App identifies mpox lesions from smartphone photos: An app that uses artificial intelligence to analyze smartphone photos of skin lesions identified mpox with 90% accuracy, researchers reported in Nature Medicine. Users can upload an image, answer questions and receive a recommendation for testing or vaccination within five (5) minutes.
Researchers to develop 3D printing of medical devices: Researchers at Penn State University and the University of Texas at Austin have been awarded a $2 million grant from the NSF’s Leading Engineering for America’s Prosperity, Health and Infrastructure program to support research into 3D printing of medical devices. The project will design and produce pediatric ventilation masks and other devices that can adjust to patients’ needs as they grow and heal, which will be more cost-effective than current production processes.
3D-printed skin take the sting out of mosquito studies: Most studies of mosquito repellents and mosquito-borne diseases involve live animals or people, but a new study at Rice University uses neither. Instead, an automated feeding platform invites mosquitoes to feed on 3D-printed synthetic skin with real human blood flowing through small vessels, while cameras record the process for machine learning algorithms to analyze.
Tech opens new treatment options for severe epilepsy: Advances in electrophysiology, including stereoelectroencephalography, have enabled more people with severe epilepsy to undergo minimally invasive brain surgery that prevents seizures. Combining SEEG information with high-resolution MRI scans reveals diseased areas and important nerve tracts that surgeons navigate using laser beams delivered through thin probes. Researchers are also borrowing from the electronics industry and developing thin, flexible grids with thousands of tiny sensors akin to a brain telescope.
FDA offers guidance on long-term neonate product studies: The FDA released draft guidance outlining recommendations for sponsors when conducting long-term neurodevelopmental studies to support the safety of medical products such as drugs, biologics or devices intended for use in neonates. According to the guidance, “although a single-arm study may be useful for collecting some types of safety information, the absence of a concurrent control arm (placebo or active comparator) will generally make clear interpretation of the results difficult, if not impossible.”
Large language model identifies people with Alzheimer’s: GPT-3, the large language model behind ChatGPT, achieved 80% accuracy in identifying people with Alzheimer’s Disease (AD) based on software-transcribed 10-second audio clips of them describing pictures in a standard dementia test. People diagnosed with AD repeated themselves, went off-topic, did not finish their thoughts and referred to objects vaguely, researchers reported in PLOS Digital Health.
3M launches new adhesive tape with 28-day wear time: 3M has released Medical Tape 4578, its new medical adhesive that has liner-free stability and can be worn on the skin for up to 28 days. The tool can also be stored for up to a year.
Artificial Intelligence (AI) predicts autism risk in bases based on Electronic Health Record (HER) data: Researchers used EHR data from 45,080 babies up to a year old to train an autism prediction model that achieved high sensitivity, specificity and positive predictive value, according to a study published in JAMA Network Open. The algorithm “could be integrated with caregiver surveys to improve the accuracy of early autism screening,” researchers wrote.
Smartphone app might detect onset of stroke: A smartphone machine learning algorithm called FAST AI detects facial asymmetry, arm weakness and speech changes associated with the onset of a stroke. Studies of FAST AI conducted at four stroke centers in Bulgaria found the app was accurate, but some experts say false negatives could be dangerous, and the technology should be used to prompt users to seek medical attention quickly, not to rule out a stroke.
Machine-learning method offers view of placental compartments: A machine-learning method for automated MRI scan processing has been developed by NIH-funded researchers to provide a detailed view of compartments in the placenta, including intervillous space and placental tissue and vessels. The technique is being studied for the potential to aid in early monitoring of placental health to identification and prevention of complications in pregnancy.