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A smartphone-based detector for mobile health applications

A smartphone-based detector for mobile health applications

Unmet Need It is estimated that there are over 6 billion smartphone users worldwide, which translates to almost 83% of the world’s population. Due to their widespread use, smartphones present a golden opportunity to redefine…

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Pallialytics, a machine learning model to determine palliative-care eligible patients

Pallialytics, a machine learning model to determine palliative-care eligible patients

Unmet Need Palliative care is specialized medical care that is focused on providing seriously ill patients with relief from symptoms, pain and stress. Palliative care can be provided at any time during an illness with…

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Memory-efficient deep learning algorithms for computed tomography image reconstruction

Memory-efficient deep learning algorithms for computed tomography image reconstruction

Unmet Need Using high resolution images such as computed tomography (CT) has become a popular tool to guide medical interventions from radiotherapy to dental implantation. The market for CT services is expected to grow from…

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A software for more easily monitoring physiological data through improved visualization and machine learning analysis

A software for more easily monitoring physiological data through improved visualization and machine learning analysis

Value Proposition With inexpensive wearable sensors, physiological data monitoring has extended beyond clinical anesthesia and emergency medicine and into in-home healthcare. Waveforms such as electrocardiography (ECG) and photoplethysmography (PPG) contain valuable information about a patient’s…

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