Integration of biomedical imaging and sensing technologies with AI-IoT for Materiovigilance and predictive modeling of stillbirth risk in maternal-fetal health monitoring

Abstract

The involvement of the Internet of Things (IoT) technology and artificial intelligence (AI) in the matter of maternal healthcare has allowed monitoring pregnant women in real-time and predicting poor pregnancy outcomes, including stillbirth and premature birth. Nevertheless, to diminish the risks of devices, the introduction of such technologies has to be accompanied by harsh safety monitoring programs. Materiovigilance, as the systematic sensing and monitoring of adverse events associated with medical instruments, is also crucial to patient safety in high-risk obstetric environments. Wearable sensors, e.g., fetal Dopplers, smart fabrics, and adhesive patches, have enhanced the prediction of stillbirth by offering continuous acquisition of physiological data but presents a hazard of ill effects if not controlled adequately. The AI introduction into the sphere of Materiovigilance enhances regulatory conformity, real-time decision-making, and raises the possibility of risk identification. Despite the massive potential, issues such as inaccurate data, poor infrastructure, and underreporting persist, particularly in low- and middle-income countries. In such circumstances, the Materiovigilance Programme of India is another staged effort to enhance the device safety supervision and reporting systems. Engagement of different stakeholders such as clinicians, engineers, regulatory agencies, and technology developers provides an opportunity to secure the safety and efficacy of AI-equipped medical devices. Enhancement of Materiovigilance systems is required to preserve proper maternal-fetal health and sustain clinics in digital obstetrics, as any error in maternal-fetal monitoring may lead to preventable death.

Keywords

artificial intelligence / Internet of Things / prediction / stillbirth

References

[1] Human M, Green S, Groenewald C, Goldstein RD, Kinney HC, Odendaal HJ. Psychosocial implications of stillbirth for the mother and her family: A CRISIS-SUPPORT APPROACH. Social Work (Stellenbosch). 2014; 50: 392.

[2] Tavares Da Silva F, Gonik B, McMillan M, Keech C, Dellicour S, Bhange S, et al.; Brighton Collaboration Stillbirth Working Group. Stillbirth: Case definition and guidelines for data collection, analysis, and presentation of maternal immunization safety data. Vaccine. 2016; 34: 6057-68.

[3] Souza JP, Bahl R. PURPOSe study: understanding the burden of stillbirths in south Asia. Lancet Glob Health. 2022; 10: e930-1.

[4] Khan M, Khurshid M, Vatsa M, Singh R, Duggal M, Singh K. On AI Approaches for Promoting Maternal and Neonatal Health in Low Resource Settings: A Review. Front Public Health. 2022; 10: 880034.

[5] Gulzar Ahmad S, Iqbal T, Javaid A, Ullah Munir E, Kirn N, Ullah Jan S, et al. Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review. Sensors (Basel). 2022; 22: 4362.

[6] Saleh SN, Elagamy MN, Saleh YNM, Osman RA. An Explainable Deep Learning-Enhanced IoMT Model for Effective Monitoring and Reduction of Maternal Mortality Risks. Future Internet. 2024; 16: 411.

[7] Li Q, Li P, Chen J, Ren R, Ren N, Xia Y. Machine Learning for Predicting Stillbirth: A Systematic Review. Reprod Sci. 2025; 32: 1388-98.

[8] Al-Fattah AN, Mahindra MP, Yusrika MU, Mapindra MP, Marizni S, Putri VP, et al. A prediction model for stillbirth based on first trimester pre-eclampsia combined screening. Int J Gynecol Obstet. 2024; 167: 1101-8.

[9] Cersonsky TEK, Ayala NK, Pinar H, Dudley DJ, Saade GR, Silver RM, et al. Identifying risk of stillbirth using machine learning. Am J Obstet Gynecol. 2023; 229: 327.e1-16.

[10] MATERIOVIGILANCE PROGRAMME OF INDIA (MvPI) [Internet]. [cited 2025 Jul 20]. Available from: https://nhsrcindia.org/hc-technology/materiovigilance-programme-of-india

[11] Never Forgotten: The situation of stillbirth around the globe [Internet]. UNICEF; c2026 [cited 2025 Apr 8]. Available from: https://data.unicef.org/resources/never-forgotten-stillbirth-estimates-report/?utm_source=chatgpt.com

[12] Hug L, You D, Blencowe H, Mishra A, Wang Z, Fix MJ, et al. Global, regional, and national estimates and trends in stillbirths from 2000 to 2019: a systematic assessment. The Lancet. 2021; 398: 772-85.

[13] Blencowe H, Campbell O, Kerac T, Stafford R, Tripathi V, Filippi V. Neglected for Too Long: Perinatal Mental Health Impacts of Stillbirth in Low- and Middle-Income Countries. BJOG. 2025; 132: 554-6.

[14] Gordon A, Raynes-Greenow C, McGeechan K, Morris J, Jeffery H. Risk factors for antepartum stillbirth and the influence of maternal age in New South Wales Australia: a population based study. BMC Pregnancy Childbirth. 2013; 13: 12.

[15] Boo YY, Bora AK, Chhabra S, Choudhury SS, Deka G, Kakoty S, et al.; MaatHRI collaborators. Maternal and fetal factors associated with stillbirth in singleton pregnancies in 13 hospitals across six states in India: A prospective cohort study. Int J Gynaecol Obstet. 2024; 165: 462-73.

[16] Genc S, Ozkahraman A, Ozalp M, Akturk E, Mihmanli V. Evaluation of obstetric outcomes and risk factors of early late and term stillbirths. Perinat J. 2024; 32: 1-8.

[17] Kebede E, Kekulawala M. Risk factors for stillbirth and early neonatal death: a case-control study in tertiary hospitals in Addis Ababa, Ethiopia. BMC Pregnancy Childbirth. 2021; 21: 641.

[18] Li J, Liu X. Fetal Health Classification Based on Machine Learning. In: 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). 2021. pp. 899-902.

[19] Khatibi T, Hanifi E, Sepehri MM, Allahqoli L. Proposing a machine-learning based method to predict stillbirth before and during delivery and ranking the features: nationwide retrospective cross-sectional study. BMC Pregnancy Childbirth. 2021; 21.

[20] Gunenc O, Dogru S, Yaman FK, Ezveci H, Metin US, Acar A. The Application of Machine Learning Models to Predict Stillbirths. Medicina (Kaunas). 2025; 61: 472.

[21] Innab N, Alsubai S, Alabdulqader EA, Alarfaj AA, Umer M, Trelova S, et al. Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI. Front Public Health. 2024; 12: 1462693.

[22] Rattanasak A, Jumphoo T, Pathonsuwan W, Kokkhunthod K, Orkweha K, Phapatanaburi K, et al. An IoT-Enabled Wearable Device for Fetal Movement Detection Using Accelerometer and Gyroscope Sensors. Sensors (Basel). 2025; 25: 1552.

[23] Pavuluri S, Sangal R, Sather J, Taylor RA. Balancing act: the complex role of artificial intelligence in addressing burnout and healthcare workforce dynamics. BMJ Health Care Inform. 2024; 31: e101120.

[24] King VJ, Bennet L, Stone PR, Clark A, Gunn AJ, Dhillon SK. Fetal growth restriction and stillbirth: Biomarkers for identifying at risk fetuses. Front Physiol. 2022; 13: 959750.

[25] Rancea A, Anghel I, Cioara T. Edge Computing in Healthcare: Innovations, Opportunities, and Challenges. Future Internet. 2024; 16: 329.

[26] Liang S, Peng J, Xu Y, Ye H. Passive Fetal Movement Recognition Approaches Using Hyperparameter Tuned LightGBM Model and Bayesian Optimization. Comput Intell Neurosci. 2021; 2021: 6252362.

[27] Mallegni N, Molinari G, Ricci C, Lazzeri A, Rosa DL, Crivello A, et al. Sensing Devices for Detecting and Processing Acoustic Signals in Healthcare. Biosensors. 2022; 12: 835.

[28] Saleh M, Xing L, Casson AJ. EEG artifact removal at the edge using AI hardware. IEEE Sensors Letters. 2025.

[29] Mikołaj KW, Christensen AN, Taksøe-Vester CA, Feragen A, Petersen OB, Lin M, et al. Predicting abnormal fetal growth using deep learning. NPJ Digit Med. 2025; 8: 318.

[30] Gude V, Corns S. Integrated Deep Learning and Supervised Machine Learning Model for Predictive Fetal Monitoring. Diagnostics. 2022; 12: 2843.

[31] Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med. 2020; 3: 17.

[32] Di Martino F, Delmastro F. Explainable AI for clinical and remote health applications: a survey on tabular and time series data. Artif Intell Rev. 2023; 56: 5261-315.

[33] Sapkota B, Palaian S, Shrestha S, Ibrahim MIM. Materiovigilance in Perspective: Understanding Its Concept and Practice in the Global Healthcare System. Ther Innov Regul Sci. 2023; 57: 886-98.

[34] Di Palma G, Scendoni R, Ferorelli D, De Benedictis A, Tambone V, De Micco F. AI-Induced Cybersecurity Risks in Healthcare: A Narrative Review of Blockchain-Based Solutions Within a Clinical Risk Management Framework. Risk Manag Healthc Policy. 2025; 18: 3479-97.

[35] Liu L, Pu Y, Fan J, Yan Y, Liu W, Luo K, et al. Wearable Sensors, Data Processing, and Artificial Intelligence in Pregnancy Monitoring: A Review. Sensors. 2024; 24: 6426.

[36] Bekker MN, Koster MPH, Keusters WR, Ganzevoort W, de Haan-Jebbink JM, Deurloo KL, et al. Home telemonitoring versus hospital care in complicated pregnancies in the Netherlands: a randomised, controlled non-inferiority trial (HoTeL). Lancet Digit Health. 2023; 5: e116-24.

[37] Stricker K, Radan AP, Surbek D. Continuous remote home monitoring solutions for mother and fetus: A scoping review. Eur J Obstet Gynecol Reprod Biol. 2025; 305: 170-7.

[38] Lei Z, Wang Q, Sun S, Zhu W, Wu P. A Bioinspired Mineral Hydrogel as a Self-Healable, Mechanically Adaptable Ionic Skin for Highly Sensitive Pressure Sensing. Adv Mater. 2017; 29.

[39] Xiong Y, Shen Y, Tian L, Hu Y, Zhu P, Sun R, et al. A flexible, ultra-highly sensitive and stable capacitive pressure sensor with convex microarrays for motion and health monitoring. Nano Energy. 2020; 70: 104436.

[40] Lee J, Kwon H, Seo J, Shin S, Koo JH, Pang C, et al. Conductive fiber-based ultrasensitive textile pressure sensor for wearable electronics. Adv Mater. 2018; 27: 2433-9.

[41] Trung TQ, Lee NE. Flexible and Stretchable Physical Sensor Integrated Platforms for Wearable Human-Activity Monitoring and Personal Healthcare. Adv Mater. 2016; 28: 4338-72.

[42] Ha M, Lim S, Cho S, Lee Y. Skin-Inspired Hierarchical Polymer Architectures with Gradient Stiffness for Spacer-Free, Ultrathin, and Highly Sensitive Triboelectric Sensors. ACS Nano. 2018; 12: 3964-74.

[43] Charlier P, Herman C, Rochedreux N, Logier R, Garabedian C, Debarge V. AcCorps: A low-cost 3D printed stethoscope for fetal phonocardiography. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2019. pp. 52-5.

[44] Khandoker A, Ibrahim E, Oshio S, Kimura Y. Validation of beat by beat fetal heart signals acquired from four-channel fetal phonocardiogram with fetal electrocardiogram in healthy late pregnancy. Sci Rep. 2018; 8: 13635.

[45] Ryu D, Kim DH, Price JT, Lee JY, Chung HU, Allen E, et al. Comprehensive pregnancy monitoring with a network of wireless, soft, and flexible sensors in high- and low-resource health settings. Proc Natl Acad Sci U S A. 2021; 118: e2100466118.

[46] Du YC, Yen LB, Kuo PL, Tsai PY. A Wearable Device for Evaluation of Relative Position, Force, and Duration of Fetal Movement for Pregnant Woman Care. IEEE Sens J. 2021; 21: 19341-50.

[47] Qin M, Xu Y, Liang Y, Sun T. A wearable fetal movement detection system for pregnant women. Front Med. 2023; 10: 1160373.

[48] Aravindan M, Alex JSR, Shwetha S, Sri Varsha S, Annis Fathima A, Reena Prabha Kokila P. Fetal Health Assessment Through Remote Fetal Phonocardiography and Electrohysterography: Wearable Wireless Device Using Shakti Processor. IEEE Sens J. 2023; 23: 16219-26.

[49] Maugeri A, Barchitta M, Agodi A. How Wearable Sensors Can Support the Research on Foetal and Pregnancy Outcomes: A Scoping Review. J Pers Med. 2023; 13: 218.

[50] Pyun KR, Park JJ, Ahn J, Lee YS, Kim H, Kim J, et al. Skin-Integrated Soft Wearable XR Interfaces for Seamless and Realistic User Experience. Chem Rev. 2025; 125: 11567-665.

[51] Boyer G, Lachmann N, Bellemère G, De Belilovsky C, Baudouin C. Effects of pregnancy on skin properties: A biomechanical approach. Skin Res Technol. 2018; 24: 551-6.

[52] Khan B, Khan W, Masrur MH, Khalid RT, Awais M, Khan B, et al. Hybrid sensor integration in wearable devices for improved cardiovascular health monitoring. J Sci: Adv Mater Devices. 2025; 10: 100889.

[53] Patel S, Conway AE, Adjei T, Abati I, Dhawan S, Yu Z, et al. Is it possible to monitor fetal movements with a wearable device? A review of novel technologies. Eur J Obstet Gynecol Reprod Biol. 2025; 305: 329-38.

[54] Albaroudi E, Mansouri T, Alameer A. A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring. AI. 2024; 5: 383-404.

[55] Zhang B, Chen C, Lee I, Lee K, Ong KL. A survey on security and privacy issues in wearable health monitoring devices. Comput Secur. 2025; 155: 104453.

[56] Heo JS, Hossain MF, Kim I. Challenges in Design and Fabrication of Flexible/Stretchable Carbon- and Textile-Based Wearable Sensors for Health Monitoring: A Critical Review. Sensors (Basel). 2020; 20: 3927.

[57] Fox D, Coddington R, Levett KM, Scarf V, Sutcliffe KL, Newnham E, et al. Tending to the machine: The impact of intrapartum fetal surveillance on women in Australia. PLoS One. 2024; 19: e0303072.

[58] Abburi R, Hatai I, Jaros R, Martinek R, Babu TA, Babu SA, et al. Adopting artificial intelligence algorithms for remote fetal heart rate monitoring and classification using wearable fetal phonocardiography. Appl Soft Comput. 2024; 165: 112049.

[59] Ahmedova A, Veleva N, Stoev S, Petkova-Gueorguieva E, Yanachkova V, Lebanova H. Wearable health-tracking technologies in pregnancy: Capabilities, clinical evidence, and implementation challenges. PHARMACIA. 2026; 73: 1-10.

[60] El Arab RA, Al Moosa OA, Albahrani Z, Alkhalil I, Somerville J, Abuadas F. Integrating Artificial Intelligence into Perinatal Care Pathways: A Scoping Review of Reviews of Applications, Outcomes, and Equity. Nurs Rep. 2025; 15: 281.

[61] Espinosa C, Becker M, Marić I, Wong RJ, Shaw GM, Gaudilliere B, et al. Prematurity Research Center at Stanford. Data-Driven Modeling of Pregnancy-Related Complications. Trends Mol Med. 2021; 27: 762-76.

[62] Koivu A, Sairanen M. Predicting risk of stillbirth and preterm pregnancies with machine learning. Health Inf Sci Syst. 2020; 8: 14.

[63] Vanamala VG, Rachel A, Pakyanadhan S, Abraham S. Biophysical profile and modified biophysical profile in predicting the fetal outcome. Int J Reprod Contracept Obstet Gynecol. 2018; 7: 3516.

[64] Jha S, Dangal G. Role of Modified Biophysical Profile in High Risk Pregnancy in Predicting Fetal Outcome. J Nepal Health Res Counc. 2020; 18: 401-5.

[65] Third Trimester Modified Biophysical Profile Scan for Predicting Fetal Outcome [Internet]. ICH GCP; [cited 2026 Feb 2]. Available from: https://ichgcp.net/clinical-trials-registry/NCT03729089

[66] Le Vance J, Emms A, Hodgetts Morton V, Morris RK, Gurney L. Remote maternal-fetal telemedicine monitoring for high-risk pregnancy care: A feasibility study. PLoS One. 2025; 20: e0336797.

[67] Performance of the Monica Novii Wireless Patch System in Threatened and Actual Pre-Term Labour [Internet]. [cited 2026 Feb 2]. Available from: https://clinicaltrials.gov/study/NCT03223324

[68] Umbilical Cord Abnormalities in the Prediction of Adverse Pregnancy Outcomes [Internet]. [cited 2026 Feb 2]. Available from: https://www.trialx.com/clinical-trials/listings/285881/umbilical-cord-abnormalities-in-the-prediction-of-adverse-pregnancy-outcomes/?utm_source=chatgpt.com

[69] Pregnancy Risk, Infant Surveillance, and Measurement Alliance (PRISMA) Investigators. Pregnancy Risk, Infant Surveillance, and Measurement Alliance (PRISMA) Maternal and Newborn Health Study: protocol for a multisite, prospective, open cohort study of pregnancy and postpartum health outcomes in South Asia and sub-Saharan Africa. BMJ Open. 2026; 16: e104512.

[70] Yang Y, Cui T, Li D, Ji S, Chen Z, Shao W, et al. Breathable Electronic Skins for Daily Physiological Signal Monitoring. Nanomicro Lett. 2022; 14: 161.

[71] Zhang S, Chhetry A, Zahed MA, Sharma S, Park C, Yoon S, et al. On-skin ultrathin and stretchable multifunctional sensor for smart healthcare wearables. npj Flex Electron. 2022; 6: 11.

[72] An X, Liu Y, Zhao Y, Lu S, Stylios GK, Liu Q. Adaptive Motion Artifact Reduction in Wearable ECG Measurements Using Impedance Pneumography Signal. Sensors. 2022; 22: 5493.

[73] Mallardi G, Calefato F, Quaranta L, Lanubile F. Integrating AI into healthcare systems: A multivocal literature review. Smart Health. 2026; 39: 100631.

[74] Vikas D, Venkataraman R, Hafis A. Advancements in materiovigilance: A comprehensive overview. Perspect Clin Res. 2025; 16: 111-7.

[75] Bhosale A, Valipay HC, Karattuthodi MS, Gangachannaiah S, Thunga G, Kalaiselvan V, et al. A systematic review and meta-analysis of knowledge, attitude, and practice survey on materiovigilance among healthcare professionals. BMC Health Serv Res. 2026; 26: 371.

[76] Mishra HP, Loriya K, Shah N, Grover S, Mishra SS. Leveraging artificial intelligence to revolutionize medical device safety. INNOSC Theranostics Pharmacol Sci. 2025; 8: 1-11.

[77] Arif M. Explainable AI in Maternal Health: Utilizing XGBoost and SHAP Values for Enhanced Risk Prediction and Interpretation. Int J Emerg Multidiscip: Comput Sci Artif Intell. 2025; 4: 16.

[78] Nandini K, Rahimunnisa K. IoT assisted fetal health classification using mother optimization algorithm with deep learning approach on cardiotocogram data. Sci Rep. 2025; 15: 38979.

[79] Medical devices [Internet]. European Medicines Agency; c1995-2026 [cited 2025 Jul 20]. Available from: https://www.ema.europa.eu/en/human-regulatory-overview/medical-devices

[80] Digital Health Software Precertification (Pre-Cert) Pilot Program [Internet]. [cited 2025 Jul 20]. Available from: https://www.fda.gov/medical-devices/digital-health-center-excellence/digital-health-software-precertification-pre-cert-pilot-program

[81] Pannonhalmi Á, Sipos B, Kurucz RI, Katona G, Kemény L, Csóka I. Advancing Regulatory Oversight of Medical Device Trials to Align with Clinical Drug Standards in the European Union. Pharmaceuticals (Basel). 2025; 18: 876.

[82] Wakefield C, Yao L, Self S, Frasch MG. Wearable technology for health monitoring during pregnancy: an observational cross-sectional survey study. Arch Gynecol Obstet. 2023; 308: 73-8.

[83] Saffarpour M, Qian W, Vali K, Kasap B, Hedriana HL, Ghiasi S. Deep Harmonic Finesse: Signal Separation in Wearable Systems with Limited Data. In: DAC '24: Proceedings of the 61st ACM/IEEE Design Automation Conference. 2024. pp. 1-6.

[84] Weissman GE. Evaluation and Regulation of Artificial Intelligence Medical Devices for Clinical Decision Support. Annu Rev Biomed Data Sci. 2025; 8: 81-99.

[85] Alzakari SA, Aldrees A, Umer M, Cascone L, Innab N, Ashraf I. Artificial intelligence-driven predictive framework for early detection of still birth. SLAS Technol. 2024; 29: 100203.

[86] d’Amati A, Baldini GM, Difonzo T, Santoro A, Dellino M, Cazzato G, et al. Artificial Intelligence in Placental Pathology: New Diagnostic Imaging Tools in Evolution and in Perspective. J Imaging. 2025; 11: 110.

[87] Nogueira M, Aparício SL, Duarte I, Silvestre M. Artificial Intelligence's Role in Improving Adverse Pregnancy Outcomes: A Scoping Review and Consideration of Ethical Issues. J Clin Med. 2025; 14: 3860.

[88] Zimmerman RM, Hernandez EJ, Yandell M, Tristani-Firouzi M, Silver RM, Grobman W, et al. AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios. BMC pregnancy and childbirth. 2025; 25: 80.

[89] Verlohren S, Brennecke SP, Galindo A, Karumanchi A, Mirkovic LB, Schlembach D, et al. Clinical interpretation and implementation of the sFlt-1/PlGF ratio in the prediction, diagnosis and management of preeclampsia. Pregnancy Hypertens. 2022; 27: 42-50.

[90] Javaid H, Petrescu CC, Schmunk LJ, Monahan JM, O’Reilly P, Garg M, et al. The impact of artificial intelligence on biomarker discovery. Emerg Top Life Sci. 2025; 8: 89-105.