AI and Machine Learning for Precision Medicine in Acute Pancreatitis

Acute pancreatitis (AP) presents a significant clinical challenge due to its wide range of severity, from mild cases to life-threatening complications such as severe acute pancreatitis (SAP), necrosis, and multi-organ failure. Traditional scoring systems, such as Ranson and BISAP, offer foundational...

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Autores: López Gordo, Sandra, Ramírez Maldonado, Elena|||0000-0001-9213-7212, Fernandez-Planas, Maria Teresa|||0000-0002-8224-0037, Bombuy Gimenez, Ernest|||0000-0003-0435-671X, Memba, Robert, Jorba, Rosa|||0000-0003-3307-4340
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:320780
Acceso en línea:https://ddd.uab.cat/record/320780
https://dx.doi.org/urn:doi:10.3390/medicina61040629
Access Level:acceso abierto
Palabra clave:Artificial intelligence
Machine learning
Acute pancreatitis
Severity
Personalized medicine
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spelling AI and Machine Learning for Precision Medicine in Acute PancreatitisA Narrative ReviewLópez Gordo, SandraRamírez Maldonado, Elena|||0000-0001-9213-7212Fernandez-Planas, Maria Teresa|||0000-0002-8224-0037Bombuy Gimenez, Ernest|||0000-0003-0435-671XMemba, RobertJorba, Rosa|||0000-0003-3307-4340Artificial intelligenceMachine learningAcute pancreatitisSeverityPersonalized medicineAcute pancreatitis (AP) presents a significant clinical challenge due to its wide range of severity, from mild cases to life-threatening complications such as severe acute pancreatitis (SAP), necrosis, and multi-organ failure. Traditional scoring systems, such as Ranson and BISAP, offer foundational tools for risk stratification but often lack early precision. This review aims to explore the transformative role of artificial intelligence (AI) and machine learning (ML) in AP management, focusing on their applications in diagnosis, severity prediction, complication management, and treatment optimization. A comprehensive analysis of recent studies was conducted, highlighting ML models such as XGBoost, neural networks, and multimodal approaches. These models integrate clinical, laboratory, and imaging data, including radiomics features, and are useful in diagnostic and prognostic accuracy in AP. Special attention was given to models addressing SAP, complications like acute kidney injury and acute respiratory distress syndrome, mortality, and recurrence. AI-based models achieved higher AUC values than traditional models in predicting acute pancreatitis outcomes. XGBoost reached an AUC of 0.93 for early SAP prediction, higher than BISAP (AUC 0.74) and APACHE II (AUC 0.81). PrismSAP, integrating multimodal data, achieved the highest AUC of 0.916. AI models also demonstrated superior accuracy in mortality prediction (AUC 0.975) and ARDS detection (AUC 0.891) AI and ML represent a transformative advance in AP management, facilitating personalized treatment, early risk stratification, and allowing resource utilization to be optimized. By addressing challenges such as model generalizability, ethical considerations, and clinical adoption, AI has the potential to significantly improve patient outcomes and redefine AP care standards globally. 22025-01-0120252025-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/320780https://dx.doi.org/urn:doi:10.3390/medicina61040629reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:3207802026-06-06T12:50:31Z
dc.title.none.fl_str_mv AI and Machine Learning for Precision Medicine in Acute Pancreatitis
A Narrative Review
title AI and Machine Learning for Precision Medicine in Acute Pancreatitis
spellingShingle AI and Machine Learning for Precision Medicine in Acute Pancreatitis
López Gordo, Sandra
Artificial intelligence
Machine learning
Acute pancreatitis
Severity
Personalized medicine
title_short AI and Machine Learning for Precision Medicine in Acute Pancreatitis
title_full AI and Machine Learning for Precision Medicine in Acute Pancreatitis
title_fullStr AI and Machine Learning for Precision Medicine in Acute Pancreatitis
title_full_unstemmed AI and Machine Learning for Precision Medicine in Acute Pancreatitis
title_sort AI and Machine Learning for Precision Medicine in Acute Pancreatitis
dc.creator.none.fl_str_mv López Gordo, Sandra
Ramírez Maldonado, Elena|||0000-0001-9213-7212
Fernandez-Planas, Maria Teresa|||0000-0002-8224-0037
Bombuy Gimenez, Ernest|||0000-0003-0435-671X
Memba, Robert
Jorba, Rosa|||0000-0003-3307-4340
author López Gordo, Sandra
author_facet López Gordo, Sandra
Ramírez Maldonado, Elena|||0000-0001-9213-7212
Fernandez-Planas, Maria Teresa|||0000-0002-8224-0037
Bombuy Gimenez, Ernest|||0000-0003-0435-671X
Memba, Robert
Jorba, Rosa|||0000-0003-3307-4340
author_role author
author2 Ramírez Maldonado, Elena|||0000-0001-9213-7212
Fernandez-Planas, Maria Teresa|||0000-0002-8224-0037
Bombuy Gimenez, Ernest|||0000-0003-0435-671X
Memba, Robert
Jorba, Rosa|||0000-0003-3307-4340
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Artificial intelligence
Machine learning
Acute pancreatitis
Severity
Personalized medicine
topic Artificial intelligence
Machine learning
Acute pancreatitis
Severity
Personalized medicine
description Acute pancreatitis (AP) presents a significant clinical challenge due to its wide range of severity, from mild cases to life-threatening complications such as severe acute pancreatitis (SAP), necrosis, and multi-organ failure. Traditional scoring systems, such as Ranson and BISAP, offer foundational tools for risk stratification but often lack early precision. This review aims to explore the transformative role of artificial intelligence (AI) and machine learning (ML) in AP management, focusing on their applications in diagnosis, severity prediction, complication management, and treatment optimization. A comprehensive analysis of recent studies was conducted, highlighting ML models such as XGBoost, neural networks, and multimodal approaches. These models integrate clinical, laboratory, and imaging data, including radiomics features, and are useful in diagnostic and prognostic accuracy in AP. Special attention was given to models addressing SAP, complications like acute kidney injury and acute respiratory distress syndrome, mortality, and recurrence. AI-based models achieved higher AUC values than traditional models in predicting acute pancreatitis outcomes. XGBoost reached an AUC of 0.93 for early SAP prediction, higher than BISAP (AUC 0.74) and APACHE II (AUC 0.81). PrismSAP, integrating multimodal data, achieved the highest AUC of 0.916. AI models also demonstrated superior accuracy in mortality prediction (AUC 0.975) and ARDS detection (AUC 0.891) AI and ML represent a transformative advance in AP management, facilitating personalized treatment, early risk stratification, and allowing resource utilization to be optimized. By addressing challenges such as model generalizability, ethical considerations, and clinical adoption, AI has the potential to significantly improve patient outcomes and redefine AP care standards globally.
publishDate 2025
dc.date.none.fl_str_mv 2
2025-01-01
2025
2025-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/320780
https://dx.doi.org/urn:doi:10.3390/medicina61040629
url https://ddd.uab.cat/record/320780
https://dx.doi.org/urn:doi:10.3390/medicina61040629
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
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https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
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repository.mail.fl_str_mv
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