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Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer.
| Content Provider | Europe PMC |
|---|---|
| Author | Das, Kriti Paltani, Maanvi Tripathi, Pankaj Kumar Kumar, Rajnish Verma, Saniya Kumar, Subodh Jain, Chakresh Kumar |
| Editor | Nardone, Valerio |
| Abstract | Irrespective of men and women, colorectal cancer (CRC), is the third most common cancer in the population with more than 1.85 million cases annually. Fewer than 20% of patients only survive beyond five years from diagnosis. CRC is a highly preventable disease if diagnosed at the early stage of malignancy. Several screening methods like endoscopy (like colonoscopy; gold standard), imaging examination [computed tomographic colonography (CTC)], guaiac-based fecal occult blood (gFOBT), immunochemical test from faeces, and stool DNA test are available with different levels of sensitivity and specificity. The available screening methods are associated with certain drawbacks like invasiveness, cost, or sensitivity. In recent years, computer-aided systems-based screening, diagnosis, and treatment have been very promising in the early-stage detection and diagnosis of CRC cases. Artificial intelligence (AI) is an enormously in-demand, cost-effective technology, that uses various tools machine learning (ML), and deep learning (DL) to screen, diagnose, and stage, and has great potential to treat CRC. Moreover, different ML algorithms and neural networks [artificial neural network (ANN), k-nearest neighbors (KNN), and support vector machines (SVMs)] have been deployed to predict precise and personalized treatment options. This review examines and summarizes different ML and DL models used for therapeutic intervention in CRC cancer along with the gap and challenges for AI. |
| Related Links | https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC10776591&blobtype=pdf |
| Volume Number | 4 |
| DOI | 10.37349/etat.2023.00197 |
| PubMed Central reference number | PMC10776591 |
| Issue Number | 6 |
| PubMed reference number | 38213536 |
| Journal | Exploration of Targeted Anti-tumor Therapy [Explor Target Antitumor Ther] |
| e-ISSN | 26923114 |
| Language | English |
| Publisher | Open Exploration Publishing |
| Publisher Date | 2023-12-27 |
| Access Restriction | Open |
| Rights License | This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. © The Author(s) 2023. |
| Subject Keyword | Artificial intelligence machine learning deep learning colorectal cancer drug discovery |
| Content Type | Text |
| Resource Type | Article |
| Subject | Oncology Cancer Research |
