White RD, Smith JA, Shepley MM. Really helpful requirements for new child ICU design, eighth version. J Perinatol. 2013;33:S2–16.
Ellsworth MA, Lang TR, Pickering BW, Herasevich V. Scientific knowledge wants within the neonatal intensive care unit digital medical document. BMC Med Inform Decis Mak. 2014;14:92.
De Georgia MA, Kaffashi F, Jacono FJ, Loparo KA. Data expertise in crucial care: evaluation of monitoring and knowledge acquisition programs for affected person care and analysis. Sci World J. 2015;2015:1–9.
Strickland NH. PACS (image archiving and communication programs): filmless radiology. Arch Dis Youngster BMJ Publ Group Ltd. 2000;83:82–6.
Fairchild KD, Aschner JL. HeRO monitoring to scale back mortality in NICU sufferers. Rrn Dove Press. 2012;2:65–76.
Griffin MP, Lake DE, Bissonette EA, Harrell FE, Shea OTM, Moorman JR. Coronary heart fee traits: novel physiomarkers to foretell neonatal an infection and dying. Pediatrics Am Acad Pediatrics. 1999;116:1070.
Fairchild KD, Lake DE. Cross-correlation of coronary heart fee and oxygen saturation in very low birthweight infants: affiliation with apnea and hostile occasions. Am J Perinatol. 2018;35:463.
Davoudi A, Malhotra KR, Shickel B, Siegel S, Williams S, Ruppert M, et al. Clever ICU for autonomous affected person monitoring utilizing pervasive sensing and deep studying. Sci Rep. 2019;9:8020. https://doi.org/10.1038/s41598-019-44004-w.
Hee Chung E, Chou J, Brown KA. Neurodevelopmental outcomes of preterm infants: a latest literature evaluation. Transl Pediatr. 2020;9:S3–8.
Web: The Way forward for Affected person Care Is Now Linked Important Care at Philips. [cited 2021 Jun 2]. pp. 1–17. Out there from: https://www.paperwork.philips.com/property/20210121/be28054cfbcf4b358e4cacb701721990.pdf?_gl=1*18u25bv*_ga*NTUxMTA4MzI3LjE2MjQxNjIzNTQ.*_ga_2NMXNNS6LE*MTYyNTIwMjI4Ny40LjEuMTYyNTIwMjQyMy40OA.&_ga=2.92528861.1923540143.1625202287-551108327.1624162354
Web: CARESCAPE ONE Usability Examine. 2020 Jun 24;:1–4. [cited 2021 Jun 2]. Out there from: https://www.gehealthcare.com/-/jssmedia/carescape-one-usability-study-wisconsin_jb76705xx_jun24.pdf?rev=-1
Web: IntelliBridge EC10 Medical system interfacing module. [cited 2021]. Out there from: https://www.philips.co.in/healthcare/product/HCNOCTN429/intellibridge-ec10-medical-device-interfacing-module
Web: BedMasterEx. Actual time scientific knowledge acquisition. [cited 2021 Jun 3]. Out there from: https://www.anandic.com/en/healthcare-it/bedmasterex/bedmasterex
Web: Capsule. [cited 2021 Jun 2]. Out there from: https://capsuletech.com/critical-care
Singh H, Kaur R, Gangadharan A, IEEE AP, 2018. Neo-bedside monitoring system for built-in neonatal intensive care unit (iNICU). ieeexploreieeeorg. 2018;7:7803–13.
Web: The iXellence. [cited 2021 Jun 2]. Out there from: (www.ixellence.com)
Vincent JL, Suter P, Bihari D, Braining H. Group of intensive care models in Europe: classes from the EPIC examine. Intensive Care Med. 1997;23:1181–4.
Carayon P, Wetterneck TB, Alyousef B, Brown RL, Cartmill RS, McGuire Ok, et al. Affect of digital well being document expertise on the work and workflow of physicians within the intensive care unit. Int J Med Inform. 2015;84:578–94.
Bodagh N, Archbold RA, Weerackody R, Hawking MKD, Barnes MR, Lee AM, et al. Feasibility of real-time seize of routine scientific knowledge within the digital well being document: a hospital-based, observational service-evaluation examine. BMJ Open. 2018;8:e019790.
Web: Meditech. [cited 2021 Jun 2]. Out there from: https://ehr.meditech.com/ehr-solutions/ehr-mobility.
Web: Allscripts Skilled. [cited 2021 Jun 2]. Out there from: https://www.allscripts.com/answer/skilled/.
Obermeyer Z, Emanuel EJ. Predicting the long run – huge knowledge, machine studying, and scientific drugs. N. Engl J Med. 2016;375:1216–9.
Mark R. The story of MIMIC. In: Knowledge MC, editor. Secondary Evaluation of Digital Well being Information. Cham: Springer Worldwide Publishing; 2016. pp. 43–9.
Saeed M, Villarroel M, Reisner AT, Clifford G, Lehman L-W, Moody G, et al. Multiparameter clever monitoring in intensive care II (MIMIC-II): a public-access intensive care unit database. Crit Care Med. 2011;39:952.
Johnson AE, Pollard TJ, Shen L, Li-Wei HL, Feng M, Ghassemi M, et al. MIMIC-III, a freely accessible crucial care database. Sci Knowledge. 2016;3:1–9.
Web: The Vermont Oxford Community. [cited 2021 Jun 2]. Out there from: https://www.vtoxford.org.
Web: The CNN Abstractor’s Handbook. [cited 2021 Jun 2]. Out there from: http://www.canadianneonatalnetwork.org.
Hanson CW third, Marshall BE. Synthetic intelligence functions within the intensive care unit. Crit Care Med. 2001;29:427–35.
Kersting Ok. Machine studying and synthetic intelligence: two fellow vacationers on the search for clever conduct in machines. Entrance Large Knowledge. 2018;1:6
Shamout F, Zhu T, Clifton D. Machine studying for scientific final result prediction. IEEE Rev Biomed Eng. 2021;14:116–26.
Shirwaikar RD, Mago N, U DA, Makkithaya Ok, Ok GH. Supervised studying strategies for evaluation of neonatal knowledge. 2nd Worldwide Convention on Utilized and Theoretical Computing and Communication Know-how (iCATccT). 2016; pp. 25–31.
Afrin R, Haddad H, Shahriar H. Supervised and unsupervised-based analytics of intensive care unit knowledge. IEEE forty third annual laptop software program and functions convention (COMPSAC). 2019; pp. 417–22.
Nemati S, Ghassemi MM, Clifford GD. Optimum remedy dosing from suboptimal scientific examples: a deep reinforcement studying strategy. Annu Int Conf IEEE Eng Med Biol Soc. 2016;2016:2978–81.
Aniruddh R, Matthieu Ok, Leo AC, Peter S, Marzyeh G. Steady state-space fashions for optimum sepsis remedy: a deep reinforcement studying strategy. proceedings of the 2nd machine studying for healthcare convention. PMLR. 2017;68:147–63.
The CRIB (scientific threat index for infants) rating: a software for assessing preliminary neonatal threat and evaluating efficiency of neonatal intensive care models. The worldwide neonatal community. Lancet.1993;342:193–8.
Lee SK, Aziz Ok, Dunn M, Clarke M, Kovacs L, Ojah C. et al. Transport threat index of physiologic stability, model II (TRIPS-II): a easy and sensible neonatal sickness severity rating. Am J Perinatol Thieme Med Publishers. 2013;30:395–400.
Gagliardi L, Cavazza A, Brunelli A, Battaglioli M, Merazzi D, Tandoi F, et al. Assessing mortality threat in very low birthweight infants: a comparability of CRIB, CRIB-II, and SNAPPE-II. Arch Dis Youngster Fetal Neonatal Ed. 2004;89:F419–22.
Parry G, Tucker J, Tarnow-Mordi W, Group UNSSC. CRIB II: an replace of the scientific threat index for infants rating. Lancet. 2003;361:1789–91.
Kim SY, Kim S, Cho J, Kim YS, Sol IS, Sung Y, et al. A deep studying mannequin for real-time mortality prediction in critically in poor health youngsters. Crit Care. 2019;23:279.
Suresh H, Hunt N, Johnson A, Celi LA, Szolovits P, Ghassemi M. Scientific intervention prediction and understanding with deep neural networks. Doshi-Velez F, Fackler J, Kale D, Ranganath R, Wallace B, Wiens J, editors. Proceedings of Machine Studying Analysis. Proceedings of Machine Studying Analysis: PMLR; 2017;68:322–37.
McGregor C. Large knowledge in neonatal intensive care. Pc. IEEE. 2013;46:54–9.
Addy DP. “Neonatal” Is the primary 28 days of life. Pediatrics. 1975;55:571.
Web page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 assertion: an up to date guideline for reporting systematic opinions. BMJ. 2021;372:n71.
Thébaud B, Goss KN, Laughon M, Whitsett JA, Abman SH, Steinhorn RH. et al. Bronchopulmonary dysplasia. Nat Rev Dis Prim. 2019;5:78.
Özek E, Kersin SG. Intraventricular hemorrhage in preterm infants. Turk Pediatr Ars Kare Publ. 2020;55:215–21.
Neu J. Necrotizing enterocolitis: the long run. Neonatology. 2020;117:240–4.
Kim SJ, Port AD, Swan R, Campbell JP, Chan RVP, Chiang MF. Retinopathy of prematurity: a evaluation of threat components and their scientific significance. Surv Ophthalmol. 2018;63:618–37.
Feldman Ok, Chawla NV. Admission period mannequin for toddler remedy (ADMIT). IEEE; 2014. pp. 583–7.
Zernikow B, Holtmannspötter Ok, Michel E, Hornschuh F, Groote Ok, Hennecke KH. Predicting size of keep in preterm neonates 52. Pediatric Res. 1997;42:393–3.
Bender GJ, Koestler D, Ombao H, McCourt M, Alskinis B, Rubin LP, et al. Neonatal intensive care unit: predictive fashions for size of keep. J Perinatol. 2012;33:147–53.
Thompson B, Elish Ok, Steele R. Machine learning-based prediction of extended size of keep in newborns. 2018 seventeenth IEEE Worldwide Convention on Machine Studying and Purposes (ICMLA). IEEE;2018,1454–9.
Lee HC, Bennett MV, Schulman J, Gould JB. Accounting for variation in size of NICU keep for very low beginning weight infants. Nat Publ Group. 2013;33:872–6.
Lee HC, Bennett MV, Schulman J, Gould JB, Revenue J. Estimating size of keep by affected person sort within the neonatal intensive care unit. Am J Perinatol. 2016;33:751–7.
Singh H, Cho SJ, Gupta S, Kaur R, Sunidhi S, Saluja S, et al. Designing a bed-side system for predicting size of keep in a neonatal intensive care unit. Sci Rep. 2021:1–13.
Geoghegan L, Scarborough A, Wormald JC, Harrison CJ, Collins D, Gardiner M. et al. Automated conversational brokers for post-intervention follow-up: a scientific evaluation. BJS Open. 2021;5:zrab070.
Kalaniti Ok, Mugarab-Samedi V, Riehl A, Bingham W, Daspal S. Net-based Digital camera (NICView) as a way of proximity software: a High quality-Enchancment initiative for fogeys of neonates admitted within the NICU. Paediatrics Youngster Well being. 2020;25:e12–3.
Singh H, Mallaiah R, Yadav G, Verma N, Sawhney A, Brahmachari SK. iCHRCloud: net & cell based mostly baby well being imprints for good healthcare. J Med Sys. 2018;42:1–12.
Singh H, Kusuda S, McAdams RM, Gupta S, Kalra J, Kaur R, et al. Machine learning-based computerized classification of video recorded neonatal manipulations and related physiological parameters: a feasibility examine. Youngster Multidiscip Digital Publ Inst. 2021;8:1.
Web: Vidyo. [cited 2021 Jun 4. Available from: https://www.vidyohealth.com/.
Moorman JR, Carlo WA, Kattwinkel J, Schelonka RL, Porcelli PJ, Navarrete CT, et al. Mortality reduction by heart rate characteristic monitoring in very low birth weight neonates: a randomized trial. J Pediatrics. 2011;159:900–1.
Moorman JR, Delos JB, Flower AA, Cao H, Kovatchev BP, Richman JS. et al. Cardiovascular oscillations at the bedside: early diagnosis of neonatal sepsis using heart rate characteristics monitoring. Physiol Measurement. 2011;32:1821–32.
Saria S, Rajani AK, Gould J, Koller D, Penn AA. Integration of early physiological responses predicts later illness severity in preterm infants. Science translational medicine. Am Assoc Advancement Sci. 2010;2:48ra65–5.
Mahieu LM, De Muynck AO, De Dooy JJ, Laroche SM, Van Acker KJ. Prediction of nosocomial sepsis in neonates by means of a computer-weighted bedside scoring system (NOSEP score). Critical Care Med. 2000;28:2026–33.
Helguera-Repetto AC, Soto-Ramírez MD, Villavicencio-Carrisoza O, Yong-Mendoza S, Yong-Mendoza A, León-Juárez M, et al. Neonatal sepsis diagnosis decision-making based on artificial neural networks. Front Pediatr Front. 2020;8:1–10.
Laughon MM, Langer JC, Bose CL, Smith PB, Ambalavanan N, Kennedy KA, et al. Prediction of bronchopulmonary dysplasia by postnatal age in extremely premature infants. Am J Respir Crit Care Med. 2011;183:1715–22.
Cuna A, Liu C, Govindarajan S, Queen M, Dai H, Truog WE. Usefulness of an online risk estimator for bronchopulmonary dysplasia in predicting corticosteroid treatment in infants born preterm. J Pediatr. 2018;197:23–8.
May C, Patel S, Kennedy C, Pollina E, Rafferty GF, Peacock JL, et al. Prediction of bronchopulmonary dysplasia. Arch Dis Child Fetal Neonatal Ed. 2011;96:F410–6.
Buzkova K, Suchomel J. Use of electrical impedance tomography for quantitative evaluation of disability level of bronchopulmonary dysplasia. IEEE; 2013, pp. 1–4.
Jensen EA, DeMauro SB, Kornhauser M, Aghai ZH, Greenspan JS, Dysart KC. Effects of multiple ventilation courses and duration of mechanical ventilation on respiratory outcomes in extremely low-birth-weight infants. JAMA Pediatrics Am Med Assoc. 2015;169:1011–7.
Tsuji M, Saul JP, Plessis du A, Eichenwald E, Sobh J, Crocker R, et al. Cerebral intravascular oxygenation correlates with mean arterial pressure in critically ill premature infants. Pediatrics Am Acad Pediatrics. 2000;106:625–32.
Sullivan BA, McClure C, Hicks J, Lake DE, Moorman JR, Fairchild KD. Early heart rate characteristics predict death and morbidities in preterm infants. J Pediatrics Elsevier. 2016;174:57–62.
Vergales BD, Zanelli SA, Matsumoto JA, Goodkin HP, Lake DE, Moorman JR, et al. Depressed heart rate variability is associated with abnormal EEG, MRI, and death in neonates with hypoxic ischemic encephalopathy. Am J Perinatol Thieme Med Publ. 2013;31:855–62.
Sortica da Costa C, Placek MM, Czosnyka M, Cabella B, Kasprowicz M, Austin T, et al. Complexity of brain signals is associated with outcome in preterm infants. J Cereb Blood Flow Metab. 2017;37:3368–79.
Galderisi A, Zammataro L, Losiouk E, Lanzola G, Kraemer K, Trevisanuto D. et al. Continuous glucose monitoring linked to an artificial intelligence risk index: early footprints of intraventricular hemorrhage in preterm neonates. Diabetes Technol Ther. 2019;21:146–53.
Tam EW, Haeusslein LA, Bonifacio SL, Glass HC, Rogers EE, Jeremy RJ, et al. Hypoglycemia is associated with increased risk for brain injury and adverse neurodevelopmental outcome in neonates at risk for encephalopathy. J Pediatr. 2012;161:88–93.
Schmid MB, Reister F, Mayer B, Hopfner RJ, Fuchs H, Hummler HD. Prospective risk factor monitoring reduces intracranial hemorrhage rates in preterm infants. Dtsch Arztebl Int. 2013;110:489.
Doheny KK, Palmer C, Browning KN, Jairath P, Liao D, He F, et al. Diminished vagal tone is a predictive biomarker of necrotizing enterocolitis‐risk in preterm infants. Neurogastroenterol Motil. 2014;26:832–40.
Stone ML, Tatum PM, Weitkamp JH, Mukherjee AB, Attridge J, McGahren ED. et al. Abnormal heart rate characteristics before clinical diagnosis of necrotizing enterocolitis. J Perinatol. 2013;33:847–50.
Ibáñez V, Couselo M, Marijuán V, Vila JJ, García-Sala C. Could clinical scores guide the surgical treatment of necrotizing enterocolitis?. Pediatr Surg Int. 2012;28:271–6.
Gephart SM, Spitzer AR, Effken JA, Dodd E, Halpern M, McGrath JM. Discrimination of GutCheck NEC: a clinical risk index for necrotizing enterocolitis. J Perinatol. 2014;34:468–75.
Hooven T, Lin YC, Salleb-Aouissi A. Multiple instance learning for predicting necrotizing enterocolitis in premature infants using microbiome data. In Proceedings of the ACM Conference on Health, Inference, and Learning 2020 (pp. 99–109).
Irles C, González-Pérez G, Carrera Muiños S, Michel Macias C, Sánchez Gómez C, Martínez-Zepeda A, et al. Estimation of neonatal intestinal perforation associated with necrotizing enterocolitis by machine learning reveals new key factors. Int J Environ Res Public Health. 2018;15:2509.
Wu C, Löfqvist C, Smith LEH, VanderVeen DK, Hellström A. WINROP Consortium FT. Importance of early postnatal weight gain for normal retinal angiogenesis in very preterm infants: a multicenter study analyzing weight velocity deviations for the prediction of retinopathy of prematurity. Arch Ophthalmol. 2012;130:992–9.
Kaempf JW, Kaempf AJ, Wu Y, Stawarz M, Niemeyer J, Grunkemeier G. Hyperglycemia, insulin and slower growth velocity may increase the risk of retinopathy of prematurity. J Perinatol. 2019;31:1–7.
Eckert GU, Filho JBF, Maia M, Procianoy RS. A predictive score for retinopathy of prematurity in very low birth weight preterm infants. Eye. 2019;26:1–7.
Binenbaum G, Ying G-S, Tomlinson LA. Group FTPGAROPG-RS. Validation of the children’s hospital of philadelphia retinopathy of prematurity (CHOP ROP) model. JAMA Ophthalmol. 2017;135:871–7.
Freitas AM, Mörschbächer R, Thorell MR, Rhoden EL. Incidence and risk factors for retinopathy of prematurity: a retrospective cohort study. Int J Retina Vitreous. 2018;4:1–8.
Sullivan BA, Wallman-Stokes A, Isler J, Sahni R, Moorman JR, Fairchild KD, et al. Early pulse oximetry data improves prediction of death and adverse outcomes in a two-center cohort of very low birth weight infants. Am J Perinatol Thieme Med Publishers. 2018;35:1331–8.
Brown JM, Campbell JP, Beers A, Chang K, Ostmo S, Chan RP, et al. Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA Ophthalmol. 2018;136:803–10.
Owen LA, Morrison MA, Hoffman RO, Yoder BA, DeAngelis MM. Retinopathy of prematurity: A comprehensive risk analysis for prevention and prediction of disease. PloS One. 2017;12:e0171467.
Richardson DK, Gray JE, McCormick MC, Workman K, Goldmann DA. Score for neonatal acute physiology: a physiologic severity index for neonatal intensive care. Pediatrics. 1993;91:617.
Richardson DK, Phibbs CS, Gray JE, McCormick MC, Workman-Daniels K, Goldmann DA. Birth weight and illness severity: independent predictors of neonatal mortality. Pediatrics Am Acad Pediatrics. 1993;91:969–75.
Wisnuwardani DN, Arif Sampurna MT, Utomo MT, Etika R. Neonatal therapeutic intervention scoring system (NTISS) in rural country: mortality and length of stay (LOS) predictive score in preterm infant. Ind J Forensic Med Toxicol. 2020;14:862–7.
Lee SK, Zupancic JAF, Pendray M, Thiessen P, Schmidt B, Whyte R, et al. Transport risk index of physiologic stability: a practical system for assessing infant transport care. J Pediatrics. 2001;139:220–6.
Parry G, Tucker J, Tarnow-Mordi W, Group UNSSC. CRIB II: an update of the clinical risk index for babies score. Lancet. 2003;361:1789–91.
Richardson DK, Corcoran JD, Escobar GJ, Lee SK. SNAP-II and SNAPPE-II: simplified newborn illness severity and mortality risk scores. J Pediatr. 2001;138:92–100.
Skarsgard ED, MacNab YC, Qiu Z, Little R, Lee SK. SNAP-II predicts mortality among infants with congenital diaphragmatic hernia. J Perinatol Nat Publ Group. 2005;25:315–9.
Muktan D, Singh RR, Bhatta NK, Shah D. Neonatal mortality risk assessment using SNAPPE-II score in a neonatal intensive care unit. BMC Pediatri. 2019;19:1–4.
Beltempo M, Shah PS, Ye XY, Afifi J, Lee S, Mcmillan DD, et al. SNAP-II for prediction of mortality and morbidity in extremely preterm infants. J Maternal Fetal Neonatal Med. 2019;32:2694–701.
Lee SK, Mcmillan DD, Ohlsson A, Pendray M, Synnes A, Whyte R, et al. Variations in practice and outcomes in the Canadian NICU network: 1996–1997. Pediatrics. 2000;106:1070.
Aliaga S, Boggess K, Ivester TS, Price WA. Influence of neonatal practice variation on outcomes of late preterm birth. Am J Perinatol. 2014;31:659–66.
Baskaran V, Bajan I, Shah B, Prescod FI, James A. A knowledge management based approach for mortality prediction in the neonatal intensive care unit. In 2011 Developments in E-systems Engineering 2011(pp. 122–5). IEEE.
Kong X, Xu F, Wu R, Wu H, Ju R, Zhao X, et al. Neonatal mortality and morbidity among infants between 24 to 31 complete weeks: a multicenter survey in China from 2013 to 2014. BMC Pediatr. 2016;26:1–8.
Podda M, Bacciu D, Micheli A, Bellù R, Placidi G, Gagliardi L. A machine learning approach to estimating preterm infants survival: development of the preterm infants survival assessment (PISA) predictor. Sci Rep. 2018;8:13743.
Shi P, Gangopadhyay A, Owens C, Blunt B, Grogan C A hybrid model using LSTM and decision tree for mortality prediction and its application in provider performance evaluation. IEEE; 2019. pp. 2773–81.
Rinta-Koski O-P, Särkkä S, Hollmén J, Leskinen M, Andersson S. Gaussian process classification for prediction of in-hospital mortality among preterm infants. Neurocomputing. 2018;298:134–41.
Jaskari J, Myllärinen J, Leskinen M, Rad AB, Hollmén J, Andersson S, et al. Machine learning methods for neonatal mortality and morbidity classification. IEEE Access. 2020;8:123347–58.
Meister AL, Doheny KK, Travagli RA. Necrotizing enterocolitis: It’s not all in the gut. Exp Biol Med. 2020;245:85–95. PMID: 31810384; PMCID: PMC7016421.
Kliegman RM, Walsh MC. Neonatal necrotizing enterocoli- tis: pathogenesis, classification, and spectrum of illness. Curr Probl Pediatr. 1987;17:213–88.
Hu Y, Lee VCS, Tan K. An application of convolutional neural networks for the early detection of late-onset neonatal sepsis. 2019 International Joint Conference on Neural Networks (IJCNN). IEEE; 2019 1–8.
Fairchild KD, Nagraj VP, Sullivan BA, Moorman JR, Lake DE. Oxygen desaturations in the early neonatal period predict development of bronchopulmonary dysplasia. Pediatr Res. 2019;85:987–93.
Shi Y, Payeur P, Frize M, Bariciak E. Thermal and RGB-D imaging for necrotizing enterocolitis detection. IEEE; 2020. pp. 1–6.
Sun Y, Kaur R, Gupta S, Paul R, Das R, Cho SJ, et al. Development and validation of high definition phenotype-based mortality prediction in critical care units. JAMIA Open. 2021;4:1–13.
Bell MJ, Ternberg JL, Feigin RD, Keating JP, Marshall R, Barton L, et al. Neonatal necrotizing enterocolitis. Therapeutic decisions based upon clinical staging. Ann Surg. 1978;187:1–7.
Hwang M, Tierradentro-García LO, Dennis RA, Anupindi SA. The role of ultrasound in necrotizing enterocolitis. Pediatr Radiol. 2021 https://doi.org/10.1007/s00247-021-05187-5. Epub ahead of print. PMID: 34654968.
Shi Y, Payeur P, Frize M, Bariciak E. “Thermal and RGB-D Imaging for Necrotizing Enterocolitis Detection,” 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2020, pp. 1–6, https://doi.org/10.1109/MeMeA49120.2020.9137344.