{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T17:16:25Z","timestamp":1790183785942,"version":"4.1.0"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-22827-0","type":"journal-article","created":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T14:02:05Z","timestamp":1762437725000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["IoT assisted fetal health classification using mother optimization algorithm with deep learning approach on cardiotocogram data"],"prefix":"10.1038","volume":"15","author":[{"given":"K.","family":"Nandini","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K.","family":"Rahimunnisa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,6]]},"reference":[{"key":"22827_CR1","doi-asserted-by":"crossref","unstructured":"Frasch, M. G., Strong, S. B., Nilosek, D., Leaverton, J. & Schifrin, B. S. Detection of preventable fetal distress during labor from scanned cardiotocogram tracings using deep learning. Front. Pediatr. 9, 736834 (2021).","DOI":"10.3389\/fped.2021.736834"},{"key":"22827_CR2","doi-asserted-by":"crossref","unstructured":"Krupa, A. J. D., Dhanalakshmi, S., Lai, K. W., Tan, Y. & Wu, X. An IoMT enabled deep learning framework for automatic detection of fetal QRS: A solution to remote prenatal care. J. King Saud Univ. Comput. Inf. Sci. 34(9), 7200\u20137211 (2022).","DOI":"10.1016\/j.jksuci.2022.07.002"},{"key":"22827_CR3","doi-asserted-by":"crossref","unstructured":"Cao, Z. et al. Intelligent antepartum fetal monitoring via deep learning and fusion of cardiotocographic signals and clinical data. Health Inf. Sci. Syst. 11(1), 16 (2023).","DOI":"10.1007\/s13755-023-00219-w"},{"key":"22827_CR4","doi-asserted-by":"crossref","unstructured":"Priya, M. & Nandhini, M. Detection of fetal brain abnormalities using data augmentation and convolutional neural network in the internet of things. Meas. Sensors 100808 (2023).","DOI":"10.1016\/j.measen.2023.100808"},{"key":"22827_CR5","doi-asserted-by":"crossref","unstructured":"Sreelakshmy, R. et al. An automated deep learning model for the cerebellum segmentation from fetal brain images. BioMed Res. Int. (2022).","DOI":"10.1155\/2022\/8342767"},{"key":"22827_CR6","doi-asserted-by":"crossref","unstructured":"Krishna, T. B. & Kokil, P. Automated classification of common maternal fetal ultrasound planes using multi layer perceptron with deep feature integration. Biomed. Signal Process. Control, 86, 105283 (2023).","DOI":"10.1016\/j.bspc.2023.105283"},{"key":"22827_CR7","doi-asserted-by":"crossref","unstructured":"Deepika, P., Suresh, R. M. & Pabitha, P. Defending Against Child Death: Deep learning-based diagnosis method for abnormal identification of fetus ultrasound Images. Comput. Intell. 37(1), 128\u2013154 (2021).","DOI":"10.1111\/coin.12394"},{"key":"22827_CR8","doi-asserted-by":"crossref","unstructured":"Vahedifard, F. et al. Automatic ventriculomegaly detection in fetal brain MRI: A step by step deep learning model for novel 2D 3D linear measurements. Diagnostics 13(14), 2355 (2023).","DOI":"10.3390\/diagnostics13142355"},{"key":"22827_CR9","doi-asserted-by":"crossref","unstructured":"Boudet, S., Houz\u00e9 de l\u2019Aulnoit, A., Peyrodie, L., Demailly, R. & Houz\u00e9, D. Use of Deep Learning to Detect the Maternal Heart Rate and False Signals on Fetal Heart Rate Recordings. Biosensors 12(9), 691 (2022).","DOI":"10.3390\/bios12090691"},{"key":"22827_CR10","doi-asserted-by":"crossref","unstructured":"Einy, S., Sen, E., Saygin, H., Hivehchi, H. & Dorostkar Navaei, Y. Local Binary Convolutional Neural Networks\u2019 Long Short Term Memory Model for Human Embryos\u2019 Anomaly Detection. Sci. Program.  (2023).","DOI":"10.1155\/2023\/2426601"},{"key":"22827_CR11","doi-asserted-by":"crossref","unstructured":"Addanke, S. & Anandan, R. Original research Article secure IoT based smart system for monitoring health care for ambulatory and fetal. J. Auton. Intell. 7(3). (2024).","DOI":"10.32629\/jai.v7i3.1217"},{"key":"22827_CR12","doi-asserted-by":"crossref","unstructured":"Edupuganti, M., Rathikarani, V. & Chaduvula, K. A Real and Accurate Ultrasound Fetal Imaging Based Heart Disease Detection Using Deep Learning Technology. Int. J. Integr. Eng.  14(7), 56\u201368 (2022).","DOI":"10.30880\/ijie.2022.14.07.005"},{"key":"22827_CR13","doi-asserted-by":"crossref","unstructured":"Krishna, T. B. & Kokil, P. Standard fetal ultrasound plane classification based on stacked ensemble of deep learning models. Expert Syst. Appl. 238, 122153 (2024).","DOI":"10.1016\/j.eswa.2023.122153"},{"key":"22827_CR14","doi-asserted-by":"crossref","unstructured":"Gupta, K., Bajaj, V. & Ansari, I. A. Integrated s transform based learning system for detection of arrhythmic fetus. IEEE Trans. Instrum. Meas. (2023).","DOI":"10.1109\/TIM.2023.3271739"},{"key":"22827_CR15","doi-asserted-by":"publisher","unstructured":"Zeng, Y., Zhang, R., Zhang, H., Qiao, S., Huang, F., Tian, Q., & Peng, Y. GCCNet: A novel network leveraging gated cross correlation for multi view classification. IEEE Trans. Multimedia 27, 1086\u20131099. https:\/\/doi.org\/10.1109\/TMM.2024.3521733  (2025).","DOI":"10.1109\/TMM.2024.3521733"},{"key":"22827_CR16","doi-asserted-by":"publisher","unstructured":"Hu, F., Yang, H., Qiu, L., Wei, S., Hu, H., &  Zhou, H. Spatial structure and organization of the medical device industry urban network in China: Evidence from specialized, refined, distinctive, and innovative firms. Front. Public Health 13, 1518327. https:\/\/doi.org\/10.3389\/fpubh.2025.1518327 (2025).","DOI":"10.3389\/fpubh.2025.1518327"},{"key":"22827_CR17","doi-asserted-by":"publisher","first-page":"0152","DOI":"10.34133\/cbsystems.0152","volume":"5","author":"W Qiu","year":"2024","unstructured":"Qiu, W. et al. Federated abnormal heart sound detection with weak to no labels. Cyborg Bionic Syst. 5, 0152. https:\/\/doi.org\/10.34133\/cbsystems.0152 (2024).","journal-title":"Cyborg Bionic Syst."},{"key":"22827_CR18","doi-asserted-by":"publisher","first-page":"0075","DOI":"10.34133\/cbsystems.0075","volume":"5","author":"K Qian","year":"2024","unstructured":"Qian, K. et al. Learning representations from heart sound: A comparative study on shallow and deep models. Cyborg Bionic Syst. 5, 0075. https:\/\/doi.org\/10.34133\/cbsystems.0075 (2024).","journal-title":"Cyborg Bionic Syst."},{"key":"22827_CR19","doi-asserted-by":"publisher","unstructured":"Ma, N., Fang, X., Zhang, Y., Xing, B., Duan, L., Lu, J., & Ma, D. Enhancing the sensitivity of spin exchange relaxation free magnetometers using phase modulated pump light with external Gaussian noise. Optics Express 32(19), 33378\u201333390. https:\/\/doi.org\/10.1364\/OE.530764 (2024).","DOI":"10.1364\/OE.530764"},{"key":"22827_CR20","doi-asserted-by":"publisher","unstructured":"Bing, P., Liu, W., Zhai, Z., Li, J., Guo, Z., Xiang, Y., & Zhu, L. A novel approach for denoising electrocardiogram signals to detect cardiovascular diseases using an efficient hybrid scheme. Front. Cardiovasc. Med. 11, 1277123. https:\/\/doi.org\/10.3389\/fcvm.2024.1277123 (2024).","DOI":"10.3389\/fcvm.2024.1277123"},{"key":"22827_CR21","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Wu, K., Wu, Z., Xiao, Y., Wang, Y., Lin, Q., & Liu, Q. A case of pioneering subcutaneous implantable cardioverter defibrillator intervention in Timothy syndrome. BMC Pediatr. 24(1), 729. https:\/\/doi.org\/10.1186\/s12887024-05216-w  (2024).","DOI":"10.1186\/s12887024-05216-w"},{"issue":"1","key":"22827_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-024-01050-0","volume":"12","author":"A Bilal","year":"2025","unstructured":"Bilal, A., Shafiq, M., Obidallah, W. J., Alduraywish, Y. A. & Long, H. Quantum computational infusion in extreme learning machines for early multi cancer detection. J. Big Data. 12 (1), 1\u201348 (2025).","journal-title":"J. Big Data"},{"key":"22827_CR23","doi-asserted-by":"publisher","unstructured":"Attar, H. et al. B5G applications and emerging services in smart IoT environments. Int. J. Crowd Sci. 9 (2), 79\u201395. https:\/\/doi.org\/10.26599\/IJCS.2024.9100007 (2025).","DOI":"10.26599\/IJCS.2024.9100007"},{"key":"22827_CR24","unstructured":"https:\/\/www.kaggle.com\/datasets\/andrewmvd\/fetalhealthclassification."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-22827-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-22827-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-22827-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T14:02:07Z","timestamp":1762437727000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-22827-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,6]]},"references-count":24,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["22827"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-22827-0","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,6]]},"assertion":[{"value":"19 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"38979"}}