Artificial intelligence is a branch of computer science involving many disciplines and technologies. Since its application in the medical field it has been constantly studied and developed. AI incorporates machine learning and neural networks to improve existing technology or create new ones. Various AI supporting systems are available for a personalized and novel strategy for the management of colorectal cancer.
Currently, surgical oncologists benefit from a number of advances in AI technologies in the clinical setting. Support vector machines (SVM) learning methodologies have been used as an AI method to differentiate colon cancer and rectal cancer, as well as classify predictions for metastasis. Computer aided diagnosis (CAD) technologies can diagnose and classify cancer using data obtained from radiologists and has been shown efficacious in cases of even very small tumors.
This Research Topic aims to summarize progress in research and clinical application of Artificial Intelligence for surgical oncologists in the investigation, early diagnosis, and prognosis of colorectal cancer (CRC), and subsequently provide clinical data as a starting point for clinical applications in the early diagnosis and planning of surgical interventions. Manuscript submissions addressing but not limited to the following themes will be considered for publication;
1) Incorporation of AI in Epidemiology (for example FeoAI) and Diagnosis (Deep learning to distinguish neoplastic polyps from non-neoplastic polyps; CAD System for screening of lesions; virtual Endoscopy)
2) Use of AI to influence choosing surgical treatments (Robotic surgery; AI Tools for provide reliable information on the metabolism of the drugs in the neoadjuvant treatment of CRC; Artificial neural networks algorithm clinical decision making; SVM system in genetic testing)
3) Data extraction to aid in planning personalised surgical interventions and therapeutic/neoadjuvant treatment
Important Note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in any of the sections of Frontiers in Oncology.
Artificial intelligence is a branch of computer science involving many disciplines and technologies. Since its application in the medical field it has been constantly studied and developed. AI incorporates machine learning and neural networks to improve existing technology or create new ones. Various AI supporting systems are available for a personalized and novel strategy for the management of colorectal cancer.
Currently, surgical oncologists benefit from a number of advances in AI technologies in the clinical setting. Support vector machines (SVM) learning methodologies have been used as an AI method to differentiate colon cancer and rectal cancer, as well as classify predictions for metastasis. Computer aided diagnosis (CAD) technologies can diagnose and classify cancer using data obtained from radiologists and has been shown efficacious in cases of even very small tumors.
This Research Topic aims to summarize progress in research and clinical application of Artificial Intelligence for surgical oncologists in the investigation, early diagnosis, and prognosis of colorectal cancer (CRC), and subsequently provide clinical data as a starting point for clinical applications in the early diagnosis and planning of surgical interventions. Manuscript submissions addressing but not limited to the following themes will be considered for publication;
1) Incorporation of AI in Epidemiology (for example FeoAI) and Diagnosis (Deep learning to distinguish neoplastic polyps from non-neoplastic polyps; CAD System for screening of lesions; virtual Endoscopy)
2) Use of AI to influence choosing surgical treatments (Robotic surgery; AI Tools for provide reliable information on the metabolism of the drugs in the neoadjuvant treatment of CRC; Artificial neural networks algorithm clinical decision making; SVM system in genetic testing)
3) Data extraction to aid in planning personalised surgical interventions and therapeutic/neoadjuvant treatment
Important Note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in any of the sections of Frontiers in Oncology.