Zhang Yahan, Chun Yi, Fu Hongyuan, Jiao Wen, Bao Jizhang, Jiang Tao, Cui Longtao, Hu Xiaojuan, Cui Ji, Qiu Xipeng, Tu Liping, Xu Jiatuo
Traditional Chinese Medicine College, Shanghai University of Traditional Chinese Medicine, No. 1200 Cailun Road, Pudong New Area, Shanghai, 201203, China, 86 021 51322143.
Clinical Research Unit, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai, China.
JMIR Med Inform. 2025 Feb 14;13:e64204. doi: 10.2196/64204.
Anemia is a global public health issue causing symptoms such as fatigue, weakness, and cognitive decline. Furthermore, anemia is associated with various diseases and increases the risk of postoperative complications and mortality. Frequent invasive blood tests for diagnosis also pose additional discomfort and risks to patients.
This study aims to assess the facial spectral characteristics of patients with anemia and to develop a predictive model for anemia risk using machine learning approaches.
Between August 2022 and September 2023, we collected facial image data from 78 anemic patients who met the inclusion criteria from the Hematology Department of Shanghai Hospital of Traditional Chinese Medicine. Between March 2023 and September 2023, we collected data from 78 healthy adult participants from Shanghai Jiading Community Health Center and Shanghai Gaohang Community Health Center. A comprehensive statistical analysis was performed to evaluate differences in spectral characteristics between the anemic patients and healthy controls. Then, we used 10 different machine learning algorithms to create a predictive model for anemia. The least absolute shrinkage and selection operator was used to analyze the predictors. We integrated multiple machine learning classification models to identify the optimal model and developed Shapley additive explanations (SHAP) for personalized risk assessment.
The study identified significant differences in facial spectral features between anemic patients and healthy controls. The support vector machine classifier outperformed other classification models, achieving an accuracy of 0.875 (95% CI 0.825-0.925) for distinguishing between the anemia and healthy control groups. In the SHAP interpretation of the model, forehead-570 nm, right cheek-520 nm, right zygomatic-570 nm, jaw-570 nm, and left cheek-610 nm were the features with the highest contributions.
Facial spectral data demonstrated clinical significance in anemia diagnosis, and the early warning model for anemia risk constructed based on spectral information demonstrated a high accuracy rate.
贫血是一个全球性的公共卫生问题，会导致疲劳、虚弱和认知能力下降等症状。此外，贫血与多种疾病相关，会增加术后并发症和死亡率的风险。频繁进行侵入性血液检测以进行诊断也会给患者带来额外的不适和风险。
本研究旨在评估贫血患者的面部光谱特征，并使用机器学习方法建立贫血风险预测模型。
在2022年8月至2023年9月期间，我们从上海中医药大学附属龙华医院血液科收集了78例符合纳入标准的贫血患者的面部图像数据。在2023年3月至2023年9月期间，我们从上海嘉定社区卫生服务中心和上海高行社区卫生服务中心收集了78名健康成年参与者的数据。进行了全面的统计分析，以评估贫血患者与健康对照者之间光谱特征的差异。然后，我们使用10种不同的机器学习算法创建了贫血预测模型。使用最小绝对收缩和选择算子分析预测因子。我们整合了多个机器学习分类模型以识别最佳模型，并开发了用于个性化风险评估的Shapley加法解释（SHAP）。
该研究发现贫血患者与健康对照者之间面部光谱特征存在显著差异。支持向量机分类器优于其他分类模型，区分贫血组和健康对照组的准确率达到0.875（95%CI 0.825-0.925）。在模型的SHAP解释中，额头-570nm、右脸颊-520nm、右颧骨-570nm、下巴-570nm和左脸颊-610nm是贡献最大的特征。
面部光谱数据在贫血诊断中具有临床意义，基于光谱信息构建的贫血风险预警模型显示出较高的准确率。
