Automatic detection and measurement of nuchal translucency
Open accessAbstract
In this paper we propose a new methodology to support the physician both to identify automatically the nuchal region and to obtain a correct thickness measurement of the nuchal translucency. The thickness of the nuchal translucency is one of the main markers for screening of chromosomal defects such as trisomy 13, 18 and 21. Its measurement is performed during ultrasound scanning in the first trimester of pregnancy. The proposed methodology is mainly based on wavelet and multi resolution analysis. The performance of our method was analysed on 382 random frames, representing mid-sagittal sections, uniformly extracted from real clinical ultrasound videos of 12 patients. According to the ground-truth provided by an expert physician, we obtained a true positive rate equal to 99.95% with respect to the nuchal region detection and about 64% of measurements present an error equal to 1 pixel (which corresponds to 0.1mm), respectively.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A potential benefit of first trimester AI models was a highly accurate non-invasive method for anomaly screening.
“The potential benefits of these models were the highly accurate non-invasive method for anomaly screening [134]”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [134]
The Abstract (p. 1) describes an automatic first-trimester ultrasound method for measuring nuchal translucency, a marker used to screen for chromosomal defects. The Conclusions (pp. 17–18) call ultrasound noninvasive and report high accuracy for locating the nuchal region, but the paper does not establish that the method itself is highly accurate at detecting fetal anomalies. (LLM, conf=0.88)
Experimental — accuracy not yet measuredWalker et al., Zhang et al., Sciortino et al., and Deng et al. introduced AI models for automated detection and measurement of NT.
“The research groups Walker et al. [132], Zhang et al. [133], Sciortino et al. [134], and Deng et al. [135] addressed the time consuming process of NT measurement by introducing AI models for its automated detection and measurement”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [134]
The Results and discussions section (p. 15) says Sciortino et al.’s methodology detects and measures nuchal translucency automatically. But the Wavelet image processing section (p. 8) describes a wavelet-based image-processing algorithm, not an AI model; that characterization is not supported by our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredA limitation of first trimester AI models was missing real-time application for clinical applicability.
“and missing real-time application for clinical applicability [133,134,138].”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [134]
The Abstract (p. 1) and Dataset description (p. 7) describe testing on frames extracted from clinical ultrasound videos, but do not assess real-time operation or identify its absence as a limitation. The Conclusions (pp. 17–18) discuss clinical use without addressing whether the method runs in real time. Searches for real-time application and processing time found no decisive passage. (LLM, conf=0.72)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Scale-space and edge detection using anisotropic diffusion
Perona, P., & Malik, J. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.56205 · Cited by 12,143 · see citing works
Morphological Image Analysis: Principles and Applications
(2000) · Sensor Review · doi:10.1108/sr.2000.08720cae.001 · Cited by 3,537 · see citing works
The discrete wavelet transform: wedding the a trous and Mallat algorithms
Shensa, M. J. (1992) · IEEE Transactions on Signal Processing · doi:10.1109/78.157290 · Cited by 1,997 · see citing works
Nicolaides, K. H., Azar, G. B., Byrne, D. L., Mansur, C. A., & Marks, K. (1992) · BMJ · doi:10.1136/bmj.304.6831.867 · Cited by 1,051 · see citing works
RANDOMISED CONTROLLED TRIAL OF GENETIC AMNIOCENTESIS IN 4606 LOW-RISK WOMEN
Tabor, A., Madsen, M. E., Obel, E., Philip, J., Bang, J., & Gaard-Pedersen, B. (1986) · The Lancet · doi:10.1016/s0140-6736(86)91218-3 · Cited by 866 · see citing works
Fast radial symmetry for detecting points of interest
Loy, G., & Zelinsky, A. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1217601 · Cited by 601 · see citing works
Nicolaides, K. H., Brizot, M. D. L., & Snijders, R. J. M. (1994) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/j.1471-0528.1994.tb11946.x · Cited by 353 · see citing works
Serum screening for Down's syndrome between 8 and 14 weeks of pregnancy
Wald, N., George, L., Smith, D., Densem, J. W., & Pettersonm, K. (1996) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/j.1471-0528.1996.tb09765.x · Cited by 199 · see citing works
González-Audícana, M., Otazu, X., Fors, O., & Seco, A. (2005) · International Journal of Remote Sensing · doi:10.1080/01431160512331314056 · Cited by 189 · see citing works
Semi‐automated system for measurement of nuchal translucency thickness
Moratalla, J., Pintoffl, K., Minekawa, R., Lachmann, R., Wright, D., & Nicolaides, K. H. (2010) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.7737 · Cited by 64 · see citing works
The Discrete Wavelet Transform
Shensa, M. J. (1991) · doi:10.21236/ada239642 · Cited by 58 · see citing works
Lee, Y.-B., Kim, M., & Kim, M. (2007) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-007-0225-7 · Cited by 37 · see citing works
A hierarchical model for automatic nuchal translucency detection from ultrasound images
Deng, Y., Wang, Y., Chen, P., & Yu, J. (2012) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2012.04.002 · Cited by 31 · see citing works
Bernardino, F., Cardoso, R., Montenegro, N. A. M. D. M., Bernardes, J., & de Sá, J. M. (1998) · Ultrasound in Medicine & Biology · doi:10.1016/s0301-5629(97)00235-4 · Cited by 31 · see citing works
Automated detection of fetal nuchal translucency based on hierarchical structural model
Deng, Y., Wang, Y., & Chen, P. (2010) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2010.6042618 · Cited by 25 · see citing works
Nicolaides, K. H., & Węgrzyn, P. (2005) · PubMed · Cited by 22 · see citing works
Ultrasonic marker pattern recognition and measurement using artificial neural network
Supriyanto, E., Lai, K. W., & Min, T. Y. (2010) · International Conference on Signal Processing · Cited by 20 · see citing works
A Semi-automated Method for the Measurement of the Fetal Nuchal Translucency in Ultrasound Images
Catanzariti, E., Fusco, G., Isgrò, F., Masecchia, S., Prevete, R., & Santoro, M. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04146-4_66 · Cited by 18 · see citing works
Anzalone, A., Fusco, G., Isgrò, F., Orlandi, E., Prevete, R., Sciortino, G., Tegolo, D., & Valenti, C. F. (2013) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2013.6627795 · Cited by 17 · see citing works
Self-citationAutomatic Nuchal Translucency Measurement from Ultrasonography
Park, J., Sofka, M., Lee, S., Kim, D.-Y., & Zhou, S. K. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40760-4_31 · Cited by 16 · see citing works
Loizou, C. P., Kasparis, T., Lazarou, T., Pattichis, C. S., & Pantziaris, M. (2014) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2014.08.003 · Cited by 16 · see citing works
Sciortino, G., Orlandi, E., Valenti, C., & Tegolo, D. (2016) · Image Analysis & Stereology · doi:10.5566/ias.1352 · Cited by 16 · see citing works
Self-citationSegmentation of colon tissue sample images using multiple graphics accelerators
Szénási, S. (2014) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2014.05.002 · Cited by 13 · see citing works
Estimating Fetal Nuchal Translucency Parameters from its Ultrasound Image
Deng, Y., Wang, Y., & Chen, P. (2008) · International Conference on Bioinformatics and Biomedical Engineering · doi:10.1109/icbbe.2008.994 · Cited by 12 · see citing works
Nirmala, S., & Palanisamy, V. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1001.1986 · Cited by 8 · see citing works
Sonia, R., & Shanthi, V. (2015) · Research Journal of Applied Sciences Engineering and Technology · doi:10.19026/rjaset.9.1385 · Cited by 5 · see citing works
Flagged first, then by citations
1 of the 27 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [23][23] R. Jain, R. Kasturi, B. Schunck, Machine Vision, McGraw-Hill, 1995. -
Cited by
Sorted by citations
Mutant p53 induces Golgi tubulo-vesiculation driving a prometastatic secretome
Capaci, V., Bascetta, L., Fantuz, M., Beznoussenko, G. V., Sommaggio, R., Cancila, V., Bisso, A., Campaner, E., Aa, M., Wiśniewski, J. R., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17596-5 · Cited by 85 · see citing works
Fully Convolutional DenseNet with Multiscale Context for Automated Breast Tumor Segmentation
Hai, J., Qiao, K., Chen, J., Tan, H., Jingbo, X., Zeng, L., Shi, D., & Yan, B. (2019) · Journal of Healthcare Engineering · doi:10.1155/2019/8415485 · Cited by 74 · see citing works
Artificial intelligence in obstetric ultrasound: A scoping review.
Horgan, R., Nehme, L., & Abuhamad, A. (2023) · Prenatal Diagnosis · doi:10.1002/pd.6411 · Cited by 72 · see citing works
Application and Progress of Artificial Intelligence in Fetal Ultrasound
Xiao, S., Zhang, J., Zhu, Y. C., Zhang, Z., Cao, H., Xie, M., & Zhang, L. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12093298 · Cited by 74 · see citing works
Artificial Intelligence in Prenatal Ultrasound Diagnosis
He, F., Wang, Y., Xiu, Y., Zhang, Y., & Chen, L. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.729978 · Cited by 62 · see citing works
Use of artificial intelligence in obstetrics: Not quite ready for prime time
Sarno, L., Neola, D., Carbone, L., Saccone, G., Carlea, A., Miceli, M., Iorio, G. G., Mappa, I., Rizzo, G., Girolamo, R. D., et al. (2023) · American Journal of Obstetrics & Gynecology MFM · doi:10.1016/j.ajogmf.2022.100792 · Cited by 47 · see citing works
Jost, E., Kosian, P., Jimenez Cruz, J., Albarqouni, S., Gembruch, U., Strizek, B., & Recker, F. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12216833 · Cited by 44 · see citing works
“Limitations could be uncovered when assessing fetal limbs due to its small anatomy and close surroundings [136–138], small data sets in rare anomalies [132,133], and missing real-time application for clinical applicability [133,134,138].”
BackgroundMethodApplications of artificial intelligence in obstetrics
Kim, H. Y., Cho, G. J., & Kwon, H. S. (2022) · ULTRASONOGRAPHY · doi:10.14366/usg.22063 · Cited by 50 · see citing works
Sangaletti, S., Iannelli, F., Zanardi, F., Cancila, V., Portararo, P., Botti, L., Vacca, D., Chiodoni, C., Napoli, A. D., Valenti, C. F., et al. (2020) · EBioMedicine · doi:10.1016/j.ebiom.2020.103055 · Cited by 33 · see citing works
Self-citationWalker, M. C., Willner, I., Miguel, O. X., Murphy, M. S. Q., El-Chaâr, D., Moretti, F., Dingwall Harvey, A. L. J., Rennicks White, R., Muldoon, K. A., Carrington, A. M., et al. (2022) · PLOS ONE · doi:10.1371/journal.pone.0269323 · Cited by 31 · see citing works
Thomas, M. C., & Arjunan, S. P. (2022) · Measurement Science Review · doi:10.2478/msr-2022-0023 · Cited by 29 · see citing works
Artificial Intelligence in Obstetrics and Gynecology: Transforming Care and Outcomes
Patel, D. J., Chaudhari, K., Acharya, N., Shrivastava, D., & Muneeba, S. (2024) · Cureus · doi:10.7759/cureus.64725 · Cited by 23 · see citing works
LinguAPP: An m-Health Application for Teledentistry Diagnostics
Fazio, M., Lombardo, C., Marino, G., Marya, A., Messina, P., Scardina, G. A., Tocco, A., Torregrossa, F., & Valenti, C. F. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19020822 · Cited by 18 · see citing works
Self-citationAutomatic measurement of fetal anterior neck lower jaw angle in nuchal translucency scans
Peng, Y., Luo, Y., Yan, J., Li, W., Liao, Y., Yan, L., Ling, H., & Long, C. (2024) · Scientific Reports · doi:10.1038/s41598-024-55974-x · Cited by 17 · see citing works
Neutrophil extracellular traps arm DC vaccination against NPM-mutant myeloproliferation
Tripodo, C., Bassani, B., Jachetti, E., Cancila, V., Chiodoni, C., Portararo, P., Botti, L., Valenti, C. F., Perrone, M., Ponzoni, M., et al. (2022) · eLife · doi:10.7554/elife.69257 · Cited by 17 · see citing works
Self-citationFully Automatic Segmentation and Three-Dimensional Reconstruction of the Liver in CT Images
Wang, Z., Zhang, C., Jiao, T., Gao, M., & Zou, G. (2018) · Journal of Healthcare Engineering · doi:10.1155/2018/6797102 · Cited by 13 · see citing works
Artificial Intelligence in Imaging in the First Trimester of Pregnancy: A Systematic Review
Umans, E., Dewilde, K., Williams, H., Deprest, J. A., & den Bosch, T. V. (2024) · Fetal Diagnosis and Therapy · doi:10.1159/000538243 · Cited by 11 · see citing works
Provision of Tailored Health Information for Patient Empowerment
Alfano, M., Lenzitti, B., Taibi, D., & Helfert, M. (2019) · International Conference on Computer Systems and Technologies (CompSysTech) · doi:10.1145/3345252.3345301 · Cited by 11 · see citing works
Direct Detection and Measurement of Nuchal Translucency with Neural Networks from Ultrasound Images
Liu, T., Xu, M., Zhang, Z., Dai, C., Wang, H., Zhang, R., Shi, L., & Wu, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32875-7_3 · Cited by 10 · see citing works
Artificial Intelligence in Obstetric Anomaly Scan: Heart and Brain
Enache, I.-A., Iovoaica-Rămescu, C., Ciobanu, Ș. G., Berbecaru, E. I. A., Vochin, A., Băluță, I. D., Istrate-Ofiţeru, A.-M., Comănescu, C. M., Nagy, R. D., & Iliescu, D. G. (2024) · Life · doi:10.3390/life14020166 · Cited by 12 · see citing works
Liu, L., Wang, T., Zhu, W., Zhang, H., Tian, H., Li, Y., Cai, W., & Yang, P. (2025) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-025-07863-y · Cited by 8 · see citing works
Graumann, O., Xin, W. C., Goudie, A., Blaivas, M., Braden, B., Westerway, S. C., Chammas, M. C., Dong, Y., Gilja, O. H., Hsieh, P. C.-C., et al. (2025) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2025.07.008 · Cited by 8 · see citing works
Sciortino, G., Tegolo, D., & Valenti, C. F. (2017) · International Conference on Information, Communication and Automation Technologies (ICAT) · doi:10.1109/icat.2017.8171631 · Cited by 6 · see citing works
Self-citationShankar, H., Narayan, A., Jain, S., Singh, D., Vyas, P., Hegde, N., Kar, P., Lad, A., Thang, J., Atada, J., et al. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761493 · Cited by 6 · see citing works
Pregitha, R. E., Kumar, R. S. V., & Kumar, C. E. S. (2023) · Soft Computing · doi:10.1007/s00500-023-08187-9 · Cited by 5 · see citing works
RetractedChen, C., Wang, C.-A., Cyue, J.-H., Chen, C.-P., Sun, F.-J., Chen, Y., Wang, L., Chang, C.-W., Tsai, B.-Y., & Chen, K. (2025) · Taiwanese Journal of Obstetrics and Gynecology · doi:10.1016/j.tjog.2025.08.001 · Cited by 4 · see citing works
Chaudhari, K., & Oza, S. K. (2024) · Journal of Pharmacology and Pharmacotherapeutics · doi:10.1177/0976500x241232530 · Cited by 4 · see citing works
Thomas, M. C., & Arjunan, S. P. (2021) · International Conference on Computer Communication and Informatics (ICCCI) · doi:10.1109/iccci50826.2021.9402299 · Cited by 4 · see citing works
Nuchal Translucency Thickness Measurement in Fetal Ultrasound Images to Analyze Down Syndrome
Thomas, M. C., Arjunan, S. P., & Viswanathan, R. (2021) · IETE Journal of Research · doi:10.1080/03772063.2021.1972847 · Cited by 4 · see citing works
Zukünftige Bildanalyse mit Hilfe automatisierter Algorithmen
Weichert, J., Rody, A., & Gembicki, M. (2020) · Der Gynäkologe · doi:10.1007/s00129-020-04563-w · Cited by 3 · see citing works
Sciortino, G., Tegolo, D., & Valenti, C. F. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59226-8_25 · Cited by 3 · see citing works
Self-citationA Comprehensive Survey on Nuchal Translucency Segmentation and Thickness Estimation
Aher, S., Agarkar, B. S., & Chaudhari, S. (2024) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict60155.2024.10544844 · Cited by 2 · see citing works
Integrative review of intelligent nuchal translucency for genetic disorder
Pawar, S. S., & Nikose, M. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2025.108597 · Cited by 2 · see citing works
A Comprehensive Survey on Down Syndrome Detection in Foetus Using Modern Technologies
Megha, I. M., Kowshik, S. V., Ali, S., & Malagi, V. P. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-5679-1_73 · Cited by 2 · see citing works
Bestehende und zukünftige Assistenzsysteme in der pränatalmedizinischen Bildgebung
Kagan, K. O., & Hoopmann, M. (2021) · Der Gynäkologe · doi:10.1007/s00129-021-04820-6 · Cited by 2 · see citing works
A Review of AI-Based Approaches for Suspected Fetal Abnormalities
Ansari, N. J. I., Edinburg, M. M., & NIKITHA, N. (2024) · Information systems engineering and management · doi:10.1007/978-3-031-61471-2_30 · Cited by 1 · see citing works
Nuchal Translucency Assessment in Prenatal Screening: An Attention-Based U-Net Approach
Sriraam, N., Chinta, B., Seshadri, S., & Suresh, S. (2024) · International Conference on Emerging Techniques in Computational Intelligence (ICETCI) · doi:10.1109/icetci62771.2024.10704101 · Cited by 1 · see citing works
Tan, L., Deng, Z., Hao, M., Zhang, P., Hou, X., Chen, C., Gu, X., Zhou, X.-H., Hou, Z., & Wang, S. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802713 · Cited by 1 · see citing works
Chen, M., Huang, Y., Zhou, T., Huang, S., Bu, G. X., Chen, X., Zhang, H., Li, W., & Xu, E. (2026) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2025.12.021 · Cited by 1 · see citing works
Kwon, H., Hur, H., Cho, H. C., Jung, Y. J., Kim, S., & Kwon, J. (2026) · Journal of Ultrasound in Medicine · doi:10.1002/jum.70281 · Cited by 1 · see citing works
A REST-based Framework to Support Non-Invasive and Early Coeliac Disease Diagnosis
Sorce, S., Gentile, V., Cascio, D., Giuliano, A., Tabacchi, M. E., Taormina, V., Tegolo, D., Valenti, C. F., & Raso, G. (2019) · International Conference on Computer Systems and Technologies (CompSysTech) · doi:10.1145/3345252.3345296 · Cited by 1 · see citing works
Self-citationEvaluating Correlations in IoT Sensors for Smart Buildings
Guastella, D. A., Verstaevel, N., Valenti, C. F., Arshad, B., & Barthélemy, J. (2021) · International Conference on Agents and Artificial Intelligence (ICAART) · doi:10.5220/0010210502240231 · Cited by 1 · see citing works
Self-citationRecent and future applications of artificial intelligence in obstetric ultrasound examination
Verma, S., Singh, A., Dobhal, K., Gairola, N., & Jakhmola, V. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-21889-7.00018-x · Cited by 1 · see citing works
A genetic approach to the maximum common subgraph problem
Valenti, C. F. (2019) · International Conference on Computer Systems and Technologies (CompSysTech) · doi:10.1145/3345252.3345272 · Cited by 1 · see citing works
Self-citationA Wavelet approach to extract main features from indirect immunofluorescence images
Palermo, R. D., Cascio, D., Raso, G., & Tegolo, D. (2019) · International Conference on Computer Systems and Technologies (CompSysTech) · doi:10.1145/3345252.3345264 · Cited by 1 · see citing works
Self-citationStellenwert der KI im pränatalen sonographischen Screening
Ritgen, J. (2022) · Die Gynäkologie · doi:10.1007/s00129-022-04995-6
Amer, M. B., Barakat, A., Alrmadyn, L., Abufraijeh, S. M., & Al‐Kharabsheh, A. M. (2025) · International Conference for Artificial Intelligence, Applications, Innovation and Ethics (AI2E) · doi:10.1109/ai2e64943.2025.10982755
The Prenatal Down Syndrome Detection Using VGG-19
Kalaiselvi, K., Saranya, S., Thilak, K. D., Devi, K. L., Kumaresan, K., & Prakash, U. M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-2697-7_43
W-ContextNet: A W-Shaped Encoder-Decoder Network for Nuchal Translucency Segmentation
Xu, J., Wang, Z., Wang, S., Liu, L., & Fang, C. (2025) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc68654.2025.11437998
Liu, L., Wang, T., Li, Y., Tian, H., Zhang, H., Zhou, C., Zhu, W., & Cai, W. (2026) · The Ultrasound Journal · doi:10.5826/tuj.2026.18334
Aher, S., Agarkar, B., & Chaudhari, S. (2026) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict68280.2026.11510926
A Review of Techniques and Trends in Nuchal Translucency Measurement for Prenatal Screening
Patil, S. U., & Patil, M. V. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-24929-6_8
FUSEP: A Multi-Center Benchmark for Diverse Tasks in Fetal Ultrasound Screening Early Pregnancy
Pu, B., Yang, J., Wang, L., Tan, Y., He, G., Dong, X., Lin, Q., Guo, J., Yang, L., Liu, Z., et al. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3817453
Tamara, Kaunang, F. J., Stefano, R., Thenata, A. P., & Hakim, B. (2026) · International Conference on Advancement in Data Science, E-learning and Information System (ICADEIS) · doi:10.1109/icadeis71120.2026.11644579
A Structure-Aware Self-Supervised Framework for Transferable Fetal Ultrasound ScreeningPreprint
Zhu, Y., Wu, J., Gao, L., Zhang, N., Wu, L., Li, H., chen, K., Luo, H., Sun, C., Daru, L., et al. (2026) · medRxiv · doi:10.64898/2026.09.21.26361576
- Authors
- Sciortino, G., Tegolo, D., & Valenti, C.
- Affiliations
- University of Palermo (IT)
- Corresponding author
- Valenti, Cesare
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2017-03
- Volume
- 82
- Pages
- 12-20
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W2576014699
- Cited by
- 55
- References
- 27
- Citations by year
- 2026: 8 · 2025: 7 · 2024: 10 · 2023: 4 · 2022: 8 · 2021: 5 · 2020: 4 · 2019: 6 · 2018: 1 · 2017: 2
- FWCI
- 1.48
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Computers in Biology and Medicine (publishedVersion)
- PubMed (publishedVersion)
- Nova Science Publishers (Nova Science Publishers, Inc.) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Prenatal Screening and Diagnostics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- nuchal translucency, thickness measurement, first-trimester ultrasound, prenatal screening, chromosomal abnormalities, Down syndrome, trisomy 18, trisomy 13, wavelet analysis, multiresolution analysis
- MeSH terms
- Machine Learning, Algorithms, Down Syndrome, Humans, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results, Nuchal Translucency Measurement, Wavelet Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
