{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T11:27:57Z","timestamp":1774956477561,"version":"3.50.1"},"reference-count":90,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis. Comput. Ind. Biomed. Art"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Placental segmentation is critical for the quantitative analysis of prenatal imaging applications. However, segmenting the placenta using magnetic resonance imaging (MRI) and ultrasound is challenging because of variations in fetal position, dynamic placental development, and image quality. Most segmentation methods define regions of interest with different shapes and intensities, encompassing the entire placenta or specific structures. Recently, deep learning has emerged as a key approach that offer high segmentation performance across diverse datasets. This review focuses on the recent advances in deep learning techniques for placental segmentation in medical imaging, specifically MRI and ultrasound modalities, and cover studies from 2019 to 2024. This review synthesizes recent research, expand knowledge in this innovative area, and highlight the potential of deep learning approaches to significantly enhance prenatal diagnostics. These findings emphasize the importance of selecting appropriate imaging modalities and model architectures tailored to specific clinical scenarios. In addition, integrating both MRI and ultrasound can enhance segmentation performance by leveraging complementary information. This review also discusses the challenges associated with the high costs and limited availability of advanced imaging technologies. It provides insights into the current state of placental segmentation techniques and their implications for improving maternal and fetal health outcomes, underscoring the transformative impact of deep learning on prenatal diagnostics.<\/jats:p>","DOI":"10.1186\/s42492-025-00197-8","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T06:49:19Z","timestamp":1752562159000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Placenta segmentation redefined: review of deep learning integration of magnetic resonance imaging and ultrasound imaging"],"prefix":"10.1186","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2712-0021","authenticated-orcid":false,"given":"Asmaa","family":"Jittou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khalid El","family":"Fazazy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jamal","family":"Riffi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"197_CR1","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1002\/ajum.12071","volume":"20","author":"T Taylor","year":"2017","unstructured":"Taylor T, Quinton A, Hyett J (2017) The developmental origins of placental function. Australas J Ultrasound Med 20:141\u2013146. https:\/\/doi.org\/10.1002\/ajum.12071","journal-title":"Australas J Ultrasound Med"},{"key":"197_CR2","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2021.780389","volume":"9","author":"A Bertini","year":"2022","unstructured":"Bertini A, Salas R, Chabert S, Sobrevia L, Pardo F (2022) Using machine learning to predict complications in pregnancy: a systematic review. Front Bioeng Biotechnol 9:780389. https:\/\/doi.org\/10.3389\/fbioe.2021.780389","journal-title":"Front Bioeng Biotechnol"},{"key":"197_CR3","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1002\/uog.26130","volume":"62","author":"R Ramirez Zegarra","year":"2023","unstructured":"Ramirez Zegarra R, Ghi T (2023) Use of artificial intelligence and deep learning in fetal ultrasound imaging. Ultrasound Obstet Gynecol 62:185\u2013194. https:\/\/doi.org\/10.1002\/uog.26130","journal-title":"Ultrasound Obstet Gynecol"},{"key":"197_CR4","doi-asserted-by":"publisher","first-page":"10521","DOI":"10.3390\/app131810521","volume":"13","author":"H Zhang","year":"2023","unstructured":"Zhang H, Qie Y (2023) Applying deep learning to medical imaging: a review. Appl Sci 13:10521. https:\/\/doi.org\/10.3390\/app131810521","journal-title":"Appl Sci"},{"key":"197_CR5","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1007\/BF01628221","volume":"210","author":"R Schuhmann","year":"1971","unstructured":"Schuhmann R, Wehler V (1971) Histologische Unterschiede an Placentazotten innerhalb der materno-fetalen Str\u00f6mungseinheit. Arch F\u00fcr Gyn\u00e4kol 210:425\u2013439. https:\/\/doi.org\/10.1007\/BF01628221","journal-title":"Arch F\u00fcr Gyn\u00e4kol"},{"key":"197_CR6","doi-asserted-by":"publisher","first-page":"720.e1","DOI":"10.1016\/j.ajog.2022.02.002","volume":"226","author":"B Salmanian","year":"2022","unstructured":"Salmanian B, Shainker SA, Hecht JL, Modes AM, Castro EC, Seaman RD (2022) The Society for Pediatric Pathology Task Force grading system for placenta accreta spectrum and its correlation with clinical outcomes. Am J Obstet Gynecol 226:720.e1\u2013720.e6. https:\/\/doi.org\/10.1016\/j.ajog.2022.02.002","journal-title":"Am J Obstet Gynecol"},{"key":"197_CR7","doi-asserted-by":"publisher","first-page":"9","DOI":"10.3390\/jpm12010009","volume":"12","author":"G Gatta","year":"2021","unstructured":"Gatta G, Di Grezia G, Cuccurullo V, Sardu C, Iovino F, Comune R et al (2021) MRI in pregnancy and precision medicine: a review from literature. J Pers Med 12:9. https:\/\/doi.org\/10.3390\/jpm12010009","journal-title":"J Pers Med"},{"key":"197_CR8","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1002\/uog.8831","volume":"37","author":"LJ Salomon","year":"2011","unstructured":"Salomon LJ, Alfirevic Z, Berghella V, Bilardo C, Hernandez-Andrade E, Johnsen SL et al (2011) Practice guidelines for performance of the routine mid-trimester fetal ultrasound scan. Ultrasound Obstet Gynecol 37:116\u2013126. https:\/\/doi.org\/10.1002\/uog.8831","journal-title":"Ultrasound Obstet Gynecol"},{"key":"197_CR9","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1002\/jcu.23424","volume":"51","author":"G Rizzo","year":"2023","unstructured":"Rizzo G, Tonni G (2023) Advances in maternal-fetal imaging. J Clin Ultrasound 51:213\u2013214. https:\/\/doi.org\/10.1002\/jcu.23424","journal-title":"J Clin Ultrasound"},{"key":"197_CR10","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1016\/S0140-6736(16)31723-8","volume":"389","author":"PD Griffiths","year":"2017","unstructured":"Griffiths PD, Bradburn M, Campbell MJ, Cooper CL, Graham R, Jarvis D et al (2017) Use of MRI in the diagnosis of fetal brain abnormalities in utero (MERIDIAN): a multicentre, prospective cohort study. The Lancet 389:538\u2013546. https:\/\/doi.org\/10.1016\/S0140-6736(16)31723-8","journal-title":"The Lancet"},{"key":"197_CR11","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1002\/uog.26129","volume":"61","author":"D Prayer","year":"2023","unstructured":"Prayer D, Malinger G, De Catte L, De Keersmaecker B, Gon\u00e7alves LF, Kasprian G et al (2023) ISUOG Practice Guidelines (updated): performance of fetal magnetic resonance imaging. Ultrasound Obstet Gynecol 61:278\u2013287. https:\/\/doi.org\/10.1002\/uog.26129","journal-title":"Ultrasound Obstet Gynecol"},{"key":"197_CR12","doi-asserted-by":"publisher","unstructured":"Guo P, Wu Y, Yuan X, Wan Z (2021) Clinical diagnostic value and analysis of MRI combined with ultrasound in prenatal pernicious placenta previa with placenta accreta. Ann Palliat Med 10:6753\u20136759. https:\/\/doi.org\/10.21037\/apm-21-1285","DOI":"10.21037\/apm-21-1285"},{"key":"197_CR13","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1177\/0284185119875644","volume":"61","author":"I Kumar","year":"2020","unstructured":"Kumar I, Verma A, Jain M, Shukla RC (2020) Structured evaluation and reporting in imaging of placenta and umbilical cord. Acta Radiol 61:685\u2013704. https:\/\/doi.org\/10.1177\/0284185119875644","journal-title":"Acta Radiol"},{"key":"197_CR14","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.ejrad.2008.06.031","volume":"68","author":"D Pugash","year":"2008","unstructured":"Pugash D, Brugger PC, Bettelheim D, Prayer D (2008) Prenatal ultrasound and fetal MRI: the comparative value of each modality in prenatal diagnosis. Eur J Radiol 68:214\u2013226. https:\/\/doi.org\/10.1016\/j.ejrad.2008.06.031","journal-title":"Eur J Radiol"},{"key":"197_CR15","doi-asserted-by":"publisher","first-page":"2639","DOI":"10.1038\/s41467-021-22695-y","volume":"12","author":"S Gong","year":"2021","unstructured":"Gong S, Gaccioli F, Dopierala J, Sovio U, Cook E, Volders PJ et al (2021) The RNA landscape of the human placenta in health and disease. Nat Commun 12:2639. https:\/\/doi.org\/10.1038\/s41467-021-22695-y","journal-title":"Nat Commun"},{"key":"197_CR16","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.placenta.2019.01.006","volume":"84","author":"C Konwar","year":"2019","unstructured":"Konwar C, Del Gobbo G, Yuan V, Robinson WP (2019) Considerations when processing and interpreting genomics data of the placenta. Placenta 84:57\u201362. https:\/\/doi.org\/10.1016\/j.placenta.2019.01.006","journal-title":"Placenta"},{"key":"197_CR17","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/s11701-024-01981-z","volume":"18","author":"G Sarwin","year":"2024","unstructured":"Sarwin G, Lussi J, Gervasoni S, Moehrlen U, Ochsenbein N, Nelson BJ et al (2024) Patient-specific placental vessel segmentation with limited data. J Robot Surg 18:237. https:\/\/doi.org\/10.1007\/s11701-024-01981-z","journal-title":"J Robot Surg"},{"key":"197_CR18","doi-asserted-by":"publisher","unstructured":"Fredriksson T, Mattos DI, Bosch J, Olsson HH (2020) Data labeling: an empirical investigation into industrial challenges and mitigation strategies. In: Morisio M, Torchiano M, Jedlitschka A (eds) Product-focused software process improvement. Springer International Publishing, Cham, pp 202\u2013216. https:\/\/doi.org\/10.1007\/978-3-030-64148-1_13","DOI":"10.1007\/978-3-030-64148-1_13"},{"key":"197_CR19","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1093\/humupd\/dmae006","volume":"30","author":"E Derisoud","year":"2024","unstructured":"Derisoud E, Jiang H, Zhao A, Chavatte-Palmer P, Deng Q (2024) Revealing the molecular landscape of human placenta: a systematic review and meta-analysis of single-cell RNA sequencing studies. Hum Reprod Update 30:410\u2013441. https:\/\/doi.org\/10.1093\/humupd\/dmae006","journal-title":"Hum Reprod Update"},{"issue":"4","key":"197_CR20","doi-asserted-by":"publisher","DOI":"10.1117\/1.NPh.10.4.044405","volume":"10","author":"C Bouchard","year":"2023","unstructured":"Bouchard C, Bernatchez R, Lavoie-Cardinal F (2023) Addressing annotation and data scarcity when designing machine learning strategies for neurophotonics. Neurophotonics 10(4): 044405. https:\/\/doi.org\/10.1117\/1.NPh.10.4.044405","journal-title":"Neurophotonics"},{"key":"197_CR21","doi-asserted-by":"publisher","first-page":"9890","DOI":"10.1109\/ACCESS.2022.3233110","volume":"11","author":"MY Ansari","year":"2023","unstructured":"Ansari MY, Chandrasekar V, Singh AV, Dakua SP (2023) Re-routing drugs to blood brain barrier: a comprehensive analysis of machine learning approaches with fingerprint amalgamation and data balancing. IEEE Access 11:9890\u20139906. https:\/\/doi.org\/10.1109\/ACCESS.2022.3233110","journal-title":"IEEE Access"},{"key":"197_CR22","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.placenta.2022.03.122","volume":"122","author":"NC Sobhani","year":"2022","unstructured":"Sobhani NC, Mernoff R, Abraha M, Okorie CN,\u00a0Marquez-Magana L,\u00a0Gaw SL et al (2022) Integrated analysis of transcriptomic datasets to identify placental biomarkers of spontaneous preterm birth. Placenta 122:66\u201373. https:\/\/doi.org\/10.1016\/j.placenta.2022.03.122","journal-title":"Placenta"},{"key":"197_CR23","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1007\/s00261-018-1755-1","volume":"44","author":"DC Oppenheimer","year":"2019","unstructured":"Oppenheimer DC, Mazaheri P, Ballard DH, Yano M, Fowler KJ (2019) Magnetic resonance imaging of the placenta and gravid uterus: a pictorial essay. Abdom Radiol 44:669\u2013684. https:\/\/doi.org\/10.1007\/s00261-018-1755-1","journal-title":"Abdom Radiol"},{"key":"197_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102639","volume":"83","author":"VA Zimmer","year":"2023","unstructured":"Zimmer VA, Gomez A, Skelton E, Wright R,\u00a0Wheeler G, Deng\u00a0S et al (2023) Placenta segmentation in ultrasound imaging: Addressing sources of uncertainty and limited field-of-view. Med Image Anal 83:102639. https:\/\/doi.org\/10.1016\/j.media.2022.102639","journal-title":"Med Image Anal"},{"key":"197_CR25","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1016\/j.placenta.2008.06.005","volume":"29","author":"M Yampolsky","year":"2008","unstructured":"Yampolsky M, Salafia CM, Shlakhter O,\u00a0Haas D,\u00a0Eucker\u00a0B,\u00a0Thorp J (2008) Modeling the variability of shapes of a human placenta. Placenta 29:790\u2013797. https:\/\/doi.org\/10.1016\/j.placenta.2008.06.005","journal-title":"Placenta"},{"key":"197_CR26","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1007\/s11548-020-02120-3","volume":"15","author":"SS Esfahani","year":"2020","unstructured":"Esfahani SS, Zhai X, Chen M, Amira A, Bensaali F, AbiNahed J et al (2020) Lattice-Boltzmann interactive blood flow simulation pipeline. Int J Comput Assist Radiol Surg 15:629\u2013639. https:\/\/doi.org\/10.1007\/s11548-020-02120-3","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"197_CR27","doi-asserted-by":"publisher","first-page":"1592","DOI":"10.1109\/JSYST.2019.2952459","volume":"14","author":"X Zhai","year":"2020","unstructured":"Zhai X, Chen M, Esfahani SS, Amira A, Bensaali F, Abinahed J et al (2020) Heterogeneous system-on-chip-based lattice-boltzmann visual simulation system. IEEE Syst J 14:1592\u20131601. https:\/\/doi.org\/10.1109\/JSYST.2019.2952459","journal-title":"IEEE Syst J"},{"key":"197_CR28","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.5184","volume":"31","author":"X Zhai","year":"2019","unstructured":"Zhai X, Amira A, Bensaali F, Al-Shibani A, Al-Nassr A, El-Sayed A et al (2019) Zynq SoC based acceleration of the lattice Boltzmann method. Concurr Comput Pract Exp 31:e5184. https:\/\/doi.org\/10.1002\/cpe.5184","journal-title":"Concurr Comput Pract Exp"},{"key":"197_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102629","volume":"83","author":"MC Fiorentino","year":"2023","unstructured":"Fiorentino MC, Villani FP, Di Cosmo M, Frontoni E, Moccia S (2023) A review on deep-learning algorithms for fetal ultrasound-image analysis. Med Image Anal 83:102629. https:\/\/doi.org\/10.1016\/j.media.2022.102629","journal-title":"Med Image Anal"},{"key":"197_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105680","volume":"88","author":"J Li","year":"2024","unstructured":"Li J, Shi Z, Zhu J, Liu J,\u00a0Qiu L, Song\u00a0Y\u00a0et al (2024) Placenta segmentation in magnetic resonance imaging: addressing position and shape of uncertainty and blurred placenta boundary. Biomed Signal Process Control 88:105680. https:\/\/doi.org\/10.1016\/j.bspc.2023.105680","journal-title":"Biomed Signal Process Control"},{"key":"197_CR31","doi-asserted-by":"publisher","unstructured":"Huang J, Do QN, Shahedi M, Xi Y, Lewis MA, Herrera CL et al (2023) Deep-learning based segmentation of the placenta and uterine cavity on prenatal MR images. In: Iftekharuddin KM, Chen W (eds) Medical Imaging 2023: Computer-Aided Diagnosis. SPIE, San Diego, p 21. https:\/\/doi.org\/10.1117\/12.2653659","DOI":"10.1117\/12.2653659"},{"key":"197_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2023.104337","volume":"145","author":"Q Ma","year":"2024","unstructured":"Ma Q, Song P, Yan C, Wang Y (2024) DCO-Net: deformable convolution-based O-shape network for fully automated placenta segmentation. Digit Signal Process 145:104337. https:\/\/doi.org\/10.1016\/j.dsp.2023.104337","journal-title":"Digit Signal Process"},{"key":"197_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107699","volume":"242","author":"C Lee","year":"2023","unstructured":"Lee C, Liao Z, Li Y, Lai Q, Guo Y, Huang J et al (2023) Placental MRI segmentation based on multi-receptive field and mixed attention separation mechanism. Comput Methods Programs Biomed 242:107699. https:\/\/doi.org\/10.1016\/j.cmpb.2023.107699","journal-title":"Comput Methods Programs Biomed"},{"key":"197_CR34","doi-asserted-by":"publisher","first-page":"180083","DOI":"10.1109\/ACCESS.2019.2958133","volume":"7","author":"M Han","year":"2019","unstructured":"Han M, Bao Y, Sun Z, Wen S, Xia L, Zhao J (2019) Automatic segmentation of human placenta images with U-Net. IEEE Access 7:180083\u2013180092. https:\/\/doi.org\/10.1109\/ACCESS.2019.2958133","journal-title":"IEEE Access"},{"key":"197_CR35","doi-asserted-by":"publisher","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A (eds) Medical image computing and computer-assisted intervention \u2013 MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9351. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"197_CR36","doi-asserted-by":"publisher","unstructured":"Feng Q, Luo L, Zhang X, Chen Y (2023) CTW-Net: a deeper multiscale feature fusion w-shaped network for medical image segmentation. In: Proceedings of 2023 IEEE 9th international conference on cloud computing and intelligent system, IEEE, Dali, 12\u201313 August 2023. https:\/\/doi.org\/10.1109\/CCIS59572.2023.10262902","DOI":"10.1109\/CCIS59572.2023.10262902"},{"key":"197_CR37","doi-asserted-by":"publisher","first-page":"5355","DOI":"10.1109\/TNNLS.2022.3204090","volume":"35","author":"H Du","year":"2024","unstructured":"Du H, Wang J, Liu M, Wang N, Meijering E (2024) SwinPA-Net: Swin transformer-based multiscale feature pyramid aggregation network for medical image segmentation. IEEE Trans Neural Netw Learn Syst 35:5355\u20135366. https:\/\/doi.org\/10.1109\/TNNLS.2022.3204090","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"197_CR38","doi-asserted-by":"publisher","unstructured":"Misra D (2019) Mish: a self regularized non-monotonic activation function. arXiv:1908.08681 [cs.LG]. https:\/\/doi.org\/10.48550\/arXiv.1908.08681","DOI":"10.48550\/arXiv.1908.08681"},{"key":"197_CR39","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.mri.2024.02.014","volume":"109","author":"J Xia","year":"2024","unstructured":"Xia J, Hu Y, Huang Z, Chen S, Huang L, Ruan Q et al (2024) A novel MRI-based diagnostic model for predicting placenta accreta spectrum. Magn Reson Imaging 109:34\u201341. https:\/\/doi.org\/10.1016\/j.mri.2024.02.014","journal-title":"Magn Reson Imaging"},{"key":"197_CR40","doi-asserted-by":"publisher","unstructured":"Huang H, Lin L, Tong R, Hu H, Zhang Q, Iwamoto Y et al (2020) UNet 3+: a full-scale connected Unet for medical image segmentation. arXiv:2004.08790 [eess.IV]. https:\/\/doi.org\/10.48550\/ARXIV.2004.08790","DOI":"10.48550\/ARXIV.2004.08790"},{"key":"197_CR41","doi-asserted-by":"publisher","unstructured":"Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H (2017) Deformable convolutional networks. In: Proceedings of 2017 IEEE international conference on computer vision, IEEE, Venice, 22\u201329 October 2017. https:\/\/doi.org\/10.1109\/ICCV.2017.89","DOI":"10.1109\/ICCV.2017.89"},{"key":"197_CR42","doi-asserted-by":"publisher","unstructured":"Xiang T, Zhang C, Liu D, Song Y, Huang H, Cai W (2020) BiO-Net: learning recurrent bi-directional connections for encoder-decoder architecture. In: Martel AL, Abolmaesumi P, Stoyanov D, Mateus D, Zuluaga MA, Zhou SK (eds) Medical image computing and computer assisted intervention \u2013 MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12261. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-59710-8_8","DOI":"10.1007\/978-3-030-59710-8_8"},{"key":"197_CR43","doi-asserted-by":"publisher","first-page":"1042","DOI":"10.1109\/TMM.2019.2937688","volume":"22","author":"Z He","year":"2020","unstructured":"He Z, Cao Y, Du L, Xu B, Yang J, Cao Y (2020) MRFN: multi-receptive-field network for fast and accurate single image super-resolution. IEEE Trans Multimed 22:1042\u20131054. https:\/\/doi.org\/10.1109\/TMM.2019.2937688","journal-title":"IEEE Trans Multimed"},{"key":"197_CR44","doi-asserted-by":"publisher","unstructured":"Liang X, Liu Z, Ouyang C (2018) A multi-sentiment classifier based on GRU and attention mechanism. In: Proceedings of 2018 IEEE 9th international conference on software engineering and service science, IEEE, Beijing, 23\u201325 November 2018. https:\/\/doi.org\/10.1109\/ICSESS.2018.8663799","DOI":"10.1109\/ICSESS.2018.8663799"},{"key":"197_CR45","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.placenta.2023.02.009","volume":"134","author":"CPS Kulseng","year":"2023","unstructured":"Kulseng CPS, Hillestad V, Eskild A, Gjesdal KI (2023) Automatic placental and fetal volume estimation by a convolutional neural network. Placenta 134:23\u201329. https:\/\/doi.org\/10.1016\/j.placenta.2023.02.009","journal-title":"Placenta"},{"key":"197_CR46","doi-asserted-by":"publisher","unstructured":"Huang G, Liu Z, van der Maaten L, Weinberger KQ (2016) Densely connected convolutional networks. In: Proceedings of 2017 IEEE conference on computer vision and pattern recognition, Honolulu, 21\u201326 July 2017. https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"197_CR47","doi-asserted-by":"publisher","unstructured":"Milletari F, Navab N, Ahmadi SA (2016) V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: Proceedings of 2016 fourth international conference on 3D vision, IEEE, Stanford, 25\u201328 October 2016. https:\/\/doi.org\/10.1109\/3DV.2016.79","DOI":"10.1109\/3DV.2016.79"},{"key":"197_CR48","doi-asserted-by":"publisher","unstructured":"Zhang Y, Yang Q (2017) A survey on multi-task learning. arXiv:1707.08114 [cs.LG]. https:\/\/doi.org\/10.48550\/arXiv.1707.08114","DOI":"10.48550\/arXiv.1707.08114"},{"key":"197_CR49","doi-asserted-by":"publisher","unstructured":"Zhang H, Dana K, Shi J, Zhang Z, Wang X, Tyagi A (2018) Context encoding for semantic segmentation. In: Proceedings of 2018 IEEE\/CVF conference on computer vision and pattern recognition, IEEE, Salt Lake City, 18\u201323 June 2018. https:\/\/doi.org\/10.1109\/CVPR.2018.00747","DOI":"10.1109\/CVPR.2018.00747"},{"key":"197_CR50","doi-asserted-by":"publisher","first-page":"2221","DOI":"10.1038\/s41598-023-29105-x","volume":"13","author":"LA Andreasen","year":"2023","unstructured":"Andreasen LA, Feragen A, Christensen AN, Thybo JK, Svendsen MBS, Zepf K et al (2023) Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization. Sci Rep 13:2221. https:\/\/doi.org\/10.1038\/s41598-023-29105-x","journal-title":"Sci Rep"},{"key":"197_CR51","doi-asserted-by":"publisher","unstructured":"He K, Gkioxari G, Dollar P, Girshick R (2017) Mask R-CNN. In: Proceedings of 2017 IEEE international conference on computer vision, IEEE, Venice, 22\u201329 October 2017. https:\/\/doi.org\/10.1109\/ICCV.2017.322","DOI":"10.1109\/ICCV.2017.322"},{"key":"197_CR52","doi-asserted-by":"publisher","first-page":"1176","DOI":"10.1002\/pd.6411","volume":"43","author":"R Horgan","year":"2023","unstructured":"Horgan R, Nehme L, Abuhamad A (2023) Artificial intelligence in obstetric ultrasound: A scoping review. Prenat Diagn 43:1176\u20131219. https:\/\/doi.org\/10.1002\/pd.6411","journal-title":"Prenat Diagn"},{"key":"197_CR53","doi-asserted-by":"publisher","first-page":"3298","DOI":"10.3390\/jcm12093298","volume":"12","author":"S Xiao","year":"2023","unstructured":"Xiao S, Zhang J, Zhu Y, Zhang Z, Cao H, Xie M et al (2023) Application and progress of artificial intelligence in fetal ultrasound. J Clin Med 12:3298. https:\/\/doi.org\/10.3390\/jcm12093298","journal-title":"J Clin Med"},{"key":"197_CR54","doi-asserted-by":"publisher","unstructured":"Hu R, Singla R, Yan R, Mayer C, Rohling RN (2019) Automated placenta segmentation with a convolutional neural network weighted by acoustic shadow detection. In: Proceedings of 41st annual international conference of the IEEE Engineering in medicine and biology society, IEEE, Berlin, 23\u201327 July 2019. https:\/\/doi.org\/10.1109\/EMBC.2019.8857448","DOI":"10.1109\/EMBC.2019.8857448"},{"key":"197_CR55","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s00330-020-07113-z","volume":"31","author":"X Zheng","year":"2021","unstructured":"Zheng X, Lyu G, Gan Y, Hu M, Liu X, Chen S et al (2021) Microcystic pattern and shadowing are independent predictors of ovarian borderline tumors and cystadenofibromas in ultrasound. Eur Radiol 31:45\u201354. https:\/\/doi.org\/10.1007\/s00330-020-07113-z","journal-title":"Eur Radiol"},{"key":"197_CR56","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.ultrasmedbio.2022.08.006","volume":"49","author":"AD Gleed","year":"2023","unstructured":"Gleed AD, Chen Q, Jackman J, Mishra D, Chandramohan V, Self A et al (2023) Automatic image guidance for assessment of placenta location in ultrasound video sweeps. Ultrasound Med Biol 49:106\u2013121. https:\/\/doi.org\/10.1016\/j.ultrasmedbio.2022.08.006","journal-title":"Ultrasound Med Biol"},{"key":"197_CR57","doi-asserted-by":"publisher","unstructured":"Zheng S, Jayasumana S, Romera-Paredes B, Vineet V, Su Z, Du D et al (2015) Conditional random fields as recurrent neural networks. In: Proceedings of 2015 IEEE international conference on computer vision, IEEE, Santiago, 7\u201313 December 2015. https:\/\/doi.org\/10.1109\/ICCV.2015.179","DOI":"10.1109\/ICCV.2015.179"},{"key":"197_CR58","doi-asserted-by":"publisher","unstructured":"Zimmer VA, Gomez A, Skelton E, Toussaint N, Zhang T, Khanal B et al (2019) Towards whole placenta segmentation at late gestation using multi-view ultrasound images. In: Shen D, Liu T, Peters TM, Staib LH, Essert C, Zhou S et al (eds) Medical image computing and computer assisted intervention \u2013 MICCAI 2019. Springer International Publishing, Cham, pp 628\u2013636. https:\/\/doi.org\/10.1007\/978-3-030-32254-0_70","DOI":"10.1007\/978-3-030-32254-0_70"},{"key":"197_CR59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1117\/1.JMI.7.1.014004","volume":"7","author":"I Oguz","year":"2020","unstructured":"Oguz I, Yushkevich N, Pouch AM, Oguz BU, Wang J, Parameshwaran S et al (2020) Minimally interactive placenta segmentation from three-dimensional ultrasound images. J Med Imaging 7:1. https:\/\/doi.org\/10.1117\/1.JMI.7.1.014004","journal-title":"J Med Imaging"},{"key":"197_CR60","doi-asserted-by":"publisher","first-page":"1539","DOI":"10.1109\/TIP.2010.2042099","volume":"19","author":"A Qiu","year":"2010","unstructured":"Qiu A, Brown T, Fischl B, Ma J, Miller MI (2010) Atlas generation for subcortical and ventricular structures with its applications in shape analysis. IEEE Trans Image Process 19:1539\u20131547. https:\/\/doi.org\/10.1109\/TIP.2010.2042099","journal-title":"IEEE Trans Image Process"},{"key":"197_CR61","doi-asserted-by":"publisher","first-page":"1869","DOI":"10.1007\/s11548-020-02256-2","volume":"15","author":"E Perera-Bel","year":"2020","unstructured":"Perera-Bel E, Ceresa M, Torrents-Barrena J, Masoller N, Valenzuela-Alcaraz B, Gratac\u00f3s E et al (2020) Segmentation of the placenta and its vascular tree in Doppler ultrasound for fetal surgery planning. Int J Comput Assist Radiol Surg 15:1869\u20131879. https:\/\/doi.org\/10.1007\/s11548-020-02256-2","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"197_CR62","doi-asserted-by":"publisher","first-page":"1768","DOI":"10.1109\/TPAMI.2006.233","volume":"28","author":"L Grady","year":"2006","unstructured":"Grady L (2006) Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell 28:1768\u20131783. https:\/\/doi.org\/10.1109\/TPAMI.2006.233","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"197_CR63","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1186\/s13059-019-1738-8","volume":"20","author":"LM Weber","year":"2019","unstructured":"Weber LM, Saelens W, Cannoodt R, Soneson C, Hapfelmeier A, Gardner PP et al (2019) Essential guidelines for computational method benchmarking. Genome Biol 20:125. https:\/\/doi.org\/10.1186\/s13059-019-1738-8","journal-title":"Genome Biol"},{"key":"197_CR64","doi-asserted-by":"publisher","unstructured":"Yan Z, Sun W, Zhou R, Yuan Z, Zhang K, Li Y et al (2024) Biomedical SAM 2: segment anything in biomedical images and videos. arXiv:2408.03286 [cs.CV]. https:\/\/doi.org\/10.48550\/arXiv.2408.03286","DOI":"10.48550\/arXiv.2408.03286"},{"key":"197_CR65","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","volume":"15","author":"J Ma","year":"2024","unstructured":"Ma J, He Y, Li F, Han L, You C, Wang B (2024) Segment anything in medical images. Nat Commun 15:654. https:\/\/doi.org\/10.1038\/s41467-024-44824-z","journal-title":"Nat Commun"},{"key":"197_CR66","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102918","volume":"89","author":"MA Mazurowski","year":"2023","unstructured":"Mazurowski MA, Dong H, Gu H, Yang J, Konz N, Zhang Y (2023) Segment anything model for medical image analysis: an experimental study. Med Image Anal 89:102918. https:\/\/doi.org\/10.1016\/j.media.2023.102918","journal-title":"Med Image Anal"},{"key":"197_CR67","doi-asserted-by":"publisher","unstructured":"Zhang Y, Shen Z, Jiao R (2024) Segment anything model for medical image segmentation: current applications and future directions. arXiv:2401.03495 [eess.IV]. https:\/\/doi.org\/10.48550\/arXiv.2401.03495","DOI":"10.48550\/arXiv.2401.03495"},{"key":"197_CR68","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2024.101504","volume":"47","author":"MdE Rayed","year":"2024","unstructured":"Rayed MdE, Islam SMS, Niha SI, Jim JR, Kabir MM, Mridha MF (2024) Deep learning for medical image segmentation: state-of-the-art advancements and challenges. Inform Med Unlocked 47:101504. https:\/\/doi.org\/10.1016\/j.imu.2024.101504","journal-title":"Inform Med Unlocked"},{"key":"197_CR69","doi-asserted-by":"publisher","unstructured":"Saavedra AC, Arroyo J, Tamayo L, Egoavil M, Ramos B, Castaneda B (2020) Automatic ultrasound assessment of placenta previa during the third trimester for rural areas. In: Proceedings of 2020 IEEE international ultrasonics symposium, IEEE, Las Vegas, 7\u201311 September 2020. https:\/\/doi.org\/10.1109\/IUS46767.2020.9251764","DOI":"10.1109\/IUS46767.2020.9251764"},{"key":"197_CR70","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1097\/JS9.0000000000000212","volume":"109","author":"Y Wu","year":"2023","unstructured":"Wu Y, Li S, Yuan J, Zhang H, Wang M, Zhang Z et al (2023) Benchmarking: a novel measuring tool for outcome comparisons in surgery. Int J Surg 109:419\u2013428. https:\/\/doi.org\/10.1097\/JS9.0000000000000212","journal-title":"Int J Surg"},{"key":"197_CR71","doi-asserted-by":"publisher","first-page":"1958","DOI":"10.1016\/j.ultrasmedbio.2013.05.017","volume":"39","author":"X Meng","year":"2013","unstructured":"Meng X, Xie L, Song W (2013) Comparing the diagnostic value of ultrasound and magnetic resonance imaging for placenta accreta: a systematic review and meta-analysis. Ultrasound Med Biol 39:1958\u20131965. https:\/\/doi.org\/10.1016\/j.ultrasmedbio.2013.05.017","journal-title":"Ultrasound Med Biol"},{"key":"197_CR72","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106478","volume":"153","author":"MY Ansari","year":"2023","unstructured":"Ansari MY, Yang Y, Meher PK, Dakua SP (2023) Dense-PSP-UNet: a neural network for fast inference liver ultrasound segmentation. Comput Biol Med 153:106478. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106478","journal-title":"Comput Biol Med"},{"key":"197_CR73","doi-asserted-by":"publisher","first-page":"14153","DOI":"10.1038\/s41598-022-16828-6","volume":"12","author":"MY Ansari","year":"2022","unstructured":"Ansari MY, Yang Y, Balakrishnan S, Abinahed J, Al-Ansari A, Warfa M et al (2022) A lightweight neural network with multiscale feature enhancement for liver CT segmentation. Sci Rep 12:14153. https:\/\/doi.org\/10.1038\/s41598-022-16828-6","journal-title":"Sci Rep"},{"key":"197_CR74","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1590\/0100-3984.2021.0115","volume":"55","author":"NH Concatto","year":"2022","unstructured":"Concatto NH, Westphalen SS, Vanceta R, Schuch A, Luersen GF, Ghezzi CLA (2022) Magnetic resonance imaging findings in placenta accreta spectrum disorders: pictorial essay. Radiol Bras 55:181\u2013187. https:\/\/doi.org\/10.1590\/0100-3984.2021.0115","journal-title":"Radiol Bras"},{"key":"197_CR75","doi-asserted-by":"publisher","first-page":"198","DOI":"10.3348\/kjr.2020.0580","volume":"22","author":"S Srisajjakul","year":"2021","unstructured":"Srisajjakul S, Prapaisilp P, Bangchokdee S (2021) Magnetic resonance imaging of placenta accreta spectrum: a step-by-step approach. Korean J Radiol 22:198. https:\/\/doi.org\/10.3348\/kjr.2020.0580","journal-title":"Korean J Radiol"},{"key":"197_CR76","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1053\/j.semperi.2015.01.004","volume":"39","author":"NN Andescavage","year":"2015","unstructured":"Andescavage NN, Du Plessis A, Limperopoulos C (2015) Advanced MR imaging of the placenta: exploring the in utero placenta-brain connection. Semin Perinatol 39:113\u2013123. https:\/\/doi.org\/10.1053\/j.semperi.2015.01.004","journal-title":"Semin Perinatol"},{"key":"197_CR77","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1002\/jmri.24850","volume":"42","author":"M Zaitsev","year":"2015","unstructured":"Zaitsev M, Maclaren J, Herbst M (2015) Motion artifacts in MRI: a complex problem with many partial solutions. J Magn Reson Imaging 42:887\u2013901. https:\/\/doi.org\/10.1002\/jmri.24850","journal-title":"J Magn Reson Imaging"},{"key":"197_CR78","doi-asserted-by":"publisher","unstructured":"Vahedifard F, Adepoju JO, Supanich M, Ai HA, Liu X, Kocak M et al (2023) Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging. World J Clin Cases 11:3725\u20133735. https:\/\/doi.org\/10.12998\/wjcc.v11.i16.3725","DOI":"10.12998\/wjcc.v11.i16.3725"},{"key":"197_CR79","doi-asserted-by":"publisher","first-page":"1426593","DOI":"10.3389\/fcvm.2024.1426593","volume":"11","author":"E Sadiku","year":"2024","unstructured":"Sadiku E, Sun L, Macgowan CK, Seed M, Morrison JL (2024) Advanced magnetic resonance imaging in human placenta: insights into fetal growth restriction and congenital heart disease. Front Cardiovasc Med 11:1426593. https:\/\/doi.org\/10.3389\/fcvm.2024.1426593","journal-title":"Front Cardiovasc Med"},{"key":"197_CR80","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.media.2019.03.008","volume":"54","author":"J Torrents-Barrena","year":"2019","unstructured":"Torrents-Barrena J, Piella G, Masoller N, Gratac\u00f3s E, Eixarch E, Ceresa M et al (2019) Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI. Med Image Anal 54:263\u2013279. https:\/\/doi.org\/10.1016\/j.media.2019.03.008","journal-title":"Med Image Anal"},{"key":"197_CR81","doi-asserted-by":"publisher","first-page":"6149","DOI":"10.1007\/s00330-019-06373-8","volume":"29","author":"N Siauve","year":"2019","unstructured":"Siauve N (2019) How and why should the radiologist look at the placenta? Eur Radiol 29:6149\u20136151. https:\/\/doi.org\/10.1007\/s00330-019-06373-8","journal-title":"Eur Radiol"},{"key":"197_CR82","doi-asserted-by":"publisher","first-page":"701","DOI":"10.2214\/AJR.17.19303","volume":"211","author":"C Bourgioti","year":"2018","unstructured":"Bourgioti C, Zafeiropoulou K, Fotopoulos S, Nikolaidou ME, Antoniou A, Tzavara C et al (2018) MRI features predictive of invasive placenta with extrauterine spread in high-risk gravid patients: a prospective evaluation. Am J Roentgenol 211:701\u2013711. https:\/\/doi.org\/10.2214\/AJR.17.19303","journal-title":"Am J Roentgenol"},{"key":"197_CR83","doi-asserted-by":"publisher","first-page":"5923","DOI":"10.1007\/s00330-020-07006-1","volume":"30","author":"ML Kromrey","year":"2020","unstructured":"Kromrey ML, Tamada D, Johno H, Funayama S, Nagata N, Ichikawa S et al (2020) Reduction of respiratory motion artifacts in gadoxetate-enhanced MR with a deep learning-based filter using convolutional neural network. Eur Radiol 30:5923\u20135932. https:\/\/doi.org\/10.1007\/s00330-020-07006-1","journal-title":"Eur Radiol"},{"key":"197_CR84","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/s13721-023-00412-7","volume":"12","author":"Y Regaya","year":"2023","unstructured":"Regaya Y, Amira A, Dakua SP (2023) Development of a cerebral aneurysm segmentation method to prevent sentinel hemorrhage. Netw Model Anal Health Inform Bioinforma 12:18. https:\/\/doi.org\/10.1007\/s13721-023-00412-7","journal-title":"Netw Model Anal Health Inform Bioinforma"},{"key":"197_CR85","doi-asserted-by":"publisher","first-page":"2165","DOI":"10.1007\/s11548-019-02030-z","volume":"14","author":"SP Dakua","year":"2019","unstructured":"Dakua SP, Abinahed J, Zakaria A, Balakrishnan S, Younes G, Navkar N et al (2019) Moving object tracking in clinical scenarios: application to cardiac surgery and cerebral aneurysm clipping. Int J Comput Assist Radiol Surg 14:2165\u20132176. https:\/\/doi.org\/10.1007\/s11548-019-02030-z","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"197_CR86","doi-asserted-by":"publisher","unstructured":"Qi H, Collins S, Noble JA (2019) UPI-Net: semantic contour detection in placental ultrasound. In: Proceedings of 2019 IEEE\/CVF international conference on computer vision workshop, IEEE, Seoul, 27\u201328 October 2019. https:\/\/doi.org\/10.1109\/ICCVW.2019.00053","DOI":"10.1109\/ICCVW.2019.00053"},{"key":"197_CR87","doi-asserted-by":"publisher","first-page":"4056","DOI":"10.1080\/14767058.2020.1846699","volume":"35","author":"E Barzilay","year":"2022","unstructured":"Barzilay E, Brandt B, Gilboa Y, Kassif E, Achiron R, Raviv-Zilka L et al (2022) Comparative analysis of ultrasound and MRI in the diagnosis of placenta accreta spectrum. J Matern Fetal Neonatal Med 35:4056\u20134059. https:\/\/doi.org\/10.1080\/14767058.2020.1846699","journal-title":"J Matern Fetal Neonatal Med"},{"key":"197_CR88","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1055\/s-0034-1371712","volume":"31","author":"E Rubesova","year":"2014","unstructured":"Rubesova E, Barth R (2014) Advances in fetal imaging. Am J Perinatol 31:567\u2013576. https:\/\/doi.org\/10.1055\/s-0034-1371712","journal-title":"Am J Perinatol"},{"key":"197_CR89","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1186\/s12913-021-06160-6","volume":"21","author":"E Reponen","year":"2021","unstructured":"Reponen E, Rundall TG, Shortell SM, Blodgett JC, Juarez A, Jokela R et al (2021) Benchmarking outcomes on multiple contextual levels in lean healthcare: a systematic review, development of a conceptual framework, and a research agenda. BMC Health Serv Res 21:161. https:\/\/doi.org\/10.1186\/s12913-021-06160-6","journal-title":"BMC Health Serv Res"},{"key":"197_CR90","doi-asserted-by":"publisher","unstructured":"Lo Storto C, Goncharuk AG (2017) Efficiency vs effectiveness: a benchmarking study on European healthcare systems. Econ Sociol 10:102\u2013115. https:\/\/doi.org\/10.14254\/2071-789X.2017\/10-3\/8","DOI":"10.14254\/2071-789X.2017\/10-3\/8"}],"container-title":["Visual Computing for Industry, Biomedicine, and Art"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42492-025-00197-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s42492-025-00197-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42492-025-00197-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T06:49:21Z","timestamp":1752562161000},"score":1,"resource":{"primary":{"URL":"https:\/\/vciba.springeropen.com\/articles\/10.1186\/s42492-025-00197-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,15]]},"references-count":90,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["197"],"URL":"https:\/\/doi.org\/10.1186\/s42492-025-00197-8","relation":{},"ISSN":["2524-4442"],"issn-type":[{"value":"2524-4442","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,15]]},"assertion":[{"value":"30 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 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 of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"17"}}