| 1. Identity statement | ||||
| Reference Type | Conference Paper (Conference Proceedings) | |||
| Site | sibgrapi.sid.inpe.br | |||
| Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH | |||
| Identifier | 8JMKD3MGPEW34M/45C6TAL | |||
| Repository | sid.inpe.br/sibgrapi/2021/09.01.22.15 | |||
| Last Update | 2021:09.01.22.15.36 (UTC) administrator | |||
| Metadata Repository | sid.inpe.br/sibgrapi/2021/09.01.22.15.36 | |||
| Metadata Last Update | 2022:06.14.00.00.19 (UTC) administrator | |||
| DOI | 10.1109/SIBGRAPI54419.2021.00060 | |||
| Citation Key | CarliniFCVHBBGT:2021:CoNeNe | |||
| Title | A Convolutional Neural Network-based Mobile Application to Bedside Neonatal Pain Assessment  | |||
| Format | On-line | |||
| Year | 2021 | |||
| Access Date | 2024, May 07 | |||
| Number of Files | 1 | |||
| Size | 4091 KiB | |||
| 2. Context | ||||
| Author | 1 Carlini, Lucas P. 2 Ferreira, Leonardo A. 3 Coutrin, Gabriel A. S. 4 Varoto, Victor V. 5 Heiderich, Tatiany M. 6 Balda, Rita C. X. 7 Barros, Marina C. M. 8 Guinsburg, Ruth 9 Thomaz, Carlos E. | |||
| Affiliation | 1 University Center of FEI 2 University Center of FEI 3 University Center of FEI 4 University Center of FEI 5 University Center of FEI 6 Federal University of São Paulo 7 Federal University of São Paulo 8 Federal University of São Paulo 9 University Center of FEI | |||
| Editor | Paiva, Afonso Menotti, David Baranoski, Gladimir V. G. Proença, Hugo Pedro Junior, Antonio Lopes Apolinario Papa, João Paulo Pagliosa, Paulo dos Santos, Thiago Oliveira e Sá, Asla Medeiros da Silveira, Thiago Lopes Trugillo Brazil, Emilio Vital Ponti, Moacir A. Fernandes, Leandro A. F. Avila, Sandra | |||
| e-Mail Address | lucaspcarlini10@gmail.com | |||
| Conference Name | Conference on Graphics, Patterns and Images, 34 (SIBGRAPI) | |||
| Conference Location | Gramado, RS, Brazil (virtual) | |||
| Date | 18-22 Oct. 2021 | |||
| Publisher | IEEE Computer Society | |||
| Publisher City | Los Alamitos | |||
| Book Title | Proceedings | |||
| Tertiary Type | Full Paper | |||
| History (UTC) | 2021-09-01 22:15:36 :: lucaspcarlini10@gmail.com -> administrator :: 2022-03-02 00:54:15 :: administrator -> menottid@gmail.com :: 2021 2022-03-02 13:22:42 :: menottid@gmail.com -> administrator :: 2021 2022-06-14 00:00:19 :: administrator -> :: 2021 | |||
| 3. Content and structure | ||||
| Is the master or a copy? | is the master | |||
| Content Stage | completed | |||
| Transferable | 1 | |||
| Version Type | finaldraft | |||
| 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. | |||
| Arrangement 1 | urlib.net > SDLA > Fonds > SIBGRAPI 2021 > A Convolutional Neural... | |||
| Arrangement 2 | urlib.net > SDLA > Fonds > Full Index > A Convolutional Neural... | |||
| doc Directory Content | access | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
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| 4. Conditions of access and use | ||||
| data URL | http://urlib.net/ibi/8JMKD3MGPEW34M/45C6TAL | |||
| zipped data URL | http://urlib.net/zip/8JMKD3MGPEW34M/45C6TAL | |||
| Language | en | |||
| Target File | paper_id_18.pdf | |||
| User Group | lucaspcarlini10@gmail.com | |||
| Visibility | shown | |||
| Update Permission | not transferred | |||
| 5. Allied materials | ||||
| Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 | |||
| Next Higher Units | 8JMKD3MGPEW34M/45PQ3RS 8JMKD3MGPEW34M/4742MCS | |||
| Citing Item List | sid.inpe.br/sibgrapi/2021/11.12.11.46 6 sid.inpe.br/sibgrapi/2022/06.10.21.49 1 | |||
| Host Collection | sid.inpe.br/banon/2001/03.30.15.38 | |||
| 6. Notes | ||||
| Empty Fields | archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume | |||