Artificial Intelligence Enabled Diagnostics Using Photoplethysmography (PPG): Beyond SpO2 and Heart Rate.
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Joshi Divya Dinesh, Saraboji Kaaviyashri, Shaik Asiya Tasleema, Sakalabaktula Krishna Sai Kiran, Purohit Shreya, Godugu Swathi, Nirmal Jasmine, Kayarkar Riya, Correa Bernardo Henrique Mendes, Gangidi Namratha, Yadav Pratibha, Yadav Jyoti, Farook Farshi, Lee Jieun, Shariff Mohammed Naveed, Sundaram Divaakar Siva Baala, Yerrapragada Gayathri, Elangovan Poonguzhali, Natarajan Thangeswaran, Janarthanan Jayarajasekaran, Jerold Wilson Sancia Mary, Karuppiah Shiva Sankari, Abbasi Zoma, Rapolu Swetha, Bumrah Simardeep Kaur, Bilgaiyan Rashi, Premkumar Chandra Rupini, Kaur Anmolpreet, Pugalenthi Lakshmi Sree, Sood Divyanshi, Shivaram Suganti, Iyer Vivek N, C M Vidhya, Masihi Simin, Helgeson Scott A, Arunachalam Shivaram P
Аннотация
Photoplethysmography (PPG) is a non-invasive optical technique commonly used to measure heart rate and oxygen saturation, but its waveform contains additional physiological information that can be analyzed using artificial intelligence (AI). This narrative review summarizes the emerging applications of AI-based PPG in cardiovascular, respiratory, sleep, hemodynamic, pregnancy-related, and portal-hypertension assessment, with the aim of evaluating its potential beyond conventional monitoring and identifying barriers to clinical translation. The literature search was conducted using PubMed, Google Scholar, IEEE Xplore, ScienceDirect, and SpringerLink. Additional relevant studies were identified through screening the reference lists of included articles. Studies published between 2002 and 2026 were identified to capture the development of PPG from conventional monitoring to newer AI-based applications. Human studies were prioritized, while relevant computational, simulated, synthetic, ex vivo, and technical studies were also included. Studies unrelated to PPG, duplicates, and studies with limited relevance were excluded. A total of 96 references were included, covering AI approaches such as convolutional and deep neural networks, ensemble methods, transfer learning, U-Net, generative adversarial networks, and Transformer-based models. Overall, the reviewed evidence suggests that AI-based PPG may support blood pressure estimation, atrial fibrillation detection, sleep and respiratory monitoring, vascular aging assessment, pulmonary hypertension screening, preeclampsia assessment, volume-status and compensatory-reserve assessment, and exploratory assessment related to portal hypertension. However, clinical translation remains limited by motion artifacts, sensor and measurement-site variability, skin-pigmentation-related bias, physiological and environmental influences, heterogeneous methods, limited external validation, and inconsistent clinical and regulatory standards.