Outcome analysis of patients with acute pancreatitis by using an artificial neural network

AI-powered innovations in pancreatitis imaging: a comprehensive literature synthesis

Early identification of pancreatitis remains a significant clinical diagnostic challenge that impacts patient outcomes. The evolution of quantitative imaging followed by deep learning models has shown great promise in the non-invasive diagnosis of pancreatitis and its complications. We provide an overview of advancements in diagnostic imaging and quantitative imaging methods along with the evolution of artificial intelligence (AI). In this article, we review the current and future states of methodology and limitations of AI in improving clinical support in the context of early detection and management of pancreatitis.

Predictive value of machine learning for the severity of acute pancreatitis: A systematic review and meta-analysis

Background

Predicting the severity of acute pancreatitis (AP) early poses a challenge in clinical practice. While there are well-established clinical scoring tools, their actual predictive performance remains uncertain. Various studies have explored the application of machine-learning methods for early AP prediction. However, a more comprehensive evidence-based assessment is needed to determine their predictive accuracy. Hence, this systematic review and meta-analysis aimed to evaluate the predictive accuracy of machine learning in assessing the severity of AP.

Methods

PubMed, EMBASE, Cochrane Library, and Web of Science were systematically searched until December 5, 2023. The risk of bias in eligible studies was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Subgroup analyses, based on different machine learning types, were performed. Additionally, the predictive accuracy of mainstream scoring tools was summarized.

Results

This systematic review ultimately included 33 original studies. The pooled c-index in both the training and validation sets was 0.87 (95 % CI: 0.84-0.89) and 0.88 (95 % CI: 0.86-0.90), respectively. The sensitivity in the training set was 0.81 (95 % CI: 0.77-0.84), and in the validation set, it was 0.79 (95 % CI: 0.71-0.85). The specificity in the training set was 0.84 (95 % CI: 0.78-0.89), and in the validation set, it was 0.90 (95 % CI: 0.86-0.93). The primary model incorporated was logistic regression; however, its predictive accuracy was found to be inferior to that of neural networks, random forests, and xgboost. The pooled c-index of the APACHE II, BISAP, and Ranson were 0.74 (95 % CI: 0.68-0.80), 0.77 (95 % CI: 0.70-0.85), and 0.74 (95 % CI: 0.68-0.79), respectively.

Conclusions

Machine learning demonstrates excellent accuracy in predicting the severity of AP, providing a reference for updating or developing a straightforward clinical prediction tool.

Artificial intelligence: Applications in critical care gastroenterology

Gastrointestinal (GI) complications frequently necessitate intensive care unit (ICU) admission. Additionally, critically ill patients also develop GI complications requiring further diagnostic and therapeutic interventions. However, these patients form a vulnerable group, who are at risk for developing side effects and complications. Every effort must be made to reduce invasiveness and ensure safety of interventions in ICU patients. Artificial intelligence (AI) is a rapidly evolving technology with several potential applications in healthcare settings. ICUs produce a large amount of data, which may be employed for creation of AI algorithms, and provide a lucrative opportunity for application of AI. However, the current role of AI in these patients remains limited due to lack of large-scale trials comparing the efficacy of AI with the accepted standards of care.

Real-time artificial intelligence predicts adverse outcomes in acute pancreatitis in the emergency department: Comparison with clinical decision rule

OBJECTIVE

Artificial intelligence (AI) prediction is increasingly used for decision making in health care, but its application for adverse outcomes in emergency department (ED) patients with acute pancreatitis (AP) is not well understood. This study aimed to clarify this aspect.

METHODS

Data from 8274 ED patients with AP in three hospitals from 2009 to 2018 were analyzed. Demographic data, comorbidities, laboratory results, and adverse outcomes were included. Six algorithms were evaluated, and the one with the highest area under the curve (AUC) was implemented into the hospital information system (HIS) for real-time prediction. Predictive accuracy was compared between the AI model and Bedside Index for Severity in Acute Pancreatitis (BISAP).

RESULTS

The mean ± SD age was 56.1 ± 16.7 years, with 67.7% being male. The AI model was successfully implemented in the HIS, with Light Gradient Boosting Machine (LightGBM) showing the highest AUC for sepsis (AUC 0.961) and intensive care unit (ICU) admission (AUC 0.973), and eXtreme Gradient Boosting (XGBoost) showing the highest AUC for mortality (AUC 0.975). Compared to BISAP, the AI model had superior AUC for sepsis (BISAP 0.785), ICU admission (BISAP 0.778), and mortality (BISAP 0.817).

CONCLUSIONS

The first real-time AI prediction model implemented in the HIS for predicting adverse outcomes in ED patients with AP shows favorable initial results. However, further external validation is needed to ensure its reliability and accuracy.

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.

A modified Ranson score to predict disease severity, organ failure, pancreatic necrosis, and pancreatic infection in patients with acute pancreatitis

Background

Although there are several scoring systems currently used to predict the severity of acute pancreatitis, each of them has limitations. Determine the accuracy of a modified Ranson score in predicting disease severity and prognosis in patients with acute pancreatitis (AP).

Methods

AP patients admitted or transferred to our institution were allocated to a modeling group (n = 304) or a validation group (n = 192). A modified Ranson score was determined by excluding the fluid sequestration parameter and including the modified computed tomography severity index (CTSI). The diagnostic performance of the modified Ranson score was compared with the Ranson score, modified CTSI, and bedside index of severity in acute pancreatitis (BISAP) score in predicting disease severity, organ failure, pancreatic necrosis and pancreatic infection.

Results

The modified Ranson score had significantly better accuracy that the Ranson score in predicting all four outcome measures in the modeling group and in the validation group (all p < 0.05). For the modeling group the modified Ranson score had the best accuracy for predicting disease severity and organ failure, and second-best accuracy for predicting pancreatic necrosis and pancreatic infection. For the verification group, it had the best accuracy for predicting organ failure, second-best accuracy for predicting disease severity and pancreatic necrosis, and third-best accuracy for predicting pancreatic infection.

Conclusion

The modified Ranson score provided better accuracy than the Ranson score in predicting disease severity, organ failure, pancreatic necrosis and pancreatic infection. Relative to the other scoring systems, the modified Ranson system was superior in predicting organ failure.

Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer

Pancreatic cancer is among the most challenging forms of cancer to treat, owing to its late diagnosis and aggressive nature that reduces the survival rate drastically. Pancreatic cancer diagnosis has been primarily based on imaging, but the current state-of-the-art imaging provides a poor prognosis, thus limiting clinicians' treatment options. The advancement of a cancer diagnosis has been enhanced through the integration of artificial intelligence and imaging modalities to make better clinical decisions. In this review, we examine how AI models can improve the diagnosis of pancreatic cancer using different imaging modalities along with a discussion on the emerging trends in an AI-driven diagnosis, based on cytopathology and serological markers. Ethical concerns regarding the use of these tools have also been discussed.

EASY-APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis

Background

Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the pancreas. Early identification of patients at high risk for developing a severe course of the disease is crucial for preventing organ failure and death. Most of the former predictive scores require many parameters or at least 24 h to predict the severity; therefore, the early therapeutic window is often missed.

Methods

The early achievable severity index (EASY) is a multicentre, multinational, prospective and observational study (ISRCTN10525246). The predictions were made using machine learning models. We used the scikit‐learn, xgboost and catboost Python packages for modelling. We evaluated our models using fourfold cross‐validation, and the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics were calculated on the union of the test sets of the cross‐validation. The most critical factors and their contribution to the prediction were identified using a modern tool of explainable artificial intelligence called SHapley Additive exPlanations (SHAP).

Results

The prediction model was based on an international cohort of 1184 patients and a validation cohort of 3543 patients. The best performing model was an XGBoost classifier with an average AUC score of 0.81 ± 0.033 and an accuracy of 89.1%, and the model improved with experience. The six most influential features were the respiratory rate, body temperature, abdominal muscular reflex, gender, age and glucose level. Using the XGBoost machine learning algorithm for prediction, the SHAP values for the explanation and the bootstrapping method to estimate confidence, we developed a free and easy‐to‐use web application in the Streamlit Python‐based framework (http://easy‐app.org/).

Conclusions

The EASY prediction score is a practical tool for identifying patients at high risk for severe AP within hours of hospital admission. The web application is available for clinicians and contributes to the improvement of the model.

Early prediction of acute necrotizing pancreatitis by artificial intelligence: a prospective cohort-analysis of 2387 cases

Pancreatic necrosis is a consistent prognostic factor in acute pancreatitis (AP). However, the clinical scores currently in use are either too complicated or require data that are unavailable on admission or lack sufficient predictive value. We therefore aimed to develop a tool to aid in necrosis prediction. The XGBoost machine learning algorithm processed data from 2387 patients with AP. The confidence of the model was estimated by a bootstrapping method and interpreted via the 10th and the 90th percentiles of the prediction scores. Shapley Additive exPlanations (SHAP) values were calculated to quantify the contribution of each variable provided. Finally, the model was implemented as an online application using the Streamlit Python-based framework. The XGBoost classifier provided an AUC value of 0.757. Glucose, C-reactive protein, alkaline phosphatase, gender and total white blood cell count have the most impact on prediction based on the SHAP values. The relationship between the size of the training dataset and model performance shows that prediction performance can be improved. This study combines necrosis prediction and artificial intelligence. The predictive potential of this model is comparable to the current clinical scoring systems and has several advantages over them.

Machine learning predictive models for acute pancreatitis: A systematic review

INTRODUCTION

Acute pancreatitis (AP) is a common clinical pancreatic disease. Patients with different severity levels have different clinical outcomes. With the advantages of algorithms, machine learning (ML) has gradually emerged in the field of disease prediction, assisting doctors in decision-making.

METHODS

A systematic review was conducted using the PubMed, Web of Science, Scopus, and Embase databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Publication time was limited from inception to 29 May 2021. Studies that have used ML to establish predictive tools for AP were eligible for inclusion. Quality assessment of the included studies was conducted in accordance with the IJMEDI checklist.

RESULTS

In this systematic review, 24 of 2,913 articles, with a total of 8,327 patients and 47 models, were included. The studies could be divided into five categories: 10 studies (42%) reported severity prediction; 10 studies (42%), complication prediction; 3 studies (13%), mortality prediction; 2 studies (8%), recurrence prediction; and 2 studies (8%), surgery timing prediction. ML showed great accuracy in several prediction tasks. However, most of the included studies were retrospective in nature, conducted at a single centre, based on database data, and lacked external validation. According to the IJMEDI checklist and our scoring criteria, two studies were considered to be of high quality. Most studies had an obvious bias in the quality of data preparation, validation, and deployment dimensions.

CONCLUSION

In the prediction tasks for AP, ML has shown great potential in assisting decision-making. However, the existing studies still have some deficiencies in the process of model construction. Future studies need to optimize the deficiencies and further evaluate the comparability of the ML systems and model performance, so as to consequently develop high-quality ML-based models that can be used in clinical practice.

Artificial intelligence in gastroenterology: A state-of-the-art review

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.

WSES project on decision support systems based on artificial neural networks in emergency surgery

The article is a scoping review of the literature on the use of decision support systems based on artificial neural networks in emergency surgery. The authors present modern literature data on the effectiveness of artificial neural networks for predicting, diagnosing and treating abdominal emergency conditions: acute appendicitis, acute pancreatitis, acute cholecystitis, perforated gastric or duodenal ulcer, acute intestinal obstruction, and strangulated hernia. The intelligent systems developed at present allow a surgeon in an emergency setting, not only to check his own diagnostic and prognostic assumptions, but also to use artificial intelligence in complex urgent clinical cases. The authors summarize the main limitations for the implementation of artificial neural networks in surgery and medicine in general. These limitations are the lack of transparency in the decision-making process; insufficient quality educational medical data; lack of qualified personnel; high cost of projects; and the complexity of secure storage of medical information data. The development and implementation of decision support systems based on artificial neural networks is a promising direction for improving the forecasting, diagnosis and treatment of emergency surgical diseases and their complications.

An Artificial Neural Networks Model for Early Predicting In-Hospital Mortality in Acute Pancreatitis in MIMIC-III

Background

Early and accurate evaluation of severity and prognosis in acute pancreatitis (AP), especially at the time of admission is very significant. This study was aimed to develop an artificial neural networks (ANN) model for early prediction of in-hospital mortality in AP.

Methods

Patients with AP were identified from the Medical Information Mart for Intensive Care-III (MIMIC-III) database. Clinical and laboratory data were utilized to perform a predictive model by back propagation ANN approach.

Results

A total of 337 patients with AP were analyzed in the study, and the in-hospital mortality rate was 11.2%. A total of 12 variables that differed between patients in survivor group and nonsurvivor group were applied to construct ANN model. Three independent variables were identified as risk factors associated with in-hospital mortality by multivariate logistic regression analysis. The predictive performance based on the area under the receiver operating characteristic curve (AUC) was 0.769 for ANN model, 0.607 for logistic regression, 0.652 for Ranson score, and 0.401 for SOFA score.

Conclusion

An ANN predictive model for in-hospital mortality in patients with AP in MIMIC-III database was first performed. The patients with high risk of fatal outcome can be screened out easily in the early stage of AP by our model.

Artificial intelligence for the management of pancreatic diseases

Novel artificial intelligence techniques are emerging in all fields of healthcare, including gastroenterology. The aim of this review is to give an overview of artificial intelligence applications in the management of pancreatic diseases. We performed a systematic literature search in PubMed and Medline up to May 2020 to identify relevant articles. Our results showed that the development of machine-learning based applications is rapidly evolving in the management of pancreatic diseases, guiding precision medicine in clinical, endoscopic and radiologic settings. Before implementation into clinical practice, further research should focus on the external validation of novel techniques, clarifying the accuracy and robustness of these models.

Emerging imaging techniques for acute pancreatitis

Acute pancreatitis (AP) is caused by acute inflammation of the pancreas and adjacent tissue and is a common source of abdominal pain. The current CT and MRI evaluation of AP is mostly based on morphologic features. Recent advances in image acquisition and analysis offer the opportunity to go beyond morphologic features. Advanced MR techniques such as diffusion-weighted imaging, as well as T1 and T2 mapping, can potentially quantify signal changes reflective of underlying tissue abnormalities. Advanced analytic techniques such as radiomics and artificial neural networks (ANNs) offer the promise of uncovering imaging biomarkers that can provide additional classification and prognostic information. The purpose of this article is to review recent advances in imaging acquisition and analytic techniques in the evaluation of AP.

Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a Web-based prognostic tool

Estimation of risk of recurrence in early-stage oral tongue squamous cell carcinoma (OTSCC) remains a challenge in the field of head and neck oncology. We examined the use of artificial neural networks (ANNs) to predict recurrences in early-stage OTSCC. A Web-based tool available for public use was also developed. A feedforward neural network was trained for prediction of locoregional recurrences in early OTSCC. The trained network was used to evaluate several prognostic parameters (age, gender, T stage, WHO histologic grade, depth of invasion, tumor budding, worst pattern of invasion, perineural invasion, and lymphocytic host response). Our neural network model identified tumor budding and depth of invasion as the most important prognosticators to predict locoregional recurrence. The accuracy of the neural network was 92.7%, which was higher than that of the logistic regression model (86.5%). Our online tool provided 88.2% accuracy, 71.2% sensitivity, and 98.9% specificity. In conclusion, ANN seems to offer a unique decision-making support predicting recurrences and thus adding value for the management of early OTSCC. To the best of our knowledge, this is the first study that applied ANN for prediction of recurrence in early OTSCC and provided a Web-based tool.

A combined paging alert and web-based instrument alters clinician behavior and shortens hospital length of stay in acute pancreatitis

OBJECTIVES

There are many published clinical guidelines for acute pancreatitis (AP). Implementation of these recommendations is variable. We hypothesized that a clinical decision support (CDS) tool would change clinician behavior and shorten hospital length of stay (LOS).

DESIGN/SETTING

Observational study, entitled, The AP Early Response (TAPER) Project. Tertiary center emergency department (ED) and hospital.

PARTICIPANTS

Two consecutive samplings of patients having ICD-9 code (577.0) for AP were generated from the emergency department (ED) or hospital admissions. Diagnosis of AP was based on conventional Atlanta criteria. The Pre-TAPER-CDS-Tool group (5/30/06-6/22/07) had 110 patients presenting to the ED with AP per 976 ICD-9 (577.0) codes and the Post-TAPER-CDS-Tool group (5/30/06-6/22/07) had 113 per 907 ICD-9 codes (7/14/10-5/5/11).

INTERVENTION

The TAPER-CDS-Tool, developed 12/2008-7/14/2010, is a combined early, automated paging-alert system, which text pages ED clinicians about a patient with AP and an intuitive web-based point-of-care instrument, consisting of seven early management recommendations.

RESULTS

The pre- vs. post-TAPER-CDS-Tool groups had similar baseline characteristics. The post-TAPER-CDS-Tool group met two management goals more frequently than the pre-TAPER-CDS-Tool group: risk stratification (P<0.0001) and intravenous fluids >6L/1st 0-24 h (P=0.0003). Mean (s.d.) hospital LOS was significantly shorter in the post-TAPER-CDS-Tool group (4.6 (3.1) vs. 6.7 (7.0) days, P=0.0126). Multivariate analysis identified four independent variables for hospital LOS: the TAPER-CDS-Tool associated with shorter LOS (P=0.0049) and three variables associated with longer LOS: Japanese severity score (P=0.0361), persistent organ failure (P=0.0088), and local pancreatic complications (<0.0001).

CONCLUSIONS

The TAPER-CDS-Tool is associated with changed clinician behavior and shortened hospital LOS, which has significant financial implications.

The use of intelligent database systems in acute pancreatitis--a systematic review

INTRODUCTION

Acute pancreatitis (AP) is a complex disease with multiple aetiological factors, wide ranging severity, and multiple challenges to effective triage and management. Databases, data mining and machine learning algorithms (MLAs), including artificial neural networks (ANNs), may assist by storing and interpreting data from multiple sources, potentially improving clinical decision-making.

AIMS

1) Identify database technologies used to store AP data, 2) collate and categorise variables stored in AP databases, 3) identify the MLA technologies, including ANNs, used to analyse AP data, and 4) identify clinical and non-clinical benefits and obstacles in establishing a national or international AP database.

METHODS

Comprehensive systematic search of online reference databases. The predetermined inclusion criteria were all papers discussing 1) databases, 2) data mining or 3) MLAs, pertaining to AP, independently assessed by two reviewers with conflicts resolved by a third author.

RESULTS

Forty-three papers were included. Three data mining technologies and five ANN methodologies were reported in the literature. There were 187 collected variables identified. ANNs increase accuracy of severity prediction, one study showed ANNs had a sensitivity of 0.89 and specificity of 0.96 six hours after admission--compare APACHE II (cutoff score ≥8) with 0.80 and 0.85 respectively. Problems with databases were incomplete data, lack of clinical data, diagnostic reliability and missing clinical data.

CONCLUSION

This is the first systematic review examining the use of databases, MLAs and ANNs in the management of AP. The clinical benefits these technologies have over current systems and other advantages to adopting them are identified.

Prognostic models for outcome following liver resection for colorectal cancer metastases: A systematic review

BACKGROUND

Liver resection provides the best chance for cure in colorectal cancer (CRC) liver metastases. A variety of factors that might influence survival and recurrence have been identified. Predictive models can help in risk stratification, to determine multidisciplinary treatment and follow-up for individual patients.

AIMS

To systematically review available prognostic models described for outcome following resection of CRC liver metastases and to assess their differences and applicability.

METHODS

The Pubmed, Embase and Cochrane Library databases were searched for articles proposing a prognostic model or risk stratification system for resection of CRC liver metastases. Search terms included 'colorectal', 'liver', 'metastasis', 'resection', 'prognosis' and 'prediction'. The articles were systematically reviewed.

RESULTS

Fifteen prognostic systems were identified, published between 1996 and 2009. The median study population was 305 patients and the median follow-up was 32 months. All studies used Cox proportional hazards for multi-variable analysis. No prognostic factor was common in all models, though there was a tendency towards the number of metastases, CRC spread to lymph nodes, maximum size of metastases, preoperative CEA level and extrahepatic spread as representing independent risk factors. Seven models assigned more weight to selected factors considered of higher predictive value.

CONCLUSION

The existing predictive models are diverse and their prognostic factors are often not weighed according to their impact. For the development of future predictive models, the complex relations within datasets and differences in relevance of individual factors should be taken into account, for example by using artificial neural networks.

Prediction of severe acute pancreatitis at admission to hospital using artificial neural networks

BACKGROUND/AIMS

Artificial neural networks (ANNs) are non-linear pattern recognition techniques, which can be used as a tool in medical decision-making. The aim of this study was to construct and validate an ANN model for early prediction of the severity of acute pancreatitis (AP).

METHODS

Patients treated for AP from 2002 to 2005 (n = 139) and from 2007 to 2009 (n = 69) were analyzed to develop and validate the ANN model. Severe AP was defined according to the Atlanta criteria.

RESULTS

ANNs selected 6 of 23 potential risk variables as relevant for severity prediction, including duration of pain until arrival at the emergency department, creatinine, hemoglobin, alanine aminotransferase, heart rate, and white blood cell count. The discriminatory power for prediction of progression to a severe course, determined from the area under the receiver-operating characteristic curve, was 0.92 for the ANN model, 0.84 for the logistic regression model (p = 0.030), and 0.63 for the APACHE II score (p < 0.001). The numbers of correctly classified patients for a sensitivity of 50 and 75% were significantly higher for the ANN model than for logistic regression (p = 0.002) and APACHE II (p < 0.001).

CONCLUSION

The ANN model identified 6 risk variables available at the time of admission, including duration of pain, a finding not being presented as a risk factor before. The severity classification developed proved to be superior to APACHE II. and IAP.

Advances in managing acute pancreatitis

This review highlights advances in acute pancreatitis (AP) made in the past year. We focus on clinical aspects of AP - severe disease especially - and risk stratification tools to guide the clinical care of patients. Most patients with AP have mild disease that requires a diagnostic evaluation, self-limited supportive care, and a short hospital stay. In patients with potentially severe AP, it is important for clinicians to use available risk-stratifying tools to identify high-risk patients and initiate timely interventions such as aggressive fluid resuscitation, close monitoring, early initiation of enteral nutrition, and appropriate use of endoscopic retrograde cholangio-pancreatography. This approach decreases morbidity and possibly mortality and is supported by evidence drawn from recent clinical guidelines, historical literature, and the highest quality studies published in the last year.

Risk assessment in acute pancreatitis

BACKGROUND

Acute pancreatitis has a variable natural history and in a proportion of patients is associated with severe complications and a significant risk of death. The various tools available for risk assessment in acute pancreatitis are reviewed.

METHODS

Relevant medical literature from PubMed, Ovid, Embase, Web of Science and The Cochrane Library websites to May 2008 was reviewed.

RESULTS AND CONCLUSION

Over the past 30 years several scoring systems have been developed to predict the severity of acute pancreatitis in the first 48-72 h. Biochemical and immunological markers, imaging modalities and novel predictive models may help identify patients at high risk of complications or death. Recently, there has been a recognition of the importance of the systemic inflammatory response syndrome and organ dysfunction.

Artificial neural networks in pancreatic disease

BACKGROUND

An artificial neural network (ANNs) is a non-linear pattern recognition technique that is rapidly gaining in popularity in medical decision-making. This study investigated the use of ANNs for diagnostic and prognostic purposes in pancreatic disease, especially acute pancreatitis and pancreatic cancer.

METHODS

PubMed was searched for articles on the use of ANNs in pancreatic diseases using the MeSH terms 'neural networks (computer)', 'pancreatic neoplasms', 'pancreatitis' and 'pancreatic diseases'. A systematic review of the articles was performed.

RESULTS

Eleven articles were identified, published between 1993 and 2007. The situations that lend themselves best to analysis by ANNs are complex multifactorial relationships, medical decisions when a second opinion is needed and when automated interpretation is required, for example in a situation of an inadequate number of experts.

CONCLUSION

Conventional linear models have limitations in terms of diagnosis and prediction of outcome in acute pancreatitis and pancreatic cancer. Management of these disorders can be improved by applying ANNs to existing clinical parameters and newly established gene expression profiles.

Pancreas and cystic fibrosis: the implications of increased survival in cystic fibrosis

Pancreatitis affects 0.5% people with cystic fibrosis (CF) in the UK and 0.01% of the normal population. Why do some with CF get pancreatitis and some not? And does pancreatitis in neonates result in pancreatic failure with no further inflammation or risk of pancreatic cancer? Review of the literature would suggest that 85% of those with CF have pancreatic destruction as children with minimal risk of further inflammatory or neoplastic changes. Those with a functioning pancreas are at risk of developing pancreatitis. There are several case series of pancreatic cancer reported in CF patients, but overall the risk is unknown. As patients with CF and pancreatic sufficiency are living longer, further studies to assess the risk of developing pancreatic cancer in this subgroup should be considered.

USEFULNESS OF INFRARED SPECTROSCOPY IN DIAGNOSIS OF ACUTE PANCREATITIS

BACKGROUND

The lack of a gold standard for the diagnosis of acute pancreatitis remains a problem. Our aim was to evaluate whether infrared spectroscopy of serum can establish the diagnosis of acute pancreatitis.

METHODS

Sixty-four patients with acute pancreatitis, 112 patients with non-pancreatic acute abdomen and 40 healthy subjects were studied. In addition to serum infrared spectral analysis, serum concentrations of amylase and lipase were measured on admission.

RESULTS

Infrared spectroscopy based on serum absorption patterns in the range 800-1000 nm successfully distinguished acute pancreatitis from acute abdominal disorders of extrapancreatic origin and from control specimens. The sensitivity, specificity and positive and negative predictive values of infrared spectroscopy on admission were 91, 91, 85, and 94%, respectively. Within 24 h of onset of symptoms, infrared spectroscopy, lipase and amylase showed similar areas under the ROC curves for infrared spectra of serum (0.93), lipase (0.96) and amylase (0.91).

CONCLUSIONS

The successful classification of infrared spectra in patients with acute pancreatitis implies that the pathophysiology of disease alters the composition of the specimen in a characteristic fashion--in this case the serum make-up reflects the presence of acute pancreatitis.

Identification of severe acute pancreatitis using an artificial neural network

BACKGROUND

The aim of this study was to construct and validate an artificial neural network (ANN) model to identify severe acute pancreatitis (AP) and predict fatal outcome.

METHODS

All patients who presented with AP from January 2000 to September 2004 were reviewed. Presentation data on admission and at 48 hours were collected. Acute Physiology and Chronic Health Evaluation (APACHE) II and Glasgow severity (GS) score were calculated. A feed-forward ANN was created and trained to predict development of severe AP and mortality from AP; 25% of the data set was withheld from training and was used to evaluate the accuracy of the ANN. Accuracy of the ANN in predicting severity of AP was compared with APACHE II and GS scores.

RESULTS

A total of 664 patients with AP were identified of whom 181 (27.3%) fulfilled the clinical and radiologic criteria for severe pancreatitis and 42 patients died (6.3%). Median APACHE II score at 48 hours was 4 (range, 0 to 23). ANN was more accurate than APACHE II or GS scoring systems at predicting progression to a severe course (P < .05 and P < .01, respectively), predicting development of multiorgan dysfunction syndrome (P < .05 and P < .01) and at predicting death from AP (P < .05).

CONCLUSIONS

An ANN was able to predict progression to severe disease, development of organ failure and mortality from acute pancreatitis with considerable accuracy and outperformed other clinical risk scoring systems. Further studies are required to assess its utility in aiding management decisions in patients with AP.

Using Generalized Additive Models for Construction of Nonlinear Classifiers in Computer-Aided Diagnosis Systems

Several investigators have pointed out the possibility of using computer-aided diagnosis (CAD) schemes, as second readers, to help radiologists in the interpretation of images. One of the most important aspects to be considered when the diagnostic imaging systems are analyzed is the evaluation of their diagnostic performance. To perform this task, receiver operating characteristic curves are the method of choice. An important step in nearly all CAD systems is the reduction of false positives, as well as the classification of lesions, using different algorithms, such as neural networks or feature analysis, and several statistical methods. A statistical model more often employed is linear discriminant analysis (LDA). However, LDA implies several limitations in the type of variables that it can analyze. In this work, we have developed a novel approach, based on generalized additive models (GAMs), as an alternative to LDA, which can deal with a broad variety of variables, improving the results produced by using the LDA model. As an application, we have used GAM techniques for reducing the number of false detections in a computerized method to detect clustered microcalcifications, and we have compared this with the results obtained when LDA was applied. Employing LDA, the system achieved a sensitivity of 80.52% at a false-positive rate of 1.90 false detections per image. With the GAM, the sensitivity increased to 83.12% and 1.46 false positives per image.

Comparative effectiveness of total population versus disease-specific neural network models in predicting medical costs

The objective of this research was to compare the accuracy of two types of neural networks in identifying individuals at risk for high medical costs for three chronic conditions. Two neural network models-a population model and three disease-specific models-were compared regarding effectiveness predicting high costs. Subjects included 33,908 health plan members with diabetes, 19,264 with asthma, and 2,605 with cardiac conditions. For model development/ testing, only members with 24 months of continuous enrollment were included. Models were developed to predict probability of high costs in 2000 (top 15% of distribution) based on 1999 claims factors. After validation, models were applied to 2000 claims factors to predict probability of high 2001 costs. Each member received two scores-population model score applied to cohort and disease model score. Receiver Operating Characteristic (ROC) curves compared sensitivity, specificity, and total performance of population model and three disease models. Diabetes-specific model accuracy, C = 0.786 (95%CI = 0.779-0.794), was greater than that of population model applied to diabetic cohort, C = 0.767 (0.759-0.775). Asthma-specific model accuracy, C = 0.835 (0.825-0.844), was no different from that of population model applied to asthma cohort, C = 0.844 (0.835-0.853). Cardiac-specific model accuracy, C = 0.651 (0.620-0.683), was lower than that of population model applied to cardiac cohort, C = 0.726 (0.697-0.756). The population model predictive power, compared to the disease model predictive power, varied by disease; in general, the larger the cohort, the greater the advantage in predictive power of the disease model compared to the population model. Given these findings, disease management program staff should test multiple approaches before implementing predictive models.

Machine learning can improve prediction of severity in acute pancreatitis using admission values of APACHE II score and C-reactive protein

BACKGROUND

Acute pancreatitis (AP) has a variable course. Accurate early prediction of severity is essential to direct clinical care. Current assessment tools are inaccurate, and unable to adapt to new parameters. None of the current systems uses C-reactive protein (CRP). Modern machine-learning tools can address these issues.

METHODS

370 patients admitted with AP in a 5-year period were retrospectively assessed; after exclusions, 265 patients were studied. First recorded values for physical examination and blood tests, aetiology, severity and complications were recorded. A kernel logistic regression model was used to remove redundant features, and identify the relationships between relevant features and outcome. Bootstrapping was used to make the best use of data and obtain confidence estimates on the parameters of the model.

RESULTS

A model containing 8 variables (age, CRP, respiratory rate, pO2 on air, arterial pH, serum creatinine, white cell count and GCS) predicted a severe attack with an area under the receiver-operating characteristic curve (AUC) of 0.82 (SD 0.01). The optimum cut-off value for predicting severity gave sensitivity and specificity of 0.87 and 0.71 respectively. The predictions were significantly better (p = 0.0036) than admission APACHE II scores in the same patients (AUC 0.74) and better than historical admission APACHE II data (AUC 0.68-0.75).

CONCLUSIONS

This system for the first time combines admission values of selected components of APACHE II and CRP for prediction of severe AP. The score is simple to use, and is more accurate than admission APACHE II alone. It is adaptable and would allow incorporation of new predictive factors.

Neural network approach to the segmentation and classification of dynamic magnetic resonance images of the breast: comparison with empiric and quantitative kinetic parameters

RATIONALE AND OBJECTIVE

An artificial neural network (ANN)-based segmentation method was developed for dynamic contrast-enhanced magnetic resonance (MR) imaging of the breast and compared with quantitative and empiric parameter mapping techniques.

MATERIALS AND METHODS

The study population was composed of 10 patients with seven malignant and three benign lesions undergoing dynamic MR imaging of the breast. All lesions were biopsied or surgically excised, and examined by means of histopathology. A T1-weighted 3D FLASH (fast low angle shot sequence) was acquired before and seven times after the intravenous administration of gadopentetate dimeglumine at a dose of 0.1 mmol/kg body weight. Motion artifacts on MR images were eliminated by voxel-based affine and nonrigid registration techniques. A two-layered feed-forward back-propagation network was created for pixel-by-pixel classification of signal intensity-time curves into benign/malignant tissue types. ANN output was statistically compared with percent-enhancement (E), signal enhancement ratio (SER), time-to-peak, subtracted signal intensity (SUB), pharmacokinetic parameter rate constant (k(ep)), and correlation coefficient to a predefined reference washout curve.

RESULTS

ANN was successfully applied to the classification of breast MR images identifying structures with benign or malignant enhancement kinetics. Correlation coefficient (logistic regression, odds ratio [OR] = 12.9; 95% CI: 7.7-21.8), k(ep) (OR = 1.8; 95% CI: 1.2-2.6), and time-to-peak (OR = 0.45; 95% CI: 0.3-0.7) were independently associated to ANN output classes. SER, E, and SUB were nonsignificant covariates.

CONCLUSION

ANN is capable of classifying breast lesions on MR images. Mapping correlation coefficient, k(ep) and time-to-peak showed the highest association with the ANN result.