The Challenges of Implementing Artificial Intelligence into Surgical Practice

Surgical strategies for challenging common bile duct stones in the endoscopic era: A comprehensive review of current evidence

While endoscopic retrograde cholangiopancreatography (ERCP) remains the primary treatment modality for common bile duct stones (CBDS) or choledocholithiasis due to advancements in instruments, surgical intervention, known as common bile duct exploration (CBDE), is still necessary in cases of difficult CBDS, failed endoscopic treatment, or altered anatomy. Recent evidence also supports CBDE in patients requesting single-step cholecystectomy and bile duct stone removal with comparable outcomes. This review elucidates relevant clinical anatomy, selection indications, and outcomes to enhance surgical understanding. The selection between trans-cystic (TC) vs trans-choledochal (TD) approaches is described, along with stone removal techniques and ductal closure. Detailed surgical techniques and strategies for both the TC and TD approaches, including instrument selection, is also provided. Additionally, this review comprehensively addresses operation-specific complications such as bile leakage, stricture, and entrapment, and focuses on preventive measures and treatment strategies. This review aims to optimize the management of CBDS through laparoscopic CBDE, with the goal of improving patient outcomes and minimizing risks.

Risk prediction of cholangitis after stent implantation based on machine learning

The risk of cholangitis after ERCP implantation in malignant obstructive jaundice patients remains unknown. To develop models based on artificial intelligence methods to predict cholangitis risk more accurately, according to patients after stent implantation in patients' MOJ clinical data. This retrospective study included 218 patients with MOJ undergoing ERCP surgery. A total of 27 clinical variables were collected as input variables. Seven models (including univariate analysis and six machine learning models) were trained and tested for classified prediction. The model' performance was measured by AUROC. The RFT model demonstrated excellent performances with accuracies up to 0.86 and AUROC up to 0.87. Feature selection in RF and SHAP was similar, and the choice of the best variable subset produced a high performance with an AUROC up to 0.89. We have developed a hybrid machine learning model with better predictive performance than traditional LR prediction models, as well as other machine learning models for cholangitis based on simple clinical data. The model can assist doctors in clinical diagnosis, adopt reasonable treatment plans, and improve the survival rate of patients.

Artificial intelligence-powered clinical decision making within gastrointestinal surgery: A systematic review

BACKGROUND

Clinical decision-making in gastrointestinal surgery is complex due to the unpredictability of tumoral behavior and postoperative complications. Artificial intelligence (AI) could aid in clinical decision-making by predicting these surgical outcomes. The current status of AI-based clinical decision-making within gastrointestinal surgery is unknown in recent literature. This review aims to provide an overview of AI models used for clinical decision-making within gastrointestinal surgery.

METHODS

A systematic literature search was performed in databases PubMed, EMBASE, Cochrane, and Web of Science. To be eligible for inclusion, studies needed to use AI models for clinical decision-making involving patients undergoing gastrointestinal surgery. Studies reporting on reviews, children, and study abstracts were excluded. The Probast risk of bias tool was used to evaluate the methodological quality of AI methods.

RESULTS

Out of 1073 studies, 10 articles were eligible for inclusion. AI models have been used to make clinical decisions between surgical procedures, selection of chemotherapy, selection of postoperative follow up programs, and implementation of a temporary ileostomy. Most studies have used a Random Forest or Gradient Boosting model with AUCs up to 0.97. All studies involved a retrospective study design, in which external validation was performed in one study.

CONCLUSIONS

This review shows that AI models have the potentiality to select the most optimal treatments for patients undergoing gastrointestinal surgery. Clinical benefits could be gained if AI models were used for clinical decision-making. However, prospective studies and randomized controlled trials will reveal the definitive role of AI models in clinical decision-making.

Challenges of Implementing Artificial Intelligence in Interventional Radiology

Artificial intelligence (AI) and deep learning (DL) remains a hot topic in medicine. DL is a subcategory of machine learning that takes advantage of multiple layers of interconnected neurons capable of analyzing immense amounts of data and "learning" patterns and offering predictions. It appears to be poised to fundamentally transform and help advance the field of diagnostic radiology, as heralded by numerous published use cases and number of FDA-cleared products. On the other hand, while multiple publications have touched upon many great hypothetical use cases of AI in interventional radiology (IR), the actual implementation of AI in IR clinical practice has been slow compared with the diagnostic world. In this article, we set out to examine a few challenges contributing to this scarcity of AI applications in IR, including inherent specialty challenges, regulatory hurdles, intellectual property, raising capital, and ethics. Owing to the complexities involved in implementing AI in IR, it is likely that IR will be one of the late beneficiaries of AI. In the meantime, it would be worthwhile to continuously engage in defining clinically relevant use cases and focus our limited resources on those that would benefit our patients the most.