Artificial Intelligence in the Diagnosis of Colorectal Cancer: A Literature Review
Background: The aim of this review is to explore the role of artificial intelligence in the diagnosis of colorectal cancer, how it impacts CRC morbidity and mortality, and why its role in clinical medicine is limited. Methods: A targeted, non-systematic review of the published literature relating to...
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/5/528 |
| _version_ | 1827319717349556224 |
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| author | Petar Uchikov Usman Khalid Krasimir Kraev Bozhidar Hristov Maria Kraeva Tihomir Tenchev Dzhevdet Chakarov Milena Sandeva Snezhanka Dragusheva Daniela Taneva Atanas Batashki |
| author_facet | Petar Uchikov Usman Khalid Krasimir Kraev Bozhidar Hristov Maria Kraeva Tihomir Tenchev Dzhevdet Chakarov Milena Sandeva Snezhanka Dragusheva Daniela Taneva Atanas Batashki |
| author_sort | Petar Uchikov |
| collection | DOAJ |
| description | Background: The aim of this review is to explore the role of artificial intelligence in the diagnosis of colorectal cancer, how it impacts CRC morbidity and mortality, and why its role in clinical medicine is limited. Methods: A targeted, non-systematic review of the published literature relating to colorectal cancer diagnosis was performed with PubMed databases that were scouted to help provide a more defined understanding of the recent advances regarding artificial intelligence and their impact on colorectal-related morbidity and mortality. Articles were included if deemed relevant and including information associated with the keywords. Results: The advancements in artificial intelligence have been significant in facilitating an earlier diagnosis of CRC. In this review, we focused on evaluating genomic biomarkers, the integration of instruments with artificial intelligence, MR and hyperspectral imaging, and the architecture of neural networks. We found that these neural networks seem practical and yield positive results in initial testing. Furthermore, we explored the use of deep-learning-based majority voting methods, such as bag of words and PAHLI, in improving diagnostic accuracy in colorectal cancer detection. Alongside this, the autonomous and expansive learning ability of artificial intelligence, coupled with its ability to extract increasingly complex features from images or videos without human reliance, highlight its impact in the diagnostic sector. Despite this, as most of the research involves a small sample of patients, a diversification of patient data is needed to enhance cohort stratification for a more sensitive and specific neural model. We also examined the successful application of artificial intelligence in predicting microsatellite instability, showcasing its potential in stratifying patients for targeted therapies. Conclusions: Since its commencement in colorectal cancer, artificial intelligence has revealed a multitude of functionalities and augmentations in the diagnostic sector of CRC. Given its early implementation, its clinical application remains a fair way away, but with steady research dedicated to improving neural architecture and expanding its applicational range, there is hope that these advanced neural software could directly impact the early diagnosis of CRC. The true promise of artificial intelligence, extending beyond the medical sector, lies in its potential to significantly influence the future landscape of CRC’s morbidity and mortality. |
| first_indexed | 2024-04-25T00:32:15Z |
| format | Article |
| id | doaj.art-7565128cb1374a2c81dc788b7d73dbfc |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-04-25T00:32:15Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-7565128cb1374a2c81dc788b7d73dbfc2024-03-12T16:42:03ZengMDPI AGDiagnostics2075-44182024-03-0114552810.3390/diagnostics14050528Artificial Intelligence in the Diagnosis of Colorectal Cancer: A Literature ReviewPetar Uchikov0Usman Khalid1Krasimir Kraev2Bozhidar Hristov3Maria Kraeva4Tihomir Tenchev5Dzhevdet Chakarov6Milena Sandeva7Snezhanka Dragusheva8Daniela Taneva9Atanas Batashki10Department of Special Surgery, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, BulgariaFaculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, BulgariaDepartment of Propaedeutics of Internal Diseases “Prof. Dr. Anton Mitov”, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, BulgariaSection “Gastroenterology”, Second Department of Internal Diseases, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, BulgariaDepartment of Otorhinolaryngology, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, BulgariaDepartment of Special Surgery, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, BulgariaDepartment of Propaedeutics of Surgical Diseases, Section of General Surgery, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, BulgariaDepartment of Midwifery, Faculty of Public Health, Medical University of Plovdiv, 4000 Plovdiv, BulgariaDepartment of Nursing Care, Faculty of Public Health, Medical University of Plovdiv, 4000 Plovdiv, BulgariaDepartment of Nursing Care, Faculty of Public Health, Medical University of Plovdiv, 4000 Plovdiv, BulgariaDepartment of Special Surgery, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, BulgariaBackground: The aim of this review is to explore the role of artificial intelligence in the diagnosis of colorectal cancer, how it impacts CRC morbidity and mortality, and why its role in clinical medicine is limited. Methods: A targeted, non-systematic review of the published literature relating to colorectal cancer diagnosis was performed with PubMed databases that were scouted to help provide a more defined understanding of the recent advances regarding artificial intelligence and their impact on colorectal-related morbidity and mortality. Articles were included if deemed relevant and including information associated with the keywords. Results: The advancements in artificial intelligence have been significant in facilitating an earlier diagnosis of CRC. In this review, we focused on evaluating genomic biomarkers, the integration of instruments with artificial intelligence, MR and hyperspectral imaging, and the architecture of neural networks. We found that these neural networks seem practical and yield positive results in initial testing. Furthermore, we explored the use of deep-learning-based majority voting methods, such as bag of words and PAHLI, in improving diagnostic accuracy in colorectal cancer detection. Alongside this, the autonomous and expansive learning ability of artificial intelligence, coupled with its ability to extract increasingly complex features from images or videos without human reliance, highlight its impact in the diagnostic sector. Despite this, as most of the research involves a small sample of patients, a diversification of patient data is needed to enhance cohort stratification for a more sensitive and specific neural model. We also examined the successful application of artificial intelligence in predicting microsatellite instability, showcasing its potential in stratifying patients for targeted therapies. Conclusions: Since its commencement in colorectal cancer, artificial intelligence has revealed a multitude of functionalities and augmentations in the diagnostic sector of CRC. Given its early implementation, its clinical application remains a fair way away, but with steady research dedicated to improving neural architecture and expanding its applicational range, there is hope that these advanced neural software could directly impact the early diagnosis of CRC. The true promise of artificial intelligence, extending beyond the medical sector, lies in its potential to significantly influence the future landscape of CRC’s morbidity and mortality.https://www.mdpi.com/2075-4418/14/5/528artificial intelligencecolorectal cancerdiagnosisautonomous learningadvanced neural software |
| spellingShingle | Petar Uchikov Usman Khalid Krasimir Kraev Bozhidar Hristov Maria Kraeva Tihomir Tenchev Dzhevdet Chakarov Milena Sandeva Snezhanka Dragusheva Daniela Taneva Atanas Batashki Artificial Intelligence in the Diagnosis of Colorectal Cancer: A Literature Review Diagnostics artificial intelligence colorectal cancer diagnosis autonomous learning advanced neural software |
| title | Artificial Intelligence in the Diagnosis of Colorectal Cancer: A Literature Review |
| title_full | Artificial Intelligence in the Diagnosis of Colorectal Cancer: A Literature Review |
| title_fullStr | Artificial Intelligence in the Diagnosis of Colorectal Cancer: A Literature Review |
| title_full_unstemmed | Artificial Intelligence in the Diagnosis of Colorectal Cancer: A Literature Review |
| title_short | Artificial Intelligence in the Diagnosis of Colorectal Cancer: A Literature Review |
| title_sort | artificial intelligence in the diagnosis of colorectal cancer a literature review |
| topic | artificial intelligence colorectal cancer diagnosis autonomous learning advanced neural software |
| url | https://www.mdpi.com/2075-4418/14/5/528 |
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