Artificial Intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify Hypertension (HTN) in its early stage, as it is a global health issue. Automated HTN detection uses socio-demographic, clinical data, and physiological signals. Additionally, signs of secondary HTN can also be identified using various imaging modalities. This systematic review examines related work on automated HTN detection. We identify datasets, techniques, and classifiers used to develop AI models from clinical data, physiological signals, and fused data (a combination of both). Image-based models for assessing secondary HTN are also reviewed. The majority of the studies have primarily utilized single-modality approaches, such as biological signals (e.g., electrocardiography, photoplethysmography), and medical imaging (e.g., magnetic resonance angiography, ultrasound). Surprisingly, only a small portion of the studies (22 out of 122) utilized a multi-modal fusion approach combining data from different sources. Even fewer investigated integrating clinical data, physiological signals, and medical imaging to understand the intricate relationships between these factors. Future research directions are discussed that could build better healthcare systems for early HTN detection through more integrated modeling of multi-modal data sources.

中文翻译：

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使用人工智能自动识别高血压并评估其副作用：系统评价（2013-2023）


人工智能 (AI) 技术越来越多地应用于医学计算机辅助诊断工具中。这些技术还可以帮助在早期阶段识别高血压（HTN），因为它是一个全球性的健康问题。自动 HTN 检测使用社会人口统计、临床数据和生理信号。此外，还可以使用各种成像方式来识别继发性高血压的迹象。本系统综述研究了自动化 HTN 检测的相关工作。我们根据临床数据、生理信号和融合数据（两者的组合）确定用于开发 AI 模型的数据集、技术和分类器。还回顾了用于评估继发性 HTN 的基于图像的模型。大多数研究主要利用单模态方法，例如生物信号（例如心电图、光电体积描记法）和医学成像（例如磁共振血管造影、超声）。令人惊讶的是，只有一小部分研究（122 项研究中的 22 项）采用了结合不同来源数据的多模态融合方法。更少有人研究如何整合临床数据、生理信号和医学成像来了解这些因素之间的复杂关系。讨论了未来的研究方向，可以通过对多模式数据源进行更集成的建模，为早期高血压检测建立更好的医疗保健系统。