[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128289-en":3,"doc-seo-128289-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128289,962085570644,"Evangeline","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",7,"Healthcare","Advances in Artificial Intelligence and Machine Learning for Precision Medicine in Necrotizing Enterocolitis and Neonatal Sepsis - A State-of-the-Art Review","Necrotizing enterocolitis remains one of the most severe gastrointestinal diseases in neonates, especially in preterm infants, combining intestinal inflammation and necrosis with high morbidity and mortality. Machine learning and artificial intelligence approaches are reviewed for enhancing NEC risk prediction, early diagnosis, and treatment optimization through analyses of high-dimensional clinical, biomarker, and imaging data. Evidence is summarized alongside limitations including dataset heterogeneity, limited model interpretability, and the need for large-scale validation.","Review  \nAdvances in Artificial Intelligence and Machine Learning for Precision Medicine in Necrotizing Enterocolitis and Neonatal Sepsis: A State-of-the-Art Review  \nMiriam Duci 1,2, Giovanna Verlato 3, Laura Moschino 3, Francesca Uccheddu 4 and Francesco Fascetti-Leon 1,2, *  \nAcademic Editor: Balagangadhar Totapally  \nReceived: 24 February 2025  \nRevised: 30 March 2025  \nAccepted: 11 April 2025  \nPublished: 13 April 2025  \nCitation: Duci, M.; Verlato, G.;  \nMoschino, L.; Uccheddu, F.; Fascetti-Leon, F. Advances in Artificial Intelligence and Machine Learning for Precision Medicine in Necrotizing Enterocolitis and Neonatal Sepsis:  \nA State-of-the-Art Review. Children 2025, 12, 498. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/children12040498](10.3390/children12040498)  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Division of Pediatric Surgery, Department of Women’s and Children’s Health, University of Padova, Via Giustiniani 2, 35128 Padova, Italy; [ducimiriam@gmail.com](ducimiriam@gmail.com)  \n2 Pediatric Surgery Unit, Division of Women’s and Children’s Health, Padova University Hospital,  \n35128 Padova, Italy  \n3 Neonatal Intensive Care Unit, Padova University Hospital, 35128 Padova, Italy; [giovanna.verlato@aopd.veneto.it](giovanna.verlato@aopd.veneto.it) (G.V.); [laura.moschino@aopd.veneto.it](laura.moschino@aopd.veneto.it) (L.M.)  \n4 Department of Industrial Engineering, Padova University, 35128 Padova, Italy; francesca.uccheddu@unipd.it  \n* Correspondence: [francesco.fascettileon@unipd.it](francesco.fascettileon@unipd.it); Tel.: +39-0498213681  \nAbstract: Necrotizing enterocolitis remains one of the most severe gastrointestinal diseases in neonates, particularly affecting preterm infants. It is characterized by intestinal inflammation and necrosis, with significant morbidity and mortality despite advancements in neonatal care. Recent advancements in artificial intelligence (AI) and machine learning (ML) have shown potential in improving NEC prediction, early diagnosis, and management. A systematic search was conducted across multiple databases to explore the application of AI and ML in predicting NEC risk, diagnosing the condition at early stages, and optimizing treatment strategies.AI-based models demonstrated enhanced accuracy in NEC risk stratification compared to traditional clinical approaches. Machine learning algorithms identified novel biomarkers associated with disease onset and severity. Additionally, deep learning applied to medical imaging improved NEC diagnosis by detecting abnormalities earlier than conventional methods. The integration of AI and ML in NEC research provides promising insights into patient-specific risk assessment. However, challenges such as data heterogeneity, model interpretability, and the need for large-scale validation studies remain. Future research should focus on translating AI-driven findings into clinical practice, ensuring ethical considerations and regulatory compliance.  \nKeywords: NEC; artificial intelligence; machine learning; precision medicine  \n1. Introduction  \nNecrotizing enterocolitis (NEC) remains one of the most devastating gastrointestinal diseases in neonates, characterized by the inflammation and necrosis of the intestinal tissue. Despite advances in neonatal care, NEC continues to have significant morbidity and mortality, particularly among preterm infants. The pathogenesis of NEC is multifactorial, involving complex interactions between immature intestinal immunity, microbial dysbiosis, and environmental factors [1] . Accurate prediction, early diagnosis, and effective management of NEC are crucial and require innovative solutions. Recent advancem","cbCainuE9QAhf1Lo","https://ap.wps.com/l/cbCainuE9QAhf1Lo","pdf",208416,5,1,12,"English","en",105,"# Abstract\n# Introduction\n## Role of AI/ML in NEC care\n## Multidisciplinary approach and precision medicine\n# Materials and Methods\n## Literature search strategy","[{\"question\":\"What problem do artificial intelligence and machine learning address in necrotizing enterocolitis care?\",\"answer\":\"They aim to improve NEC risk prediction, support earlier diagnosis, and optimize management by extracting patterns from high-dimensional datasets.\"},{\"question\":\"How do AI/ML models improve NEC prediction and diagnosis according to the review?\",\"answer\":\"AI-based risk stratification models show improved accuracy over traditional clinical approaches, while machine learning identifies biomarkers and deep learning applied to imaging detects abnormalities earlier than conventional methods.\"},{\"question\":\"What challenges remain before AI-driven NEC results can be applied clinically?\",\"answer\":\"Key challenges include data heterogeneity, limited model interpretability, and the need for large-scale validation studies, along with attention to ethical and regulatory requirements.\"}]","Advances in Artificial Intelligence and Machine Learning for Precision Medicine in Necrotizing Enterocolitis and Neonatal Sepsis - 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