 2017, International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases.

[62]  Niranjan Konduri,et al.  User experience analysis of an eHealth system for tuberculosis in resource-constrained settings: A nine-country comparison , 2017, Int. J. Medical Informatics.

[63]  K. Kam,et al.  Clinical Application of Genexpert on BAL Samples in Management of TB in Intermediate Burden Area , 2017 .

[64]  V. Cook,et al.  Preventing Tuberculosis in a Low Incidence Setting: Evaluation of a Multi-lingual, Online, Educational Video on Latent Tuberculosis , 2018, Journal of Immigrant and Minority Health.

[65]  K. Dheda,et al.  Impact of the GeneXpert MTB/RIF Technology on Tuberculosis Control. , 2017, Microbiology spectrum.

[66]  H. Shewade,et al.  Digital chest X-ray through a mobile van: public private partnership to detect sputum negative pulmonary TB , 2017, BMC Research Notes.

[67]  Aihua Liu,et al.  Use of Digital Droplet PCR to Detect Mycobacterium tuberculosis DNA in Whole Blood-Derived DNA Samples from Patients with Pulmonary and Extrapulmonary Tuberculosis , 2017, Front. Cell. Infect. Microbiol..

[68]  Q. Meng,et al.  [The development and application of digital PCR used in Mycobacterium tuberculosis detection]. , 2017, Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases.

[69]  Mauro Giacomini,et al.  A Web Based Tool to Enhance Monitoring and Retention in Care for Tuberculosis Affected Patients , 2017, pHealth.

[70]  B. Tessema,et al.  Computer-aided reading of tuberculosis chest radiography: moving the research agenda forward to inform policy , 2017, European Respiratory Journal.

[71]  Elizabeth Holzschuh,et al.  Use of Video Directly Observed Therapy for Treatment of Latent Tuberculosis Infection — Johnson County, Kansas, 2015 , 2017, MMWR. Morbidity and mortality weekly report.

[72]  M. Grobusch,et al.  Ultrasound for patients in a high HIV/tuberculosis prevalence setting: a needs assessment and review of focused applications for Sub-Saharan Africa. , 2017, International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases.

[73]  A. Daftary,et al.  Using mHealth for HIV/TB Treatment Support in Lesotho: Enhancing Patient–Provider Communication in the START Study , 2016, Journal of acquired immune deficiency syndromes.

[74]  Duc Cuong Pham,et al.  Video Directly Observed Therapy to support adherence with treatment for tuberculosis in Vietnam: A prospective cohort study. , 2017, International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases.

[75]  H. Ayles,et al.  Digital CXR with computer aided diagnosis versus symptom screen to define presumptive tuberculosis among household contacts and impact on tuberculosis diagnosis , 2017, BMC Infectious Diseases.

[76]  M. Gagnon,et al.  Evaluating clinicians' user experience and acceptability of LearnTB, a smartphone application for tuberculosis in India. , 2017, mHealth.

[77]  D. Falzon,et al.  Video-observed treatment for tuberculosis patients in Belarus: findings from the first programmatic experience , 2017, European Respiratory Journal.

[78]  K. Dheda,et al.  Effect of new tuberculosis diagnostic technologies on community-based intensified case finding: a multicentre randomised controlled trial. , 2017, The Lancet. Infectious diseases.

[79]  Hiroyuki Yamada,et al.  Laboratory evaluation of the Anyplex™ II MTB/MDR and MTB/XDR tests based on multiplex real-time PCR and melting-temperature analysis to identify Mycobacterium tuberculosis and drug resistance. , 2017, Diagnostic microbiology and infectious disease.

[80]  G. Joos,et al.  Digital health to end tuberculosis in the Sustainable Development Goals era: achievements, evidence and future perspectives , 2017, European Respiratory Journal.

[81]  David Mark,et al.  Feasibility, Acceptability, and Adoption of Digital Fingerprinting During Contact Investigation for Tuberculosis in Kampala, Uganda: A Parallel-Convergent Mixed-Methods Analysis , 2018, Journal of medical Internet research.

[82]  Heng Zhang,et al.  A point-of-need enzyme linked aptamer assay for Mycobacterium tuberculosis detection using a smartphone , 2018 .

[83]  S. Cotter,et al.  Evaluation and comparison of the National Tuberculosis (TB) Surveillance System in Ireland before and after the introduction of the Computerised Electronic Reporting System (CIDR) , 2018, Epidemiology and Infection.

[84]  Jing Wang,et al.  Development and application of a rapid Mycobacterium tuberculosis detection technique using polymerase spiral reaction , 2018, Scientific Reports.

[85]  D. Ingram Video directly observed therapy: Enhancing care for patients with active tuberculosis. , 2018, Nursing.

[86]  Eric S. Ramos,et al.  Mobile phone interventions for tuberculosis should ensure access to mobile phones to enhance equity – a prospective, observational cohort study in Peruvian shantytowns , 2018, Tropical medicine & international health : TM & IH.

[87]  C. Signorelli,et al.  Information and communication technology to enhance TB control in migrant populations , 2018, European Journal of Public Health.

[88]  Feng Zhu,et al.  Application Values of T-SPOT.TB in Clinical Rapid Diagnosis of Tuberculosis , 2018, Iranian journal of public health.

[89]  F. Raab,et al.  Tuberculosis Treatment Monitoring by Video Directly Observed Therapy in 5 Health Districts, California, USA , 2018, Emerging infectious diseases.

[90]  C. Viscoli,et al.  Time to change the single-centre approach to management of patients with tuberculosis: a novel network platform with automatic data import and data sharing , 2018, ERJ Open Research.

[91]  Su‐Kyung Lee,et al.  Application of next-generation sequencing to detect variants of drug-resistant Mycobacterium tuberculosis: genotype–phenotype correlation , 2019, Annals of Clinical Microbiology and Antimicrobials.

[92]  Domingos Alves,et al.  Towards a Clinical Trial Protocol to Evaluate Health Information Systems: Evaluation of a Computerized System for Monitoring Tuberculosis from a Patient Perspective in Brazil , 2018, Journal of Medical Systems.

[93]  M. Lobato,et al.  A National Survey on the Use of Electronic Directly Observed Therapy for Treatment of Tuberculosis , 2017, Journal of public health management and practice : JPHMP.

[94]  J. Burzynski,et al.  Using Video Technology to Increase Treatment Completion for Patients With Latent Tuberculosis Infection on 3-Month Isoniazid and Rifapentine: An Implementation Study , 2018, Journal of medical Internet research.

[95]  G. Churchyard,et al.  Using mHealth to improve tuberculosis case identification and treatment initiation in South Africa: Results from a pilot study , 2018, PloS one.

[96]  S. Heysell,et al.  Levofloxacin Pharmacokinetics/Pharmacodynamics, Dosing, Susceptibility Breakpoints, and Artificial Intelligence in the Treatment of Multidrug-resistant Tuberculosis. , 2018, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[97]  Payal Dande,et al.  Acquaintance to Artificial Neural Networks and use of artificial intelligence as a diagnostic tool for tuberculosis: A review. , 2018, Tuberculosis.

[98]  S. Khaparde,et al.  Enhancing TB surveillance with mobile technology: Opportunities and challenges. , 2018, The Indian journal of tuberculosis.

[99]  Y. Xiong,et al.  Automatic detection of mycobacterium tuberculosis using artificial intelligence. , 2018, Journal of thoracic disease.

[100]  D. Falzon,et al.  The impact of digital health technologies on tuberculosis treatment: a systematic review , 2018, European Respiratory Journal.

[101]  K. R. Amico,et al.  Pilot evaluation of a second-generation electronic pill box for adherence to Bedaquiline and antiretroviral therapy in drug-resistant TB/HIV co-infected patients in KwaZulu-Natal, South Africa , 2018, BMC Infectious Diseases.

[102]  B. van Ginneken,et al.  Computer-assisted chest radiography reading for tuberculosis screening in people living with diabetes mellitus. , 2018, The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.

[103]  Nor Azah Yusof,et al.  An intelligent mobile-enabled expert system for tuberculosis disease diagnosis in real time , 2018, Expert Syst. Appl..

[104]  J. Haberer,et al.  Text Messages Sent to Household Tuberculosis Contacts in Kampala, Uganda: Process Evaluation , 2018, JMIR mHealth and uHealth.

[105]  S. Sofat,et al.  Tuberculosis Detection from Chest Radiographs: A Comprehensive Survey on Computer-Aided Diagnosis Techniques , 2017, Current Medical Imaging Reviews.

[106]  T. Frauenfelder,et al.  Detection of tuberculosis patterns in digital photographs of chest X-ray images using Deep Learning: feasibility study. , 2018, The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.

[107]  Qing Liu,et al.  Label-free, real-time and multiplex detection of Mycobacterium tuberculosis based on silicon photonic microring sensors and asymmetric isothermal amplification technique (SPMS-AIA) , 2018 .

[108]  A. Nunn,et al.  Data in TB have to be both big and good. , 2018, The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.

[109]  Jennifer A Davidson,et al.  Creating a web-based electronic tool to aid tuberculosis (TB) cluster investigation: data integration in TB surveillance activities in the United Kingdom, 2013 to 2016 , 2018, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[110]  H. Koornhof,et al.  Feasibility of using postal and web-based surveys to estimate the prevalence of tuberculosis among health care workers in South Africa , 2018, PloS one.

[111]  H. Ho,et al.  Automated real-time detection of drug-resistant Mycobacterium tuberculosis on a lab-on-a-disc by Recombinase Polymerase Amplification. , 2018, Analytical biochemistry.

[112]  Tania S. Douglas,et al.  Mobile phone-based evaluation of latent tuberculosis infection: Proof of concept for an integrated image capture and analysis system , 2018, Comput. Biol. Medicine.

[113]  T. Ciach,et al.  Detection of tuberculosis in patients with the use of portable SPR device , 2018 .

[114]  Léonie Goeminne,et al.  Potential Application of Digitally Linked Tuberculosis Diagnostics for Real-Time Surveillance of Drug-Resistant Tuberculosis Transmission: Validation and Analysis of Test Results , 2018, JMIR medical informatics.

[115]  William B. Lober,et al.  What Do Patients and Experts Want in a Smartphone-Based Application to Support Tuberculosis Treatment Completion? , 2018, Nursing Informatics.

[116]  Laura M Lechuga,et al.  Label-Free and Real-Time Detection of Tuberculosis in Human Urine Samples Using a Nanophotonic Point-of-Care Platform. , 2018, ACS sensors.

[117]  R. Lester,et al.  The effect of text messaging on latent tuberculosis treatment adherence: a randomised controlled trial , 2018, European Respiratory Journal.

[118]  D. Falzon,et al.  Evaluating the potential costs and impact of digital health technologies for tuberculosis treatment support , 2018, European Respiratory Journal.

[119]  B. van Ginneken,et al.  Evaluation of the diagnostic accuracy of Computer-Aided Detection of tuberculosis on Chest radiography among private sector patients in Pakistan , 2018, Scientific Reports.

[120]  Effectiveness of Tuberculosis Screening Technology in the Initiation of Correct Diagnosis of Pulmonary Tuberculosis at a Tertiary Care Hospital in Thailand: Comparative Analysis of Xpert MTB/RIF Versus Sputum AFB Smear , 2018, Asia-Pacific journal of public health.

[121]  Syed Mustafa Ali,et al.  Data Quality: A Negotiator between Paper-Based and Digital Records in Pakistan's TB Control Program , 2018, Data.

[122]  Samuel B Holzman,et al.  Advancing Patient-Centered Care in Tuberculosis Management: A Mixed-Methods Appraisal of Video Directly Observed Therapy , 2018, Open forum infectious diseases.

[123]  Ravi Seethamraju,et al.  Intention to Use a Mobile-Based Information Technology Solution for Tuberculosis Treatment Monitoring – Applying a UTAUT Model , 2017, Information Systems Frontiers.

[124]  S. Basu mHealth to enhance TB referrals: challenge in scaling up. , 2018, Public health action.

[125]  Chang-Hyun Jang,et al.  Liquid crystal-based aptasensor for the detection of interferon-γ and its application in the diagnosis of tuberculosis using human blood , 2019, Sensors and Actuators B: Chemical.

[126]  L. Fiebig,et al.  Assessment of the use and need for an integrated molecular surveillance of tuberculosis: an online survey in Germany , 2019, BMC Public Health.

[127]  Dongqing Wei,et al.  Artificial Neural Networks for Prediction of Tuberculosis Disease , 2019, Front. Microbiol..

[128]  J DhaliaSweetlin,et al.  Computer aided diagnosis of drug sensitive pulmonary tuberculosis with cavities, consolidations and nodular manifestations on lung CT images , 2019, Int. J. Bio Inspired Comput..

[129]  I. Bassett,et al.  Test and Treat TB: a pilot trial of GeneXpert MTB/RIF screening on a mobile HIV testing unit in South Africa , 2019, BMC Infectious Diseases.

[130]  S. Negi,et al.  Diagnostic Evaluation of Multiplex Real Time PCR, GeneXpert MTB/RIF Assay and Conventional Methods in Extrapulmonary Tuberculosis , 2019, JOURNAL OF CLINICAL AND DIAGNOSTIC RESEARCH.

[131]  B. Datta,et al.  Comparison of clinical and cost-effectiveness of two strategies using mobile digital x-ray to detect pulmonary tuberculosis in rural India , 2019, BMC Public Health.

[132]  R. S. Chithra,et al.  Fractional crow search-based support vector neural network for patient classification and severity analysis of tuberculosis , 2019, IET Image Process..

[133]  C. Wattal,et al.  Newer Diagnostic Tests and their Application in Pediatric TB , 2019, The Indian Journal of Pediatrics.

[134]  Richard G. White,et al.  Application of provincial data in mathematical modelling to inform sub-national tuberculosis program decision-making in South Africa , 2019, PloS one.

[135]  Dharitri Thakkar,et al.  A pilot project: 99DOTS information communication technology-based approach for tuberculosis treatment in Rajkot district , 2019, Lung India : official organ of Indian Chest Society.

[136]  Zhaolei Zhang,et al.  Integrating exosomal microRNAs and electronic health data improved tuberculosis diagnosis , 2019, EBioMedicine.

[137]  Carolin T. Turner,et al.  Spatial Network Mapping of Pulmonary Multidrug-Resistant Tuberculosis Cavities Using RNA Sequencing , 2019, American journal of respiratory and critical care medicine.

[138]  M. Bhargava,et al.  N-TB: A mobile-based application to simplify nutritional assessment, counseling and care of patients with tuberculosis in India. , 2019, The Indian journal of tuberculosis.

[139]  Rashmi Rodrigues,et al.  Mobile Health for Tuberculosis Management in South India: Is Video-Based Directly Observed Treatment an Acceptable Alternative? , 2019, JMIR mHealth and uHealth.

[140]  S. Islam,et al.  Toward Developing a Standardized Core Set of Outcome Measures in Mobile Health Interventions for Tuberculosis Management: Systematic Review , 2018, JMIR mHealth and uHealth.

[141]  J. Luo,et al.  Rapid direct drug susceptibility testing of Mycobacterium tuberculosis based on culture droplet digital polymerase chain reaction. , 2019, The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.

[142]  J. van Beek,et al.  Evaluation of whole genome sequencing and software tools for drug susceptibility testing of Mycobacterium tuberculosis. , 2019, Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases.

[143]  R. Garfein,et al.  Smartphone-enabled video-observed versus directly observed treatment for tuberculosis: a multicentre, analyst-blinded, randomised, controlled superiority trial , 2019, The Lancet.

[144]  A. Story,et al.  A sputum sample processing method for community and mobile tuberculosis diagnosis using the Xpert MTB/RIF assay , 2019, ERJ Open Research.

[145]  Inkyung Jung,et al.  Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers’ Health Examination Data , 2019, International journal of environmental research and public health.

[146]  Mirko Zimic,et al.  Automatic diagnostics of tuberculosis using convolutional neural networks analysis of MODS digital images , 2019, PloS one.

[147]  H. Fraser,et al.  Video-observed therapy for tuberculosis: strengthening care , 2019, The Lancet.

[148]  R. Garfein,et al.  Change in Patient Comfort Using Mobile Phones Following the Use of an App to Monitor Tuberculosis Treatment Adherence: Longitudinal Study , 2019, JMIR mHealth and uHealth.