Speaker 1: Now then, tuberculosis remains the world's deadliest infectious disease, and
Speaker 1: here in South Africa the numbers are staggering four hundred
Speaker 1: and sixty eight out of every one hundred thousand people affected.
Speaker 1: But despite this, TB remains massively underdiagnosed in our health
Speaker 1: and wellness segment. Today, we're focusing on a groundbreaking new
Speaker 1: initiative from Stellenbosch University, where researchers are helping lead a
Speaker 1: global trial using AI artificial intelligence to improve TB diagnoses,
Speaker 1: particularly at primary care level. The project, involving ten institutions
Speaker 1: across Africa and Europe, aims to develop a diagnostic tool
Speaker 1: that uses handheld ultrasound devices and smartphones to help detect
Speaker 1: TV quickly and more accurately, even in rural or low
Speaker 1: resource settings. Joining us now to explain more is Professor
Speaker 1: Grant Thron, Professor and Clinical Microbacteriology and Epidemiology at Stellenbosh Universities,
Speaker 1: also the char coordinator for this innovative study. Good to
Speaker 1: have you with us this afternoon. Thanks for making time.
Speaker 2: Thank you, mich Thank you so much. Sarah Jane, Good
Speaker 2: afternoon to you and your listeners.
Speaker 1: It's incredible to think that although it is the world's
Speaker 1: sort of most deadliest infectious disease, it's still underdiagnosed. Why
Speaker 1: is that.
Speaker 2: Rov The main reason is that TB has such a
Speaker 2: diverse way of presenting. Most TB, in fact, probably about
Speaker 2: half TB is in people who don't yet feel sick
Speaker 2: enough to seek care. These are people who do not
Speaker 2: yet have full blown symptoms, and traditionally we've unfortunately relied
Speaker 2: on symptoms to trigger TB testing. But with projects such
Speaker 2: as this one, we are trying to test people earlier
Speaker 2: and earlier before they are even actually aware that they
Speaker 2: may have TB disease.
Speaker 1: So what would and what would that look like? Then?
Speaker 1: How did how? How does that work?
Speaker 2: So how that would look like? Is that you would
Speaker 2: move away from a symptom based approach to really a
Speaker 2: risk factor based approach. Right, So, regardless of how someone
Speaker 2: is feeling, if they are in a certain area, or
Speaker 2: they have maybe a clinical condition like diabetes or HIV
Speaker 2: that increases their risk of TV, you would evaluate them
Speaker 2: using a two step process. The first process is really
Speaker 2: just working out would that person actually benefit from TV testing?
Speaker 2: And that is called a screening tool where you take
Speaker 2: someone who is healthy and you see, do they actually
Speaker 2: need that crucial yet really expensive follow on commatory testing
Speaker 2: or can they go about dead with their day and
Speaker 2: we can be confident they don't have TV. So this
Speaker 2: project is really aiming to target people in those risk
Speaker 2: groups to make sure that we get the scarce but
Speaker 2: expensive tests that we have applied to the right combination
Speaker 2: of people in an efficient manner.
Speaker 1: For a while testing the wrong people at the wrong time.
Speaker 1: I suppose that's what you were saying just then, is
Speaker 1: that this is about getting to people before the point
Speaker 1: at which they're presenting with symptoms exactly.
Speaker 2: This is one of the paradoxes in TB care is
Speaker 2: that most people who we test for TB do not
Speaker 2: actually have TV. But at the same time, we just
Speaker 2: never test and laugh people for TV. So we need
Speaker 2: to make TB testing itself more efficient, so make sure
Speaker 2: that we don't waste unnecessary resources testing people who would
Speaker 2: probably not benefit, and then at the same time use
Speaker 2: those freed up resources to expand the knit for TV
Speaker 2: testing as wide as possible. But in people who have
Speaker 2: those risk factors that mean that the odds are finding
Speaker 2: someone are the highest that they can be.
Speaker 1: You mentioned some people who are more predisposed than others.
Speaker 1: Who else might be on that list who is particularly at.
Speaker 2: Risk for TV, So it depends, right, I think the
Speaker 2: most important thing is traditionally people who have some form
Speaker 2: of compromised immunity are at risk of TB. But crucially
Speaker 2: that is not just HIV. It can also be malnutrition.
Speaker 2: It can also be poor health for other reasons because
Speaker 2: of smoking or you're exposed to pollution. It can also
Speaker 2: be the fact that you maybe had TB before and
Speaker 2: that itself increases your risk for future episodes. So the
Speaker 2: risk factors for TB are diverse. But having said that,
Speaker 2: I'm really much just emphasized is that TB, even though
Speaker 2: it's more likely to be found in certain people, there
Speaker 2: are many people who themselves lead act active, healthy, normal
Speaker 2: lives who actually start to come down with a cough
Speaker 2: and often TB is not suspected in those people because
Speaker 2: they don't fit that stereotypical picture. But they often turn
Speaker 2: out to have had full blown TV all along. So
Speaker 2: there's nothing absolute with TV. It can affect everyone to
Speaker 2: different extents.
Speaker 1: Yeah, South Africa is now part of this major international project.
Speaker 1: So what's involved in the study and what exactly is
Speaker 1: the main goal.
Speaker 2: The main goal of the study is to catalyze on
Speaker 2: recent advances in ultimo ultrasound technology. So ultrasound technology which
Speaker 2: many of us are may be familiar with when it
Speaker 2: comes to you know, ultrasounds in trolleys on hospitals that
Speaker 2: are maybe used for looking at pregnancy. Those devices have
Speaker 2: become miniaturized and have become handheld. They can be moved
Speaker 2: over the body to acquire images to look for small
Speaker 2: early signs in the anatomy of people for TV. And
Speaker 2: where AI comes in is that traditionally to acquire those
Speaker 2: ultrasilved images is a lot of information that's generated and
Speaker 2: there is a very high level of training and specialized
Speaker 2: expertise that is required to analyze that large amount of information,
Speaker 2: and that is quite frankly expertise, but we do not
Speaker 2: have available in South Africa at the scale that we
Speaker 2: need it. So the AI comes in and being able
Speaker 2: to process that information as it is captured by these
Speaker 2: point of care ultrasolt machines to say that this person
Speaker 2: perhaps has a shadow on their legs, or there's evidence
Speaker 2: of tissue damage in their body, and whether or not
Speaker 2: that specific signal or that specific type of damage that
Speaker 2: is seen is itself indicative of TV And so this
Speaker 2: project will try and show that as a proof of
Speaker 2: concept and generate the evidence to say that this is
Speaker 2: a technology or maybe it would not be a technology
Speaker 2: that really requires large scale adoption because it helps us
Speaker 2: to tist more people and importantly, it may not be
Speaker 2: constrained by the huge shortage of people with irrelevant training
Speaker 2: mast the health workers in South Africa that are in
Speaker 2: such a shortage.
Speaker 1: So I was wondering how this could then, what would
Speaker 1: this sort of look like in local clinics, And I
Speaker 1: mean how soon this type of technology might be available
Speaker 1: to be rolled out in local clinics, But how would
Speaker 1: it help with with the challenges that we face here
Speaker 1: in South Africa.
Speaker 2: So the visionire is that in approximately five years or so,
Speaker 2: anyone who's coming into a clinic primary care clinic like
Speaker 2: in other words, their local community clinic, anyone who is
Speaker 2: coming in there for any reason, the seas this ultrasound
Speaker 2: scan which can potentially be done through the clothes, and
Speaker 2: it can be done by someone with only a few
Speaker 2: days of training. So perhaps while you're in the queue,
Speaker 2: while you're waiting for other clinical services, you could receive
Speaker 2: this scan of your body and that could tell you
Speaker 2: whether or not you need to be prioritized for other
Speaker 2: further testing. But that's the initial use case that we
Speaker 2: are targeting, where we are able to check anyone who's
Speaker 2: coming into the facility. And then further down the line,
Speaker 2: we know that there's a lot of TV in communities
Speaker 2: that never comes into facilities and you can maybe deploy
Speaker 2: these devices in communities where people check the torso for
Speaker 2: signs or TV and use that to refer people on it.
Speaker 1: It's really exciting stuff, isn't it. And I wonder, I mean,
Speaker 1: what if we were to look forward, what might be
Speaker 1: the long term hope for how guess AI. That's the
Speaker 1: the buzzword at the moment. How AI could transform TB
Speaker 1: care or even more broadly broader healthcare diagnostics, particularly in
Speaker 1: resource limited environments like ones that we see here in
Speaker 1: South Africa.
Speaker 2: So this is a tool that is by no means
Speaker 2: going to replace doctors or right yeah, it's a tool
Speaker 2: to simply support them where people with very specialized expertise
Speaker 2: of skits. It also is something that almost democratizers access
Speaker 2: to new diagnostic technologies because that patient to benefit from
Speaker 2: for example, the advances in ultrasound does not need to
Speaker 2: go to a university hospital perhaps where a professor who
Speaker 2: is a specialist in ultrasound is based, where easily orchese
Speaker 2: level of expertise is required to interpret that ultrasound. So
Speaker 2: it's allowed their access to new technologies by removing these
Speaker 2: bottlenix that we have in terms of human capacity and importantly, AI,
Speaker 2: even though itself is relatively complex, a lot of that
Speaker 2: complexity is behind the scenes. In other words, there I
Speaker 2: itself can be in a very user friendly tool which
Speaker 2: can simplify what would otherwise be fairly complex decision making
Speaker 2: and also importantly help frontline health workers at faster with
Speaker 2: also greater accuracy.
Speaker 1: RAP appreciate your time this afternoon, what a fascinating area
Speaker 1: of work and certainly looking forward to seeing how that
Speaker 1: is rolled out across South Africa. Speaking to Professor Granthron,
Speaker 1: professor in Clinical Microbacteriology and Epidemiology at Stelleinbosh University talking
Speaker 1: about this project that researchers at Stelleinbosh University are involved
Speaker 1: in leading really a global trial using artificial intelligence to
Speaker 1: improve tuberculosis diagnosis. As we heard there, the idea that
Speaker 1: that could be rolled out at primary care level. Number
Speaker 1: of institutions across the continent and in Europe aiming to
Speaker 1: develop a tool that essentially is a handheld ultrasound device
Speaker 1: and which could help detect TB more quickly and more accurately,
Speaker 1: particularly in rural or low resource settings. What a fascinating conversation.