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Shukun Quickly Shifts Strategy to Fight COVID-19 Pandemic NVIDIA Blog

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The COVID-19 pandemic has disrupted the world like few events before it. But for Shukun Technology, a response required "a minor change in our strategy," according to its chief technology officer, Chao Zheng. That's because Shukun, a startup founded by some of China's brightest AI and medical minds, was busy refining its AI-powered platform to diagnose heart disease and strokes when the global pandemic struck. The company quickly shifted resources to develop a system that analyzes chest CT scans to help speed up diagnoses of COVID-19 patients. That system, called Lung Doc – pneumonia edition, has already been rolled out to 30 hospitals in China over the past few months, where it will grow more accurate as it learns from more data.


New machine learning method allows hospitals to share patient data -- privately

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PHILADELPHIA - To answer medical questions that can be applied to a wide patient population, machine learning models rely on large, diverse datasets from a variety of institutions. However, health systems and hospitals are often resistant to sharing patient data, due to legal, privacy, and cultural challenges. An emerging technique called federated learning is a solution to this dilemma, according to a study published Tuesday in the journal Scientific Reports, led by senior author Spyridon Bakas, PhD, an instructor of Radiology and Pathology & Laboratory Medicine in the Perelman School of Medicine at the University of Pennsylvania. Federated learning -- an approach first implemented by Google for keyboards' autocorrect functionality -- trains an algorithm across multiple decentralized devices or servers holding local data samples, without exchanging them. While the approach could potentially be used to answer many different medical questions, Penn Medicine researchers have shown that federated learning is successful specifically in the context of brain imaging, by being able to analyze magnetic resonance imaging (MRI) scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions.


New machine learning method allows hospitals to share patient data--privately

#artificialintelligence

To answer medical questions that can be applied to a wide patient population, machine learning models rely on large, diverse datasets from a variety of institutions. However, health systems and hospitals are often resistant to sharing patient data, due to legal, privacy, and cultural challenges. An emerging technique called federated learning is a solution to this dilemma, according to a study published Tuesday in the journal Scientific Reports, led by senior author Spyridon Bakas, Ph.D., an instructor of Radiology and Pathology & Laboratory Medicine in the Perelman School of Medicine at the University of Pennsylvania. Federated learning--an approach first implemented by Google for keyboards' autocorrect functionality--trains an algorithm across multiple decentralized devices or servers holding local data samples, without exchanging them. While the approach could potentially be used to answer many different medical questions, Penn Medicine researchers have shown that federated learning is successful specifically in the context of brain imaging, by being able to analyze magnetic resonance imaging (MRI) scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions.


Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence

arXiv.org Artificial Intelligence

Title: Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence One sentence summary: An efficient and effective privacy-preserving AI framework is proposed for CT-based COVID-19 diagnosis, based on 9,573 CT scans of 3,336 patients, from 23 hospitals in China and the UK. Abstract Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health. MAIN TEXT Introduction As the gold standard for identifying COVID-19 carriers, reverse transcription-polymerase chain reaction (RT-PCR) is the primary diagnostic modality to detect viral nucleotide in specimens from cases with suspected infection. It has been reported that coronavirus carriers present certain radiological features in chest CTs, including ground-glass opacity, interlobular septal thickening, and consolidation, which can be exploited to identify COVID-19 cases.


World first for AI and machine learning to treat COVID-19 patients worldwide

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Addenbrooke's Hospital in Cambridge and 20 other hospitals from across the world and healthcare technology leader NVIDIA have used artificial intelligence (AI) to predict COVID patients' oxygen needs on a global scale. The research was sparked by the pandemic and set out to build an AI tool to predict how much extra oxygen a COVID-19 patient might need in the first days of hospital care, using data from across four continents. The technique, known as federated learning, used an algorithm to analyze chest X-rays and electronic health data from hospital patients with COVID symptoms. To maintain strict patient confidentiality, the patient data was fully anonymized and an algorithm was sent to each hospital so no data was shared or left its location. Once the algorithm had "learned" from the data, the analysis was brought together to build an AI tool which could predict the oxygen needs of hospital COVID patients anywhere in the world.