Tuberculosis conundrum - current and future scenarios: A proposed comprehensive approach combining laboratory, imaging, and computing advances

Abstract

Tuberculosis (TB) remains a global threat, with the rise of multiple and extensively drug resistant TB posing additional challenges. The International health community has set various 5-yearly targets for TB elimination: mathematical modelling suggests that a 2050 target is feasible with a strategy combining better diagnostics, drugs, and vaccines to detect and treat both latent and active infection. The availability of rapid and highly sensitive diagnostic tools (Gene-Xpert, TB-Quick) will vastly facilitate population-level identification of TB (including rifampicin resistance and through it, multi-drug-resistant TB). Basic-research advances have illuminated molecular mechanisms in TB, including the protective role of Vitamin D. Also, Mycobacterium tuberculosis impairs the host immune response through epigenetic mechanisms (histone-binding modulation). Imaging will continue to be key, both for initial diagnosis and follow-up. We discuss advances in multiple imaging modalities to evaluate TB tissue changes, such as molecular imaging techniques (including pathogen-specific positron emission tomography imaging agents), non-invasive temporal monitoring, and computing enhancements to improve data acquisition and reduce scan times. Big data analysis and Artificial Intelligence (AI) algorithms, notably in the AI sub-field called “Deep Learning”, can potentially increase the speed and accuracy of diagnosis. Additionally, Federated learning makes multi-institutional/multi-city AI-based collaborations possible without sharing identifiable patient data. More powerful hardware designs - e.g. , Edge and Quantum Computing- will facilitate the role of computing applications in TB. However, “Artificial Intelligence needs real Intelligence to guide it!” To have maximal impact, AI must use a holistic approach that incorporates time tested human wisdom gained over decades from the full gamut of TB, i.e. , key imaging and clinical parameters, including prognostic indicators, plus bacterial and epidemiologic data. We propose a similar holistic approach at the level of national/international policy formulation and implementation, to enable effective culmination of TB’s endgame, summarizing it with the acronym “TB - REVISITED”.

Características imagenológicas actuales de resonancia magnética en los granulomas cerebrales más frecuentes

“…La infección tuberculosa del SNC es causada por diseminación hematógena intracraneal a partir de un foco primario, en la mayoría de los casos de los pulmones (6). Entre el 5 y 15 % de las TBC extrapulmonares corresponden al compromiso del SNC, y la forma de presentación más frecuente es la meningitis tuberculosa (7,8). Las de compromiso parenquimal son menos frecuentes, aunque se encuentran mayormente en pacientes infectados con el VIH, como oportunista, que puede darse con o sin meningitis.…”

“…Asimismo, ha aumentado la resistencia a la terapia antituberculosa (6). La incidencia de TBC del SNC alcanza hasta el 10 % en pacientes con infección por VIH vs. tuberculosos sin infección por VIH (7). Según la Organización Mundial de la Salud (OMS), la TBC causa la muerte del 11 % de los pacientes con sida.…”

Augmentation of literature review of COVID-19 radiology

“…Telemedicine has advanced by leaps and bounds. AI algorithms enable faster diagnosis (including remote diagnosis), with a fair degree of accuracy[ 48 ]. While the application of AI to medical imaging of cancers and other diseases is being developed over the past decades, the recent COVID-19 pandemic hastened the: (1) Need; (2) Development; (3) Training; and (4) Testing of AI algorithms, within a relatively shorter time-span of less than 2 years[ 49 ].…”

“…However limitations such as: (1) Limited datasets; (2) Inaccurate execution of training and testing procedures; and (3) Use of incorrect performance criteria needed to be dealt with. The above limitations can be overcome by the utilization of federated learning[ 48 , 51 , 52 ].…”

“…If AI is to make an even greater impact, Merchant et al [ 48 ] suggested getting down to the basics and incorporating time tested key medical ‘teaching’ and/or key ‘clinical’ parameters, including prognostic indicators, for more effective AI algorithms and their better clinical utility. They also stated that “Artificial Intelligence needs real Intelligence to guide it!”.…”