@InProceedings{CarliniFCVHBBGT:2021:CoNeNe,
               author = "Carlini, Lucas P. and Ferreira, Leonardo A. and Coutrin, Gabriel 
                         A. S. and Varoto, Victor V. and Heiderich, Tatiany M. and Balda, 
                         Rita C. X. and Barros, Marina C. M. and Guinsburg, Ruth and 
                         Thomaz, Carlos E.",
          affiliation = "{University Center of FEI } and {University Center of FEI } and 
                         {University Center of FEI } and {University Center of FEI } and 
                         {University Center of FEI } and {Federal University of S{\~a}o 
                         Paulo } and {Federal University of S{\~a}o Paulo } and {Federal 
                         University of S{\~a}o Paulo } and {University Center of FEI}",
                title = "A Convolutional Neural Network-based Mobile Application to Bedside 
                         Neonatal Pain Assessment",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "neonatal pain, convolutional neural network, mobile application, 
                         explainable AI.",
             abstract = "More than 500 painful interventions are carried out during the 
                         hospitalisation of a newborn baby in a neonatal intensive care 
                         unit. Since neonates are not able to verbally communicate pain, 
                         some studies have been done to identify the presence and intensity 
                         of pain by behavioural analysis, mainly by facial expression. 
                         These studies allow a better understanding of this painful 
                         experience faced by the neonate. In this context, this work 
                         proposes and implements a mobile application for smartphones that 
                         uses Artificial Intelligence (AI) techniques to automatically 
                         identify the facial expression of pain in neonates, presenting 
                         feasibility in real clinical situations. Firstly, a Convolutional 
                         Neural Network architecture was adapted and trained with face 
                         images captured before and after painful clinical procedures 
                         carried out routinely. Then, this computational model was 
                         optimised to a mobile environment to make it practical for 
                         everyday use. Moreover, we used an explainable AI method to 
                         identify facial regions that might be relevant to pain assessment. 
                         Our results showed that is possible to classify the facial 
                         expression of the pain of neonates with high accuracy. 
                         Additionally, our methodology presented novel results highlighting 
                         as well sound facial regions that agree with pain scales used by 
                         neonatologists and with the visual perception of adults when 
                         assessing pain in neonates, whether they are health professionals 
                         or not.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00060",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00060",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45C6TAL",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45C6TAL",
           targetfile = "paper_id_18.pdf",
        urlaccessdate = "2024, May 06"
}