Lyu Xuening, Goperma Rimsa, Wang Dandan, Wan Chunling, Zhao Liang
Graduate School of Advanced Integrated Studies in Human Survivability, Kyoto University, Kyoto, Japan.
Bio-X Institutes, Shanghai Jiao Tong University, No. 1954, Huashan Road, 200030, Shanghai, China.
BMC Psychiatry. 2025 Aug 4;25(1):757. doi: 10.1186/s12888-025-07196-2.
Niacin Skin-Flushing Response (NSR) has emerged as a promising objective biomarker for the precise diagnosis of mental disorders. However, its diagnostic potential has been constrained by the limitations of traditional statistical approaches. The advent of Artificial Intelligence (AI) offers a transformative opportunity to overcome these challenges. This study presents a novel contribution to the field by establishing an open-access dataset and developing advanced AI-driven tools to enhance the diagnostic accuracy of psychiatric disorders through NSR analysis.
This study introduces the world's first open dataset specifically developed for AI studies of Niacin Skin-Flushing Response (NSR), a physiological biomarker associated with mental illnesses including depression, bipolar disorder, and schizophrenia. Leveraging this dataset, we developed an advanced Machine Learning (ML) approach designed for the broad diagnosis of mental disorders. Distinct from prior studies which are often limited to First Episode Schizophrenia and depend on specific devices, our approach champions device independence. The core of our methodology involves a novel algorithm featuring an Efficient-Unet based Deep Learning model for the precise segmentation of NSR areas. This segmentation is significantly enhanced by runtime data augmentation and trained on a robust train/validation/test dataset split. Subsequently, a Support Vector Machine (SVM) method is employed for psychiatric disorder classification utilizing feature vectors extracted from the segmentation of NSR areas with a 3-scale quantization. The SVM training incorporates 5-fold cross-validation, Synthetic Minority Over-sampling Technique (SMOTE) for managing class imbalance, and hyperparameter tuning to optimize balanced accuracy.
The established dataset comprises 600 high-quality NSR images from 120 individuals, encompassing a diverse cohort of healthy controls and patients with various mental illnesses. The developed AI tools offer an objective, swift, and highly accurate approach that is demonstrably independent of the diagnosed condition or the specific device used for image acquisition. Comparative results demonstrate that the ML-based diagnostic approach achieves a sensitivity ranging from 60.0 to 65.0% and a specificity from 75.0 to 88.3% across various types of illnesses, further underscoring its broad applicability and device independence.
This research conclusively demonstrates the significant potential of advanced AI tools in achieving precise diagnosis of psychiatric disorders, potentially surpassing human capabilities in both speed and accuracy. With the provision of the proposed open dataset and the introduction of novel methodologies, this study marks substantial progress in developing an objective and accurate NSR-based screening process for a wide spectrum of psychiatric disorders. Its enhanced applicability and independence from specific devices hold profound potential to substantially advance mental health diagnostics and contribute to improved patient outcomes globally.
烟酸皮肤潮红反应（NSR）已成为一种有前景的客观生物标志物，用于精神障碍的精确诊断。然而，其诊断潜力受到传统统计方法局限性的制约。人工智能（AI）的出现为克服这些挑战提供了变革性机遇。本研究通过建立一个开放获取数据集并开发先进的人工智能驱动工具，以通过NSR分析提高精神障碍的诊断准确性，为该领域做出了新贡献。
本研究推出了世界上首个专门为烟酸皮肤潮红反应（NSR）的人工智能研究开发的开放数据集，NSR是一种与包括抑郁症、双相情感障碍和精神分裂症在内的精神疾病相关的生理生物标志物。利用这个数据集，我们开发了一种先进的机器学习（ML）方法，用于广泛的精神障碍诊断。与以往通常限于首发精神分裂症且依赖特定设备的研究不同，我们的方法支持设备独立性。我们方法的核心涉及一种新颖算法，其具有基于高效Unet的深度学习模型，用于精确分割NSR区域。通过运行时数据增强显著增强了这种分割，并在强大的训练/验证/测试数据集划分上进行训练。随后，采用支持向量机（SVM）方法，利用从NSR区域分割中提取的特征向量进行3尺度量化，对精神障碍进行分类。SVM训练纳入了5折交叉验证、用于处理类别不平衡的合成少数过采样技术（SMOTE）以及超参数调整，以优化平衡准确率。
所建立的数据集包含来自120个人的600张高质量NSR图像，涵盖了不同的健康对照和患有各种精神疾病的患者群体。所开发的人工智能工具提供了一种客观、快速且高度准确的方法，该方法明显独立于诊断病情或用于图像采集的特定设备。比较结果表明，基于机器学习的诊断方法在各种类型疾病中的敏感性范围为60.0%至65.0%，特异性为75.0%至88.3%，进一步强调了其广泛的适用性和设备独立性。
本研究确凿地证明了先进人工智能工具在实现精神障碍精确诊断方面的巨大潜力，在速度和准确性方面可能超越人类能力。通过提供所提议的开放数据集和引入新方法，本研究在为广泛的精神障碍开发基于NSR的客观准确筛查过程方面取得了重大进展。其增强的适用性和不依赖特定设备具有极大潜力，可大幅推进心理健康诊断，并为全球改善患者预后做出贡献。
