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

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).Basicresearch 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 subfield 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”.

Enhancing medical-imaging artificial intelligence through holistic use of time-tested key imaging and clinical parameters:Future insights

Much of the published literature in Radiology-related Artificial Intelligence(AI)focuses on single tasks,such as identifying the presence or absence or severity of specific lesions.Progress comparable to that achieved for general-purpose computer vision has been hampered by the unavailability of large and diverse radiology datasets containing different types of lesions with possibly multiple kinds of abnormalities in the same image.Also,since a diagnosis is rarely achieved through an image alone,radiology AI must be able to employ diverse strategies that consider all available evidence,not just imaging information.Using key imaging and clinical signs will help improve their accuracy and utility tremendously.Employing strategies that consider all available evidence will be a formidable task;we believe that the combination of human and computer intelligence will be superior to either one alone.Further,unless an AI application is explainable,radiologists will not trust it to be either reliable or bias-free;we discuss some approaches aimed at providing better explanations,as well as regulatory concerns regarding explainability(“transparency”).Finally,we look at federated learning,which allows pooling data from multiple locales while maintaining data privacy to create more generalizable and reliable models,and quantum computing,still prototypical but potentially revolutionary in its computing impact.

Augmentation of literature review of COVID-19 radiology

We suggest an augmentation of the excellent comprehensive review article titled“Comprehensive literature review on the radiographic findings,imaging modalities,and the role of radiology in the coronavirus disease 2019(COVID-19)pandemic”under the following categories:(1)“Inclusion of additional radiological features,related to pulmonary infarcts and to COVID-19 pneumonia”;(2)“Amplified discussion of cardiovascular COVID-19 manifestations and the role of cardiac magnetic resonance imaging in monitoring and prognosis”;(3)“Imaging findings related to fluorodeoxyglucose positron emission tomography,optical,thermal and other imaging modalities/devices,including‘intelligent edge’and other remote monitoring devices”;(4)“Artificial intelligence in COVID-19 imaging”;(5)“Additional annotations to the radiological images in the manuscript to illustrate the additional signs discussed”;and(6)“A minor correction to a passage on pulmonary destruction”.