Specificity is a measure of how often a test correctly gives a negative result in someone who does not have the condition being tested for). Two of the CAD systems (QXR and CAD4TB) also met the aspirational 90% sensitivity and 70% specificity target product profile set by the WHO for a TB triage test (sensitivity is a measure of how often a test correctly detects a condition in someone who has the condition. She says none of the five CAD systems were retrained in Bangladesh prior to the study since they wanted to see how off-the-shelf solutions perform out in the real world.Īll five of the CAD systems were found to "significantly outperform" the panel of human radiologists in the study.
"We sort of did a blind test - we used a dataset that has never been seen by any AI company," explains Qin.
People who were X-rayed were also asked about their symptoms and given the gold standard GeneXpert molecular test - although this information was, of course, withheld from the CAD systems and panel of radiologists. The images were from three treatment centres in Bangladesh and date from 2014 to 2016.
KASPAROV CHESS GAME DOWNLOAD SERIES
All the CAD systems and the panel of radiologists evaluated the same series of X-ray images from just under 24 000 people.
The Lancet Digital Health study compared the performance of five different computer-aided detection (CAD) systems with each other and with a panel of three human radiologists. The World Health Organization in March recommended the wider use of such computer-aided detection systems - but they will bolster the case for making these technologies more widely available more quickly and may help governments in deciding which of a number of competing systems to choose. Indeed, drug-resistant TB cannot be diagnosed using the X‑ray approach," he says. Thus, sputum-based testing and confirmation of the diagnosis are still required. It can only infer the likelihood of TB and not definitively prove it. "For example, if the computer-assisted system detects shadowing in the upper parts of the lung or detects cavities (holes in the lungs), it is more likely to report probable TB. Putting it a bit differently, Professor Keertan Dheda, general physician, pulmonologist, and a critical care specialist who heads up the Division of Pulmonology at the University of Cape Town, says such computer-assisted systems produce a textured probabilistic heat map with a score outlining the likelihood of TB being present. In this case, that task is detecting signs of TB on chest X-rays. Most of the AI that is currently used is called 'narrow' AI, meaning it is a computer programme trained to perform one particular task. Zhi Zhen Qin, technical officer at the Stop TB Partnership, explains AI is a broad term that simply refers to machines demonstrating human-like intelligence. This development may have major implications for TB control efforts, since it may set the stage for more people being diagnosed early when they have not yet developed symptoms. Though the exact details of the study are quite technical, it is fair to say that as with chess, interpreting chest X-rays for TB is no longer exclusively the domain of clever, well-trained humans. They were responding to a landmark study (led by them) published in the journal Lancet Digital Health. The Stop TB Partnership (a unique UN-linked partnership) recently proclaimed that "the results are in: artificial intelligence outperforms humans at reading chest X-rays for signs of tuberculosis". But according to some, computers are now better than humans at this as well.