Telehealth is a valuable tool for primary health care (PHC), where depression is a common condition. PHC is the first point of contact for most people with depression, but about 25% of diagnoses made by PHC physicians are inaccurate. Many other barriers also hinder depression detection and treatment in PHC. Artificial intelligence (AI) may help reduce depression misdiagnosis in PHC and improve overall diagnosis and treatment outcomes. Telehealth consultations often have video issues, such as poor connectivity or dropped calls. Audio-only telehealth is often more practical for lower-income patients who may lack stable internet connections. Thus, our study focused on using audio data to predict depression risk. The objectives were to: 1) Collect audio data from 24 people (12 with depression and 12 without mental health or major health condition diagnoses); 2) Build a machine learning model to predict depression risk. TPOT, an autoML tool, was used to select the best machine learning algorithm, which was the K-nearest neighbors classifier. The selected model had high performance in classifying depression risk (Precision: 0.98, Recall: 0.93, F1-Score: 0.96). These findings may lead to a range of tools to help screen for and treat depression. By developing tools to detect depression risk, patients can be routed to AI-driven chatbots for initial screenings. Partnerships with a range of stakeholders are crucial to implementing these solutions. Moreover, ethical considerations, especially around data privacy and potential biases in AI models, need to be at the forefront of any AI-driven intervention in mental health care.

翻译：远程医疗是初级卫生保健（PHC）中一项有价值的工具，而抑郁症是该领域的常见病症。PHC是多数抑郁症患者首次就诊的医疗环节，但PHC医生的诊断准确率存在约25%的误差。此外，诸多其他障碍也制约着PHC中抑郁症的识别与治疗。人工智能（AI）可能有助于减少PHC中的抑郁症误诊，并改善整体诊疗效果。远程医疗问诊常存在视频故障（如连接不良或通话中断），而对于缺乏稳定互联网连接的低收入患者而言，纯音频远程医疗更为实用可行。因此，本研究聚焦于利用音频数据预测抑郁症风险。研究目标包括：1）采集24名受试者（12名抑郁症患者与12名无精神健康或重大健康诊断者）的音频数据；2）构建预测抑郁症风险的机器学习模型。采用自动机器学习工具TPOT筛选最优算法，最终选定K近邻分类器。该模型在抑郁症风险分类中表现优异（精确率：0.98，召回率：0.93，F1分数：0.96）。这些发现有望推动开发一系列辅助抑郁症筛查与治疗的工具。通过构建抑郁症风险检测工具，患者可被引导至AI驱动的聊天机器人进行初步筛查。此类方案的落地需各利益相关方的协同合作，且在精神卫生领域的AI干预中，数据隐私与模型偏见等伦理考量必须置于首位。