{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T10:14:55Z","timestamp":1789467295442,"version":"build-2803163510"},"reference-count":103,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/life16091530","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T08:16:48Z","timestamp":1789460208000},"page":"1530","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence Enabled Diagnostics Using Photoplethysmography (PPG): Beyond SpO2 and Heart Rate"],"prefix":"10.3390","volume":"16","author":[{"given":"Divya Dinesh","family":"Joshi","sequence":"first","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7884-2505","authenticated-orcid":false,"given":"Kaaviyashri","family":"Saraboji","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7658-6173","authenticated-orcid":false,"given":"Asiya Tasleema","family":"Shaik","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krishna Sai Kiran","family":"Sakalabaktula","sequence":"additional","affiliation":[{"name":"Department of Medicine, Tulane University School of Medicine, New Orleans, LA 70112, 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Meas."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s13534-019-00097-w","article-title":"Current Progress of Photoplethysmography and SPO2 for Health Monitoring","volume":"9","author":"Tamura","year":"2019","journal-title":"Biomed. Eng. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"14","DOI":"10.2174\/157340312801215782","article-title":"On the Analysis of Fingertip Photoplethysmogram Signals","volume":"8","author":"Elgendi","year":"2012","journal-title":"Curr. Cardiol. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Rovas, G., Bikia, V., and Stergiopulos, N. (2023). Quantification of the Phenomena Affecting Reflective Arterial Photoplethysmography. Bioengineering, 10.","DOI":"10.3390\/bioengineering10040460"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lee, H., Ko, H., Thap, T., and Lee, J. (2016). Multiple Switching Light Sources Based Motion Artifacts Reduction in Reflectance Photoplethysmography. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE.","DOI":"10.1109\/EMBC.2016.7591457"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1038\/s41591-021-01614-0","article-title":"AI in Health and Medicine","volume":"28","author":"Rajpurkar","year":"2022","journal-title":"Nat. Med."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Manetas-Stavrakakis, N., Sotiropoulou, I.M., Paraskevas, T., Maneta Stavrakaki, S., Bampatsias, D., Xanthopoulos, A., Papageorgiou, N., and Briasoulis, A. (2023). Accuracy of Artificial Intelligence-Based Technologies for the Diagnosis of Atrial Fibrillation: A Systematic Review and Meta-Analysis. J. Clin. Med., 12.","DOI":"10.3390\/jcm12206576"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Liang, Y., Chen, Z., Ward, R., and Elgendi, M. (2018). Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification. Biosensors, 8.","DOI":"10.3390\/bios8040101"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Slapni\u010dar, G., Mlakar, N., and Lu\u0161trek, M. (2019). Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network. Sensors, 19.","DOI":"10.3390\/s19153420"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"106677","DOI":"10.1016\/j.cmpb.2022.106677","article-title":"Application of Photoplethysmography Signals for Healthcare Systems: An in-Depth Review","volume":"216","author":"Loh","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_11","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2017). Deep Learning, The MIT Press."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2235","DOI":"10.1088\/1361-6579\/aa9772","article-title":"Multiscale Network Representation of Physiological Time Series for Early Prediction of Sepsis","volume":"38","author":"Shashikumar","year":"2017","journal-title":"Physiol. Meas."},{"key":"ref_13","unstructured":"Vaswani, A., Brain, G., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention Is All You Need. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"11805","DOI":"10.1109\/ACCESS.2017.2707460","article-title":"HeartID: A Multiresolution Convolutional Neural Network for ECG-Based Biometric Human Identification in Smart Health Applications","volume":"5","author":"Zhang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1001\/jama.2016.19043","article-title":"Global Burden of Hypertension and Systolic Blood Pressure of at Least 110 to 115 Mm Hg, 1990\u20132015","volume":"317","author":"Forouzanfar","year":"2017","journal-title":"JAMA"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1109\/TBME.2015.2441951","article-title":"Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice","volume":"62","author":"Mukkamala","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1109\/TBME.2016.2580904","article-title":"Cuffless Blood Pressure Estimation Algorithms for Continuous Health-Care Monitoring","volume":"64","author":"Kachuee","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e20101","DOI":"10.1002\/advs.202520101","article-title":"Intrinsic PPG\u2013ECG Coupling for Accurate and Low-Power Blood Pressure Monitoring","volume":"13","author":"Chen","year":"2026","journal-title":"Adv. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1111\/jch.13294","article-title":"Validation Protocols for Blood Pressure Measuring Devices in the 21st Century","volume":"20","author":"Stergiou","year":"2018","journal-title":"J. Clin. Hypertens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1159\/000522660","article-title":"Challenges Presented by Cuffless Measurement of Blood Pressure If Adopted for Diagnosis and Treatment of Hypertension","volume":"10","author":"Avolio","year":"2022","journal-title":"Pulse"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mahmud, S., Ibtehaz, N., Khandakar, A., Tahir, A.M., Rahman, T., Islam, K.R., Hossain, M.S., Rahman, M.S., Musharavati, F., and Ayari, M.A. (2022). A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals. Sensors, 22.","DOI":"10.3390\/s22030919"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"138077","DOI":"10.1109\/ACCESS.2020.3012384","article-title":"Pulse Transit Time-Pulse Wave Analysis Fusion Based on Wearable Wrist Ballistocardiogram for Cuff-Less Blood Pressure Trend Tracking","volume":"8","author":"Yousefian","year":"2020","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Arjomand, A., Boudesh, A., Bayatmakou, F., Krylov, G., Kent, K., and Mohammadi, A. (2024). Transforhythm: A Transformer Architecture Conductive to Blood Pressure Estimation Through Solo Ppg Signal Capturing. arXiv.","DOI":"10.2139\/ssrn.5371308"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Haque, R., Wang, C., and Pala, N. (2025). An Ensemble-Based AI Approach for Continuous Blood Pressure Estimation in Health Monitoring Applications. Sensors, 25.","DOI":"10.3390\/s25154574"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1016\/S0895-7061(02)02979-5","article-title":"Evaluation of the Performance of a Wrist Blood Pressure Measuring Device with a Position Sensor Compared to Ambulatory 24-Hour Blood Pressure Measurements","volume":"15","author":"UEN","year":"2002","journal-title":"Am. J. Hypertens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Samimi, H., and Dajani, H.R. (2023). A PPG-Based Calibration-Free Cuffless Blood Pressure Estimation Method Using Cardiovascular Dynamics. Sensors, 23.","DOI":"10.3390\/s23084145"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhou, B., Li, Y., Tang, M., and Miao, F. (2020). Continuous Blood Pressure Estimation From Electrocardiogram and Photoplethysmogram During Arrhythmias. Front. Physiol., 11.","DOI":"10.3389\/fphys.2020.575407"},{"key":"ref_28","unstructured":"World Health Organization (2025). Hypertension, World Health Organization."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1161\/JAHA.118.008585","article-title":"Contact-Free Screening of Atrial Fibrillation by a Smartphone Using Facial Pulsatile Photoplethysmographic Signals","volume":"7","author":"Yan","year":"2018","journal-title":"J. Am. Heart Assoc."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Temko, A. (2015). Estimation of Heart Rate from Photoplethysmography during Physical Exercise Using Wiener Filtering and the Phase Vocoder. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE.","DOI":"10.1109\/EMBC.2015.7318655"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"21434","DOI":"10.1364\/OE.16.021434","article-title":"Remote Plethysmographic Imaging Using Ambient Light","volume":"16","author":"Verkruysse","year":"2008","journal-title":"Opt. Express"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"10762","DOI":"10.1364\/OE.18.010762","article-title":"Non-Contact, Automated Cardiac Pulse Measurements Using Video Imaging and Blind Source Separation","volume":"18","author":"Poh","year":"2010","journal-title":"Opt. Express"},{"key":"ref_33","first-page":"e45111","article-title":"Evaluation of Atrial Fibrillation Detection in Short-Term Photoplethysmography (PPG) Signals Using Artificial Intelligence","volume":"15","author":"Talukdar","year":"2023","journal-title":"Cureus"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.jacep.2024.01.008","article-title":"Continuous Atrial Fibrillation Monitoring From Photoplethysmography","volume":"10","author":"Antiperovitch","year":"2024","journal-title":"JACC Clin. Electrophysiol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1016\/j.jacc.2019.08.019","article-title":"Mobile Photoplethysmographic Technology to Detect Atrial Fibrillation","volume":"74","author":"Guo","year":"2019","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1001\/jamacardio.2018.0136","article-title":"Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch","volume":"3","author":"Tison","year":"2018","journal-title":"JAMA Cardiol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1056\/NEJMoa1901183","article-title":"Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation","volume":"381","author":"Perez","year":"2019","journal-title":"N. Engl. J. Med."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s13665-024-00361-0","article-title":"Artificial Intelligence for Diagnosis of Obstructive Sleep Apnea","volume":"13","author":"May","year":"2024","journal-title":"Curr. Pulmonol. Rep."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Jiang, X., Ren, Y., Wu, H., Li, Y., and Liu, F. (2023). Convolutional Neural Network Based on Photoplethysmography Signals for Sleep Apnea Syndrome Detection. Front. Neurosci., 17.","DOI":"10.3389\/fnins.2023.1222715"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1109\/JBHI.2022.3225363","article-title":"SleepPPG-Net: A Deep Learning Algorithm for Robust Sleep Staging From Continuous Photoplethysmography","volume":"27","author":"Kotzen","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.5664\/jcsm.7228","article-title":"Use of Actigraphy for the Evaluation of Sleep Disorders and Circadian Rhythm Sleep-Wake Disorders: An American Academy of Sleep Medicine Systematic Review, Meta-Analysis, and GRADE Assessment","volume":"14","author":"Smith","year":"2018","journal-title":"J. Clin. Sleep Med."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1053\/smrv.2001.0182","article-title":"The Role of Actigraphy in Sleep Medicine","volume":"6","author":"Sadeh","year":"2002","journal-title":"Sleep Med. Rev."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1560","DOI":"10.1093\/sleep\/27.8.1560","article-title":"A Novel Adaptive Wrist Actigraphy Algorithm for Sleep-Wake Assessment in Sleep Apnea Patients","volume":"27","author":"Hedner","year":"2004","journal-title":"Sleep"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1088\/1361-6579\/aa670e","article-title":"Extraction of Respiratory Signals from the Electrocardiogram and Photoplethysmogram: Technical and Physiological Determinants","volume":"38","author":"Charlton","year":"2017","journal-title":"Physiol. Meas."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3109\/03091902.2011.638965","article-title":"Photoplethysmographic Derivation of Respiratory Rate: A Review of Relevant Physiology","volume":"36","author":"Meredith","year":"2012","journal-title":"J. Med. Eng. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1946","DOI":"10.1109\/TBME.2013.2246160","article-title":"Multiparameter Respiratory Rate Estimation From the Photoplethysmogram","volume":"60","author":"Karlen","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1213\/ANE.0b013e31828098b2","article-title":"Respiration Signals from Photoplethysmography","volume":"117","author":"Nilsson","year":"2013","journal-title":"Anesth. Analg."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1093\/sleep\/30.4.519","article-title":"Practice Parameters for the Use of Actigraphy in the Assessment of Sleep and Sleep Disorders: An Update for 2007","volume":"30","author":"Morgenthaler","year":"2007","journal-title":"Sleep"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1088\/0967-3334\/31\/9\/015","article-title":"Photoplethysmography Pulse Rate Variability as a Surrogate Measurement of Heart Rate Variability during Non-Stationary Conditions","volume":"31","author":"Gil","year":"2010","journal-title":"Physiol. Meas."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"044004","DOI":"10.1088\/1361-6579\/abf1b0","article-title":"Transfer Learning from ECG to PPG for Improved Sleep Staging from Wrist-Worn Wearables","volume":"42","author":"Li","year":"2021","journal-title":"Physiol. Meas."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"479","DOI":"10.5664\/jcsm.6506","article-title":"Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline","volume":"13","author":"Kapur","year":"2017","journal-title":"J. Clin. Sleep Med."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"33915","DOI":"10.1109\/JIOT.2024.3433500","article-title":"ApSense: Data-Driven Algorithm in PPG-Based Sleep Apnea Sensing","volume":"11","author":"Choksatchawathi","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Attaripour Esfahani, S., Baba Ali, N., Farina, J.M., Scalia, I.G., Pereyra, M., Abbas, M.T., Javadi, N., Bismee, N.N., Abdelfattah, F.E., and Awad, K. (2025). A Comprehensive Review of Artificial Intelligence (AI) Applications in Pulmonary Hypertension (PH). Medicina, 61.","DOI":"10.3390\/medicina61010085"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1093\/ehjdh\/ztae020","article-title":"Photoplethysmography and Intracardiac Pressures: Early Insights from a Pilot Study","volume":"5","author":"Scholte","year":"2024","journal-title":"Eur. Heart J.-Digit. Health"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Fadilah, A., Putri, V.Y.S., Puling, I.M.D.R., and Willyanto, S.E. (2024). Assessing the Precision of Machine Learning for Diagnosing Pulmonary Arterial Hypertension: A Systematic Review and Meta-Analysis of Diagnostic Accuracy Studies. Front. Cardiovasc. Med., 11.","DOI":"10.3389\/fcvm.2024.1422327"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"105071","DOI":"10.1016\/j.bspc.2023.105071","article-title":"Classification of Pulmonary Arterial Pressure Using Photoplethysmography and Bi-Directional LSTM","volume":"86","author":"Zhang","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Nemati, N., Burton, T., Fathieh, F., Gillins, H.R., Shadforth, I., Ramchandani, S., and Bridges, C.R. (2024). Pulmonary Hypertension Detection Non-Invasively at Point-of-Care Using a Machine-Learned Algorithm. Diagnostics, 14.","DOI":"10.3390\/diagnostics14090897"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"01287","DOI":"10.1183\/23120541.01287-2024","article-title":"Clinical Validation of a Machine-Learned, Point-of-Care System to IDENTIFY Pulmonary Hypertension","volume":"11","author":"McLean","year":"2025","journal-title":"ERJ Open Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"11377","DOI":"10.1038\/s41598-022-15240-4","article-title":"Photoplethysmogram Based Vascular Aging Assessment Using the Deep Convolutional Neural Network","volume":"12","author":"Shin","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.medengphy.2019.07.009","article-title":"Data-Driven Assessment of Cardiovascular Ageing through Multisite Photoplethysmography and Electrocardiography","volume":"73","author":"Chiarelli","year":"2019","journal-title":"Med. Eng. Phys."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Jakarbet, U.P., Khan, A.A., Sumathi, R., and BP, A. (2025). Smartphone-Based Photoplethysmography: A Signal Processing Approach for Low-Cost Cardiovascular Monitoring. Proceedings of the 2025 Third International Conference on Networks, Multimedia and Information Technology (NMITCON), IEEE.","DOI":"10.1109\/NMITCON65824.2025.11187532"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"e026361","DOI":"10.1161\/JAHA.122.026361","article-title":"Aortic Distensibility Measured by Automated Analysis of Magnetic Resonance Imaging Predicts Adverse Cardiovascular Events in UK Biobank","volume":"11","author":"Cecelja","year":"2022","journal-title":"J. Am. Heart Assoc."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Huttunen, J.M.J., K\u00e4rkk\u00e4inen, L., and Lindholm, H. (2019). Pulse Transit Time Estimation of Aortic Pulse Wave Velocity and Blood Pressure Using Machine Learning and Simulated Training Data. PLoS Comput. Biol., 15.","DOI":"10.1371\/journal.pcbi.1007259"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1213\/ANE.0000000000002532","article-title":"Photoplethysmography and Heart Rate Variability for the Diagnosis of Preeclampsia","volume":"126","author":"Euliano","year":"2018","journal-title":"Anesth. Analg."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2225617","DOI":"10.1080\/10641955.2023.2225617","article-title":"Distinguishing Preeclampsia Using the Falling Scaled Slope (FSS)\u2014A Novel Photoplethysmographic Morphological Parameter","volume":"42","author":"Chen","year":"2023","journal-title":"Hypertens. Pregnancy"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1007\/s43926-024-00063-8","article-title":"Real-Time Pre-Eclampsia Prediction Model Based on IoT and Machine Learning","volume":"4","author":"Munyao","year":"2024","journal-title":"Discov. Internet Things"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Butler, L., Gunturkun, F., Chinthala, L., Karabayir, I., Tootooni, M.S., Bakir-Batu, B., Celik, T., Akbilgic, O., and Davis, R.L. (2024). AI-Based Preeclampsia Detection and Prediction with Electrocardiogram Data. Front. Cardiovasc. Med., 11.","DOI":"10.3389\/fcvm.2024.1360238"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.preghy.2019.03.005","article-title":"Prediction Models for Preeclampsia: A Systematic Review","volume":"16","author":"Hirst","year":"2019","journal-title":"Pregnancy Hypertens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2012\/691089","article-title":"Noninvasive Evaluation of Portal Hypertension: Emerging Tools and Techniques","volume":"2012","author":"Snowdon","year":"2012","journal-title":"Int. J. Hepatol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"583","DOI":"10.21037\/atm.2019.09.22","article-title":"Portal Pressure Monitoring\u2014State-of-the-Art and Future Perspective","volume":"7","author":"Xu","year":"2019","journal-title":"Ann. Transl. Med."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"e15223","DOI":"10.14814\/phy2.15223","article-title":"The Answer at Our Fingertips: Volume Status in Cirrhosis Determined by Machine Learning and Pulse Oximeter Waveform","volume":"10","author":"Mazumder","year":"2022","journal-title":"Physiol. Rep."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"e7045","DOI":"10.1097\/MD.0000000000007045","article-title":"The Photoplethysmographic Amplitude to Pulse Pressure Ratio Can Track Sudden Changes in Vascular Compliance and Resistance during Liver Graft Reperfusion","volume":"96","author":"Kim","year":"2017","journal-title":"Medicine"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1038\/s41598-022-05080-7","article-title":"Imaging Photoplethysmography as an Easy-to-Use Tool for Monitoring Changes in Tissue Blood Perfusion during Abdominal Surgery","volume":"12","author":"Kamshilin","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1152\/japplphysiol.00668.2013","article-title":"Estimation of Individual-Specific Progression to Impending Cardiovascular Instability Using Arterial Waveforms","volume":"115","author":"Convertino","year":"2013","journal-title":"J. Appl. Physiol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"S98","DOI":"10.1097\/TA.0000000000004377","article-title":"Physiologic Validation of the Compensatory Reserve Metric Obtained from Pulse Oximetry: A Step toward Advanced Medical Monitoring on the Battlefield","volume":"97","author":"Roden","year":"2024","journal-title":"J. Trauma Acute Care Surg."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Convertino, V.A., Schauer, S.G., Weitzel, E.K., Cardin, S., Stackle, M.E., Talley, M.J., Sawka, M.N., and Inan, O.T. (2020). Wearable Sensors Incorporating Compensatory Reserve Measurement for Advancing Physiological Monitoring in Critically Injured Trauma Patients. Sensors, 20.","DOI":"10.3390\/s20226413"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Convertino, V.A., Techentin, R.W., Poole, R.J., Dacy, A.C., Carlson, A.N., Cardin, S., Haider, C.R., Holmes, D.R., Wiggins, C.C., and Joyner, M.J. (2022). AI-Enabled Advanced Development for Assessing Low Circulating Blood Volume for Emergency Medical Care: Comparison of Compensatory Reserve Machine-Learning Algorithms. Sensors, 22.","DOI":"10.3390\/s22072642"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1097\/SHK.0000000000002188","article-title":"THE COMPENSATORY RESERVE INDEX FOR PREDICTING HEMORRHAGIC SHOCK IN PREHOSPITAL TRAUMA","volume":"60","author":"Latimer","year":"2023","journal-title":"Shock"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Bedolla, C.N., Gonzalez, J.M., Vega, S.J., Convertino, V.A., and Snider, E.J. (2023). An Explainable Machine-Learning Model for Compensatory Reserve Measurement: Methods for Feature Selection and the Effects of Subject Variability. Bioengineering, 10.","DOI":"10.3390\/bioengineering10050612"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1097\/SHK.0000000000000959","article-title":"Compensatory Reserve Index: Performance of A Novel Monitoring Technology to Identify the Bleeding Trauma Patient","volume":"49","author":"Johnson","year":"2018","journal-title":"Shock"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Trieu, H.T., Khanh, L.P., Ming, D.K.Y., Quang, C.H., Phan, T.Q., Van, V.C.N., Deniz, E., Mulligan, J., Wills, B.A., and Moulton, S. (2022). The Compensatory Reserve Index Predicts Recurrent Shock in Patients with Severe Dengue. BMC Med., 20.","DOI":"10.1186\/s12916-022-02311-6"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Wang, M., Li, Z., Zhang, Q., and Wang, G. (2019). Removal of Motion Artifacts in Photoplethysmograph Sensors during Intensive Exercise for Accurate Heart Rate Calculation Based on Frequency Estimation and Notch Filtering. Sensors, 19.","DOI":"10.3390\/s19153312"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1007\/s10916-010-9505-0","article-title":"Relationship Between Measurement Site and Motion Artifacts in Wearable Reflected Photoplethysmography","volume":"35","author":"Maeda","year":"2011","journal-title":"J. Med. Syst."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"2477","DOI":"10.1056\/NEJMc2029240","article-title":"Racial Bias in Pulse Oximetry Measurement","volume":"383","author":"Sjoding","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Lee, J., Kim, M., Park, H.-K., and Kim, I.Y. (2020). Motion Artifact Reduction in Wearable Photoplethysmography Based on Multi-Channel Sensors with Multiple Wavelengths. Sensors, 20.","DOI":"10.3390\/s20051493"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"2570","DOI":"10.1038\/s41598-021-82124-4","article-title":"Synthetic Photoplethysmography (PPG) of the Radial Artery through Parallelized Monte Carlo and Its Correlation to Body Mass Index (BMI)","volume":"11","author":"Rodriguez","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Wang, Y., Azimi, I., Kazemi, K., Rahmani, A.M., and Liljeberg, P. (2022). PPG Signal Reconstruction Using Deep Convolutional Generative Adversarial Network. Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE.","DOI":"10.1109\/EMBC48229.2022.9871678"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1038\/s41597-023-02020-6","article-title":"A Benchmark for Machine-Learning Based Non-Invasive Blood Pressure Estimation Using Photoplethysmogram","volume":"10","author":"Hsieh","year":"2023","journal-title":"Sci. Data"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"101007","DOI":"10.1016\/j.iot.2023.101007","article-title":"Tiny-PPG: A Lightweight Deep Neural Network for Real-Time Detection of Motion Artifacts in Photoplethysmogram Signals on Edge Devices","volume":"25","author":"Wu","year":"2024","journal-title":"Internet Things"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1038\/s43856-024-00555-2","article-title":"Recommendations for Evaluating Photoplethysmography-Based Algorithms for Blood Pressure Assessment","volume":"4","author":"Elgendi","year":"2024","journal-title":"Commun. Med."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Wang, W., Mohseni, P., Kilgore, K.L., and Najafizadeh, L. (2023). PulseDB: A Large, Cleaned Dataset Based on MIMIC-III and VitalDB for Benchmarking Cuff-Less Blood Pressure Estimation Methods. Front. Digit. Health, 4.","DOI":"10.3389\/fdgth.2022.1090854"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.jacc.2023.04.054","article-title":"Consumer Wearable Health and Fitness Technology in Cardiovascular Medicine","volume":"82","author":"Petek","year":"2023","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1161\/CIRCRESAHA.122.322389","article-title":"Wearable Devices in Cardiovascular Medicine","volume":"132","author":"Hughes","year":"2023","journal-title":"Circ. Res."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"110991","DOI":"10.1016\/j.bspc.2026.110991","article-title":"Standardizing Photoplethysmography (PPG) for Clinical-Grade Wearable Sensing","volume":"127","author":"Kyriacou","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"e129","DOI":"10.1017\/cts.2023.547","article-title":"Clinical Applications of Contactless Photoplethysmography for Monitoring in Adults: A Systematic Review and Meta-Analysis","volume":"7","author":"Bautista","year":"2023","journal-title":"J. Clin. Transl. Sci."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1097\/HJH.0000000000003775","article-title":"Finger Pulse Plethysmography Predicts Gestational Hypertension, Preeclampsia and Gestational Diabetes","volume":"42","author":"Shahrbabaki","year":"2024","journal-title":"J. Hypertens."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1016\/j.jacc.2020.01.046","article-title":"Wearable Devices for Ambulatory Cardiac Monitoring","volume":"75","author":"Sana","year":"2020","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"111861","DOI":"10.1016\/j.compbiomed.2026.111861","article-title":"Non-Invasive Arterial Blood Pressure Waveform Generation in Critically Ill Patients: A Sensor-Based Deep Learning Approach","volume":"213","author":"Harris","year":"2026","journal-title":"Comput. Biol. Med."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Pugalenthi, L.S., Senapati, S.G., Gohri, J., Anam, H.L., Madan, H., Arora, A., Arora, A., Lee, J., Yerrapragada, G., and Elangovan, P. (2026). Blood Pressure Variability (BPV) as a Novel Digital Biomarker of Multisystem Risk and Diagnostic Insight: Measurement, Mechanisms, and Emerging Artificial Intelligence Methods. Biomedicines, 14.","DOI":"10.3390\/biomedicines14020317"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Msokar, S., Davydov, R., and Davydov, V. (2026). Interpretable Photoplethysmography Feature Engineering for Multi-Class Blood Pressure Staging. Computers, 15.","DOI":"10.20944\/preprints202602.0969.v1"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"20552076261456613","DOI":"10.1177\/20552076261456613","article-title":"Clinical Performance Evaluation of a Cuffless Continuous Non-Invasive Blood Pressure Monitoring Device in ICU Patients","volume":"12","author":"Chen","year":"2026","journal-title":"Digit. Health"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1038\/s41371-025-01082-w","article-title":"Performance of a Cuffless Photoplethysmography-Based Device for Continuous Monitoring of Blood Pressure after Cardiac Surgery: A Preliminary Validation Study","volume":"39","author":"Hellou","year":"2025","journal-title":"J. Hum. Hypertens."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1038\/s41746-021-00462-z","article-title":"Evaluation of Biases in Remote Photoplethysmography Methods","volume":"4","author":"Dasari","year":"2021","journal-title":"npj Digit. Med."}],"container-title":["Life"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-1729\/16\/9\/1530\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T08:44:29Z","timestamp":1789461869000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-1729\/16\/9\/1530"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,15]]},"references-count":103,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["life16091530"],"URL":"https:\/\/doi.org\/10.3390\/life16091530","relation":{},"ISSN":["2075-1729"],"issn-type":[{"value":"2075-1729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,15]]}}}