By SAURABH JHA, MD
No one knows who gave Rahul Roy tuberculosis. Roy’s charmed life as a successful trader involved traveling in his Mercedes C class between his apartment on the plush Nepean Sea Road in South Mumbai and offices in Bombay Stock Exchange. He cared little for Mumbai’s weather. He seldom rolled down his car windows – his ambient atmosphere, optimized for his comfort, rarely changed.
Historically TB, or “consumption” as it was known, was a Bohemian malady; the chronic suffering produced a rhapsody which produced fine art. TB was fashionable in Victorian Britain, in part, because consumption, like aristocracy, was thought to be hereditary. Even after Robert Koch discovered that the cause of TB was a rod-shaped bacterium – Mycobacterium Tuberculosis (MTB), TB had a special status denied to its immoral peer, Syphilis, and unaesthetic cousin, leprosy.
TB became egalitarian in the early twentieth century but retained an aristocratic noblesse oblige. George Orwell may have contracted TB when he voluntarily lived with miners in crowded squalor to understand poverty. Unlike Orwell, Roy had no pretentions of solidarity with poor people. For Roy, there was nothing heroic about getting TB. He was embarrassed not because of TB’s infectivity; TB sanitariums are a thing of the past. TB signaled social class decline. He believed rickshawallahs, not traders, got TB.
“In India, many believe TB affects only poor people, which is a dangerous misconception,” said Rhea Lobo – film maker and TB survivor.
Tuberculosis is the new leprosy. The stigma has consequences, not least that it’s difficult diagnosing a disease that you don’t want diagnosed. TB, particularly extra-pulmonary TB, mimics many diseases.
“TB can cause anything except pregnancy,” quips Dr. Justy – a veteran chest physician. “If doctors don’t routinely think about TB they’ll routinely miss TB.”
In Lobo, the myocobacteria domiciled in the bones of her feet, giving her heel pain, which was variously ascribed to bone bruise, bone cancer, and staphylococcal infection. Only when a lost biopsy report resurfaced, and after receiving the wrong antibiotics, was TB diagnosed, by which time the settlers had moved to her neck, creating multiple pockets of pus. After multiple surgeries and a protracted course of antibiotics, she was free of TB.
“If I revealed I had TB no one would marry me, I was advised” laughed Lobo. “So, I made a documentary on TB and started ‘Bolo Didi’ (speak sister), a support group for women with TB. Also, I got married!”
Mycobacterium tuberculosis is an astute colonialist which lets the body retain control of its affairs. The mycobacteria arrive in droplets, legitimately, through the airways and settle in the breezy climate of the upper lobes and superior segment of the lower lobes of the lungs. If they sense weakness they attack, and if successful, cause primary TB. Occasionally they so overpower the body that an avalanche of small, discrete snowballs, called miliary TB, spread. More often, they live silently in calcified lymph nodes as latent TB. When apt, they reappear, causing secondary TB. The clues to their presence are calcified mediastinal nodes or a skin rash after injection of mycobacterial protein.
MTB divides every 20 hours. In the bacterial world that’s Monk-like libido. E. Coli, in comparison, divides every 20 minutes. Their sexual ennui makes them frustratingly difficult to culture. Their tempered fecundity also means they don’t overwhelm their host with their presence, permitting them to write fiction and live long enough to allow the myocobacteria to jump ship.
TB has been around for a while. The World Health Organization (WHO) wants TB eradicated but the myocobacteria have no immediate plans for retirement. Deaths from TB are declining at a tortoise pace of 2 % a year. TB affects 10 million and kills 1.6 million every year – it is still the number one infectious cause of death.
The oldest disease’s nonchalance to the medical juggernaut is not for the lack of a juggernaut effort. Mass screening for TB using chest radiographs started before World War 2, and still happens in Japan. The search became fatigued by the low detection of TB. The challenge wasn’t just in looking for needles in haystacks, but getting to the haystacks which, in developing countries, are dispersed like needles.
The battleground for TB eradication is India, which has the highest burden of TB – a testament not just to its large population. Because TB avoids epidemics, it never scares the crap out of people. Its distribution and spread match society’s wealth distribution and aspirations. And in that regard India is most propitious for its durability.
Few miles north of Nepean Sea Road is Dharavi – Asia’s largest slum, made famous by the Oscar-winning film, Slumdog Millionaire. From atop, Dharavi looks like thousand squashed coke cans beside thousand crumpled cardboard boxes. On the ground, it’s a hot bed of economic activity. No one wants to stay in Dharavi forever, its people want to become Bollywood stars, or gangsters, or just very rich. Dharavi is a reservoir of hope.
Dharavi is a reservoir also of active TB. In slums, which are full of houses packed like sardines in which live people packed like sardines, where cholera spreads like wildfire and wildfire spreads like cholera, myocobacteria travel much further. Familiarity breeds TB. One person with active TB can infect nine – and none are any the wiser of the infection because unlike cholera, which is wildfire, TB is a slow burn and its symptoms are indistinguishable from the maladies of living in a slum.
Slum dwellers with active TB often continue working – there’s no safety net in India to cushion the illness – and often travel afar to work. They could be selling chai and samosas outside the Bombay Stock Exchange. With the habit of expectoration – in India, spitting on the streets isn’t considered bad manners – sputum is aplenty, and mycobacteria-laden droplets from Dharavi can easily reach Roy’s lungs. TB, the great leveler, bridges India’s wealth divide. Mycobacteria unite Nepean Sea Road with Dharavi.
Rat in Matrix Algebra
The major challenges in fighting tuberculosis are finding infected people and ensuring they take the treatment for the prescribed duration, often several months. Both obstacles can wear each other– if patients don’t take their treatment what’s the point finding TB? If TB can’t be found what good is the treatment?
The two twists in the battle against TB, drug resistant TB and concurrent TB and HIV, favor the mycobacteria. But TB detection is making a resurgence with the reemergence of the old warrior – the chest radiograph, which now has a new ally – artificial intelligence (AI). Artificial Intelligence is chest radiograph’s Sancho Panza.
Ten miles north of Dharavi in slick offices in Goregaon, Mumbai’s leafy suburb, data scientists training algorithms to read chest radiographs are puzzled by AI’s leap in performance.
“The algorithm we developed,” says Preetham Sreenivas incredulously, “has an AUC of 1 on the new set of radiographs!”
AUC, or area under the receiver operator characteristic curve, measures diagnostic accuracy. The two types of diagnostic errors are false negatives – mistaking abnormal for normal, and false positives – mistaking normal for abnormal. In general, fewer false negatives (FNs) means more false positives (FPs); trade-off of errors. A higher AUC implies fewer “false” errors, AUC of 1 is perfect accuracy; no false positives, no false negatives.
Chest radiograph are two-dimensional images on which three dimensional structures, such as lungs, are collapsed and which, like Houdini, hide stuff in plain sight. Pathology literally hides behind normal structures. It’s nearly impossible for radiologists to have an AUC of 1. Not even God knows what’s going on in certain parts of the lung, such as the posterior segment of the left lower lobe.
Here, AI seemed better than God at interpreting chest radiographs. But Sreenivas, who leads the chest radiograph team in Qure.ai – a start-up in Mumbai which solves healthcare problems using artificial intelligence, refused to open the champagne.
“Algorithms can’t jump from an AUC of 0.84 to 1. It should be the other way round – their performance should drop when they see data (radiographs) from a new hospital,” explains Sreenivas.
Algorithms mature in three stages. First, training – data (x-rays), labelled with ground truth, are fed to a deep neural network (the brain), Labels, such as pleural effusion, pulmonary edema, pneumonia, or no abnormality, teach AI. After seeing enough cases AI is ready for the second step, validation – in which it is tested on different cases taken from the same source as the training set – like same hospital. If AI performs respectably, it is ready for the third stage – the test.
Training radiology residents is like training AI. First, residents see cases knowing the answer. Then they see cases on call from the institution they’re training at, without knowing the answer. Finally, released into the world, they see cases from different institutions and give an answer.
The test and training cases come from different sources. The algorithm invariably performs worse on test than training set because of “overfitting” – a phenomenon where the algorithm tries hard fitting to the local culture. It thinks the rest of the world is exactly like the place it trained, and can’t adapt to subtle differences in images because of different manufacturers, different acquisition parameters, or acquisition on different patient populations. To reduce overfitting, AI is regularly fed cases from new institutions.
When AI’s performance on radiographs from a new hospital mysteriously improved, Sreenivas smelt a rat.
“AI is matrix algebra. It’s not corrupt like humans – it doesn’t cheat. The problem must be the data,” Sreenivas pondered.
Birth of a company
“I wish I could say we founded this company to fight TB,” says Pooja Rao, co-founder of Qure.ai, apologetically. “But I’d be lying. The truth is that we saw in an international public health problem a business case for AI.”
Qure.ai was founded by Prashant Warier and Pooja Rao. After graduating from the Indian Institute of Technology (IIT), Warier, a natural born mathematician, did his PhD from Georgia Tech. He had no plans of returning to India, until he faced the immigration department’s bureaucratic incompetence. Someone had tried entering the US illegally on his wife’s stolen passport. The bureaucracy, unable to distinguish the robber from the robbed, denied her a work visa. Warier reluctantly left the US.
In India, Warier founded a company which used big data to find preferences of niche customers. His company was bought by Fractal, a data analytic giant – the purchase motivated largely by the desire to recruit Warier.
Warier wanted to develop an AI-enabled solution for healthcare. In India, data-driven decisions are common in retail but sparse in healthcare. In a move unusual in industry and uncommon even in academia, Fractal granted him freedom to tinker, with no strings attached. Qure.ai was incubated by Fractal.
Warier discovered Rao, a physician-scientist and bioinformatician, on LinkedIn and invited her to lead the research and development. Rao became a doctor to become a scientist because she believed that deep knowledge of medicine helps join the dots in the biomedical sciences. After her internship, she did a PhD at the Max Planck Institute in Germany. For her thesis, she applied deep learning to predict Alzheimer’s disease from RNA. Though frustrated by Alzheimer’s, which seemed uncannily difficult to predict, she fell in love with deep learning.
Rao and Warier were initially uncertain what their start-up should focus on. There were many applications of AI in healthcare, such as genomic analysis, analysis of electronic medical records, insurance claims data, Rao recalled two lessons from her PhD.
“Diseases such as Alzheimer’s are heterogeneous, so the ground truth, the simple question – is there Alzheimer’s – is messy. The most important thing I realized is that without the ground truth AI is useless.”
Rao echoed the sentiments of Lady Lovelace, the first computer programmer, from the nineteenth century. When Lovelace saw the analytical engine, the first “algorithm”, invented by Charles Babbage, she said: “The analytical Engine has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform. It can follow analysis; but it has no power of anticipating any analytical relations or truths.”
The second lesson Rao learnt was that the ground truth must be available immediately, not in the future – i.e. AI must be trained on diseases of the present, not outcomes, which are nebulous and take time to reveal. The immediacy of their answer, which must be now, right away, reduced their choices to two – radiology and pathology. Pathology had yet to be digitized en masse.
“The obvious choice for AI was radiology”, revealed Warier.
Why “Qure” with a Q, not “Cure” with a C, I asked. Was it a tribute to Arabic medicine?
“We’re not that erudite,” laughed Warier. “The internet domain for ‘cure’ had already been taken.”
Qure.ai was founded in 2016 during peak AI euphoria. In those days deep learning seemed magical to those who understood it, and to those who didn’t. Geoffrey Hinton, deep learning’s titan, famously predicted radiologists’ extinction – he advised that radiologists should stop being trained because AI would interpret the images just as well.
Bioethicist and architect of Obamacare, Ezekiel Emanuel, told radiologists that their profession faced an existential threat from AI. UK’s health secretary, Jeremy Hunt, drunk on the Silicon Valley cool aid, prophesized that algorithms will outperform general practitioners. Venture capitalist, Vinod Khosla predicted modestly that algorithms will replace 80 % of doctors.
Amidst the metastasizing hype, Warier and Rao remained circumspect. Both understood AI’s limitations. Rao was aware that radiologists hedged in their reports – which often made the ground truth a coin toss. They concluded that AI would be an incremental technology. AI would help radiologists become better radiologists.
“We were firing arrows in the dark. Radiology is vast. We didn’t know where to start,” recalls Rao.
Had Qure.ai been funded by venture capitalists, they’d have a deadline to have a product. But Fractal prescribed no fixed timeline. This gave the founders an opportunity to explore radiology. The exploration was instructive.
They spoke to several radiologists to better understand radiology, find the profession’s pain points, see what could be automated, and what might be better dealt by AI. The advice ranged from the flippant to the esoteric. One radiologist recommended using AI to quantify lung fibrosis in interstitial pulmonary fibrosis, another, knee cartilage for precision anti-rheumatoid therapy. Qure.ai has a stockpile of unused, highly niche, esoteric algorithms.
Every radiologist’s idea of augmentation was unique. Importantly, few of their ideas comprised mainstream practice. Augmentation seemed a way of expanding radiologist’s possibilities, rather than dealing with radiology’s exigencies – no radiologist, for instance, suggested that AI should look for TB on chest radiographs.
Augmentation doesn’t excite venture capitalists as much as replacement, transformation, or disruption. And augmentation didn’t excite Rao and Warier, either. When you have your skin in the commercial game, relevance is the only currency.
“Working for start-ups is different from being a scientist in an academic medical center. We do science, too. But before we take a project, we think about the return of investment. Just because an endeavor is academically challenging doesn’t mean that it’s commercially useful. If product don’t sell, start-ups have to close shop,” said Rao.
The small size of start-ups means they don’t have to run decisions through bulky corporate governance. It doesn’t take weeks convening meetings through Doodle polls. Like free climbers who aren’t encumbered by climbing equipment, they can reach their goal sooner. Because a small start-up is nimble it can fail fast, fail without faltering, fail a few time. But it can’t fail forever. Qure needed a product it could democratize. Then an epiphany.
In World War 2, after allied aircrafts sustained bullets in enemy fire, some returned to the airbase and others crashed. Engineers wanted the aircrafts reinforced at their weakest points to increase their chances of surviving enemy fire. A renowned statistician of the time, Abraham Wald, analyzed the distribution of the bullets and advised that reinforcements be placed where the plane hadn’t been shot. Wald realized that the planes which didn’t return were likely shot at the weakest points. On the planes which returned the bullets marked their strongest point.
Warier and Rao realized that they needed to think about scenarios where radiologists were absent, not where radiologists were abundant. They had asked the wrong people the wrong question. The imminent need wasn’t replacing or even augmenting radiologists, but in supplying near-radiologist expertise where not a radiologist was in sight. The epiphany changed their strategy.
“It’s funny – when I’m asked whether I see AI replacing radiologists, I point out that in most of the rest of the world there aren’t any radiologists to replace,” said Rao.
The choice of modality – chest radiographs – followed logically because chest radiographs are the most commonly ordered imaging test worldwide. They’re useful for a number of clinical problems and seem deceptively easy to interpret. Their abundance also meant that AI would have a large sample size to learn from.
“There just weren’t enough radiologists to read the daily chest radiograph volume at Christian Medical College, Vellore, where I worked. I can read chest x-rays because I’m a chest physician, but reading radiographs takes away time I could be spending with my patients, and I just couldn’t keep up with the volumes,” recalls Dr. Justy. Several radiographs remained unread for several weeks, many hid life-threatening conditions such as pneumothorax or lung cancer. The hospital was helpless – their budget was constrained and as important as radiologists were, other physicians and services were more important. Furthermore, even if they wanted they couldn’t recruit radiologists because the supply of radiologists in India is small.
Justy believes AI can offer two levels of service. For expert physicians like her, it can take away the normal radiographs, leaving her to read the abnormal ones, which reduces the workload because the majority of the radiographs are normal. For novice physicians, and non-physicians, AI could provide an interpretation – diagnosis, or differential diagnoses, or just point abnormalities on the radiograph.
The Qure.ai team imagined those scenarios, too. First they needed the ingredients, the data, i.e. the chest radiographs. But the start-up comprised only a few data scientists, none of whom had any hospital affiliations.
“I was literally on the road for two years asking hospitals for chest radiographs. I barely saw my family,” recalls Warier. “Getting the hospitals to share data was the most difficult part of building Qure.ai.”
Warier became a traveling salesman and met with leadership of over hundred healthcare facilities of varying sizes, resources, locations, and patient populations. He explained what Qure.ai wanted to achieve and why they needed radiographs. There were long waits outside the leadership office, last minute meeting cancellations, unanswered e-mails, lukewarm receptions, and enthusiasm followed by silence. But he made progress, and many places agreed to give him the chest radiographs. The data came with stipulations. Some wanted to share revenue. Some wanted research collaborations. Some had unrealistic demands such as share of the company. It was trial and error for Warier, as he had done nothing of this nature before.
Actually it was Warier’s IIT alumni network which opened doors. IITians (graduates of the Indian Institutes of Technology) practically run India’s business, commerce, and healthcare. Heads of private equity which funds corporate hospitals are often IITians, as are the CEOs of these hospitals.
“Without my IIT alumni network, I don’t think we could have pulled it off. Once an IITian introduces an IITian to an IITian, it’s an unwritten rule that they must help,” said Warier.
Warier’s efforts paid. Qure has now acquired over 2.5 million chest radiographs from over 100 sites for training, validation and testing the chest radiograph algorithm.
“As a data scientist my ethos is that there’s no such thing as ‘too much data.’ More the merrier,” smiled Warier.
“The mobile phone reached many parts of India before the landline could get there,” explains Warier. “Similarly, AI will reach parts of India before radiologists.”
Soon, a few others, including Srinivas, joined the team. Whilst the data scientists were educating AI, Rao and Warier were figuring their customer base. It was evident that radiologists would not be their customers. Radiologists didn’t need AI. Their customers were those who needed radiologists but were prepared to settle for AI.
“The secret to commercialization in healthcare is need, real need, not induced demand. But it’s tricky because the neediest are least likely to generate revenues,” said Warier in a pragmatic tone. Unless the product can be scaled at low marginal costs. An opportunity for Qure.ai arose in the public health space – the detection of tuberculosis on chest radiographs in the global fight against TB. It was an indication that radiologists in developing worlds didn’t mind conceding – they had plenty on their plates, already.
“It was serendipity,” recalls Rao. “A consultant suggested that we use our algorithm to detect TB. We then met people working in the TB space – advocates, activists, social workers, physicians, and epidemiologists. We were inspired particularly by Dr. Madhu Pai, Professor of Epidemiology at McGill University. His passion to eradicate TB made us believe that the fight against TB was personal.”
Qure.ai started with four people. Today 35 people work for it. They even have a person dedicated to regulatory affairs. Rao remembers the early days. “We were lucky to have been supported by Fractal. Had we been operating out of a garage, we might not have survived. Building algorithms isn’t easy.”
Hamlet’s modified opening soliloquy, “TB or not TB, that is the question”, simplifies the dilemma facing TB detection, which is a choice between fewer false positives and fewer false negatives. Ideally, one wants neither. The treatment for tuberculosis – quadruple therapy – exacts several month commitment. It’s not a walk in the park. Patients have to be monitored to confirm they are treatment compliant, and though directly observed therapy, medicine’s big brother, has become less intrusive, it still consumes resources. Taking TB treatment when one doesn’t have TB is unfortunate. But not taking TB treatment when one has TB can be tragic, and defeats the purpose of detection, and perpetuates the reservoir of TB.
Hamlet’s soliloquy can be broken into two parts – screening and confirmation. When screening for TB, “not TB is the question”. The screening test must be sensitive –capable of finding TB in those with TB, i.e. have a high negative predictive value (NPV), so that when it says “no TB” – we’re (nearly) certain the person doesn’t have TB.
Those positive on screening tests comprise two groups – true positives (TB) and false positives (not TB). We don’t want antibiotics frivolously given, so the soliloquy reverses; it is now “TB, that is the question.” The confirmatory test must be specific, highly capable of finding “not TB” in those without TB, i.e. have a high positive predictive value (PPV), so that when it says “TB” – we’re (nearly) certain that the person has TB. Confirmatory tests should not be used to screen, and vice versa.
Tuberculosis can be inferred on chest radiographs or myocobacteria TB can be seen on microscopy. Seeing is believing and seeing the bacteria by microscopy was once the highest level of proof of infection. In one method, slide containing sputum is stained with carbol fuchsin, rendering it red. MTB retains its glow even after the slide is washed with acid alcohol, a property responsible for its other name – acid fast bacilli.
Sputum microscopy, once heavily endorsed by the WHO for the detection of TB, is cheap but complicated. The sputum specimen must contain sputum, not saliva, which is easily mistaken for sputum. Patients have to be taught how to bring up the sputum from deep inside their chest. The best time to collect sputum is early morning, so the collection needs discipline, which means that the yield of sputum depends on the motivation of the patient. Inspiring patients to provide sputum is hard because even those who regularly cough phlegm can find its sight displeasing.
Which is to say nothing about the analysis part, which requires attention to detail. It’s easier seeing mycobacteria when they’re abundant. Sputum microscopy is best at detecting the most infectious of the most active of the active TB sufferers. Its accuracy depends on the spectrum of disease. If you see MTB, the patient has TB. If you don’t see MTB, the patient could still have TB. Sputum microscopy, alone, is too insensitive and cumbersome for mass screening – yet, in many parts of the world, that’s all they have.
The gold standard test for TB – the unfailing truth that the patient has TB, independent of the spectrum of disease – is culture of mycobacteria, which was deemed impractical because on the Löwenstein–Jensen medium, the agar made specially for MTB, it took six weeks to grow MTB, which is too long for treatment decisions. Culture has made a comeback, in order to detect drug resistant mycobacteria. On newer media, such as MGIT, the mycobacteria grow much faster.
The detection of TB was revolutionized by molecular diagnostics, notably the nucleic acid amplification test, also known as GeneXpert MTB/ RIF, shortened to Xpert, which simultaneously detects mycobacterial DNA and assesses whether the mycobacteria are resistant to rifampicin – one of the mainline anti-tuberculosis drugs.
Xpert boasts a specificity of 98 %, and with a sensitivity of 90 % it is nearly gold standard material, or at least good enough for confirmation of TB. It gives an answer in 2 hours – a dramatically reduced turnaround time compared to agar. Xpert can detect 131 colony-forming units of MTB per ml of specimen – which is a marked improvement from microscopy, where there should be 10, 000 colony-forming units of MTB per ml of specimen for reliable detection. However, Xpert can’t be used on everyone, not just because its sensitivity isn’t high enough – 90 % is a B plus, and for screening we need an A plus sensitivity. But also its price, which ranges from $10 – $20 per cartridge, and is too expensive for mass screening in developing countries.
This brings us back to the veteran warrior, the chest radiograph, which has a long history. Shortly after Wilhelm Röntgen’s discovery, x-rays were used to see the lungs, the lungs were a natural choice because there was natural contrast between the air, through which the rays passed, and the bones, which stopped the rays. Pathology in the lungs stopped the rays, too – so the ‘stopping of rays’ became a marker for lung disease, chief of which was tuberculosis.
X-rays were soon conscripted to the battlefield in the Great War to locate bullets in wounded soldiers, making them war heroes. But it was the writer, Thomas Mann, who elevated the radiograph to literary fame in Magic Mountain – a story about a TB sanitarium. The chest radiograph and tuberculosis became intertwined in people’s imagination. By World War 2, chest radiographs were used for national TB screening in the US.
The findings of TB on chest radiographs include consolidation (whiteness), big lymph nodes in the mediastinum, cavitation (destruction of lung), nodules, shrunken lung, and pleural effusion. These findings, though sensitive for TB – if the chest radiograph is normal, active TB is practically excluded, aren’t terribly specific, as they’re shared by other diseases, such as sarcoid.
Chest radiographs became popular with immigration authorities in Britain and Australia to screen for TB in immigrants from high TB burden countries at the port of entry. But the WHO remained unimpressed by chest radiographs, preferring sputum analysis instead. The inter- and intra-observer variation in the interpretation of the radiograph didn’t inspire confidence. Radiologists would often disagree with each other, and sometimes disagree with themselves. WHO had other concerns.
“One reason that the WHO is weary of chest radiographs is that they fear that if radiographs alone are used for decision making, TB will be overtreated. This is common practice in the private medical sector in India,” explains Professor Madhu Pai.
Nonetheless, Pai advocates that radiographs triage for TB, to select patients for Xpert, which is cost effective because radiographs, presently, are cheaper than molecular tests. Using Xpert only on patients with abnormal chest radiographs would increase its diagnostic yield – i.e. percentage of cases which test positive. Chest radiograph’s high sensitivity compliments Xpert’s high specificity. But this combination isn’t 100 % – nothing in diagnostic medicine is. The highly infective endobronchial TB can’t be seen on chest radiograph, because the mycobacteria never make it to the lungs, and remain stranded in the airway.
“Symptoms such as cough are even more non-specific than chest radiographs for TB. Cough means shit in New Delhi, because of the air pollution which gives everyone a cough,” explains Pai, basically emphasizing that neither the chest radiograph nor clinical acumen, can be removed from the diagnostic pathway for TB.
A test can’t be judged just by its AUC. How likely people – doctors and patients – are to adopt a test is also important and here the radiograph outshines sputum microscopy, because despite its limitations, well known to radiologists, radiographs still carry a certain aura, particularly in India. In the Bollywood movie, Anand, an oncologist played by Amitabh Bachchan diagnosed terminal cancer by glancing at the patient’s radiograph for couple of seconds. Not CT, not PET, but a humble old radiograph. Bollywood has set a very high bar for Artificial Intelligence.
Saurabh Jha (aka @RogueRad) is a contributing editor for THCB. This is part 1 of a two-part story.
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