TY - JOUR
T1 - Artificial Intelligence in Colorectal Cancer Surgery: Present and Future Perspectives
AU - Quero, Giuseppe
AU - Mascagni, Pietro
AU - Kolbinger, Fiona R.
AU - Fiorillo, Claudio
AU - De Sio, Davide
AU - Longo, Fabio
AU - Schena, Carlo Alberto
AU - Laterza, Vito
AU - Rosa, Fausto
AU - Menghi, Roberta
AU - Papa, Valerio
AU - Tondolo, Vincenzo
AU - Cina, Caterina
AU - Distler, Marius
AU - Weitz, Juergen
AU - Speidel, Stefanie
AU - Padoy, Nicolas
AU - Alfieri, Sergio
PY - 2022
Y1 - 2022
N2 - Simple Summary Computer vision (CV) is a field of artificial intelligence (AI) that deals with the automatic analysis of videos and images. Recent advances in AI and CV methods coupled with the growing availability of surgical videos of minimally invasive procedures have led to the development of AI-based algorithms to improve surgical care. Initial proofs of concept have focused on fairly standardized procedures such as laparoscopic cholecystectomy. However, the real value of CV in surgery resides in analyzing and providing assistance in more complex and variable procedures such as colorectal resections. This manuscript provides a brief introduction to AI for surgeons and a comprehensive overview of CV solutions for colorectal cancer surgery. Artificial intelligence (AI) and computer vision (CV) are beginning to impact medicine. While evidence on the clinical value of AI-based solutions for the screening and staging of colorectal cancer (CRC) is mounting, CV and AI applications to enhance the surgical treatment of CRC are still in their early stage. This manuscript introduces key AI concepts to a surgical audience, illustrates fundamental steps to develop CV for surgical applications, and provides a comprehensive overview on the state-of-the-art of AI applications for the treatment of CRC. Notably, studies show that AI can be trained to automatically recognize surgical phases and actions with high accuracy even in complex colorectal procedures such as transanal total mesorectal excision (TaTME). In addition, AI models were trained to interpret fluorescent signals and recognize correct dissection planes during total mesorectal excision (TME), suggesting CV as a potentially valuable tool for intraoperative decision-making and guidance. Finally, AI could have a role in surgical training, providing automatic surgical skills assessment in the operating room. While promising, these proofs of concept require further development, validation in multi-institutional data, and clinical studies to confirm AI as a valuable tool to enhance CRC treatment.
AB - Simple Summary Computer vision (CV) is a field of artificial intelligence (AI) that deals with the automatic analysis of videos and images. Recent advances in AI and CV methods coupled with the growing availability of surgical videos of minimally invasive procedures have led to the development of AI-based algorithms to improve surgical care. Initial proofs of concept have focused on fairly standardized procedures such as laparoscopic cholecystectomy. However, the real value of CV in surgery resides in analyzing and providing assistance in more complex and variable procedures such as colorectal resections. This manuscript provides a brief introduction to AI for surgeons and a comprehensive overview of CV solutions for colorectal cancer surgery. Artificial intelligence (AI) and computer vision (CV) are beginning to impact medicine. While evidence on the clinical value of AI-based solutions for the screening and staging of colorectal cancer (CRC) is mounting, CV and AI applications to enhance the surgical treatment of CRC are still in their early stage. This manuscript introduces key AI concepts to a surgical audience, illustrates fundamental steps to develop CV for surgical applications, and provides a comprehensive overview on the state-of-the-art of AI applications for the treatment of CRC. Notably, studies show that AI can be trained to automatically recognize surgical phases and actions with high accuracy even in complex colorectal procedures such as transanal total mesorectal excision (TaTME). In addition, AI models were trained to interpret fluorescent signals and recognize correct dissection planes during total mesorectal excision (TME), suggesting CV as a potentially valuable tool for intraoperative decision-making and guidance. Finally, AI could have a role in surgical training, providing automatic surgical skills assessment in the operating room. While promising, these proofs of concept require further development, validation in multi-institutional data, and clinical studies to confirm AI as a valuable tool to enhance CRC treatment.
KW - artificial intelligence
KW - colorectal cancer
KW - colorectal surgery
KW - artificial intelligence
KW - colorectal cancer
KW - colorectal surgery
UR - http://hdl.handle.net/10807/230891
U2 - 10.3390/cancers14153803
DO - 10.3390/cancers14153803
M3 - Article
SN - 2072-6694
VL - 14
SP - N/A-N/A
JO - Cancers
JF - Cancers
ER -