通过机器学习与深度学习模型评估患者接受Inspire疗法的资格
Assessing Patient Eligibility for Inspire Therapy through Machine Learning and Deep Learning Models
AI总结：
针对阻塞性睡眠呼吸暂停的Inspire疗法患者适配性判定难题，首次结合机器学习与深度学习技术，基于127名患者的DISE内镜视频及临床数据，对11种模型算法开展基准测试，验证了AI辅助判定患者疗法资格的潜力。
摘要：
Inspire疗法是经FDA批准的用于阻塞性睡眠呼吸暂停的植入式神经刺激治疗方案。然而，并非所有患者都对该疗法有应答，即便是经验丰富的耳鼻喉科医生也难以判定患者是否适合接受治疗。本文首次尝试结合机器学习与深度学习技术，利用医疗数据以及药物诱导睡眠内镜检查（DISE，Inspire疗法的必备流程）拍摄的视频，识别患者对Inspire疗法的应答情况。为此，我们收集并标注了来自127名患者的三个数据集：其中两个包含聚焦于舌根和腭咽的内镜视频，第三个数据集涵盖患者的临床信息。基于这些数据集，我们对6种深度学习模型和5种经典机器学习算法的性能进行了基准测试与比较。结果表明，运用机器学习和深度学习技术判定患者接受Inspire疗法的资格具备可行性，为该领域的未来发展奠定了基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
