O127 Application of machine learning approaches in the diagnosis and management of colorectal cancer

Abstract Introduction Colorectal cancer (CRC) is one of the most common cancers worldwide. In recent years, Artificial Intelligence (AI) and Machine Learning (ML) algorithms have gained traction in CRC research for their ability to efficiently analyse large volumes variables offer objective data driven models optimise clinical decision making. The present review provides a contemporary summary current application these approaches CRC. Methods A literature search was performed on AI ML learning relevant articles were scrutinized. Results aided detection systems successful identifying CRC, able outperform endoscopists detecting adenomas (detection rate 0.29 vs. 0.20, p < 0.001). based radiomics showed promise metastasis even outperforming national guideline-based (AUC 0.83 0.73, p<0.01) predicting risk T1 lymph nodes. also identify novel genomic targets, predict response oncological therapy, 5-year disease free survival stage II III 0.698). operative context, can be trained surgical steps actions with accuracy scores over 80% as well as. Conclusion modelling takes advantage increasing digital medical keeping allow rapid high-volume analysis which has found diagnosis, staging, treatment, prognosis may useful development intra-operative guidance optimize training.