Paris, April 28, 2020 /PRNewswire/ — Chronolife, an artificial intelligence company specializing in digital health, today announced that it has secured Class IIa medical certification from the European Union for its groundbreaking medical-grade smart T-shirt, KeeSense™. The multi-sensor wearable device continuously monitors electrocardiography (ECG), thoracic respiration, abdominal respiration, skin temperature, thoracic impedance, and physical activity, enabling healthcare practitioners to remotely track vital clinical data about their patients. The platform’s receipt of a medical Conformité Européenne (CE) mark allows KeeSense™ to be marketed in the European Economic Area as a wearable medical device for healthcare purposes including remote monitoring of patients with chronic diseases and support for diagnostics.
The KeeSense™ T-shirt is designed for comfortable round-the-clock use, and is fully reusable and washable, mimicking similar daily use garments. The unique T-shirt transmits data to its paired smartphone app via Bluetooth, which then sends the data to a secure and certified server for live or time-delayed analysis by the wearer’s healthcare team. The platform’s multi-parametric medical data enables researchers and healthcare teams to develop meaningful insights into patients’ long-term health, while also allowing prompt responses to medical emergencies.
Besides providing healthcare practitioners with a powerful new way to follow their patients, Chronolife’s Remote Patient Monitoring (RPM) platform will also enable pharmaceutical and medtech researchers to leverage a rich new source of round-the-clock, real-world physiological data to run more robust and efficient therapeutic efficiency programs and clinical trials. KeeSense™ monitoring is intended to be unobtrusive and to blend seamlessly with daily life, which could greatly improve both quality of life of the patients and their adherence to RPM devices. Healthcare stakeholders would be able to remotely and continuously monitor patients under real-life conditions, which could contribute to increased efficiency and improved outcomes of the RPM services.
“KeeSense™ delivers an unrivalled stream of real-world medical-grade data. This medical CE mark enables doctors to use KeeSense™ to provide more convenient and effective medical care, and gives researchers access to a rich new source of vital signs intelligence,” said Laurent Vandebrouck, CEO of Chronolife.
Chronolife also recently announced the launch of a major, pan-European clinical trial of a predictive solution using KeeSense™ to provide early warning of cardiac emergencies in patients with chronic heart failure. Researchers at 25 hospitals across Europe will collect KeeSense™ data to develop and validate the ability of Chronolife’s predictive HOTS algorithm to accurately alert healthcare providers of clinically significant events that signal deterioration in their patients’ conditions.
With KeeSense’s medical certification, Chronolife is now well-positioned for partnerships with a wide range of telemedicine services and telehealth providers to innovate and deliver end-to-end, continuous RPM programs. This will not only improve outcomes for patients with conditions that require constant monitoring, such as cardiovascular dysfunctions and respiratory illnesses, but also reduce hospital (re)admissions and help alleviate the ongoing shortage of healthcare resources and staff.
Co-founded with iBionext, Chronolife is an artificial intelligence company specializing in digital health. Its patented technology is a unique neuromorphic algorithm called HOTS (Hierarchy Of event-based Time Surfaces), which analyzes several data flows continuously, to characterize clinical events. Chronolife has developed a smart wearable that integrates various sensors to monitor physiological data continuously. This data is analyzed by the smartphone application on a patient’s phone that uses HOTS technology to conduct data fusion. It is capable of detecting changes in a patient’s health and triggering alerts to healthcare professionals to predict acute pathological episodes.