毎週水曜の夜は、英語に親しむ「英活」の時間。ビジネスパーソンから英語教師、英語学習者の知的好奇心を刺激する番組です。 「今週のニュース」では、「英語と経済」を同時に学びます。『Nikkei Asia』(日本経済新聞社)の英字記事で、「時事英語」や「ビジネス英語」など、生きた英語をお伝えします。 『日本経済新聞』水曜夕刊2面「Step Up ENGLISH」と企画連動しています。
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Trial to see whether AI app accurately detects TB cough
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At the Kenya Medical Research Institute, research is underway to create a mobile phone application that uses AI to diagnose tuberculosis and other respiratory diseases. In a specially contained quiet room, Dr. Videlis Nduba and his team record coughs from people with respiratory diseases like tuberculosis as well as people without disease. The aim is to create software that can differentiate between the two and make a mobile phone application that can accurately recognize a cough connected to TB and other serious diseases. Natural or forced coughs are collected using three microphones, including a cheap version, a high-definition one and a microphone on a smartphone. The results are sent to the University of Washington which puts them through an existing computer software system called ResNet 18. Nduba believes that if the software can be proven in trials to perform accurately, it can shorten the time before a patient can get a diagnosis and treatment, and that will help curb the spread of TB. "The biggest achievement is reduced time to diagnosis. So, from when someone develops TB symptoms, to when a doctor determines they have TB and they need treatment sometimes the average can run from 3 to 2 months to one year. And when they are in the community they are infectious and they are transmitting TB. The moment they get a cough, if you can just expose them to this software and determine this is TB would reduce TB transmission in the community and a lot of TB is due to transmission," he says. But the software is not yet accurate enough to meet the standard required by the World Health Organization. The WHO says the application must be at least 90% accurate in recognizing a TB infection and it must be at least 80% accurate at detecting if no infection exists. Nduba’s trials so far have shown 80% accuracy at detecting TB and 70% accuracy for detecting there is no TB. This article was provided by The Associated Press.
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2167 episoade
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Manage episode 408965068 series 2530089
Content provided by レアジョブ英会話. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by レアジョブ英会話 or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ro.player.fm/legal.
At the Kenya Medical Research Institute, research is underway to create a mobile phone application that uses AI to diagnose tuberculosis and other respiratory diseases. In a specially contained quiet room, Dr. Videlis Nduba and his team record coughs from people with respiratory diseases like tuberculosis as well as people without disease. The aim is to create software that can differentiate between the two and make a mobile phone application that can accurately recognize a cough connected to TB and other serious diseases. Natural or forced coughs are collected using three microphones, including a cheap version, a high-definition one and a microphone on a smartphone. The results are sent to the University of Washington which puts them through an existing computer software system called ResNet 18. Nduba believes that if the software can be proven in trials to perform accurately, it can shorten the time before a patient can get a diagnosis and treatment, and that will help curb the spread of TB. "The biggest achievement is reduced time to diagnosis. So, from when someone develops TB symptoms, to when a doctor determines they have TB and they need treatment sometimes the average can run from 3 to 2 months to one year. And when they are in the community they are infectious and they are transmitting TB. The moment they get a cough, if you can just expose them to this software and determine this is TB would reduce TB transmission in the community and a lot of TB is due to transmission," he says. But the software is not yet accurate enough to meet the standard required by the World Health Organization. The WHO says the application must be at least 90% accurate in recognizing a TB infection and it must be at least 80% accurate at detecting if no infection exists. Nduba’s trials so far have shown 80% accuracy at detecting TB and 70% accuracy for detecting there is no TB. This article was provided by The Associated Press.
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