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Proietti J, O'Toole JM, Murray DM, Boylan GB. Advances in Electroencephalographic Biomarkers of Neonatal Hypoxic Ischemic Encephalopathy. Clin Perinatol 2024; 51:649-663. [PMID: 39095102 DOI: 10.1016/j.clp.2024.04.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Electroencephalography (EEG) is a key objective biomarker of newborn brain function, delivering critical, cotside insights to aid the management of encephalopathy. Access to continuous EEG is limited, forcing reliance on subjective clinical assessments. In hypoxia ischaemia, the primary cause of encephalopathy, alterations in EEG patterns correlate with. injury severity and evolution. As HIE evolves, causing secondary neuronal death, EEG can track injury progression, informing neuroprotective strategies, seizure management and prognosis. Despite its value, challenges with interpretation and lack of on site expertise has limited its broader adoption. Technological advances, particularly in digital EEG and machine learning, are enhancing real-time analysis. This will allow EEG to expand its role in HIE diagnosis, management and outcome prediction.
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Affiliation(s)
- Jacopo Proietti
- Department of Engineering for Innovation Medicine, University of Verona, Strada le Grazie, Verona 37134, Italy; INFANT Research Centre, University College Cork, Cork, Ireland
| | - John M O'Toole
- INFANT Research Centre, University College Cork, Cork, Ireland; Cergenx Ltd., Dublin, Ireland
| | - Deirdre M Murray
- INFANT Research Centre, University College Cork, Cork, Ireland; Department of Paediatrics & Child Health, University College Cork, Paediatric Academic Unit, Cork University Hospital, Wilton, Cork, T12 DC4A, Ireland
| | - Geraldine B Boylan
- INFANT Research Centre, University College Cork, Cork, Ireland; Department of Paediatrics & Child Health, University College Cork, Paediatric Academic Unit, Cork University Hospital, Wilton, Cork, T12 DC4A, Ireland.
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2
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Roychaudhuri S, Hannon K, Sunwoo J, Garvey AA, El-Dib M. Quantitative EEG and prediction of outcome in neonatal encephalopathy: a review. Pediatr Res 2024; 96:73-80. [PMID: 38503980 DOI: 10.1038/s41390-024-03138-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 02/18/2024] [Accepted: 03/01/2024] [Indexed: 03/21/2024]
Abstract
Electroencephalogram (EEG) is an important biomarker for neonatal encephalopathy (NE) and has significant predictive value for brain injury and neurodevelopmental outcomes. Quantitative analysis of EEG involves the representation of complex EEG data in an objective, reproducible and scalable manner. Quantitative EEG (qEEG) can be derived from both a limited channel EEG (as available during amplitude integrated EEG) and multi-channel conventional EEG. It has the potential to enable bedside clinicians to monitor and evaluate details of cortical function without the necessity of continuous expert input. This is particularly useful in NE, a dynamic and evolving condition. In these infants, continuous, detailed evaluation of cortical function at the bedside is a valuable aide to management especially in the current era of therapeutic hypothermia and possible upcoming neuroprotective therapies. This review discusses the role of qEEG in newborns with NE and its use in informing monitoring and therapy, along with its ability to predict imaging changes and short and long-term neurodevelopmental outcomes. IMPACT: Quantitative representation of EEG data brings the evaluation of continuous brain function, from the neurophysiology lab to the NICU bedside and has a potential role as a biomarker for neonatal encephalopathy. Clinical and research applications of quantitative EEG in the newborn are rapidly evolving and a wider understanding of its utility is valuable. This overview summarizes the role of quantitative EEG at different timepoints, its relevance to management and its predictive value for short- and long-term outcomes in neonatal encephalopathy.
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Affiliation(s)
- Sriya Roychaudhuri
- Division of Newborn Medicine, Department of Pediatrics, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Katie Hannon
- Division of Newborn Medicine, Department of Pediatrics, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - John Sunwoo
- Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Aisling A Garvey
- Division of Newborn Medicine, Department of Pediatrics, Brigham and Women's Hospital, Boston, MA, USA
- INFANT Research Centre, Cork, Ireland
- Department of Paediatrics and Child Health, University College Cork, Cork, Ireland
- Department of Neonatology, Cork University Maternity Hospital, Cork, Ireland
| | - Mohamed El-Dib
- Division of Newborn Medicine, Department of Pediatrics, Brigham and Women's Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
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3
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Danthine V, Cottin L, Berger A, Germany Morrison EI, Liberati G, Ferrao Santos S, Delbeke J, Nonclercq A, El Tahry R. Electroencephalogram synchronization measure as a predictive biomarker of Vagus nerve stimulation response in refractory epilepsy: A retrospective study. PLoS One 2024; 19:e0304115. [PMID: 38861500 PMCID: PMC11166337 DOI: 10.1371/journal.pone.0304115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 05/06/2024] [Indexed: 06/13/2024] Open
Abstract
There are currently no established biomarkers for predicting the therapeutic effectiveness of Vagus Nerve Stimulation (VNS). Given that neural desynchronization is a pivotal mechanism underlying VNS action, EEG synchronization measures could potentially serve as predictive biomarkers of VNS response. Notably, an increased brain synchronization in delta band has been observed during sleep-potentially due to an activation of thalamocortical circuitry, and interictal epileptiform discharges are more frequently observed during sleep. Therefore, investigation of EEG synchronization metrics during sleep could provide a valuable insight into the excitatory-inhibitory balance in a pro-epileptogenic state, that could be pathological in patients exhibiting a poor response to VNS. A 19-channel-standard EEG system was used to collect data from 38 individuals with Drug-Resistant Epilepsy (DRE) who were candidates for VNS implantation. An EEG synchronization metric-the Weighted Phase Lag Index (wPLI)-was extracted before VNS implantation and compared between sleep and wakefulness, and between responders (R) and non-responders (NR). In the delta band, a higher wPLI was found during wakefulness compared to sleep in NR only. However, in this band, no synchronization difference in any state was found between R and NR. During sleep and within the alpha band, a negative correlation was found between wPLI and the percentage of seizure reduction after VNS implantation. Overall, our results suggest that patients exhibiting a poor VNS efficacy may present a more pathological thalamocortical circuitry before VNS implantation. EEG synchronization measures could provide interesting insights into the prerequisites for responding to VNS, in order to avoid unnecessary implantations in patients showing a poor therapeutic efficacy.
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Affiliation(s)
- Venethia Danthine
- Institute of NeuroScience (IoNS), Université Catholique de Louvain, Ottignies-Louvain-la-Neuve, Belgium
| | - Lise Cottin
- Bio- Electro- And Mechanical Systems (BEAMS), Université Libre de Bruxelles, Brussels, Belgium
| | - Alexandre Berger
- Institute of NeuroScience (IoNS), Université Catholique de Louvain, Ottignies-Louvain-la-Neuve, Belgium
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Center-in Vivo Imaging, University of Liège, Liège, Belgium
| | - Enrique Ignacio Germany Morrison
- Institute of NeuroScience (IoNS), Université Catholique de Louvain, Ottignies-Louvain-la-Neuve, Belgium
- Walloon Excellence in Life Sciences and Biotechnology (WELBIO) department, WEL Research Institute, Wavre, Belgium
| | - Giulia Liberati
- Institute of NeuroScience (IoNS), Université Catholique de Louvain, Ottignies-Louvain-la-Neuve, Belgium
- Institute of Psychology (IPSY), Université Catholique de Louvain, Ottignies-Louvain-la-Neuve, Belgium
| | - Susana Ferrao Santos
- Institute of NeuroScience (IoNS), Université Catholique de Louvain, Ottignies-Louvain-la-Neuve, Belgium
- Department of Neurology, Cliniques Universitaires Saint Luc, Woluwe-Saint-Lambert, Belgium
| | - Jean Delbeke
- Institute of NeuroScience (IoNS), Université Catholique de Louvain, Ottignies-Louvain-la-Neuve, Belgium
| | - Antoine Nonclercq
- Bio- Electro- And Mechanical Systems (BEAMS), Université Libre de Bruxelles, Brussels, Belgium
| | - Riëm El Tahry
- Institute of NeuroScience (IoNS), Université Catholique de Louvain, Ottignies-Louvain-la-Neuve, Belgium
- Department of Neurology, Cliniques Universitaires Saint Luc, Woluwe-Saint-Lambert, Belgium
- Walloon Excellence in Life Sciences and Biotechnology (WELBIO) department, WEL Research Institute, Wavre, Belgium
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Gunn ER, Battin MR, Gunn AJ. Is there a problem with how we select patients for therapeutic hypothermia? Pediatr Res 2024:10.1038/s41390-024-03313-1. [PMID: 38849487 DOI: 10.1038/s41390-024-03313-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 05/22/2024] [Indexed: 06/09/2024]
Affiliation(s)
- Eleanor R Gunn
- Newborn Services, Te Whatu Ora Counties Manukau, Auckland, New Zealand
| | | | - Alistair J Gunn
- Department of Physiology, The University of Auckland, Auckland, New Zealand.
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Abbasi H, Davidson JO, Dhillon SK, Zhou KQ, Wassink G, Gunn AJ, Bennet L. Deep Learning for Generalized EEG Seizure Detection after Hypoxia-Ischemia-Preclinical Validation. Bioengineering (Basel) 2024; 11:217. [PMID: 38534490 DOI: 10.3390/bioengineering11030217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 02/12/2024] [Accepted: 02/23/2024] [Indexed: 03/28/2024] Open
Abstract
Brain maturity and many clinical treatments such as therapeutic hypothermia (TH) can significantly influence the morphology of neonatal EEG seizures after hypoxia-ischemia (HI), and so there is a need for generalized automatic seizure identification. This study validates efficacy of advanced deep-learning pattern classifiers based on a convolutional neural network (CNN) for seizure detection after HI in fetal sheep and determines the effects of maturation and brain cooling on their accuracy. The cohorts included HI-normothermia term (n = 7), HI-hypothermia term (n = 14), sham-normothermia term (n = 5), and HI-normothermia preterm (n = 14) groups, with a total of >17,300 h of recordings. Algorithms were trained and tested using leave-one-out cross-validation and k-fold cross-validation approaches. The accuracy of the term-trained seizure detectors was consistently excellent for HI-normothermia preterm data (accuracy = 99.5%, area under curve (AUC) = 99.2%). Conversely, when the HI-normothermia preterm data were used in training, the performance on HI-normothermia term and HI-hypothermia term data fell (accuracy = 98.6%, AUC = 96.5% and accuracy = 96.9%, AUC = 89.6%, respectively). Findings suggest that HI-normothermia preterm seizures do not contain all the spectral features seen at term. Nevertheless, an average 5-fold cross-validated accuracy of 99.7% (AUC = 99.4%) was achieved from all seizure detectors. This significant advancement highlights the reliability of the proposed deep-learning algorithms in identifying clinically translatable post-HI stereotypic seizures in 256Hz recordings, regardless of maturity and with minimal impact from hypothermia.
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Affiliation(s)
- Hamid Abbasi
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
- Auckland Bioengineering Institute (ABI), University of Auckland, Auckland 1010, New Zealand
| | - Joanne O Davidson
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
| | - Simerdeep K Dhillon
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
| | - Kelly Q Zhou
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
| | - Guido Wassink
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
| | - Alistair J Gunn
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
| | - Laura Bennet
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
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Sullivan BA, Beam K, Vesoulis ZA, Aziz KB, Husain AN, Knake LA, Moreira AG, Hooven TA, Weiss EM, Carr NR, El-Ferzli GT, Patel RM, Simek KA, Hernandez AJ, Barry JS, McAdams RM. Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities. J Perinatol 2024; 44:1-11. [PMID: 38097685 PMCID: PMC10872325 DOI: 10.1038/s41372-023-01848-5] [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] [Received: 09/11/2023] [Revised: 11/21/2023] [Accepted: 11/30/2023] [Indexed: 12/17/2023]
Abstract
Artificial intelligence (AI) offers tremendous potential to transform neonatology through improved diagnostics, personalized treatments, and earlier prevention of complications. However, there are many challenges to address before AI is ready for clinical practice. This review defines key AI concepts and discusses ethical considerations and implicit biases associated with AI. Next we will review literature examples of AI already being explored in neonatology research and we will suggest future potentials for AI work. Examples discussed in this article include predicting outcomes such as sepsis, optimizing oxygen therapy, and image analysis to detect brain injury and retinopathy of prematurity. Realizing AI's potential necessitates collaboration between diverse stakeholders across the entire process of incorporating AI tools in the NICU to address testability, usability, bias, and transparency. With multi-center and multi-disciplinary collaboration, AI holds tremendous potential to transform the future of neonatology.
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Affiliation(s)
- Brynne A Sullivan
- Division of Neonatology, Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Kristyn Beam
- Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Zachary A Vesoulis
- Division of Newborn Medicine, Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, USA
| | - Khyzer B Aziz
- Division of Neonatology, Department of Pediatrics, Johns Hopkins University, Baltimore, MD, USA
| | - Ameena N Husain
- Division of Neonatology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Lindsey A Knake
- Division of Neonatology, Department of Pediatrics, University of Iowa, Iowa City, IA, USA
| | - Alvaro G Moreira
- Division of Neonatology, Department of Pediatrics, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Thomas A Hooven
- Division of Newborn Medicine, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Elliott M Weiss
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
- Treuman Katz Center for Pediatric Bioethics and Palliative Care, Seattle Children's Research Institute, Seattle, WA, USA
| | - Nicholas R Carr
- Division of Neonatology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - George T El-Ferzli
- Division of Neonatology, Department of Pediatrics, Ohio State University, Nationwide Children's Hospital, Columbus, OH, USA
| | - Ravi M Patel
- Division of Neonatology, Department of Pediatrics, Emory University School of Medicine and Children's Healthcare of Atlanta, Atlanta, GA, USA
| | - Kelsey A Simek
- Division of Neonatology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Antonio J Hernandez
- Division of Neonatology, Department of Pediatrics, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - James S Barry
- Division of Neonatology, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Ryan M McAdams
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
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Barsh GR, Wusthoff CJ. Can electronic medical records predict neonatal seizures? Lancet Digit Health 2023; 5:e175-e176. [PMID: 36963906 DOI: 10.1016/s2589-7500(23)00041-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 02/13/2023] [Indexed: 03/26/2023]
Affiliation(s)
- Gabrielle R Barsh
- Department of Neurology, Division of Child Neurology, Stanford University, Palo Alto, CA 94304, USA
| | - Courtney J Wusthoff
- Department of Neurology, Division of Child Neurology, Stanford University, Palo Alto, CA 94304, USA.
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Murphy E, Healy DB, Chioma R, Dempsey EM. Evaluation of the Hypotensive Preterm Infant: Evidence-Based Practice at the Bedside? CHILDREN (BASEL, SWITZERLAND) 2023; 10:519. [PMID: 36980077 PMCID: PMC10047557 DOI: 10.3390/children10030519] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/28/2023] [Accepted: 02/28/2023] [Indexed: 03/09/2023]
Abstract
Choosing the appropriate management approach for the preterm infant with low blood pressure during the transition period generally involved intervening when the blood pressure drifted below a certain threshold. It is now clear that this approach is too simplistic and does not address the underlying physiology. In this chapter, we explore the many monitoring tools available for evaluation of the hypotensive preterm and assess the evidence base supporting or refuting their use. The key challenge relates to incorporating these outputs with the clinical status of the patient and choosing the appropriate management strategy.
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Affiliation(s)
- Elizabeth Murphy
- Department of Paediatrics & Child Health, University College Cork, T12 YT20 Cork, Ireland
| | - David B. Healy
- Department of Paediatrics & Child Health, University College Cork, T12 YT20 Cork, Ireland
- INFANT Research Centre, University College Cork, T12 DC4A Cork, Ireland
| | - Roberto Chioma
- INFANT Research Centre, University College Cork, T12 DC4A Cork, Ireland
- Department of Life Sciences and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Eugene M. Dempsey
- Department of Paediatrics & Child Health, University College Cork, T12 YT20 Cork, Ireland
- INFANT Research Centre, University College Cork, T12 DC4A Cork, Ireland
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Chioma R, Sbordone A, Patti ML, Perri A, Vento G, Nobile S. Applications of Artificial Intelligence in Neonatology. APPLIED SCIENCES 2023; 13:3211. [DOI: 10.3390/app13053211] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/17/2023]
Abstract
The development of artificial intelligence methods has impacted therapeutics, personalized diagnostics, drug discovery, and medical imaging. Although, in many situations, AI clinical decision-support tools may seem superior to rule-based tools, their use may result in additional challenges. Examples include the paucity of large datasets and the presence of unbalanced data (i.e., due to the low occurrence of adverse outcomes), as often seen in neonatal medicine. The most recent and impactful applications of AI in neonatal medicine are discussed in this review, highlighting future research directions relating to the neonatal population. Current AI applications tested in neonatology include tools for vital signs monitoring, disease prediction (respiratory distress syndrome, bronchopulmonary dysplasia, apnea of prematurity) and risk stratification (retinopathy of prematurity, intestinal perforation, jaundice), neurological diagnostic and prognostic support (electroencephalograms, sleep stage classification, neuroimaging), and novel image recognition technologies, which are particularly useful for prompt recognition of infections. To have these kinds of tools helping neonatologists in daily clinical practice could be something extremely revolutionary in the next future. On the other hand, it is important to recognize the limitations of AI to ensure the proper use of this technology.
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Affiliation(s)
- Roberto Chioma
- Department of Life Sciences and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Annamaria Sbordone
- Department of Life Sciences and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Maria Letizia Patti
- Department of Life Sciences and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Alessandro Perri
- Department of Life Sciences and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Giovanni Vento
- Department of Life Sciences and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Stefano Nobile
- Department of Life Sciences and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
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