The clinical application of artificial intelligence through machine learning, deep learning and natural language processing offers enormous potential for improving surgical decision-making processes in the pre-, intra- and post-operative phases. Surgery is facing a paradigm shift, determined by its rapid development in recent decades, which has allowed it to become a high-tech discipline.
Increasingly powerful technological developments, such as modern operating rooms, equipped with digital and interconnected equipment, new imaging systems, as well as robotic procedures, allow enormous potential for the improvement of therapy and surgical outcome.
ML and in particular DL have recently achieved considerable success in the medical field, but the field of surgery has not yet been explored in a similar way. In surgery we have multimodal procedural data coming from a dynamic environment that includes the patient, various monitoring devices and sensors and the operating room team, therefore there is a high variability of both patient data (e.g. anatomy, tumor location, comorbidities) and the surgical team (e.g. surgical procedure, surgical strategy, surgical experience). This poses several challenges regarding technical infrastructure for data acquisition, storage, access, annotation, sharing, analyses and clinical application aspects.
The research topic is aimed to verify if the application of machine learning in visceral surgery is able to improve:
-The clinical support in the correct interpretation of investigations and in formulating a precise diagnosis;
-The optimization of decision-making processes and risk analysis minimization of complications;
-A better orientation in complex anatomies with advanced visualization supports;
-The analysis and improvement of diagnostic and therapeutic processes of complications;
-The predictive analysis of response to treatment;
-The easy access to big data for scientific research purposes;
-The improvement of training programs;
-The better patient compliance with surgical procedures.
The scope of the research topic is to focus on the outcomes of clinical application of machine learning in the field of visceral surgery, particularly in hepato-pancreato-biliary and colorectal surgery.
Original and review papers could be accepted for publication, but are not limited to.
Keywords:
Artificial Intelligence, Machine Learning, Deep Learning, Neural Network, Visceral Surgery, Computer Vision, Natural Launguage Processing, Colorectal Surgery
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
The clinical application of artificial intelligence through machine learning, deep learning and natural language processing offers enormous potential for improving surgical decision-making processes in the pre-, intra- and post-operative phases. Surgery is facing a paradigm shift, determined by its rapid development in recent decades, which has allowed it to become a high-tech discipline.
Increasingly powerful technological developments, such as modern operating rooms, equipped with digital and interconnected equipment, new imaging systems, as well as robotic procedures, allow enormous potential for the improvement of therapy and surgical outcome.
ML and in particular DL have recently achieved considerable success in the medical field, but the field of surgery has not yet been explored in a similar way. In surgery we have multimodal procedural data coming from a dynamic environment that includes the patient, various monitoring devices and sensors and the operating room team, therefore there is a high variability of both patient data (e.g. anatomy, tumor location, comorbidities) and the surgical team (e.g. surgical procedure, surgical strategy, surgical experience). This poses several challenges regarding technical infrastructure for data acquisition, storage, access, annotation, sharing, analyses and clinical application aspects.
The research topic is aimed to verify if the application of machine learning in visceral surgery is able to improve:
-The clinical support in the correct interpretation of investigations and in formulating a precise diagnosis;
-The optimization of decision-making processes and risk analysis minimization of complications;
-A better orientation in complex anatomies with advanced visualization supports;
-The analysis and improvement of diagnostic and therapeutic processes of complications;
-The predictive analysis of response to treatment;
-The easy access to big data for scientific research purposes;
-The improvement of training programs;
-The better patient compliance with surgical procedures.
The scope of the research topic is to focus on the outcomes of clinical application of machine learning in the field of visceral surgery, particularly in hepato-pancreato-biliary and colorectal surgery.
Original and review papers could be accepted for publication, but are not limited to.
Keywords:
Artificial Intelligence, Machine Learning, Deep Learning, Neural Network, Visceral Surgery, Computer Vision, Natural Launguage Processing, Colorectal Surgery
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.