The field of visceral surgery is currently undergoing a significant transformation due to the integration of artificial intelligence, specifically machine learning, deep learning, and natural language processing. These advancements have the potential to enhance surgical decision-making processes in pre-, intra-, and post-operative phases. Despite the rapid technological developments in surgery, such as modern operating rooms equipped with digital and interconnected equipment, new imaging systems, and robotic procedures, the application of machine learning in surgery remains underexplored. The dynamic environment of surgery, characterized by multimodal procedural data from patients, monitoring devices, sensors, and the operating room team, presents unique challenges in terms of data acquisition, storage, access, annotation, sharing, analysis, and clinical application.
The primary objective of this Research Topic is to investigate the potential of machine learning in improving various aspects of visceral surgery. This includes enhancing the accuracy of clinical interpretations and diagnoses, optimizing decision-making processes, minimizing risk of complications, improving navigation in complex anatomies with advanced visualization supports, and enhancing the analysis and treatment of complications. Additionally, the research aims to explore the potential of machine learning in predictive analysis of treatment responses, facilitating access to big data for research purposes, improving training programs, and increasing patient compliance with surgical procedures.
The Research Topic is specifically focused on the outcomes of the clinical application of machine learning in visceral surgery, particularly in hepato-pancreato-biliary and colorectal surgery. We welcome submissions that address the following themes:
• Clinical support in the interpretation of investigations and formulation of precise diagnoses;
• Optimization of decision-making processes and risk analysis;
• Minimization of complications;
• Navigation in complex anatomies with advanced visualization supports;
• Analysis and improvement of diagnostic and therapeutic processes of complication;
• Predictive analysis of treatment responses;
• Access to big data for scientific research purposes;
• Improvement of training programs;
• Patient compliance with surgical procedures.
Original and review papers are welcomed for submission.
The field of visceral surgery is currently undergoing a significant transformation due to the integration of artificial intelligence, specifically machine learning, deep learning, and natural language processing. These advancements have the potential to enhance surgical decision-making processes in pre-, intra-, and post-operative phases. Despite the rapid technological developments in surgery, such as modern operating rooms equipped with digital and interconnected equipment, new imaging systems, and robotic procedures, the application of machine learning in surgery remains underexplored. The dynamic environment of surgery, characterized by multimodal procedural data from patients, monitoring devices, sensors, and the operating room team, presents unique challenges in terms of data acquisition, storage, access, annotation, sharing, analysis, and clinical application.
The primary objective of this Research Topic is to investigate the potential of machine learning in improving various aspects of visceral surgery. This includes enhancing the accuracy of clinical interpretations and diagnoses, optimizing decision-making processes, minimizing risk of complications, improving navigation in complex anatomies with advanced visualization supports, and enhancing the analysis and treatment of complications. Additionally, the research aims to explore the potential of machine learning in predictive analysis of treatment responses, facilitating access to big data for research purposes, improving training programs, and increasing patient compliance with surgical procedures.
The Research Topic is specifically focused on the outcomes of the clinical application of machine learning in visceral surgery, particularly in hepato-pancreato-biliary and colorectal surgery. We welcome submissions that address the following themes:
• Clinical support in the interpretation of investigations and formulation of precise diagnoses;
• Optimization of decision-making processes and risk analysis;
• Minimization of complications;
• Navigation in complex anatomies with advanced visualization supports;
• Analysis and improvement of diagnostic and therapeutic processes of complication;
• Predictive analysis of treatment responses;
• Access to big data for scientific research purposes;
• Improvement of training programs;
• Patient compliance with surgical procedures.
Original and review papers are welcomed for submission.