NeuTigers Announces Study Exploring the Use of AI and Wearables to Detect and Prevent Sickle Cell Anemia Exacerbation Crisis

NeuTigers, an Artificial Intelligence company spun out of Princeton University, has announced a study to investigate the use of AI and everyday wearables such as the Withings ScanWatch to detect early symptoms of the Sickle Cell Anemia Vaso-Occlusive Crisis (VOC), and prevent the exacerbation of the disease and the costly hospitalization of patients.

Conducted at Assistance Publique- Hôpitaux de Paris (AP-HP) by Prof. Frédéric Galacteros, the study will investigate NeuTigers’ StartDeep patients’ remote detection and prevention health platform to identify the digital signatures of impending chronic and/or acute crisis to enable early interventions, improve patients’ health outcome and overall quality of life.

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The StarDeep platform miniaturizes AI using an advanced form of machine learning called Deep Neural Networks (DNNs). It can detect and identify the digital physiological signatures of various disease states deciphered from the collection of off-the shelf wearable medical devices data. Following successful studies and commercial solutions developed to detect respiratory conditions such as COVID-19, analysis of biomarkers for Diabetes, and assessment of mental health disorders for schizoaffective, major depressive, and bipolar, all with 90%+ accuracy, the technology will be investigated as part of a new integrated healthcare intervention paradigm in Sickle Cell Anemia.

Unlike traditional forms of linear machine learning, DNNs mimic how a brain works based on a principle called grow and prune developed at the Princeton Department of Engineering and Computing with co-Founder Prof. Niraj K. Jha. It is similar to how a toddler interprets the world and humans create energy efficient ‘’predictive models’’, that are very accurate, despite using during the learning phase very small data size.

Patients with Sickle Cell Anemia lack enough healthy red blood cells to carry oxygen around the body. The initial study, a technological equivalent of a phase 1 trial, will explore and model the relationship of physiological signals that are predictive with how an impending chronic and/or acute episodes of Sickle Cell Anemia impacts patients’ disease conditions and quality of life. It will look at signals such as Galvanic Skin Response (GSR), Skin temperature, Heart Inter-beat Interval (IBI), Blood Oxygen levels, Quality of Sleep, Physical Activities and others.

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COVID-19 affects people’s biometrics and physiological markers in both obvious and nearly imperceptible ways. Using advanced algorithms of machine learning, CovidDeep is so sensitive it can detect changes in these physiological patterns even before they are felt by the patient and all with real-time analysis.

The second phase of the project is to expand with prospective studies across different sites in EU, Africa and US to explore the models’ accuracy and clinical effectiveness.

“The best way to deal with a crisis is to avoid it happening in the first place. We are now entering a new era where medical early warning systems have become a reality,” said Adel Laoui, CEO and founder of NeuTigers. “We are excited at the possibility of deploying a technology that can save lives of patients dealing with Sickle Cell Anemia. The potential of the StarDeep platform to dramatically improve patient outcomes while slashing some of the highest costs of healthcare makes it one of the most exciting developments in preventative personal medicine.”

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