Background: Sleep is essential for overall health and plays a critical role in the diagnosis of psychiatric disorders. Although polysomnography remains the gold standard for measuring sleep, its reliance on laboratory settings limits its…
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What You Should Know:
– Joy Parenting Club, the platform aiming to revolutionize the parenting experience, has announced its acquisition of Heba Care, a technology platform that supports families navigating complex health and developmental needs.
– This acquisition…
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Sleep disturbances are an underrecognized factor associated with reduced workplace productivity (“presenteeism”). Previous studies have largely relied on self-reported sleep data, limiting their scalability and accuracy. We investigated associations between smartphone-based sleep metrics and…
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News
Microsoft Research releases GigaTIME, an open-source, multi-modal AI system that researchers used to convert pathology slides into virtual images, then analyze them against patient data from Providence. The resulting virtual population allows researchers to…
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What You Should Know:
– Tether has launched QVAC Health, a new personal wellness platform designed to aggregate data from disparate fitness trackers and apps into a single, encrypted, and offline-capable environment.
– By utilizing local,…
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What You Should Know:
– Sleep.ai, the company behind the world’s most validated sleep intelligence platform, today announced the launch of Sleep Sense, a groundbreaking technology that turns any smartphone into a seamless, privacy-aware sleep sensor.
– Sleep…
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Digital health interventions (DHIs) offer scalable, accessible approaches to promote healthy sleep in adolescents and to prevent or treat poor sleep and insomnia. Given the high prevalence of sleep problems and their bidirectional links…
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Sleep disturbances and mental health issues represent escalating global health challenges, exacerbated by the COVID-19 pandemic and increased digital media consumption. Digital mindfulness-based interventions (DMBIs) have emerged as a scalable and accessible alternative, but…
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We developed a deep learning model trained on over two million ultrasound images from 78,531 pregnancies from Australia, India, and the UK to estimate gestational age (GA) directly from any fetal ultrasound image, regardless…
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This study presents an ensemble learning approach for automated screening and severity classification of chronic kidney disease (CKD) using polysomnographic (PSG) phenotypes. We analyzed PSG data from 358 subjects (179 CKD, 179 early-CKD) in…
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