TwinEDA: a sustainable deep-learning approach for limb-position estimation in preterm infants’ depth images
Author:
Funder
EU POR FESR funding program
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Biomedical Engineering
Link
https://link.springer.com/content/pdf/10.1007/s11517-022-02696-9.pdf
Reference38 articles.
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2. Gibbs R, Romero R, Hillier S, Eschenbach D, Sweet RL (1992) A review of premature birth and subclinical infection. Am J Obstet Gynecol 166(5):1515–1528
3. DeMaster D, Bick J, Johnson U, Montroy JJ, Landry S, Duncan AF (2019) Nurturing the preterm infant brain: leveraging neuroplasticity to improve neurobehavioral outcomes. Pediatr Res 85(2):166–175
4. Porro M, Fontana C, Giannì ML, Pesenti N, Boggini T, De Carli A, De Bon G, Lucco G, Mosca F, Fumagalli M et al (2020) Early detection of general movements trajectories in very low birth weight infants. Sci Rep 10(1):1–7
5. Fontana C, Ottaviani V, Veneroni C, Sforza SE, Pesenti N, Mosca F, Picciolini O, Fumagalli M, Dellacà RL (2021) Front Pediatr 868
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