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基于面部表情的抑郁症识别和诊断研究
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Abstract:
抑郁症是一种常见但会影响患者生活质量的精神疾病，它常表现为面部表情和行为上的变化。然而，目前抑郁症的诊断主要依靠患者自我报告和医师主观判断，这种方式存在较大局限性。面部表情可以传递丰富的非语言信息，在抑郁症的识别和评估中具有重要作用，人工智能在图像特征提取和分类等方面有独特优势，可为研究面部表情与抑郁症之间的关系提供有力支撑。该文基于人工智能技术的抑郁症患者面部特征研究，并对未来研究方向进行展望，以期为日后抑郁症临床智能化诊断和跟踪提供参考。
Depression is a prevalent mental illness that significantly impacts patients’ quality of life, often manifesting through alterations in facial expressions and behavior. However, the current diagnostic approach for depression predominantly relies on patient self-reporting and subjective assessments by physicians, presenting notable limitations. Facial expressions are capable of conveying nuanced non-verbal cues and thus hold considerable promise in the detection and evaluation of depression. Leveraging artificial intelligence, with its distinct capabilities in image feature extraction and classification, offers potent tools for investigating the correlation between facial expressions and depression. This study employs artificial intelligence technology to examine the facial characteristics of individuals with depression, and anticipates future research avenues, aiming to furnish insights for the advancement of clinically intelligent diagnosis and monitoring of depression.
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