Assessing Patient Eligibility for Inspire Therapy through Machine Learning and Deep Learning Models
This work addresses the problem of accurately selecting patients for Inspire therapy, which is incremental as it applies existing ML/DL methods to a new medical dataset.
The paper tackled the challenge of predicting patient eligibility for Inspire therapy for obstructive sleep apnea by using machine learning and deep learning models on medical data and endoscopic videos from 127 patients, demonstrating the potential of these techniques to assist in determining candidacy.
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.