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    "headline": "Nexus Intelligence scales AI chest X-ray screening across Africa to fill radiologist gap",
    "dek": "Pretoria startup Nexus Intelligence has deployed AI chest X-ray screening at 47 sites across six African countries.",
    "prose": "By 2026, Nexus Intelligence, a Pretoria-based healthtech startup founded in 2022 by Gerhard Ferreira and Andries Vorster, had deployed its Nexus AI CXR platform to about 47 sites across six countries and analysed more than 50,000 chest X-rays. [^1]\n\nScientists at Scripps Research developed a new AI model called ECG-CLIP to improve the detection and prediction of various heart diseases. [^2]\n\nA prospective study across three sites in Lusaka, Zambia, published in NEJM AI, found that Nexus's TB model achieved 87% sensitivity and 70% specificity at its high-sensitivity threshold, while none of the 10 independent radiologists it was compared with reached the World Health Organization's 90% sensitivity target. [^3]\n\nAn independent evaluation published in The Lancet Digital Health found that Nexus AI CXR had an AUC of 0.897 and the highest sensitivity among 12 computer-aided detection products at a fixed 0.5 threshold, reaching 89.1% sensitivity and 67.1% specificity at 90% sensitivity. [^4]\n\nGerhard Ferreira told BusinessDay that an adequately resourced healthcare system may require 100 to 120 radiologists per million people, while many low-income countries have fewer than two, and said that at least 14 African countries have had no practising radiologist, with Nigeria having an estimated 300 radiologists for its 240 million people and South Africa about 700 for its 62 million. [^5]\n\nIn tests on detecting acute myocardial infarction, cardiac amyloidosis, and hypertrophic cardiomyopathy, ECG-CLIP consistently performed better than standard deep learning and linear models. [^6]\n\nSenior author Giorgio Quer, an assistant professor of digital medicine at Scripps Research, stated that the new algorithm only needs to see on the order of a dozen confirmed ECGs of a specific disease to detect that disease in the future. [^7]\n\nThe ECG-CLIP model was trained using more than 1.7 million electrocardiograms collected from more than 540,000 people and paired with clinicians' notes. [^8]",
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