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Bethell GS, Jones IH, Battersby C, Knight M, Hall NJ. Methods of identifying surgical Necrotizing Enterocolitis-a systematic review and meta-analysis. Pediatr Res 2024:10.1038/s41390-024-03292-3. [PMID: 38849483 DOI: 10.1038/s41390-024-03292-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 04/02/2024] [Accepted: 05/15/2024] [Indexed: 06/09/2024]
Abstract
BACKGROUND Current data suggests potential benefit of earlier surgery for necrotizing enterocolitis (NEC) however this requires accurate prognostication early in the disease course. This study aims to identify and determine the effectiveness of previously reported methods or tests for the identification of surgical NEC. METHODS Systematic review and meta-analysis with registration on PROSPERO including articles describing a method of identifying surgical NEC. Outcomes of interest were effectiveness and repeatability of index test. RESULTS Of the 190 full-text articles screened, 90 studies were included which contained 114 methods of identifying surgical NEC in 9546 infants. Of these methods, 44 were a scoring system, 37 a single biomarker, 24 an imaging method, and 9 an invasive method. Sensitivity and specificity ranged from 12.8-100% to 13-100%, respectively. Some methods (9.6%) provided insufficient methods for repeatability within clinical practice or research. Meta-analyses were possible for only 2 methods, the metabolic derangement 7 score and abdominal ultrasound. CONCLUSIONS A range of methods for identifying surgical NEC have been identified with varying overall performance and uncertainties about reproducibility and superiority of any method. External validation in large multicentre datasets should allow direct comparison of accuracy and prospective study should evaluate impact on clinical outcomes. IMPACT Earlier identification of need for surgery in necrotizing enterocolitis (NEC) has the potential to improve the unfavourable outcomes in this condition. As such, many methods have been developed and reported to allow earlier identification of surgical NEC. This study is the first synthesis of the literature which identifies previously reported methods and the effectiveness of these. Many methods, including scoring systems and biomarkers, appear effective for prognostication in NEC and external validation is now required in multicentre datasets prior to clinical utility.
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Affiliation(s)
- George S Bethell
- University Surgical Unit, Faculty of Medicine, University of Southampton, Southampton, UK
- Department of Paediatric Surgery and Urology, Southampton Children's Hospital, Southampton, UK
| | - Ian H Jones
- Department of Paediatric Surgery and Urology, Birmingham Children's Hospital, Birmingham, UK
| | - Cheryl Battersby
- Neonatal Medicine, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Marian Knight
- Nuffield Department of Population Health, National Perinatal Epidemiology Unit, University of Oxford, Oxford, UK
| | - Nigel J Hall
- University Surgical Unit, Faculty of Medicine, University of Southampton, Southampton, UK.
- Department of Paediatric Surgery and Urology, Southampton Children's Hospital, Southampton, UK.
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2
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De Rose DU, Lapillonne A, Iacobelli S, Capolupo I, Dotta A, Salvatori G. Nutritional Strategies for Preterm Neonates and Preterm Neonates Undergoing Surgery: New Insights for Practice and Wrong Beliefs to Uproot. Nutrients 2024; 16:1719. [PMID: 38892652 PMCID: PMC11174646 DOI: 10.3390/nu16111719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 05/22/2024] [Accepted: 05/30/2024] [Indexed: 06/21/2024] Open
Abstract
The nutrition of preterm infants remains contaminated by wrong beliefs that reflect inexactitudes and perpetuate old practices. In this narrative review, we report current evidence in preterm neonates and in preterm neonates undergoing surgery. Convictions that necrotizing enterocolitis is reduced by the delay in introducing enteral feeding, a slow advancement in enteral feeds, and the systematic control of residual gastric volumes, should be abandoned. On the contrary, these practices prolong the time to reach full enteral feeding. The length of parenteral nutrition should be as short as possible to reduce the infectious risk. Intrauterine growth restriction, hemodynamic and respiratory instability, and patent ductus arteriosus should be considered in advancing enteral feeds, but they must not translate into prolonged fasting, which can be equally dangerous. Clinicians should also keep in mind the risk of refeeding syndrome in case of high amino acid intake and inadequate electrolyte supply, closely monitoring them. Conversely, when preterm infants undergo surgery, nutritional strategies are still based on retrospective studies and opinions rather than on randomized controlled trials. Finally, this review also highlights how the use of adequately fortified human milk is strongly recommended, as it offers unique benefits for immune and gastrointestinal health and neurodevelopmental outcomes.
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Affiliation(s)
- Domenico Umberto De Rose
- Neonatal Intensive Care Unit, “Bambino Gesù” Children’s Hospital IRCCS, 00165 Rome, Italy; (I.C.); (A.D.); (G.S.)
- PhD Course in Microbiology, Immunology, Infectious Diseases, and Transplants (MIMIT), Faculty of Medicine and Surgery, “Tor Vergata” University of Rome, 00133 Rome, Italy
| | - Alexandre Lapillonne
- Department of Neonatology, APHP, Necker-Enfants Malades University Hospital, EHU 7328 Paris Cite University Paris, 75015 Paris, France;
- Children’s Nutrition Research Center, Baylor College of Medicine, Houston, TX 77024, USA
| | - Silvia Iacobelli
- Réanimation Néonatale et Pédiatrique, Centre Hospitalier Universitaire Saint-Pierre, BP 350, 97448 Saint Pierre CEDEX, France;
- Centre d’Études Périnatales de l’Océan Indien (UR 7388), Université de La Réunion, BP 350, 97448 Saint Pierre CEDEX, France
| | - Irma Capolupo
- Neonatal Intensive Care Unit, “Bambino Gesù” Children’s Hospital IRCCS, 00165 Rome, Italy; (I.C.); (A.D.); (G.S.)
| | - Andrea Dotta
- Neonatal Intensive Care Unit, “Bambino Gesù” Children’s Hospital IRCCS, 00165 Rome, Italy; (I.C.); (A.D.); (G.S.)
| | - Guglielmo Salvatori
- Neonatal Intensive Care Unit, “Bambino Gesù” Children’s Hospital IRCCS, 00165 Rome, Italy; (I.C.); (A.D.); (G.S.)
- Donor Human Milk Bank, “Bambino Gesù” Children’s Hospital IRCCS, 00165 Rome, Italy
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3
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Conte L, Amodeo I, De Nunzio G, Raffaeli G, Borzani I, Persico N, Griggio A, Como G, Cascio D, Colnaghi M, Mosca F, Cavallaro G. Congenital diaphragmatic hernia: automatic lung and liver MRI segmentation with nnU-Net, reproducibility of pyradiomics features, and a machine learning application for the classification of liver herniation. Eur J Pediatr 2024; 183:2285-2300. [PMID: 38416256 PMCID: PMC11035462 DOI: 10.1007/s00431-024-05476-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 02/29/2024]
Abstract
Prenatal assessment of lung size and liver position is essential to stratify congenital diaphragmatic hernia (CDH) fetuses in risk categories, guiding counseling, and patient management. Manual segmentation on fetal MRI provides a quantitative estimation of total lung volume and liver herniation. However, it is time-consuming and operator-dependent. In this study, we utilized a publicly available deep learning (DL) segmentation system (nnU-Net) to automatically contour CDH-affected fetal lungs and liver on MRI sections. Concordance between automatic and manual segmentation was assessed by calculating the Jaccard coefficient. Pyradiomics standard features were then extracted from both manually and automatically segmented regions. The reproducibility of features between the two groups was evaluated through the Wilcoxon rank-sum test and intraclass correlation coefficients (ICCs). We finally tested the reliability of the automatic-segmentation approach by building a ML classifier system for the prediction of liver herniation based on support vector machines (SVM) and trained on shape features computed both in the manual and nnU-Net-segmented organs. We compared the area under the classifier receiver operating characteristic curve (AUC) in the two cases. Pyradiomics features calculated in the manual ROIs were partly reproducible by the same features calculated in nnU-Net segmented ROIs and, when used in the ML procedure, to predict liver herniation (both AUC around 0.85). Conclusion: Our results suggest that automatic MRI segmentation is feasible, with good reproducibility of pyradiomics features, and that a ML system for liver herniation prediction offers good reliability. Trial registration: https://clinicaltrials.gov/ct2/show/NCT04609163?term=NCT04609163&draw=2&rank=1 ; Clinical Trial Identification no. NCT04609163. What is Known: • Magnetic resonance imaging (MRI) is crucial for prenatal congenital diaphragmatic hernia (CDH) assessment. It enables the quantification of the total lung volume and the extent of liver herniation, which are essential for stratifying the severity of CDH, guiding counseling, and patient management. • The manual segmentation of MRI scans is a time-consuming process that is heavily reliant upon the skill set of the operator. What is New: • MRI lung and liver automatic segmentation using the deep learning nnU-Net system is feasible, with good Jaccard coefficient values and satisfactory reproducibility of pyradiomics features compared to manual results. • A feasible ML system for predicting liver herniation could improve prenatal assessments and CDH patient management.
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Affiliation(s)
- Luana Conte
- Department of Mathematics and Physics "E. De Giorgi", Laboratory of Biomedical Physics and Environment, Università del Salento, Lecce, Italy
- Advanced Data Analysis in Medicine (ADAM), Laboratory of Interdisciplinary Research Applied to Medicine (DReAM), Local Health Authority (ASL) Lecce and Università del Salento, Lecce, Italy
| | - Ilaria Amodeo
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Giorgio De Nunzio
- Department of Mathematics and Physics "E. De Giorgi", Laboratory of Biomedical Physics and Environment, Università del Salento, Lecce, Italy.
- Advanced Data Analysis in Medicine (ADAM), Laboratory of Interdisciplinary Research Applied to Medicine (DReAM), Local Health Authority (ASL) Lecce and Università del Salento, Lecce, Italy.
| | - Genny Raffaeli
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Clinical Sciences and Community Health, Università Degli Studi Di Milano, Milan, Italy
| | - Irene Borzani
- Pediatric Radiology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Nicola Persico
- Department of Clinical Sciences and Community Health, Università Degli Studi Di Milano, Milan, Italy
- Department of Obstetrics and Gynecology, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Alice Griggio
- ASST Fatebenefratelli Sacco, Ospedale Macedonio Melloni, Milan, Italy
| | - Giuseppe Como
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Donato Cascio
- Department of Physics and Chemistry, Università Degli Studi Di Palermo, Palermo, Italy
| | - Mariarosa Colnaghi
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Fabio Mosca
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Clinical Sciences and Community Health, Università Degli Studi Di Milano, Milan, Italy
| | - Giacomo Cavallaro
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
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Cuna A, Premkumar MH, Sampath V. Artificial intelligence to classify acquired intestinal injury in preterm neonates-a new perspective. Pediatr Res 2024:10.1038/s41390-024-03148-w. [PMID: 38499626 DOI: 10.1038/s41390-024-03148-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 03/02/2024] [Indexed: 03/20/2024]
Affiliation(s)
- Alain Cuna
- Division of Neonatology, Children's Mercy Kansas City, Kansas City, MO, USA
- School of Medicine, University of Missouri Kansas City, Kansas City, MO, USA
| | - Muralidhar H Premkumar
- Division of Neonatology, Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA
| | - Venkatesh Sampath
- Division of Neonatology, Children's Mercy Kansas City, Kansas City, MO, USA.
- School of Medicine, University of Missouri Kansas City, Kansas City, MO, USA.
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5
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Nayak SP, Sánchez-Rosado M, Reis JD, Brown LS, Mangona KL, Sharma P, Nelson DB, Wyckoff MH, Pandya S, Mir IN, Brion LP. Development of a Prediction Model for Surgery or Early Mortality at the Time of Initial Assessment for Necrotizing Enterocolitis. Am J Perinatol 2024. [PMID: 38272063 DOI: 10.1055/a-2253-8656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
OBJECTIVE No available scale, at the time of initial evaluation for necrotizing enterocolitis (NEC), accurately predicts, that is, with an area under the curve (AUC) ≥0.9, which preterm infants will undergo surgery for NEC stage III or die within a week. STUDY DESIGN This is a retrospective cohort study (n = 261) of preterm infants with <33 weeks' gestation or <1,500 g birthweight with either suspected or with definite NEC born at Parkland Hospital between 2009 and 2021. A prediction model using the new HASOFA SCORE (H: yperglycemia, H: yperkalemia, use of inotropes for H: ypotension during the prior week, A: cidemia, Neonatal S: equential O: rgan F: ailure A: ssessment [nSOFA: ] score) was compared with a similar model using the nSOFA score. RESULTS Among 261 infants, 112 infants had NEC stage I, 68 with NEC stage II, and 81 with NEC stage III based on modified Bell's classification. The primary outcome, surgery for NEC stage III or death within a week, occurred in 81 infants (surgery in 66 infants and death in 38 infants). All infants with pneumoperitoneum or abdominal compartment syndrome either died or had surgery. The HASOFA and the nSOFA scores were evaluated in 254 and 253 infants, respectively, at the time of the initial workup for NEC. Both models were internally validated. The HASOFA model was a better predictor of surgery for NEC stage III or death within a week than the nSOFA model, with greater AUC 0.909 versus 0.825, respectively, p < 0.001. Combining HASOFA at initial assessment with concurrent or later presence of abdominal wall erythema or portal gas improved the prediction surgery for NEC stage III or death with AUC 0.942 or 0.956, respectively. CONCLUSION Using this new internally validated prediction model, surgery for NEC stage III or death within a week can be accurately predicted at the time of initial assessment for NEC. KEY POINTS · No available scale, at initial evaluation, accurately predicts which preterm infants will undergo surgery for NEC stage III or die within a week.. · In this retrospective cohort study of 261 preterm infants with either suspected or definite NEC we developed a new prediction model (HASOFA score).. · The HASOFA-model had high discrimination (AUC 0.909) and excellent calibration and was internally validated..
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Affiliation(s)
- Sujir P Nayak
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Mariela Sánchez-Rosado
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas
- Division of Neonatology, Joe DiMaggio Children's Hospital, Hollywood, Florida
| | - Jordan D Reis
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas
- Department of Pediatrics, Baylor Scott and White, Dallas, Texas
| | - L Steven Brown
- Department of Research, Parkland Health and Hospital System, Dallas, Texas
| | - Kate L Mangona
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Priya Sharma
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas
- Department of Pediatrics, Baylor Scott and White, Dallas, Texas
| | - David B Nelson
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, and Parkland Health, Dallas, Texas
| | - Myra H Wyckoff
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Samir Pandya
- Division of Pediatric Surgery, Department of Surgery, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Imran N Mir
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Luc P Brion
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas
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6
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Beam K, Sharma P, Levy P, Beam AL. Artificial intelligence in the neonatal intensive care unit: the time is now. J Perinatol 2024; 44:131-135. [PMID: 37443271 DOI: 10.1038/s41372-023-01719-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 06/24/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023]
Abstract
Artificial intelligence (AI) has the potential to revolutionize the neonatal intensive care unit (NICU) care by leveraging the large-scale, high-dimensional data that are generated by NICU patients. There is an emerging recognition that the confluence of technological progress, commercialization pathways, and rich data sets provides a unique opportunity for AI to make a lasting impact on the NICU. In this perspective article, we discuss four broad categories of AI applications in the NICU: imaging interpretation, prediction modeling of electronic health record data, integration of real-time monitoring data, and documentation and billing. By enhancing decision-making, streamlining processes, and improving patient outcomes, AI holds the potential to transform the quality of care for vulnerable newborns, making the excitement surrounding AI advancements well-founded and the potential for significant positive change stronger than ever before.
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Affiliation(s)
- Kristyn Beam
- Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Puneet Sharma
- Division of Newborn Medicine, Department of Pediatrics Boston Children's Hospital, Boston, MA, USA
| | - Phil Levy
- Division of Newborn Medicine, Department of Pediatrics Boston Children's Hospital, Boston, MA, USA
| | - Andrew L Beam
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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7
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Cui C, Chen FL, Li LQ. [Recent research on machine learning in the diagnosis and treatment of necrotizing enterocolitis in neonates]. ZHONGGUO DANG DAI ER KE ZA ZHI = CHINESE JOURNAL OF CONTEMPORARY PEDIATRICS 2023; 25:767-773. [PMID: 37529961 PMCID: PMC10414163 DOI: 10.7499/j.issn.1008-8830.2302165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 05/08/2023] [Indexed: 08/03/2023]
Abstract
Necrotizing enterocolitis (NEC), with the main manifestations of bloody stool, abdominal distension, and vomiting, is one of the leading causes of death in neonates, and early identification and diagnosis are crucial for the prognosis of NEC. The emergence and development of machine learning has provided the potential for early, rapid, and accurate identification of this disease. This article summarizes the algorithms of machine learning recently used in NEC, analyzes the high-risk predictive factors revealed by these algorithms, evaluates the ability and characteristics of machine learning in the etiology, definition, and diagnosis of NEC, and discusses the challenges and prospects for the future application of machine learning in NEC.
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Affiliation(s)
- Cheng Cui
- Department of Neonatology, Children's Hospital of Chongqing Medical University/National Clinical Research Center for Child Health and Disorders/Ministry of Education Key Laboratory of Child Development and Disorders/Key Laboratory of Pediatrics in Chongqing, Chongqing 400014, China
| | - Fei-Long Chen
- Department of Neonatology, Children's Hospital of Chongqing Medical University/National Clinical Research Center for Child Health and Disorders/Ministry of Education Key Laboratory of Child Development and Disorders/Key Laboratory of Pediatrics in Chongqing, Chongqing 400014, China
| | - Lu-Quan Li
- Department of Neonatology, Children's Hospital of Chongqing Medical University/National Clinical Research Center for Child Health and Disorders/Ministry of Education Key Laboratory of Child Development and Disorders/Key Laboratory of Pediatrics in Chongqing, Chongqing 400014, China
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8
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Klerk DH, van Varsseveld OC, Offringa M, Modi N, Lacher M, Zani A, Pakarinen MP, Koivusalo A, Jester I, Spruce M, Derikx JPM, Bakx R, Ksia A, Vermeulen MJ, Kooi EMW, Hulscher JBF. Development of an international core outcome set for treatment trials in necrotizing enterocolitis-a study protocol. Trials 2023; 24:367. [PMID: 37259112 DOI: 10.1186/s13063-023-07413-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 05/24/2023] [Indexed: 06/02/2023] Open
Abstract
AIM Necrotizing enterocolitis (NEC) is the most lethal disease of the gastrointestinal tract of preterm infants. New and existing management strategies need clinical evaluation. Large heterogeneity exists in the selection, measurement, and reporting of outcome measures in NEC intervention studies. This hampers meta-analyses and the development of evidence-based management guidelines. We aim to develop a Core Outcome Set (COS) for NEC that includes the most relevant outcomes for patients and physicians, from moment of diagnosis into adulthood. This COS is designed for use in NEC treatment trials, in infants with confirmed NEC. METHODS This study is designed according to COS-STAD (Core Outcome Set-STAndards for Development) recommendations and the COMET (Core Outcome Measures in Effectiveness Trials) Initiative Handbook. We obtained a waiver from the Ethics Review Board and prospectively registered this study with COMET (Study 1920). We will approach 125 clinicians and/or researchers from low-middle and high-income countries based on their scientific output (using SCIVAL, a bibliometric tool). Patients and parents will be approached through local patient organisations. Participants will be separated into three panels, to assess differences in priorities between former patients and parents (1. lay panel), clinicians and researchers involved in the neonatal period (2. neonatal panel) and after the neonatal period (3. post-neonatal panel). They will be presented with outcomes currently used in NEC research, identified through a systematic review, in a Delphi process. Eligible outcome domains are also identified from the patients and parents' perspectives. Using a consensus process, including three online Delphi rounds and a final face-to-face consensus meeting, the COS will be finalised and include outcomes deemed essential to all stakeholders: health care professionals, parents and patients' representatives. The final COS will be reported in accordance with the COS-Standards for reporting (COS-STAR) statement. CONCLUSIONS Development of an international COS will help to improve homogeneity of outcome measure reporting in NEC, will enable adequate and efficient comparison of treatment strategies, and will help the interpretation and implementation of clinical trial results. This will contribute to high-quality evidence regarding the best treatment strategy for NEC in preterm infants.
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Affiliation(s)
- Daphne H Klerk
- Division of Neonatology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
| | - Otis C van Varsseveld
- Department of Surgery, Division of Paediatric Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Martin Offringa
- Division of Neonatology, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Neena Modi
- Section of Neonatal Medicine, School of Public Health, Chelsea and Westminster Hospital campus, Imperial College London, London, UK
| | - Martin Lacher
- Department of Paediatric Surgery, University Hospital Leipzig, University of Leipzig, Leipzig, Germany
| | - Augusto Zani
- Department of General and Thoracic Surgery, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Mikko P Pakarinen
- Department of Paediatric Surgery, Children's Hospital, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Antti Koivusalo
- Department of Paediatric Surgery, Children's Hospital, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Ingo Jester
- Departments of Paediatric Surgery, Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
| | | | - Joep P M Derikx
- Department of Paediatric Surgery, UMC, Emma Children's Hospital, Amsterdam, University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Roel Bakx
- Department of Paediatric Surgery, UMC, Emma Children's Hospital, Amsterdam, University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Amine Ksia
- Department of Surgery, Department of Paediatric Surgery, Monastir Medical School, Fattouma Bourguiba Hospital, Monastir University, Monastir, Tunisia
| | - Marijn J Vermeulen
- Care4Neo, Neonatal Patient and Parent Organization, Rotterdam, the Netherlands
- Department of Neonatal and Pediatric Intensive Care, Division of Neonatology, Erasmus Medical Centre, Rotterdam, the Netherlands
| | - Elisabeth M W Kooi
- Division of Neonatology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Jan B F Hulscher
- Department of Surgery, Division of Paediatric Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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9
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McElroy SJ, Lueschow SR. State of the art review on machine learning and artificial intelligence in the study of neonatal necrotizing enterocolitis. Front Pediatr 2023; 11:1182597. [PMID: 37303753 PMCID: PMC10250644 DOI: 10.3389/fped.2023.1182597] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 04/25/2023] [Indexed: 06/13/2023] Open
Abstract
Necrotizing Enterocolitis (NEC) is one of the leading causes of gastrointestinal emergency in preterm infants. Although NEC was formally described in the 1960's, there is still difficulty in diagnosis and ultimately treatment for NEC due in part to the multifactorial nature of the disease. Artificial intelligence (AI) and machine learning (ML) techniques have been applied by healthcare researchers over the past 30 years to better understand various diseases. Specifically, NEC researchers have used AI and ML to predict NEC diagnosis, NEC prognosis, discover biomarkers, and evaluate treatment strategies. In this review, we discuss AI and ML techniques, the current literature that has applied AI and ML to NEC, and some of the limitations in the field.
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Affiliation(s)
- Steven J. McElroy
- Department of Pediatrics, University of California Davis, Sacramento, CA, United States
| | - Shiloh R. Lueschow
- Stead Family Department of Pediatrics, University of Iowa, Iowa City, IA, United States
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10
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Xiao T, Dong X, Lu Y, Zhou W. High-Resolution and Multidimensional Phenotypes Can Complement Genomics Data to Diagnose Diseases in the Neonatal Population. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:204-215. [PMID: 37197647 PMCID: PMC10110825 DOI: 10.1007/s43657-022-00071-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 08/03/2022] [Accepted: 08/08/2022] [Indexed: 05/19/2023]
Abstract
Advances in genomic medicine have greatly improved our understanding of human diseases. However, phenome is not well understood. High-resolution and multidimensional phenotypes have shed light on the mechanisms underlying neonatal diseases in greater details and have the potential to optimize clinical strategies. In this review, we first highlight the value of analyzing traditional phenotypes using a data science approach in the neonatal population. We then discuss recent research on high-resolution, multidimensional, and structured phenotypes in neonatal critical diseases. Finally, we briefly introduce current technologies available for the analysis of multidimensional data and the value that can be provided by integrating these data into clinical practice. In summary, a time series of multidimensional phenome can improve our understanding of disease mechanisms and diagnostic decision-making, stratify patients, and provide clinicians with optimized strategies for therapeutic intervention; however, the available technologies for collecting multidimensional data and the best platform for connecting multiple modalities should be considered.
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Affiliation(s)
- Tiantian Xiao
- Division of Neonatology, Children’s Hospital of Fudan University, National Children’s Medical Center, 399 Wanyuan Road, Shanghai, 201102 China
- Department of Neonatology, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610000 China
| | - Xinran Dong
- Center for Molecular Medicine, Pediatric Research Institute, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102 China
| | - Yulan Lu
- Center for Molecular Medicine, Pediatric Research Institute, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102 China
| | - Wenhao Zhou
- Division of Neonatology, Children’s Hospital of Fudan University, National Children’s Medical Center, 399 Wanyuan Road, Shanghai, 201102 China
- Center for Molecular Medicine, Pediatric Research Institute, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102 China
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11
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Leiva T, Lueschow S, Burge K, Devette C, McElroy S, Chaaban H. Biomarkers of necrotizing enterocolitis in the era of machine learning and omics. Semin Perinatol 2023; 47:151693. [PMID: 36604292 PMCID: PMC9975050 DOI: 10.1016/j.semperi.2022.151693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Necrotizing enterocolitis (NEC) continues to be a major cause of morbidity and mortality in preterm infants. Despite decades of research in NEC, no reliable biomarkers can accurately diagnose NEC or predict patient prognosis. The recent emergence of multi-omics could potentially shift NEC biomarker discovery, particularly when evaluated using systems biology techniques. Furthermore, the use of machine learning and artificial intelligence in analyzing this 'big data' could enable novel interpretations of NEC subtypes, disease progression, and potential therapeutic targets, allowing for integration with personalized medicine approaches. In this review, we evaluate studies using omics technologies and machine learning in the diagnosis of NEC. Future implications and challenges inherent to the field are also discussed.
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Affiliation(s)
- Tyler Leiva
- Department of Surgery, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Shiloh Lueschow
- Department of Microbiology and Immunology, Stead Family Department of Pediatrics, University of Iowa, Iowa City, IA, USA
| | - Kathryn Burge
- Department of Pediatrics, The University of Oklahoma Health Sciences Center, 1200 N. Everett Dr., ETNP 7504, Oklahoma City, OK 73104, USA
| | - Christa Devette
- Department of Pediatrics, The University of Oklahoma Health Sciences Center, 1200 N. Everett Dr., ETNP 7504, Oklahoma City, OK 73104, USA
| | - Steven McElroy
- Department of Pediatrics, University of California Davis, Sacramento, CA, USA
| | - Hala Chaaban
- Department of Pediatrics, The University of Oklahoma Health Sciences Center, 1200 N. Everett Dr., ETNP 7504, Oklahoma City, OK 73104, USA.
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12
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Using machine learning to impact on long-term clinical care: principles, challenges, and practicalities. Pediatr Res 2023; 93:324-333. [PMID: 35906306 PMCID: PMC9937918 DOI: 10.1038/s41390-022-02194-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 06/10/2022] [Accepted: 06/22/2022] [Indexed: 11/08/2022]
Abstract
The rise of machine learning in healthcare has significant implications for paediatrics. Long-term conditions with significant disease heterogeneity comprise large portions of the routine work performed by paediatricians. Improving outcomes through discovery of disease and treatment prediction models, alongside novel subgroup clustering of patients, are some of the areas in which machine learning holds significant promise. While artificial intelligence has percolated into routine use in our day to day lives through advertising algorithms, song or movie selections and sifting of spam emails, the ability of machine learning to utilise highly complex and dimensional data has not yet reached its full potential in healthcare. In this review article, we discuss some of the foundations of machine learning, including some of the basic algorithms. We emphasise the importance of correct utilisation of machine learning, including adequate data preparation and external validation. Using nutrition in preterm infants and paediatric inflammatory bowel disease as examples, we discuss the evidence and potential utility of machine learning in paediatrics. Finally, we review some of the future applications, alongside challenges and ethical considerations related to application of artificial intelligence. IMPACT: Machine learning is a widely used term; however, understanding of the process and application to healthcare is lacking. This article uses clinical examples to explore complex machine learning terms and algorithms. We discuss limitations and potential future applications within paediatrics and neonatal medicine.
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13
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Artificial intelligence in the diagnosis of necrotising enterocolitis in newborns. Pediatr Res 2023; 93:376-381. [PMID: 36195629 DOI: 10.1038/s41390-022-02322-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 09/03/2022] [Indexed: 11/09/2022]
Abstract
Necrotising enterocolitis (NEC) is one of the most common diseases in neonates and predominantly affects premature or very-low-birth-weight infants. Diagnosis is difficult and needed in hours since the first symptom onset for the best therapeutic effects. Artificial intelligence (AI) may play a significant role in NEC diagnosis. A literature search on the use of AI in the diagnosis of NEC was performed. Four databases (PubMed, Embase, arXiv, and IEEE Xplore) were searched with the appropriate MeSH terms. The search yielded 118 publications that were reduced to 8 after screening and checking for eligibility. Of the eight, five used classic machine learning (ML), and three were on the topic of deep ML. Most publications showed promising results. However, no publications with evident clinical benefits were found. Datasets used for training and testing AI systems were small and typically came from a single institution. The potential of AI to improve the diagnosis of NEC is evident. The body of literature on this topic is scarce, and more research in this area is needed, especially with a focus on clinical utility. Cross-institutional data for the training and testing of AI algorithms are required to make progress in this area. IMPACT: Only a few publications on the use of AI in NEC diagnosis are available although they offer some evidence that AI may be helpful in NEC diagnosis. AI requires large, multicentre, and multimodal datasets of high quality for model training and testing. Published results in the literature are based on data from single institutions and, as such, have limited generalisability. Large multicentre studies evaluating broad datasets are needed to evaluate the true potential of AI in diagnosing NEC in a clinical setting.
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14
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McAdams RM, Kaur R, Sun Y, Bindra H, Cho SJ, Singh H. Predicting clinical outcomes using artificial intelligence and machine learning in neonatal intensive care units: a systematic review. J Perinatol 2022; 42:1561-1575. [PMID: 35562414 DOI: 10.1038/s41372-022-01392-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 03/30/2022] [Accepted: 04/01/2022] [Indexed: 01/19/2023]
Abstract
BACKGROUND Advances in technology, data availability, and analytics have helped improve quality of care in the neonatal intensive care unit. OBJECTIVE To provide an in-depth review of artificial intelligence (AI) and machine learning techniques being utilized to predict neonatal outcomes. METHODS The PRISMA protocol was followed that considered articles from established digital repositories. Included articles were categorized based on predictions of: (a) major neonatal morbidities such as sepsis, bronchopulmonary dysplasia, intraventricular hemorrhage, necrotizing enterocolitis, and retinopathy of prematurity; (b) mortality; and (c) length of stay. RESULTS A total of 366 studies were considered; 68 studies were eligible for inclusion in the review. The current set of predictor models are primarily built on supervised learning and mostly used regression models built on retrospective data. CONCLUSION With the availability of EMR data and data-sharing of NICU outcomes across neonatal research networks, machine learning algorithms have shown breakthrough performance in predicting neonatal disease.
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Affiliation(s)
- Ryan M McAdams
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Ravneet Kaur
- Child Health Imprints (CHIL) USA Inc, Madison, WI, USA
| | - Yao Sun
- Division of Neonatology, University of California San Francisco, San Francisco, CA, USA
| | | | - Su Jin Cho
- College of Medicine, Ewha Womans University Seoul, Seoul, Korea
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15
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Moschino L, Verlato G, Duci M, Cavicchiolo ME, Guiducci S, Stocchero M, Giordano G, Fascetti Leon F, Baraldi E. The Metabolome and the Gut Microbiota for the Prediction of Necrotizing Enterocolitis and Spontaneous Intestinal Perforation: A Systematic Review. Nutrients 2022; 14:nu14183859. [PMID: 36145235 PMCID: PMC9506026 DOI: 10.3390/nu14183859] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 09/13/2022] [Accepted: 09/13/2022] [Indexed: 11/26/2022] Open
Abstract
Necrotizing enterocolitis (NEC) is the most devastating gastrointestinal emergency in preterm neonates. Research on early predictive biomarkers is fundamental. This is a systematic review of studies applying untargeted metabolomics and gut microbiota analysis to evaluate the differences between neonates affected by NEC (Bell’s stage II or III), and/or by spontaneous intestinal perforation (SIP) versus healthy controls. Five studies applying metabolomics (43 cases, 95 preterm controls) and 20 applying gut microbiota analysis (254 cases, 651 preterm controls, 22 term controls) were selected. Metabolomic studies utilized NMR spectroscopy or mass spectrometry. An early urinary alanine/histidine ratio >4 showed good sensitivity and predictive value for NEC in one study. Samples collected in proximity to NEC diagnosis demonstrated variable pathways potentially related to NEC. In studies applying untargeted gut microbiota analysis, the sequencing of the V3−V4 or V3 to V5 regions of the 16S rRNA was the most used technique. At phylum level, NEC specimens were characterized by increased relative abundance of Proteobacteria compared to controls. At genus level, pre-NEC samples were characterized by a lack or decreased abundance of Bifidobacterium. Finally, at the species level Bacteroides dorei, Clostridium perfringens and perfringens-like strains dominated early NEC specimens, whereas Clostridium butyricum, neonatale and Propionibacterium acnei those at disease diagnosis. Six studies found a lower Shannon diversity index in cases than controls. A clear separation of cases from controls emerged based on UniFrac metrics in five out of seven studies. Importantly, no studies compared NEC versus SIP. Untargeted metabolomics and gut microbiota analysis are interrelated strategies to investigate NEC pathophysiology and identify potential biomarkers. Expression of quantitative measurements, data sharing via biorepositories and validation studies are fundamental to guarantee consistent comparison of results.
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Affiliation(s)
- Laura Moschino
- Neonatal Intensive Care Unit, Department of Women’s and Children’s Health, Padova University Hospital, 35128 Padova, Italy
- Institute of Paediatric Research, Città della Speranza, Laboratory of Mass Spectrometry and Metabolomics, 35127 Padova, Italy
- Correspondence: ; Tel.: +39-049-821-3548
| | - Giovanna Verlato
- Neonatal Intensive Care Unit, Department of Women’s and Children’s Health, Padova University Hospital, 35128 Padova, Italy
| | - Miriam Duci
- Paediatric Surgery, Department of Women’s and Children’s Health, Padova University Hospital, 35128 Padova, Italy
| | - Maria Elena Cavicchiolo
- Neonatal Intensive Care Unit, Department of Women’s and Children’s Health, Padova University Hospital, 35128 Padova, Italy
| | - Silvia Guiducci
- Neonatal Intensive Care Unit, Department of Women’s and Children’s Health, Padova University Hospital, 35128 Padova, Italy
| | - Matteo Stocchero
- Institute of Paediatric Research, Città della Speranza, Laboratory of Mass Spectrometry and Metabolomics, 35127 Padova, Italy
- Laboratory of Mass Spectrometry and Metabolomics, Department of Women’s and Children’s Health, Padova University Hospital, 35128 Padova, Italy
| | - Giuseppe Giordano
- Institute of Paediatric Research, Città della Speranza, Laboratory of Mass Spectrometry and Metabolomics, 35127 Padova, Italy
- Laboratory of Mass Spectrometry and Metabolomics, Department of Women’s and Children’s Health, Padova University Hospital, 35128 Padova, Italy
| | - Francesco Fascetti Leon
- Paediatric Surgery, Department of Women’s and Children’s Health, Padova University Hospital, 35128 Padova, Italy
| | - Eugenio Baraldi
- Neonatal Intensive Care Unit, Department of Women’s and Children’s Health, Padova University Hospital, 35128 Padova, Italy
- Institute of Paediatric Research, Città della Speranza, Laboratory of Mass Spectrometry and Metabolomics, 35127 Padova, Italy
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16
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Guo C, Li H. Application of 5G network combined with AI robots in personalized nursing in China: A literature review. Front Public Health 2022; 10:948303. [PMID: 36091551 PMCID: PMC9449115 DOI: 10.3389/fpubh.2022.948303] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 08/08/2022] [Indexed: 01/21/2023] Open
Abstract
The medical and healthcare industry is currently developing into digitization. Attributed to the rapid development of advanced technologies such as the 5G network, cloud computing, artificial intelligence (AI), and big data, and their wide applications in the medical industry, the medical model is shifting into an intelligent one. By combining the 5G network with cloud healthcare platforms and AI, nursing robots can effectively improve the overall medical efficacy. Meanwhile, patients can enjoy personalized medical services, the supply and the sharing of medical and healthcare services are promoted, and the digital transformation of the healthcare industry is accelerated. In this paper, the application and practice of 5G network technology in the medical industry are introduced, including telecare, 5G first-aid remote medical service, and remote robot applications. Also, by combining application characteristics of AI and development requirements of smart healthcare, the overall planning, intelligence, and personalization of the 5G network in the medical industry, as well as opportunities and challenges of its application in the field of nursing are discussed. This paper provides references to the development and application of 5G network technology in the field of medical service.
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Affiliation(s)
- Caixia Guo
- Presidents' Office, China-Japan Union Hospital, Jilin University, Changchun, China
| | - Hong Li
- Department of Emergency Medicine, China-Japan Union Hospital, Jilin University, Changchun, China,*Correspondence: Hong Li
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17
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Development of artificial neural networks for early prediction of intestinal perforation in preterm infants. Sci Rep 2022; 12:12112. [PMID: 35840701 PMCID: PMC9287325 DOI: 10.1038/s41598-022-16273-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 07/07/2022] [Indexed: 11/09/2022] Open
Abstract
Intestinal perforation (IP) in preterm infants is a life-threatening condition that may result in serious complications and increased mortality. Early Prediction of IP in infants is important, but challenging due to its multifactorial and complex nature of the disease. Thus, there are no reliable tools to predict IP in infants. In this study, we developed new machine learning (ML) models for predicting IP in very low birth weight (VLBW) infants and compared their performance to that of classic ML methods. We developed artificial neural networks (ANNs) using VLBW infant data from a nationwide cohort and prospective web-based registry. The new ANN models, which outperformed all other classic ML methods, showed an area under the receiver operating characteristic curve (AUROC) of 0.8832 for predicting IP associated with necrotizing enterocolitis (NEC-IP) and 0.8797 for spontaneous IP (SIP). We tested these algorithms using patient data from our institution, which were not included in the training dataset, and obtained an AUROC of 1.0000 for NEC-IP and 0.9364 for SIP. NEC-IP and SIP in VLBW infants can be predicted at an excellent performance level with these newly developed ML models. https://github.com/kdhRick2222/Early-Prediction-of-Intestinal-Perforation-in-Preterm-Infants.
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18
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Artificial Intelligence in NICU and PICU: A Need for Ecological Validity, Accountability, and Human Factors. Healthcare (Basel) 2022; 10:healthcare10050952. [PMID: 35628089 PMCID: PMC9140402 DOI: 10.3390/healthcare10050952] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 05/17/2022] [Accepted: 05/19/2022] [Indexed: 02/04/2023] Open
Abstract
Pediatric patients, particularly in neonatal and pediatric intensive care units (NICUs and PICUs), are typically at an increased risk of fatal decompensation. That being said, any delay in treatment or minor errors in medication dosage can overcomplicate patient health. Under such an environment, clinicians are expected to quickly and effectively comprehend large volumes of medical information to diagnose and develop a treatment plan for any baby. The integration of Artificial Intelligence (AI) into the clinical workflow can be a potential solution to safeguard pediatric patients and augment the quality of care. However, before making AI an integral part of pediatric care, it is essential to evaluate the technology from a human factors perspective, ensuring its readiness (technology readiness level) and ecological validity. Addressing AI accountability is also critical to safeguarding clinicians and improving AI acceptance in the clinical workflow. This article summarizes the application of AI in NICU/PICU and consecutively identifies the existing flaws in AI (from clinicians’ standpoint), and proposes related recommendations, which, if addressed, can improve AIs’ readiness for a real clinical environment.
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19
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A critical evaluation of current definitions of necrotizing enterocolitis. Pediatr Res 2022; 91:590-597. [PMID: 34021272 DOI: 10.1038/s41390-021-01570-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 04/19/2021] [Accepted: 04/26/2021] [Indexed: 01/24/2023]
Abstract
BACKGROUND Necrotizing enterocolitis (NEC) is a devastating intestinal disease of premature infants, with significant mortality and long-term morbidity among survivors. Multiple NEC definitions exist, but no formal head-to-head evaluation has been performed. We hypothesized that contemporary definitions would perform better in evaluation metrics than Bell's and range features would be more frequently identified as important than yes/no features. METHODS Two hundred and nineteen patients from the University of Iowa hospital with NEC, intestinal perforation, or NEC concern were identified from a 10-year retrospective cohort. NEC presence was confirmed by a blinded investigator. Evaluation metrics were calculated using statistics and six supervised machine learning classifiers for current NEC definitions. Feature importance evaluation was performed on each decision tree classifier. RESULTS Newer definitions outperformed Bell's staging using both standard statistics and most machine learning classifiers. The decision tree classifier had the highest overall machine learning scores, which resulted in Non-Bell definitions having high sensitivity (0.826, INC) and specificity (0.969, ST), while Modified Bell (IIA+) had reasonable sensitivity (0.783), but poor specificity (0.531). Feature importance evaluation identified nine criteria as important for diagnosis. CONCLUSIONS This preliminary study suggests that Non-Bell NEC definitions may be better at diagnosing NEC and calls for further examination of definitions and important criteria. IMPACT This article is the first formal head-to-head evaluation of current available definitions of NEC. Non-Bell NEC definitions may be more effective in identifying NEC based on findings from traditional measures of diagnostic performance and machine learning techniques. Nine features were identified as important for diagnosis from the definitions evaluated within the decision tree when performing supervised classification machine learning. This article serves as a preliminary study to formally evaluate the definitions of NEC utilized and should be expounded upon with a larger and more diverse patient cohort.
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20
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Adegboro CO, Choudhury A, Asan O, Kelly MM. Artificial Intelligence to Improve Health Outcomes in the NICU and PICU: A Systematic Review. Hosp Pediatr 2022; 12:93-110. [PMID: 34890453 DOI: 10.1542/hpeds.2021-006094] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
CONTEXT Artificial intelligence (AI) technologies are increasingly used in pediatrics and have the potential to help inpatient physicians provide high-quality care for critically ill children. OBJECTIVE We aimed to describe the use of AI to improve any health outcome(s) in neonatal and pediatric intensive care. DATA SOURCE PubMed, IEEE Xplore, Cochrane, and Web of Science databases. STUDY SELECTION We used peer-reviewed studies published between June 1, 2010, and May 31, 2020, in which researchers described (1) AI, (2) pediatrics, and (3) intensive care. Studies were included if researchers assessed AI use to improve at least 1 health outcome (eg, mortality). DATA EXTRACTION Data extraction was conducted independently by 2 researchers. Articles were categorized by direct or indirect impact of AI, defined by the European Institute of Innovation and Technology Health joint report. RESULTS Of the 287 publications screened, 32 met inclusion criteria. Approximately 22% (n = 7) of studies revealed a direct impact and improvement in health outcomes after AI implementation. Majority were in prototype testing, and few were deployed into an ICU setting. Among the remaining 78% (n = 25) AI models outperformed standard clinical modalities and may have indirectly influenced patient outcomes. Quantitative assessment of health outcomes using statistical measures, such as area under the receiver operating curve (56%; n = 18) and specificity (38%; n = 12), revealed marked heterogeneity in metrics and standardization. CONCLUSIONS Few studies have revealed that AI has directly improved health outcomes for pediatric critical care patients. Further prospective, experimental studies are needed to assess AI's impact by using established implementation frameworks, standardized metrics, and validated outcome measures.
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Affiliation(s)
- Claudette O Adegboro
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
| | - Avishek Choudhury
- Division of Engineering Management, School of Systems and Enterprise, Stevens Institute of Technology, Hoboken, New Jersey
| | - Onur Asan
- Division of Engineering Management, School of Systems and Enterprise, Stevens Institute of Technology, Hoboken, New Jersey
| | - Michelle M Kelly
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
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21
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Amodeo I, De Nunzio G, Raffaeli G, Borzani I, Griggio A, Conte L, Macchini F, Condò V, Persico N, Fabietti I, Ghirardello S, Pierro M, Tafuri B, Como G, Cascio D, Colnaghi M, Mosca F, Cavallaro G. A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study. PLoS One 2021; 16:e0259724. [PMID: 34752491 PMCID: PMC8577746 DOI: 10.1371/journal.pone.0259724] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 10/25/2021] [Indexed: 02/04/2023] Open
Abstract
INTRODUCTION Outcome predictions of patients with congenital diaphragmatic hernia (CDH) still have some limitations in the prenatal estimate of postnatal pulmonary hypertension (PH). We propose applying Machine Learning (ML), and Deep Learning (DL) approaches to fetuses and newborns with CDH to develop forecasting models in prenatal epoch, based on the integrated analysis of clinical data, to provide neonatal PH as the first outcome and, possibly: favorable response to fetal endoscopic tracheal occlusion (FETO), need for Extracorporeal Membrane Oxygenation (ECMO), survival to ECMO, and death. Moreover, we plan to produce a (semi)automatic fetus lung segmentation system in Magnetic Resonance Imaging (MRI), which will be useful during project implementation but will also be an important tool itself to standardize lung volume measures for CDH fetuses. METHODS AND ANALYTICS Patients with isolated CDH from singleton pregnancies will be enrolled, whose prenatal checks were performed at the Fetal Surgery Unit of the Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (Milan, Italy) from the 30th week of gestation. A retrospective data collection of clinical and radiological variables from newborns' and mothers' clinical records will be performed for eligible patients born between 01/01/2012 and 31/12/2020. The native sequences from fetal magnetic resonance imaging (MRI) will be collected. Data from different sources will be integrated and analyzed using ML and DL, and forecasting algorithms will be developed for each outcome. Methods of data augmentation and dimensionality reduction (feature selection and extraction) will be employed to increase sample size and avoid overfitting. A software system for automatic fetal lung volume segmentation in MRI based on the DL 3D U-NET approach will also be developed. ETHICS AND DISSEMINATION This retrospective study received approval from the local ethics committee (Milan Area 2, Italy). The development of predictive models in CDH outcomes will provide a key contribution in disease prediction, early targeted interventions, and personalized management, with an overall improvement in care quality, resource allocation, healthcare, and family savings. Our findings will be validated in a future prospective multicenter cohort study. REGISTRATION The study was registered at ClinicalTrials.gov with the identifier NCT04609163.
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Affiliation(s)
- Ilaria Amodeo
- NICU, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Giorgio De Nunzio
- Department of Mathematics and Physics “E. De Giorgi”, Laboratory of Biomedical Physics and Environment, Università del Salento, Lecce, Italy
- Advanced Data Analysis in Medicine (ADAM), Laboratory of Interdisciplinary Research Applied to Medicine (DReAM), Università del Salento, Lecce, Italy
- Azienda Sanitaria Locale (ASL), Lecce, Italy
| | - Genny Raffaeli
- NICU, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy
| | - Irene Borzani
- Pediatric Radiology Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Alice Griggio
- Monza and Brianza Mother and Child Foundation, San Gerardo Hospital, Università degli Studi di Milano-Bicocca, Monza, Italy
| | - Luana Conte
- Department of Mathematics and Physics “E. De Giorgi”, Laboratory of Biomedical Physics and Environment, Università del Salento, Lecce, Italy
- Advanced Data Analysis in Medicine (ADAM), Laboratory of Interdisciplinary Research Applied to Medicine (DReAM), Università del Salento, Lecce, Italy
- Azienda Sanitaria Locale (ASL), Lecce, Italy
| | - Francesco Macchini
- Department of Pediatric Surgery, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Valentina Condò
- NICU, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Nicola Persico
- Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy
- Department of Obstetrics and Gynecology, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Isabella Fabietti
- Department of Obstetrics and Gynecology, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Stefano Ghirardello
- NICU, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Maria Pierro
- NICU, Bufalini Hospital, Azienda Unità Sanitaria Locale della Romagna, Cesena, Italy
| | - Benedetta Tafuri
- Department of Mathematics and Physics “E. De Giorgi”, Laboratory of Biomedical Physics and Environment, Università del Salento, Lecce, Italy
- Advanced Data Analysis in Medicine (ADAM), Laboratory of Interdisciplinary Research Applied to Medicine (DReAM), Università del Salento, Lecce, Italy
- Azienda Sanitaria Locale (ASL), Lecce, Italy
| | - Giuseppe Como
- NICU, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Donato Cascio
- Department of Physics and Chemistry, Università degli Studi di Palermo, Palermo, Italy
| | - Mariarosa Colnaghi
- NICU, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Fabio Mosca
- NICU, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy
| | - Giacomo Cavallaro
- NICU, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
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Satardien M, Van Wyk L, Sidler D, Van Zyl JI. Outcomes of Neonates Requiring Neonatal Intensive Care Admission for Necrotizing Enterocolitis in a Resource-Restricted Hospital in Cape Town, South Africa. J Trop Pediatr 2021; 67:6161350. [PMID: 33693891 DOI: 10.1093/tropej/fmaa130] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
AIM The aim of this study is to describe the 30-day mortality, neurodevelopmental outcome and composite outcome (mortality or abnormal neurodevelopmental outcome) of neonates with necrotizing enterocolitis (NEC), requiring neonatal intensive care (NICU) admission, in a resource-restricted environment. METHODS All neonates admitted to Tygerberg Hospital, NICU, with a presumptive diagnosis of NEC Bell stage IIB or more, over a 5-year period, were included. RESULTS One hundred and thirty-five neonates were included with a mean gestational age of 29 ± 2.7 weeks and mean birth weight of 1185 g ± 446 g. The 30-day mortality was 52%, neurodevelopment abnormalities occurred in 35% of survivors and adverse composite outcome in 63%. The 30-day mortality and adverse composite outcome risk were increased by small for gestational age, shock, metabolic acidosis, inotrope requirement and first feed >9 days after surgery. CONCLUSION In resource-restricted environments, mortality and abnormal neurodevelopmental outcome of neonates with NEC, remain high. However, outcomes are comparable with international literature. Neonates with NEC, requiring NICU admission and surgery, require neurodevelopmental follow-up.
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Affiliation(s)
- M Satardien
- Department of Paediatrics and Child Health, Stellenbosch University, Cape Town, South Africa
| | - L Van Wyk
- Department of Paediatrics and Child Health, Stellenbosch University, Cape Town, South Africa
| | - D Sidler
- Division of Paediatric Surgery, Stellenbosch University, Cape Town, South Africa
| | - J I Van Zyl
- Department of Paediatrics and Child Health, Stellenbosch University, Cape Town, South Africa
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23
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Berrington JE, Embleton ND. Time of Onset of Necrotizing Enterocolitis and Focal Perforation in Preterm Infants: Impact on Clinical, Surgical, and Histological Features. Front Pediatr 2021; 9:724280. [PMID: 34540772 PMCID: PMC8446643 DOI: 10.3389/fped.2021.724280] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 07/27/2021] [Indexed: 01/10/2023] Open
Abstract
Objective: There is no gold standard test for diagnosis of necrotizing enterocolitis (NEC). Timing of onset is used in some definitions and studies in an attempt to separate NEC from focal intestinal perforation (FIP) with 14 days used as a cutoff. In a large, detailed data set we aimed to compare NEC and FIP in preterm infants born <32 weeks gestation, presenting before 14 days of life in comparison to cases presenting later. Design: Infants with NEC or FIP when parents had consented to enrollment in an observational and sample collection study were included from 2009 to 2019. Clinical, surgical, histological, and outcome data were extracted and reviewed by each author independently. Patients/Episodes: In 785 infants, 174 episodes of NEC or FIP were identified of which 73 (42%) occurred before 14 days, including 54 laparotomies and 19 episodes of medically managed NEC ("early"). There were 56 laparotomies and 45 episodes of medically managed NEC presenting on or after 14 days age ("late"). Results: In early cases, 41% of laparotomies were for NEC (22 cases) and 59% for FIP (32 cases), and in late cases, 91% of laparotomies (51 cases) were for NEC and 9% (five cases) were for FIP. NEC presenting early was more likely to present with an initial septic presentation rather than discrete abdominal pathology and less likely to have clear pneumatosis. Early cases did not otherwise differ clinically, surgically, or histologically or in outcomes compared with later cases. FIP features did not differ by age at presentation. Conclusions: Although most FIP occurred early, 14% occurred later, whereas almost one third (29%) of NEC cases (surgical and medical) presented early. Infant demographics and surgical and histological findings of early- and late-presenting disease did not differ, suggesting that early and late cases are not necessarily different subtypes of the same disease although a common pathway of different pathogenesis cannot be excluded. Timing of onset does not accurately distinguish NEC from FIP, and caution should be exercised in including timing of onset in diagnostic criteria.
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Affiliation(s)
- Janet Elizabeth Berrington
- Newcastle upon Tyne Hospitals National Health Service Foundation Trust, Newcastle upon Tyne, United Kingdom.,Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Nicholas David Embleton
- Newcastle upon Tyne Hospitals National Health Service Foundation Trust, Newcastle upon Tyne, United Kingdom.,Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
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24
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Abstract
Necrotizing enterocolitis (NEC) is a leading cause of morbidity and mortality in hospitalized infants. First classified through Bell staging in 1978, a number of additional definitions of NEC have been proposed in the subsequent decades. In this review, we summarize eight current definitions of NEC, and explore similarities and differences in clinical signs and radiographic features included within these definitions, as well as their limitations. We highlight the importance of a global consensus on defining NEC to improve NEC research and outcomes, incorporating input from participants at an international NEC conference. We also highlight the important role of patient-families in helping to redefine NEC.
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25
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Helguera-Repetto AC, Soto-Ramírez MD, Villavicencio-Carrisoza O, Yong-Mendoza S, Yong-Mendoza A, León-Juárez M, González-Y-Merchand JA, Zaga-Clavellina V, Irles C. Neonatal Sepsis Diagnosis Decision-Making Based on Artificial Neural Networks. Front Pediatr 2020; 8:525. [PMID: 33042902 PMCID: PMC7518045 DOI: 10.3389/fped.2020.00525] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 07/24/2020] [Indexed: 12/21/2022] Open
Abstract
Neonatal sepsis remains difficult to diagnose due to its non-specific signs and symptoms. Traditional scoring systems help to discriminate between septic or not patients, but they do not consider every single patient particularity. Thus, the purpose of this study was to develop an early- and late-onset neonatal sepsis diagnosis model, based on clinical maternal and neonatal data from electronic records, at the time of clinical suspicion. A predictive model was obtained by training and validating an artificial Neural Networks (ANN) algorithm with a balanced dataset consisting of preterm and term non-septic or septic neonates (early- and late-onset), with negative and positive culture results, respectively, using 25 maternal and neonatal features. The outcome of the model was sepsis or not. The performance measures of the model, evaluated with an independent dataset, outperformed physician's diagnosis using the same features based on traditional scoring systems, with a 93.3% sensitivity, an 80.0% specificity, a 94.4% AUROC, and a regression coefficient of 0.974 between actual and simulated results. The model also performed well-relative to the state-of-the-art methods using similar maternal/neonatal variables. The top 10 factors estimating sepsis were maternal age, cervicovaginitis and neonatal: fever, apneas, platelet counts, gender, bradypnea, band cells, catheter use, and birth weight.
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Affiliation(s)
| | - María Dolores Soto-Ramírez
- Department of Immunobiochemistry, Instituto Nacional de Perinatología, Mexico City, Mexico.,Department of Microbiology, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Mexico City, Mexico
| | - Oscar Villavicencio-Carrisoza
- Department of Immunobiochemistry, Instituto Nacional de Perinatología, Mexico City, Mexico.,Department of Microbiology, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Mexico City, Mexico
| | - Samantha Yong-Mendoza
- Department of Immunobiochemistry, Instituto Nacional de Perinatología, Mexico City, Mexico.,Department of Microbiology, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Mexico City, Mexico
| | - Angélica Yong-Mendoza
- Department of Immunobiochemistry, Instituto Nacional de Perinatología, Mexico City, Mexico
| | - Moisés León-Juárez
- Department of Immunobiochemistry, Instituto Nacional de Perinatología, Mexico City, Mexico
| | - Jorge A González-Y-Merchand
- Department of Microbiology, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Mexico City, Mexico
| | - Verónica Zaga-Clavellina
- Department of Physiology and Cellular Development, Instituto Nacional de Perinatología, Mexico City, Mexico
| | - Claudine Irles
- Department of Physiology and Cellular Development, Instituto Nacional de Perinatología, Mexico City, Mexico
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26
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Alcalá-Rmz V, Zanella-Calzada LA, Galván-Tejada CE, García-Hernández A, Cruz M, Valladares-Salgado A, Galván-Tejada JI, Gamboa-Rosales H. Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16030381. [PMID: 30700010 PMCID: PMC6388177 DOI: 10.3390/ijerph16030381] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Revised: 01/20/2019] [Accepted: 01/21/2019] [Indexed: 01/16/2023]
Abstract
Diabetes is a chronic and noncommunicable but preventable disease that is affecting the Mexican population at worrying levels, being the first place in prevalence worldwide. Early diabetes detection has become important to prevent other health conditions that involve low organ yield until the patient death. Based on this problem, this work proposes the architecture of an Artificial Neural Network (ANN) for the automated classification of healthy patients from diabetics patients. The analysis was performed used a set of 19 para-clinical features to determine the health status of the patients. The developed model was evaluated through a statistical analysis based on the calculation of the loss function, accuracy, area under the curve (AUC) and receiving operating characteristics (ROC) curve. The results obtained present statistically significant values, with accuracy of 0.94 and AUC values of 0.98. Based on these results, it is possible to conclude that the ANN implemented in this work can classify patients with presence of diabetes from controls with significant accuracy, presenting preliminary results for the development of a diagnostic tool that can be supportive for health specialists.
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Affiliation(s)
- Vanessa Alcalá-Rmz
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, Mexico.
| | - Laura A Zanella-Calzada
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, Mexico.
| | - Carlos E Galván-Tejada
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, Mexico.
| | - Alejandra García-Hernández
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, Mexico.
| | - Miguel Cruz
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Av. Cuauhtémoc 330, Col. Doctores, Del. Cuauhtémoc, Ciudad de México CP 06720, Mexico.
| | - Adan Valladares-Salgado
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Av. Cuauhtémoc 330, Col. Doctores, Del. Cuauhtémoc, Ciudad de México CP 06720, Mexico.
| | - Jorge I Galván-Tejada
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, Mexico.
| | - Hamurabi Gamboa-Rosales
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, Mexico.
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