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Review
. 2023;4(6):1286-1300.
doi: 10.37349/etat.2023.00197. Epub 2023 Dec 27.

Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer

Kriti Das  1 , Maanvi Paltani  1 , Pankaj Kumar Tripathi  2 , Rajnish Kumar  3 , Saniya Verma  3 , Subodh Kumar  3 , Chakresh Kumar Jain  2
Affiliations
Review

Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer

Kriti Das et al. Explor Target Antitumor Ther. 2023.

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.

Keywords: Artificial intelligence; colorectal cancer; deep learning; drug discovery; machine learning.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
How and where AE in cancer research is being used
Figure 2
Figure 2
Applications of AI in different disciplines, utilizing DL and ML
Figure 3
Figure 3
Categorization of ML algorithms with its subtypes and their applications
Figure 4
Figure 4
Applications of SVM in drug discovery
Figure 5
Figure 5
AI in drug screening, DD, drug repurposing, and chemical synthesis
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