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Multimodal AI for Brain Disorders

The diagnosis and care processes for brain conditions such as depression or dementia are complex, involve multiple tests, data types and specialists; and they are, in some cases, very dependent on the subjective ability of the patient to report on their condition. Some of these challenges have become at the same time an opportunity to improve the healthcare processes by leveraging AI.

Multimodal Machine Learning solutions can assist specialists and provide them with new tools and insights to make the healthcare pathway more efficient and objective. In this post, we introduce one of the projects we are developing at PickleTech focused on such improvements: HALO. Starting from a cohort of elite athletes, we leverage multimodal AI and the development of biomarkers, aiming to provide athletes and preparation teams with objective measures of their mental health.

Individualization matters: the gradient adjusted pace model of Kilian Jornet

Gradient Adjusted Pace (GAP) models are integrated as a common feature in many running sports devices and companion apps. Recently, Kilian Jornet announced his movement from Suunto to Coros, and one of the anecdotal quotes to motivate the change was Coros interest on Kilian’s adjusted pace model. How useful is it to look at a GAP model not derived from your own individual data?

Kilian-Coros’ example highlights the importance of individualized performance modeling for athletes. In this post, we derive the individual GAP model for Kilian. Thanks to his collaboration, we have computed the model using only his own recorded data from last year’s activities. We compare his individualized GAP version with the public version by Strava, focusing on what makes Kilian the ultimate mountain athlete.

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