研究背景：近年來台灣乳癌人口增加，加上罹病女性年輕化、疾病慢性化，乳癌病人的心理健康逐漸成為照護重點之一。乳癌病人的負面情緒、憂鬱為在化療期間常出現的心理困擾，而華人隱忍的特質可能造成情緒表露的改變、甚至出現情緒壓抑而影響癌症調適。然而，目前針對乳癌患者情感表達與癌症調適關係的臨床證據仍然有限。
研究目的：本研究預計觀察化療期間乳癌婦女的憂鬱程度、情感表露、情感壓抑與癌症調適，並檢視其與在臉部情緒、口語表達的憂鬱程度與傳統問卷結果的相關性，進一步找出癌症調適的預測因子。
研究設計：本研究採橫斷面設計，在台灣南部一所醫學中心招募正在接受化療的乳癌患者。使用標準化流程錄製與壓力相關的敘述視頻及面部表情，並結合情感壓抑量表和癌症調適量表進行數據收集。使用臉部和語意憂鬱預測工具進行面部表情及語意內容的憂鬱預測，藉由相關性分析與逐步迴歸分析來識別癌症適應的預測因素。
結果： 共有44名參與者，憂鬱預測AI工具與傳統心理憂鬱量表之間具有顯著相關性，但AI檢測出的憂鬱率更高，顯示其對細微情感線索的敏感性，若結合臉部與語意憂鬱則辨識較為嚴謹，較趨近量表評估憂鬱程度。研究發現參與者普遍存在情感壓抑，且情感壓抑與較差的適應結果相關。迴歸模型顯示，HDRS評定的憂鬱、心理因子和中性情感表達可解釋41.2%的癌症調適變異量，而AI預測的憂鬱結合心理因子則解釋了37%的變異量。
結論：AI工具在大規模、客觀的憂鬱評估方面展現出潛力。儘管AI工具在捕捉細微情感線索方面表現優異，其對接受化療的年長患者可能高估憂鬱的現象需進一步優化。結合AI與臨床評估工具可提升癌症適應的評估精確性，並為針對調適不良核心信念及情感調節的介入措施提供指導，最終改善乳癌患者的心理健康照護。

Research Background: The prevalence of breast cancer in Taiwan is rising, with patients being diagnosed at younger ages and facing a more chronic disease course. Emotional suppression, a cultural trait in Chinese individuals, may exacerbate psychological distress, including depression, during chemotherapy, negatively impacting cancer adaptation. Limited evidence exists on the role of emotional expression in cancer adjustment within the Taiwanese breast cancer population.
Research Purposes: Firstly, to examine the correlation between depression prediction with facial-semantic features and the psychometric testing scores in WBCs. Secondly, to identify the predictors of WBC cancer adjustment strategies, especially the predicting power of depression prediction with facial-semantic, emotional suppression, and psychometric testing scores.
Methods: A cross-sectional study recruited 44 breast cancer patients undergoing chemotherapy from a southern Taiwan medical center. Standardized video recordings captured stress-related narratives and facial expressions. Questionnaires supplemented data collection, including Hamilton Depression Rating Scale, Beck’s Depression Inventory, Emotional Suppression Scale and the Mini-Mental Adjustment to Cancer Scale. Depression detection tools analyzed facial expressions and semantic content. A combined multimodal depression prediction was also used. Spearman’s correlation and multiple stepwise regression identified predictors of cancer adjustment.
Results: AI tools correlated significantly with traditional psychometric scales but overestimated depression severity, reflecting higher susceptibility to subtle emotional cues. Regression models showed HDRS-rated depression, maladaptive Psychological Factors, and Neutral emotion expressions explained 41.2% of the variance in cancer adjustment, while AI-predicted depression combined with maladaptive Psychological Factors explained 37%. Unexpectedly, emotional suppression was not a significant predictor of cancer adjustment.
Conclusion: AI tools demonstrate objective depression assessment, complementing the nuanced insights of professional-rated scales. While AI excels in detecting subtle emotional cues, its overestimation of depression in older patients undergoing chemotherapy highlights the need for optimization. Combining AI and clinician-rated tools could enhance the assessment of cancer adjustment and guide interventions targeting maladaptive Psychological Factors and emotional regulation, ultimately improving psychological care for breast cancer patients.

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