Human vs. Machine Learning Based Detection of Facial Weakness Using Video Analysis.

Background: Current EMS stroke screening tools facilitate early detection and triage, but the tools' accuracy and reliability are limited and highly variable. An automated stroke screening tool could improve stroke outcomes by facilitating more accurate prehospital diagnosis and delivery. We hypothesize that a machine learning algorithm using video analysis can detect common signs of stroke.

Video-Based Facial Weakness Analysis.

Objective: Facial weakness is a common sign of neurological diseases such as Bell's palsy and stroke. However, recognizing facial weakness still remains as a challenge, because it requires experience and neurological training.

Methods: We propose a framework for facial weakness detection, which models the temporal dynamics of both shape and appearance-based features of each target frame through a bi-directional long short-term memory network (Bi-LSTM).

Facial Weakness Analysis and Quantification of Static Images.

Facial weakness is a symptom commonly associated to lack of facial muscle control due to neurological injury. Several diseases are associated with facial weakness such as stroke and Bell's palsy. The use of digital imaging through mobile phones, tablets, personal computers and other devices could provide timely opportunity for detection, which if accurate enough can improve treatment by enabling faster patient triage and recovery progress monitoring.

Modeling integrin and plasma-polymerized pyrrole interactions: chemical diversity relevance for cell regeneration.

Protein-engineered biomaterials represent a powerful approach to increase biofunctional activity like tissue repair and celular proliferation. Among these materials, integrins and the development of their specific interactions with plasma-polymerized pyrrole (PPPy) are promising biomaterial for tissue regeneration. In this paper, we studied the molecular recognition in the active site of three integrins (α5β1, αvβ3 and αIIbβ3) with PPPy using the structure proposed by Kumar et al.