Symposium 8:Precision Diagnosis of TB and LTBI: Non-sequencing-based Methods
Prompt diagnosis of TB and LTBI is urgent and could facilitate timely therapeutic intervention and minimizes community transmission, particularly when encountering the disruptions caused by the COVID-19 pandemic that resulted in a large global fall in the number of people newly diagnosed with TB and reported (i.e. officially notified) in 2020, compared with 2019. Thus, the development of new diagnostic tools to overcome the diagnostic obstacles of TB and LTBI caused by COVID-19 pandemic and to target ending TB in 2035 is crucial. This symposium will bring the information on the advancement of the diagnosis of TB and LTBI to the health professionals.
Time (GMT+8) |
Topic | Speaker | Country / Region |
---|---|---|---|
14:50-15:20 | The Advancement of AI-Augmented Imaging Technology in the Diagnosis of Pulmonary TB | Dr. Yu-Sen Lin | Taiwan |
15:20-15:50 | Precision Diagnosis of Tuberculosis: Empowering Personalized Healthcare with Nano and Micro Technologies | Dr. Tony Hu | USA |
15:50-16:20 | Immune Based Diagnosis of LTBI- Where We Are and Where We Are Going | Dr. Jennifer Ann Mendoza-Wi | Philippines |
The Advancement of AI-Augmented Imaging Technology in the Diagnosis of Pulmonary TB
Abstract:
Recent breakthroughs in AI and TB diagnostics have revolutionized the field with the introduction of AI-augmented tools. These tools employ advanced machine learning algorithms to detect TB infections in chest X-rays, CT scans, and sputum samples. The use of deep learning allows these tools to analyze medical images and identify TB infections with remarkable precision. In addition to this, AI-augmented tools have been instrumental in the development of a virtual assistant for health workers. This has significantly improved the diagnosis of TB in remote areas, where access to medical facilities is limited, helping health workers diagnose TB quickly and accurately in low-resource settings. For example, the detection of acid-fast bacillus in TB smears by AI imaging system has been validated and published in the literature. These tools are designed to address the challenges posed by the COVID-19 pandemic, which has led to a decrease in the detection of new TB cases. In conclusion, the integration of AI in TB diagnostics is paving the way for early detection and effective treatment strategies, thereby contributing to the global fight against TB.
Dr. Yu-Sen Lin
Taiwan
Immune Based Diagnosis of LTBI- Where We Are and Where We Are Going
Abstract:
Precision diagnostic medicine occupies the frontline for the clinical campaign against disease. Historically, Tuberculosis is humanity’s leading infectious nemesis, in terms of morbidity/mortality. Today, ~25% of the global population harbors latent Mycobacterium tuberculosis (Mtb) infections, with risks for re-activation and spread through close contact. Despite grave impacts, scant research evaluates mechanisms or biomarkers to advance insights into tuberculosis diagnosis, activation, and progression, severely limiting clinical patient management, and perpetuating dire outcomes. Addressing these challenges, my team employs a variety of cutting-edge platforms, including high-resolution mass spectrometry, nanomaterial probes, and CRISPR to elucidate ultrasensitive and quantitative readouts. Translating these advances, we envision simple point-of-care assays, deployed into resource-limited endemic regions, allowing rapid diagnosis and precision guidance for therapeutics, augmenting global pandemic eradication efforts.
Dr. Tony Hu
USA
Immune Based Diagnosis of LTBI- Where We Are and Where We Are Going
Abstract:
The difficulty in eliminating TB can be attributed to the diverse mechanisms of immune evasion and immune response manipulation by MTB. MTB can persist in the human body for years without causing clinical symptoms, leading to a condition known as latent tuberculosis infection (LTBI).
The Problem: Latent tuberculosis infection (LTBI) has become a major source of active tuberculosis (ATB). The two currently available classes of tests – tuberculin skin test (TST) and interferon-gamma release assay (IGRA) – require a competent immune response to accurately identify TB infection. However, a positive test result by either method is not, by itself, a reliable indicator of the risk of progression to TB disease. The diagnostic difficulties of LTBI include issues such as cost, detection time, sensitivity, and specificity.
Where We are: In 2015, WHO updated its recommendations on the use of TST and IGRA for the diagnosis of TB infection, and in 2022 issued a policy statement extending these recommendations to cover the use of new and updated versions of blood-based IGRA. Newer Mtb antigen-based skin tests (TBST) have been developed to measure the cell-mediated immunological response to Mtb specific antigens. Comparative studies have been done and there is emerging evidence that these tests may offer similar specificity to IGRA, and when compared with TST they may provide more reliable results in children and in people living with HIV. The new IGRA tests available will be discussed.
Where are we going: Machine learning (ML) in LTBI diagnosis? ML is becoming an important tool in the field of identifying and diagnosing LTBI. ML uses algorithmic training forming a structured knowledge system to provide personalized decision support and rapid diagnosis. It represents a promising approach to accurately discriminate and diagnose LTBI and ATB.
Dr. Jennifer Ann Mendoza-Wi
Philippines
Dr. Bo-Shiun Yan
Taiwan
Dr. Masae Kawamura
USA