![]() “We found that this deep-learning system performs really well with all of them with a single operating point that was pre-selected based on a development dataset, something that other medical imaging AI systems have found challenging.” “What’s especially promising in this study is that we looked at a range of different datasets that reflected the breadth of TB presentation, different equipment and different clinical workflows,” Kazemzadeh said. Trends were similar across different patient subgroups, including a test set from gold miners in South Africa, a group with a high prevalence of TB, compared to the general public. “AI performed really well with a variety of patients.” “We wanted to see if this system predicts TB on par with radiologists, and that’s what the study is showing,” Pilgrim said. Over 165,000 images from more than 22,000 patients were used for model development and testing.Īnalysis with 14 international radiologists showed that the deep-learning method was comparable to radiologists for the determination of active TB on chest X-rays. They then tested it on data from five countries, covering multiple high-TB-burden countries, various clinical settings and a wide range of races and ethnicities. ![]() ![]() The researchers developed the system using data from nine countries. The system uses deep learning, a type of AI that can be applied to teach the computer to recognize and predict medical conditions. Kazemzadeh and colleagues developed and assessed an AI system that can quickly and automatically evaluate chest X-rays for TB. “We can teach computers to recognize tuberculosis from X-rays so that in these low-resource settings a patient’s X-ray can be interpreted within seconds.”Įarlier Screening May Lead to Better Outcomes Target presentation expert job description software#“Bridging the expert shortage is where AI comes in,” said first author Sahar Kazemzadeh, BS, software engineer at Google Health. “We have effective drugs for treating TB, but large-scale screening programs to detect TB are not always feasible in low-income countries due to cost and availability of expert radiologists,” said study co-author Rory Pilgrim, BEng, a product manager at Google Health AI in Mountain View, CA.Ĭost-effective TB screening using chest X-rays and AI has the potential to improve access to healthcare, Pilgrim said, particularly in difficult-to-reach populations. Almost 90% of the active TB infections occur in about 30 countries, many with scarce resources needed to address this public health problem. ![]() The COVID-19 pandemic has exacerbated the problem, with recent reports indicating that 21% fewer people received care for TB in 2020 than in 2019. TB kills more than a million people worldwide every year. Researchers said the AI system may be able to aid screening in areas with limited radiologist resources. An AI system detects tuberculosis (TB) in chest X-rays at a level comparable to radiologists, according to a study published in Radiology. ![]()
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