⚡ Quick Summary
The MINERVA study aims to develop a machine learning model to predict the risk of recurrent acute pancreatitis (RAP) in patients with mild acute biliary pancreatitis (MABP). By utilizing advanced techniques, this study seeks to enhance clinical decision-making and potentially reduce healthcare costs associated with RAP.
🔍 Key Details
- 📊 Study Population: Adult patients diagnosed with MABP who have not undergone early cholecystectomy (EC)
- 🧩 Data Sources: Retrospective data from the MANCTRA-1 study and prospective data collection
- ⚙️ Technology: Convolutional Neural Networks (CNN) for feature extraction and risk prediction
- 🏆 Performance Metrics: Accuracy, precision, recall, and area under the ROC curve (AUC)
🔑 Key Takeaways
- 📊 The MINERVA study addresses the gap in predicting RAP risk in MABP patients.
- 💡 Machine learning techniques are being leveraged to create a predictive model.
- 👩🔬 Data collection will occur across multiple academic and community hospitals in Italy.
- 🏆 The model aims to provide a reliable and cost-effective tool for healthcare professionals.
- 🌍 The study emphasizes the practical application of AI in clinical settings.
- 🆔 ClinicalTrials.gov Identifier: NCT06124989.
📚 Background
Mild acute biliary pancreatitis (MABP) poses significant clinical and economic challenges, particularly due to its potential for relapse. Current guidelines recommend early cholecystectomy (EC) during the same hospital admission to prevent recurrent acute pancreatitis (RAP). However, the implementation of these guidelines varies, underscoring the need for reliable predictive tools to aid in clinical decision-making.
🗒️ Study
The MINERVA study will be conducted in Italy, involving adult patients diagnosed with MABP according to the revised Atlanta Criteria. Patients who have not undergone EC during their initial admission will be included, while those with non-biliary aetiology or severe pancreatitis will be excluded. The study will utilize both retrospective and prospective data, captured through the REDCap system, to develop a robust machine learning model.
📈 Results
The machine learning model will employ convolutional neural networks (CNN) for feature extraction and risk prediction. The process includes spatial transformation of variables, creation of 2D images, application of convolutional filters, and final risk prediction through a fully connected layer. Performance will be evaluated using metrics such as accuracy, precision, recall, and area under the ROC curve (AUC).
🌍 Impact and Implications
The MINERVA study has the potential to significantly impact clinical practice by providing a reliable and accessible tool for predicting RAP risk in MABP patients. By integrating a wide range of clinical and demographic variables, this model could enhance decision-making processes, ultimately leading to a reduction in the incidence of RAP and associated healthcare costs. The practical application of AI in this context highlights the transformative potential of technology in healthcare.
🔮 Conclusion
The MINERVA study represents a promising advancement in the use of machine learning for clinical decision-making in the context of mild acute biliary pancreatitis. By developing a predictive model for RAP risk, healthcare professionals may be better equipped to manage patient care, leading to improved outcomes and reduced healthcare expenditures. Continued research in this area is essential to fully realize the benefits of AI in medicine.
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Machine learning for the rElapse risk eValuation in acute biliary pancreatitis: The deep learning MINERVA study protocol.
Abstract
BACKGROUND: Mild acute biliary pancreatitis (MABP) presents significant clinical and economic challenges due to its potential for relapse. Current guidelines advocate for early cholecystectomy (EC) during the same hospital admission to prevent recurrent acute pancreatitis (RAP). Despite these recommendations, implementation in clinical practice varies, highlighting the need for reliable and accessible predictive tools. The MINERVA study aims to develop and validate a machine learning (ML) model to predict the risk of RAP (at 30, 60, 90 days, and at 1-year) in MABP patients, enhancing decision-making processes.
METHODS: The MINERVA study will be conducted across multiple academic and community hospitals in Italy. Adult patients with a clinical diagnosis of MABP, in accordance with the revised Atlanta Criteria, who have not undergone EC during index admission will be included. Exclusion criteria encompass non-biliary aetiology, severe pancreatitis, and the inability to provide informed consent. The study involves both retrospective data from the MANCTRA-1 study and prospective data collection. Data will be captured using REDCap. The ML model will utilise convolutional neural networks (CNN) for feature extraction and risk prediction. The model includes the following steps: the spatial transformation of variables using kernel Principal Component Analysis (kPCA), the creation of 2D images from transformed data, the application of convolutional filters, max-pooling, flattening, and final risk prediction via a fully connected layer. Performance metrics such as accuracy, precision, recall, and area under the ROC curve (AUC) will be used to evaluate the model.
DISCUSSION: The MINERVA study aims to address the specific gap in predicting RAP risk in MABP patients by leveraging advanced ML techniques. By incorporating a wide range of clinical and demographic variables, the MINERVA score aims to provide a reliable, cost-effective, and accessible tool for healthcare professionals. The project emphasises the practical application of AI in clinical settings, potentially reducing the incidence of RAP and associated healthcare costs.
TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT06124989.
Author: [‘Podda M’, ‘Pisanu A’, ‘Pellino G’, ‘De Simone A’, ‘Selvaggi L’, ‘Murzi V’, ‘Locci E’, ‘Rottoli M’, ‘Calini G’, ‘Cardelli S’, ‘Catena F’, ‘Vallicelli C’, ‘Bova R’, ‘Vigutto G’, “D’Acapito F”, ‘Ercolani G’, ‘Solaini L’, ‘Biloslavo A’, ‘Germani P’, ‘Colutta C’, ‘Occhionorelli S’, ‘Lacavalla D’, ‘Sibilla MG’, ‘Olmi S’, ‘Uccelli M’, ‘Oldani A’, ‘Giordano A’, ‘Guagni T’, ‘Perini D’, ‘Pata F’, ‘Nardo B’, ‘Paglione D’, ‘Franco G’, ‘Donadon M’, ‘Di Martino M’, ‘Bruzzese D’, ‘Pacella D’]
Journal: World J Emerg Surg
Citation: Podda M, et al. Machine learning for the rElapse risk eValuation in acute biliary pancreatitis: The deep learning MINERVA study protocol. Machine learning for the rElapse risk eValuation in acute biliary pancreatitis: The deep learning MINERVA study protocol. 2025; 20:17. doi: 10.1186/s13017-025-00594-7