
Machine learning diagnoses COVID-19 from chest CTs
Scientists have developed an algorithm for rapid, computerized diagnosis of COVID-19, overcoming the limitations of reverse transcription polymerase chain reaction.
Scientists have developed an algorithm for rapid, computerized diagnosis of COVID-19, overcoming the limitations of reverse transcription polymerase chain reaction.
The neural network detects anomalies in medical images more successfully than general-purpose solutions.
Researchers have designed a miniaturized 3D-printed device to inactivate Pseudomonas aeruginosa, a common bacterium that causes the infection.
Research using machine learning on images of everyday items is improving the accuracy and speed of detecting respiratory diseases, reducing the need for specialist medical expertise.
The Fraunhofer Institutes project M³Infekt aims to develop a multi-modal, modular and mobile system of sensors for monitoring infectious diseases.
Two deep learning algorithms that identify patterns of COVID-19 in lung images and breath sounds, may help in the fight against other respiratory diseases and the growing challenge of antibiotic resistance.
Researchers have developed a new AI platform that detects COVID-19 by analyzing X-ray images of the lungs.
Researchers have found that people who are asymptomatic for Covid-19 may differ from healthy individuals in the way that they cough.
Find out more about how scientists and physician are using AI to make contributions in the fight against the coronavirus.
Using specialized nanoparticles, engineers have developed a way to monitor pneumonia or other lung diseases by analyzing the breath exhaled by the patient.
Thanks to a variety of smart technologies, high-tech clothing today is capable of analyzing body functions or actively optimizing the microclimate.
Students at Cranfield University have designed computer models that can identify COVID-19 in X-rays.
Researchers are collaborating with local partners to establish a network of portable, handheld ultrasound scanners that can soon accelerate COVID-19 diagnosis.
Establishing whether a patient is suffering from COVID-19 within a few minutes is possible using ultrasound machines that are enhanced with artificial intelligence.
Radiologists are investigating people's medical conditions and pregnancies remotely thanks to an ESA-backed robotic technology.
Researchers have developed a predictive artificial intelligence model that can tell the difference between healthy patients, those who are ill with pneumonia and those who have COVID-19, from chest X-rays.
A study showed that an AI algorithm provides results comparable with lung function tests, which measure how forcefully a person can exhale.
3D printing fuels efforts to rapidly increase ventilator capacity while providing each patient on vent support with individually tailored gas pressures and pressure monitoring.
Scientists plan to use high-tech biometric sensors for 24-hour monitoring of COVID-19 patients in home isolation.
Researchers have developed a new approach to early diagnosis of lung cancer: a urine test that can detect the presence of proteins linked to the disease.
A coronavirus app coupled with machine intelligence will soon enable an individual to get an at-home risk assessment based on how they feel and where they've been in about a minute.
A researcher has developed a multiple sensor fusion device for non-contact measurement of vital signs and its clinical applications.
In a matter of seconds, a new algorithm read chest X-rays for 14 pathologies, performing as well as radiologists in most cases, a Stanford-led study says.
A "Hive Mind" of doctors, moderated by AI algorithms, makes more accurate diagnoses than the doctors or machine learning alone, according to a new study from Stanford and Unanimous AI.
Using machine learning, researchers have developed a new computational tool to screen patients with common but blinding retinal diseases, potentially speeding diagnoses and treatment.