Mobile App for Assessing Hemifacial Spasm Treatment Response Using Machine Learning

It is challenging to assess hemifacial spasm (HFS) patients as they exhibit high-frequency and heterogeneous anomalous eyelid movements. This study aimed to develop an application for a smartphone to objectively determine eyelid movements frequency so that treatment responses in these patients can be assessed accurately. The smartphone application was developed mainly using Python, a prominent and broadly used programming language focused on machine learning and data science tasks. The application can precisely predict the movement of the patient’s eyes using an SVM regressor and classifier. The results are plotted for better visual inspection by using data visualization techniques. Thus, the application enables a continuous study of each patient using an integrated database in Google spreadsheets, which could better track the results of each treatment response. The application showed to be an efficient method to identify and represent eyelid movement occurrences in patients, objectively measuring the eyelid movement frequency and, thus, assessing the treatment response in patients with hemifacial spasms. This system could enable customized and fine adjustments to botulinum toxin doses based on each patient’s needs.