Risk prediction platform for pancreatic fistula after pancreatoduodenectomy using artificial intelligence

Artificial intelligence in the diagnosis and treatment of acute appendicitis: a narrative review

Artificial intelligence is transforming healthcare. Artificial intelligence can improve patient care by analyzing large amounts of data to help make more informed decisions regarding treatments and enhance medical research through analyzing and interpreting data from clinical trials and research projects to identify subtle but meaningful trends beyond ordinary perception. Artificial intelligence refers to the simulation of human intelligence in computers, where systems of artificial intelligence can perform tasks that require human-like intelligence like speech recognition, visual perception, pattern-recognition, decision-making, and language processing. Artificial intelligence has several subdivisions, including machine learning, natural language processing, computer vision, and robotics. By automating specific routine tasks, artificial intelligence can improve healthcare efficiency. By leveraging machine learning algorithms, the systems of artificial intelligence can offer new opportunities for enhancing both the efficiency and effectiveness of surgical procedures, particularly regarding training of minimally invasive surgery. As artificial intelligence continues to advance, it is likely to play an increasingly significant role in the field of surgical learning. Physicians have assisted to a spreading role of artificial intelligence in the last decade. This involved different medical specialties such as ophthalmology, cardiology, urology, but also abdominal surgery. In addition to improvements in diagnosis, ascertainment of efficacy of treatment and autonomous actions, artificial intelligence has the potential to improve surgeons' ability to better decide if acute surgery is indicated or not. The role of artificial intelligence in the emergency departments has also been investigated. We considered one of the most common condition the emergency surgeons have to face, acute appendicitis, to assess the state of the art of artificial intelligence in this frequent acute disease. The role of artificial intelligence in diagnosis and treatment of acute appendicitis will be discussed in this narrative review.

Optimizing prediction models for pancreatic fistula after pancreatectomy: Current status and future perspectives

Postoperative pancreatic fistula (POPF) is a frequent complication after pancreatectomy, leading to increased morbidity and mortality. Optimizing prediction models for POPF has emerged as a critical focus in surgical research. Although over sixty models following pancreaticoduodenectomy, predominantly reliant on a variety of clinical, surgical, and radiological parameters, have been documented, their predictive accuracy remains suboptimal in external validation and across diverse populations. As models after distal pancreatectomy continue to be progressively reported, their external validation is eagerly anticipated. Conversely, POPF prediction after central pancreatectomy is in its nascent stage, warranting urgent need for further development and validation. The potential of machine learning and big data analytics offers promising prospects for enhancing the accuracy of prediction models by incorporating an extensive array of variables and optimizing algorithm performance. Moreover, there is potential for the development of personalized prediction models based on patient- or pancreas-specific factors and postoperative serum or drain fluid biomarkers to improve accuracy in identifying individuals at risk of POPF. In the future, prospective multicenter studies and the integration of novel imaging technologies, such as artificial intelligence-based radiomics, may further refine predictive models. Addressing these issues is anticipated to revolutionize risk stratification, clinical decision-making, and postoperative management in patients undergoing pancreatectomy.

Deep learning-based prediction of post-pancreaticoduodenectomy pancreatic fistula

Postoperative pancreatic fistula is a life-threatening complication with an unmet need for accurate prediction. This study was aimed to develop preoperative artificial intelligence-based prediction models. Patients who underwent pancreaticoduodenectomy were enrolled and stratified into model development and validation sets by surgery between 2016 and 2017 or in 2018, respectively. Machine learning models based on clinical and body composition data, and deep learning models based on computed tomographic data, were developed, combined by ensemble voting, and final models were selected comparison with earlier model. Among the 1333 participants (training, n = 881; test, n = 452), postoperative pancreatic fistula occurred in 421 (47.8%) and 134 (31.8%) and clinically relevant postoperative pancreatic fistula occurred in 59 (6.7%) and 27 (6.0%) participants in the training and test datasets, respectively. In the test dataset, the area under the receiver operating curve [AUC (95% confidence interval)] of the selected preoperative model for predicting all and clinically relevant postoperative pancreatic fistula was 0.75 (0.71-0.80) and 0.68 (0.58-0.78). The ensemble model showed better predictive performance than the individual ML and DL models.

Preoperative evaluation to determine the difficulty of No. 6 lymphadenectomy in laparoscopic gastrectomy

BACKGROUND

Laparoscopic gastrectomy (LG) requires a long learning curve because of the complicated surgical procedures. Infrapyloric (No. 6) lymph node dissection (LND) is one of the difficult procedures in LG, especially for trainees. This study investigated the impact of the prediction of the difficulty of No. 6 LND.

METHODS

We retrospectively reviewed the preoperative computed tomography (CT) images and individual operative video records of 57 patients who underwent LG with No. 6 LND to define and predict the No. 6 LND difficulty. To evaluate whether prediction of the difficulty of No. 6 LND could improve surgical outcomes, 48 patients who underwent laparoscopic distal gastrectomy were assessed (30 patients without prediction by a qualified surgeon and 18 patients with prediction by a trainee).

RESULTS

The anatomical characteristic that LND required > 2 cm of dissection along the right gastroepiploic vein was defined as difficulty of No. 6 LND. Of the 57 LG patients, difficulty was identified intraoperatively in 21 patients (36.8%). Among the several evaluated anatomical parameters, the length between the right gastroepiploic vein and the right gastroepiploic artery in the maximum intensity projection in contrast-enhanced CT images was significantly correlated with the intraoperative difficulty of No. 6 LND (p < 0.0001). Surgical outcomes, namely intraoperative minor bleeding, postoperative pancreatic fistula, and drain amylase concentration were not significantly different between LG performed by a trainee with prediction compared with that by a specialist without prediction.

CONCLUSIONS

Preoperative evaluation of the difficulty of No. 6 LND is useful for trainees, to improve surgical outcomes.

Exploring the application and future outlook of Artificial intelligence in pancreatic cancer

Pancreatic cancer, an exceptionally malignant tumor of the digestive system, presents a challenge due to its lack of typical early symptoms and highly invasive nature. The majority of pancreatic cancer patients are diagnosed when curative surgical resection is no longer possible, resulting in a poor overall prognosis. In recent years, the rapid progress of Artificial intelligence (AI) in the medical field has led to the extensive utilization of machine learning and deep learning as the prevailing approaches. Various models based on AI technology have been employed in the early screening, diagnosis, treatment, and prognostic prediction of pancreatic cancer patients. Furthermore, the development and application of three-dimensional visualization and augmented reality navigation techniques have also found their way into pancreatic cancer surgery. This article provides a concise summary of the current state of AI technology in pancreatic cancer and offers a promising outlook for its future applications.

Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery

Complications after surgery have a major impact on short- and long-term outcomes, and decades of technological advancement have not yet led to the eradication of their risk. The accurate prediction of complications, recently enhanced by the development of machine learning algorithms, has the potential to completely reshape surgical patient management. In this paper, we reflect on multiple issues facing the implementation of machine learning, from the development to the actual implementation of machine learning models in daily clinical practice, providing suggestions on the use of machine learning models for predicting postoperative complications after major abdominal surgery.

A Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis analysis to evaluate the quality of reporting of postoperative pancreatic fistula prediction models after pancreatoduodenectomy: A systematic review

BACKGROUND

Postoperative pancreatic fistula is a frequent and potentially lethal complication after pancreatoduodenectomy. Several models have been developed to predict postoperative pancreatic fistula risk. This study was performed to evaluate the quality of reporting of postoperative pancreatic fistula prediction models after pancreatoduodenectomy using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist that provides guidelines on reporting prediction models to enhance transparency and to help in the decision-making regarding the implementation of the appropriate risk models into clinical practice.

METHODS

Studies that described prediction models to predict postoperative pancreatic fistula after pancreatoduodenectomy were searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The TRIPOD checklist was used to evaluate the adherence rate. The area under the curve and other performance measures were extracted if reported. A quadrant matrix chart is created to plot the area under the curve against TRIPOD adherence rate to find models with a combination of above-average TRIPOD adherence and area under the curve.

RESULTS

In total, 52 predictive models were included (23 development, 15 external validation, 4 incremental value, and 10 development and external validation). No risk model achieved 100% adherence to the TRIPOD. The mean adherence rate was 65%. Most authors failed to report on missing data and actions to blind assessment of predictors. Thirteen models had an above-average performance for TRIPOD checklist adherence and area under the curve.

CONCLUSION

Although the average TRIPOD adherence rate for postoperative pancreatic fistula models after pancreatoduodenectomy was 65%, higher compared to other published models, it does not meet TRIPOD standards for transparency. This study identified 13 models that performed above average in TRIPOD adherence and area under the curve, which could be the appropriate models to be used in clinical practice.

Machine learning versus logistic regression for the prediction of complications after pancreatoduodenectomy

BACKGROUND

Machine learning is increasingly advocated to develop prediction models for postoperative complications. It is, however, unclear if machine learning is superior to logistic regression when using structured clinical data. Postoperative pancreatic fistula and delayed gastric emptying are the two most common complications with the biggest impact on patient condition and length of hospital stay after pancreatoduodenectomy. This study aimed to compare the performance of machine learning and logistic regression in predicting pancreatic fistula and delayed gastric emptying after pancreatoduodenectomy.

METHODS

This retrospective observational study used nationwide data from 16 centers in the Dutch Pancreatic Cancer Audit between January 2014 and January 2021. The area under the curve of a machine learning and logistic regression model for clinically relevant postoperative pancreatic fistula and delayed gastric emptying were compared.

RESULTS

Overall, 799 (16.3%) patients developed a postoperative pancreatic fistula, and 943 developed (19.2%) delayed gastric emptying. For postoperative pancreatic fistula, the area under the curve of the machine learning model was 0.74, and the area under the curve of the logistic regression model was 0.73. For delayed gastric emptying, the area under the curve of the machine learning model and logistic regression was 0.59.

CONCLUSION

Machine learning did not outperform logistic regression modeling in predicting postoperative complications after pancreatoduodenectomy.

Prediction of postoperative pancreatic fistula and pancreatitis after pancreatoduodenectomy or distal pancreatectomy: A review

BACKGROUND AND OBJECTIVE

Postoperative pancreatic fistula (POPF) is the leading cause of morbidity and early mortality in patients undergoing pancreatic resection. In addition, recent studies have identified postoperative acute pancreatitis (POAP) as an independent contributor to morbidity. Most perioperative mitigation strategies experimented for POPF have been shown to be in vain with no consensus on the best perioperative management. Clinical prediction models have been developed with the hope of identifying high POPF risk patients with the leading idea of finding subpopulations possibly benefiting from pre-existing or novel mitigation strategies. The aim of this review was to map out the existing prediction modeling studies to better understand the current stage of POPF prediction modeling, and the methodology behind them.

METHODS

A narrative review of the existing POPF prediction model studies was performed. Studies published before September 2022 were included.

RESULTS

While the number of POPF prediction models for pancreatoduodenectomy has increased, none of the currently existing models stand out from the crowd. For distal pancreatectomy, two unique POPF prediction models exist, but due to their freshness, no further external validation or adoption in clinics or research has been reported. There seems to be a lack of adherence to correct methodology or reporting guidelines in most of the studies, which has rendered external validity-if assessed-low. Few of the most recent studies have demonstrated preoperative assessment of pancreatic aspects from computed tomography (CT) scans to provide relatively strong predictors of POPF.

CONCLUSIONS

Main goal for the future would be to reach a consensus on the most important POPF predictors and prediction model. At their current state, few models have demonstrated adequate transportability and generalizability to be up to the task. Better understanding of POPF pathophysiology and the possible driving force of acute inflammation and POAP might be required before such a prediction model can be accessed.

Development of a Pancreatic Fistula Prediction Model After Pancreaticoduodenectomy Based on a Decision Tree and Random Forest Algorithm

BACKGROUND

The incidence of postoperative pancreatic fistula (POPF) after pancreaticoduodenectomy (PD) is high. We sought to develop a POPF prediction model based on a decision tree (DT) and random forest (RF) algorithm after PD and to explore its clinical value.

METHODS

The case data of 257 patients who underwent PD in a tertiary general hospital from 2013 to 2021 were retrospectively collected in China. The RF model was used to select features by ranking the importance of variables, and both algorithms were used to build the prediction model after automatic adjustment of parameters by setting the respective hyperparameter intervals and resampling as a 10-fold cross-validation method, etc. The prediction model's performance was assessed by the receiver operating characteristic curve (ROC) and the area under curve (AUC).

RESULTS

Postoperative pancreatic fistula occurred in 56 cases (56/257, 21.8%). The DT model had an AUC of .743 and an accuracy of .840, while the RF model had an AUC of .977 and an accuracy of .883. The DT plot visualized the process of inferring the risk of pancreatic fistula from the DT model on independent individuals. The top 10 important variables were selected for ranking in the RF variable importance ranking.

CONCLUSION

This study successfully developed a DT and RF algorithm for the POPF prediction model, which can be used as a reference for clinical health care professionals to optimize treatment strategies to reduce the incidence of POPF.

PREDICTION OF EXTERNAL PANCREATIC FISTULA DEVELOPMENT IN PATIENTS WITH ACUTE INFECTED NECROTISING PANCREATITIS

OBJECTIVE

The aim: To search for risk factors for the development of external pancreatic fistula (EPF) in patients with acute infected necrotizing pancreatitis.

PATIENTS AND METHODS

Materials and methods: A prospective single-center observational study was conducted with the participation of 160 adult patients with infected acute necrotizing pancreatitis (ANP) who required intervention at different stages of the disease. Depending on the amylase activity of the drainage secretions, the groups with and without diagnosed EPF were compared according to 15 demographic, clinical, laboratory and instrumental parameters of the disease. To identify predictors of the risk of developing EPF in ANP, a regression logistic analysis was performed and logistic regression models were built for each factor attribute.

RESULTS

Results: We did not find statistically significant differences in the indicators characterising patients on the first day of illness, as well as in the comparison of indicators characterising local complications of AP, the timing of AP infection, the incidence of sepsis and the level of PON in sepsis. When comparing the groups by the frequency of the type of microbial agent of infected APB, no statistically significant differences were found. Comparative pairwise analysis between the groups by morphological characteristics revealed statistical differences in the frequency of focal superficial and transmural PN (p < 0.001). Next, we performed a regression logistic analysis and built logistic regression models for each factor attribute, namely age, gender, BMI, BISAP score and Charlson comorbidity index, morphological characteristics of necrotizing pancreatitis. After univariate regression analysis, a statistically significant association between the depth of PN and the risk of developing PFN was found (OR 2.7 (1.4-5.2), p=0.001).

CONCLUSION

Conclusions: We found that the risk of developing an external pancreatic fistula was associated with the depth of pancreatic necrosis.

Personal predictive model based on systemic inflammation markers for estimation of postoperative pancreatic fistula following pancreaticoduodenectomy

BACKGROUND

Postoperative pancreatic fistula (PF) is a serious life-threatening complication after pancreaticoduodenectomy (PD). Our research aimed to develop a machine learning (ML)-aided model for PF risk stratification.

AIM

To develop an ML-aided model for PF risk stratification.

METHODS

We retrospectively collected 618 patients who underwent PD from two tertiary medical centers between January 2012 and August 2021. We used an ML algorithm to build predictive models, and subject prediction index, that is, decision curve analysis, area under operating characteristic curve (AUC) and clinical impact curve to assess the predictive efficiency of each model.

RESULTS

A total of 29 variables were used to build the ML predictive model. Among them, the best predictive model was random forest classifier (RFC), the AUC was [0.897, 95% confidence interval (CI): 0.370–1.424], while the AUC of the artificial neural network, eXtreme gradient boosting, support vector machine, and decision tree were between 0.726 (95%CI: 0.191–1.261) and 0.882 (95%CI: 0.321–1.443).

CONCLUSION

Fluctuating serological inflammatory markers and prognostic nutritional index can be used to predict postoperative PF.

Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis

Purpose

We evaluated the related research on artificial intelligence (AI) in pancreatic cancer (PC) through bibliometrics analysis and explored the research hotspots and current status from 1997 to 2021.

Methods

Publications related to AI in PC were retrieved from the Web of Science Core Collection (WoSCC) during 1997-2021. Bibliometrix package of R software 4.0.3 and VOSviewer were used to bibliometrics analysis.

Results

A total of 587 publications in this field were retrieved from WoSCC database. After 2018, the number of publications grew rapidly. The United States and Johns Hopkins University were the most influential country and institution, respectively. A total of 2805 keywords were investigated, 81 of which appeared more than 10 times. Co-occurrence analysis categorized these keywords into five types of clusters: (1) AI in biology of PC, (2) AI in pathology and radiology of PC, (3) AI in the therapy of PC, (4) AI in risk assessment of PC and (5) AI in endoscopic ultrasonography (EUS) of PC. Trend topics and thematic maps show that keywords " diagnosis ", “survival”, “classification”, and “management” are the research hotspots in this field.

Conclusion

The research related to AI in pancreatic cancer is still in the initial stage. Currently, AI is widely studied in biology, diagnosis, treatment, risk assessment, and EUS of pancreatic cancer. This bibliometrics study provided an insight into AI in PC research and helped researchers identify new research orientations.

Differential Performance of Machine Learning Models in Prediction of Procedure-Specific Outcomes

BACKGROUND

Procedure-specific complications can have devastating consequences. Machine learning-based tools have the potential to outperform traditional statistical modeling in predicting their risk and guiding decision-making. We sought to develop and compare deep neural network (NN) models, a type of machine learning, to logistic regression (LR) for predicting anastomotic leak after colectomy, bile leak after hepatectomy, and pancreatic fistula after pancreaticoduodenectomy (PD).

METHODS

The colectomy, hepatectomy, and PD National Surgical Quality Improvement Program (NSQIP) databases were analyzed. Each dataset was split into training, validation, and testing sets in a 60/20/20 ratio, with fivefold cross-validation. Models were created using NN and LR for each outcome. Models were evaluated primarily with area under the receiver operating characteristic curve (AUROC).

RESULTS

A total of 197,488 patients were included for colectomy, 25,403 for hepatectomy, and 23,333 for PD. For anastomotic leak, AUROC for NN was 0.676 (95% 0.666-0.687), compared with 0.633 (95% CI 0.620-0.647) for LR. For bile leak, AUROC for NN was 0.750 (95% CI 0.739-0.761), compared with 0.722 (95% CI 0.698-0.746) for LR. For pancreatic fistula, AUROC for NN was 0.746 (95% CI 0.733-0.760), compared with 0.713 (95% CI 0.703-0.723) for LR. Variables related to intra-operative information, such as surgical approach, biliary reconstruction, and pancreatic gland texture were highly important for model predictions.

DISCUSSION

Machine learning showed a marginal advantage over traditional statistical techniques in predicting procedure-specific outcomes. However, models that included intra-operative information performed better than those that did not, suggesting that NSQIP procedure-targeted datasets may be strengthened by including relevant intra-operative information.

The role of artificial intelligence in pancreatic surgery: a systematic review

Artificial intelligence (AI), including machine learning (ML), is being slowly incorporated in medical practice, to provide a more precise and personalized approach. Pancreatic surgery is an evolving field, which offers the only curative option for patients with pancreatic cancer. Increasing amounts of data are available in medicine: AI and ML can help incorporate large amounts of information in clinical practice. We conducted a systematic review, based on PRISMA criteria, of studies that explored the use of AI or ML algorithms in pancreatic surgery. To our knowledge, this is the first systematic review on this topic. Twenty-five eligible studies were included in this review; 12 studies with implications in the preoperative diagnosis, while 13 studies had implications in patient evolution. Preoperative diagnosis, such as predicting the malignancy of IPMNs, differential diagnosis between pancreatic cystic lesions, classification of different pancreatic tumours, and establishment of the correct management for each of these lesions, can be facilitated through different AI or ML algorithms. Postoperative evolution can also be predicted, and some studies reported prediction models for complications, including postoperative pancreatic fistula, while other studies have analysed the implications for prognosis evaluation (from predicting a textbook outcome, the risk of metastasis or relapse, or the mortality rate and survival). One study discussed the possibility of predicting an intraoperative complication-massive intraoperative bleeding. Artificial intelligence and machine learning models have promising applications in pancreatic surgery, in the preoperative period (high-accuracy diagnosis) and postoperative setting (prognosis evaluation and complication prediction), and the intraoperative applications have been less explored.

The prediction of surgical complications using artificial intelligence in patients undergoing major abdominal surgery: A systematic review

BACKGROUND

Conventional statistics are based on a simple cause-and-effect principle. Postoperative complications, however, have a multifactorial and interrelated etiology. The application of artificial intelligence might be more accurate to predict postoperative outcomes. The objective of this study was to determine the current quality of studies describing the use of artificial intelligence in predicting complications in patients undergoing major abdominal surgery.

METHODS

A literature search was performed in PubMed, Embase, and Web of Science. Inclusion criteria were (1) empirical studies including patients undergoing (2) any type of gastrointestinal surgery, including hepatopancreaticobiliary surgery, whose (3) complications or mortality were predicted with the use of (4) any artificial intelligence system. Studies were screened for description of method of validation and testing in methodology. Outcome measurements were sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve.

RESULTS

From a total of 1,537 identified articles, 15 were included for the review. Among a large variety of algorithms used by the included studies, sensitivity was between 0.06 and 0.96, specificity was between 0.61 and 0.98, accuracy was between 0.78 and 0.95, and area under the receiver operating characteristic curve varied between 0.50 and 0.96.

CONCLUSION

Artificial intelligence algorithms have the ability to accurately predict postoperative complications. Nevertheless, algorithms should be properly tested and validated, both internally and externally. Furthermore, a complete database and the absence of unsampled imbalanced data are absolute prerequisites for algorithms to predict accurately.

A machine learning risk model based on preoperative computed tomography scan to predict postoperative outcomes after pancreatoduodenectomy

Clinically relevant postoperative pancreatic fistula (CR-POPF) is a life-threatening complication following pancreaticoduodenectomy (PD). Individualized preoperative risk assessment could improve clinical management and prevent or mitigate adverse outcomes. The aim of this study is to develop a machine learning risk model to predict occurrence of CR-POPF after PD from preoperative computed tomography (CT) scans. A total of 100 preoperative high-quality CT scans of consecutive patients who underwent pancreaticoduodenectomy in our institution between 2011 and 2019 were analyzed. Radiomic and morphological features extracted from CT scans related to pancreatic anatomy and patient characteristics were included as variables. These data were then assessed by a machine learning classifier to assess the risk of developing CR-POPF. Among the 100 patients evaluated, 20 had CR-POPF. The predictive model based on logistic regression demonstrated specificity of 0.824 (0.133) and sensitivity of 0.571 (0.337), with an AUC of 0.807 (0.155), PPV of 0.468 (0.310) and NPV of 0.890 (0.084). The performance of the model minimally decreased utilizing a random forest approach, with specificity of 0.914 (0.106), sensitivity of 0.424 (0.346), AUC of 0.749 (0.209), PPV of 0.502 (0.414) and NPV of 0.869 (0.076). Interestingly, using the same data, the model was also able to predict postoperative overall complications and a postoperative length of stay over the median with AUCs of 0.690 (0.209) and 0.709 (0.160), respectively. These findings suggest that preoperative CT scans evaluated by machine learning may provide a novel set of information to help clinicians choose a tailored therapeutic pathway in patients candidated to pancreatoduodenectomy.

Realistic Advantages of Early Surgical Drain Removal after Pancreatoduodenectomy: A Single-Institution Retrospective Study

The latest guidelines from the Enhanced Recovery After Surgery (ERAS^®) Society stated that early drain removal after pancreatoduodenectomy (PD) is beneficial in decreasing complications including postoperative pancreatic fistulas (POPFs). This study aimed to ascertain the actual benefits of early drain removal after PD. The data of 450 patients who underwent PD between 2018 and 2020 were retrospectively reviewed. The surgical outcomes were compared between patients whose drains were removed within 3 postoperative days (early removal group) and after 5 days (late removal group). Logistic regression analysis was performed to identify the risk factors for clinically relevant POPFs (CR-POPFs). Among the patients with drain fluid amylase < 5000 IU on the first postoperative day, the early removal group had fewer complications and shorter hospital stays than the late removal group (30.9% vs. 54.5%, p < 0.001; 9.8 vs. 12.5 days, p = 0.030, respectively). The incidences of specific complications including CR-POPFs were comparable between the two groups. Risk factor analysis showed that early drain removal did not increase CR-POPFs (p = 0.163). Although early drain removal has not been identified as apparently beneficial, this study showed that it may contribute to an early return to normal life without increasing complications.

Artificial intelligence in perioperative management of major gastrointestinal surgeries

Artificial intelligence (AI) demonstrated by machines is based on reinforcement learning and revolves around the usage of algorithms. The purpose of this review was to summarize concepts, the scope, applications, and limitations in major gastrointestinal surgery. This is a narrative review of the available literature on the key capabilities of AI to help anesthesiologists, surgeons, and other physicians to understand and critically evaluate ongoing and new AI applications in perioperative management. AI uses available databases called “big data” to formulate an algorithm. Analysis of other data based on these algorithms can help in early diagnosis, accurate risk assessment, intraoperative management, automated drug delivery, predicting anesthesia and surgical complications and postoperative outcomes and can thus lead to effective perioperative management as well as to reduce the cost of treatment. Perioperative physicians, anesthesiologists, and surgeons are well-positioned to help integrate AI into modern surgical practice. We all need to partner and collaborate with data scientists to collect and analyze data across all phases of perioperative care to provide clinical scenarios and context. Careful implementation and use of AI along with real-time human interpretation will revolutionize perioperative care, and is the way forward in future perioperative management of major surgery.

Preoperative prediction of clinically relevant postoperative pancreatic fistula after pancreaticoduodenectomy

OBJECTIVES

To develop a radiomics model and a combined model for preoperative prediction of clinically relevant postoperative pancreatic fistula (CR-POPF) in patients undergoing pancreaticoduodenectomy and to compare the predictive performance of the two models with the traditional Fistula Risk Score system.

METHODS

A total of 250 patients who underwent pancreaticoduodenectomy (PD) with preoperative computed tomography (CT) were divided into a training set (n = 175) and validation set (n = 75). The pancreatic area was automatically segmented on the portal venous phase CT images using a 3D U-Net segmentation model. A radiomics model was developed using radiomics features extracted from the volume of interest (VOI) and a combined model was developed using radiomics features, demographic information and radiological features. The FRS was also used to predict POPF. The predictive performance of the prediction models was assessed using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA).

RESULTS

Eleven and 18 features were extracted for the radiomics model and combined model, respectively. The combined model showed excellent predictive value, with an AUC of 0.871 (95 %CI 0.816,0.926) and 0.869 (95 %CI 0.779,0.958) in the training cohort and validation cohort, respectively. Calibration curves and DCA showed that the combined model outperformed the traditional FRS system and radiomics model.

CONCLUSION

The combined model exhibited excellent predictive performance and outperformed the traditional FRS system and radiomics model in the preoperative prediction of CR-POPF.

Applications of artificial intelligence in pancreatic and biliary diseases

The application of artificial intelligence (AI) in medicine has increased rapidly with respect to tasks including disease detection/diagnosis, risk stratification, and prognosis prediction. With recent advances in computing power and algorithms, AI has shown promise in taking advantage of vast electronic health data and imaging studies to supplement clinicians. Machine learning and deep learning are the most widely used AI methodologies for medical research and have been applied in pancreatobiliary diseases for which diagnosis and treatment selection are often complicated and require joint consideration of data from multiple sources. The aim of this review is to provide a concise introduction of the major AI methodologies and the current landscape of AI research in pancreatobiliary diseases.