{"type":"ScholarlyArticle","title":"AI-Assisted Tool for Early Diagnosis and Prevention of Colorectal Cancer in Africa","authors":[{"name":"Bushra Ibnauf","slug":"bushra-ibnauf","given":"Bushra","family":"Ibnauf","affiliation":"Soba University Hospital , Khartoum , Sudan"},{"name":"Mohammed Aboul Ezz","slug":"mohammed-aboul-ezz","given":"Mohammed","family":"Aboul Ezz","affiliation":"Billharz Resarch Institute , Giza , Egypt"},{"name":"Ayman Abdel Aziz","slug":"ayman-abdel-aziz","given":"Ayman","family":"Abdel Aziz","affiliation":"Billharz Resarch Institute , Giza , Egypt"},{"name":"Khalid Elgazzar","slug":"khalid-elgazzar","given":"Khalid","family":"Elgazzar","affiliation":"Ontario Tech University , Oshawa , Canada"},{"name":"Mennatullah Siam","slug":"mennatullah-siam","given":"Mennatullah","family":"Siam","affiliation":"Ontario Tech University , Oshawa , Canada"}],"abstract":"Colorectal cancer (CRC) is considered the third most common cancer worldwide and is recently increasing in Africa. It is mostly diagnosed at an advanced state causing high fatality rates, which highlights the importance of CRC early diagnosis. There are various methods used to enable early diagnosis of CRC, which are vital to increase survival rates such as colonoscopy. Recently, there are calls to start an early detection program in Egypt using colonoscopy. It can be used for diagnosis and prevention purposes to detect and remove polyps, which are benign growths that have the risk of turning into cancer. However, there tends to be a high miss rate of polyps from physicians, which motivates machine learning guided polyp segmentation methods in colonoscopy videos to aid physicians. To date, there are no large-scale video polyp segmentation dataset that is focused on African countries. It was shown in AI-assisted systems that under-served populations such as patients with African origin can be misdiagnosed. There is also a potential need in other African countries beyond Egypt to provide a cost efficient tool to record colonoscopy videos using smart phones without relying on video recording equipment. Since most of the equipment used in Africa are old and refurbished, and video recording equipment can get defective. Hence, why we propose to curate a colonoscopy video dataset focused on African patients, provide expert annotations for video polyp segmentation and provide an AI-assisted tool to record colonoscopy videos using smart phones. Our project is based on our core belief in developing research by Africans and increasing the computer vision research capacity in Africa.","doi":"10.24963/ijcai.2023/706","url":"https://ijcai.org/proceedings/2023/706","pdf":"https://ijcai.org/proceedings/2023/0706.pdf","markdown":"https://ijcai.org/proceedings/2023/706.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/706","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"AI for Good - Projects","track":"AI for Good - Projects","number":706,"pages":{"first":6362,"last":6369},"keywords":["AI for Good - Projects: Computer Vision"],"videos":[],"references":[{"position":1,"raw":"[Awedew et al., 2022] Atalel Fentahun Awedew, Zelalem Asefa, and Woldemariam Beka Belay. 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