Assessing Patient Eligibility for Inspire Therapy through Machine
  Learning and Deep Learning Models
Mohsena Chowdhury Tejas Vyas Rahul Alapati Andrés M Bur Guanghui Wang
Inspire therapy is an FDA-approved internal neurostimulation treatment for
obstructive sleep apnea. However, not all patients respond to this therapy,
posing a challenge even for experienced otolaryngologists to determine
candidacy. This paper makes the first attempt to leverage both machine learning
and deep learning techniques in discerning patient responsiveness to Inspire
therapy using medical data and videos captured through Drug-Induced Sleep
Endoscopy (DISE), an essential procedure for Inspire therapy. To achieve this,
we gathered and annotated three datasets from 127 patients. Two of these
datasets comprise endoscopic videos focused on the Base of the Tongue and
Velopharynx. The third dataset composes the patient's clinical information. By
utilizing these datasets, we benchmarked and compared the performance of six
deep learning models and five classical machine learning algorithms. The
results demonstrate the potential of employing machine learning and deep
learning techniques to determine a patient's eligibility for Inspire therapy,
paving the way for future advancements in this field.