Current implicationsãnd challenges ofãrtificial intelligence technologies in therapeutic intervention of colorectal cancer

Abstract

Irrespective of menãnd women, colorectal cancer (CRC), is the third most common cancer in the population with more than 1.85 million casesãnnually. Fewer than 20% of patients only survive beyond five years from diagnosis. CRC isã highly preventable disease if diagnosedãt 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,ãnd stool DNA testãreãvailable with different levels of sensitivityãnd specificity. Theãvailable screening methodsãreãssociated with certain drawbacks like invasiveness, cost, or sensitivity. In recent years, computer-aided systems-based screening, diagnosis,ãnd treatment have been very promising in the early-stage detectionãnd diagnosis of CRC cases. Artificial intelligence (AI) isãn enormously in-demand, cost-effective technology, that uses various tools machine learning (ML),ãnd deep learning (DL) to screen, diagnose,ãnd stage,ãnd has great potential to treat CRC. Moreover, different MLãlgorithmsãnd neural networks [artificial neural network (ANN), k-nearest neighbors (KNN),ãnd support vector machines (SVMs)] have been deployed to predict preciseãnd personalized treatment options. This review examinesãnd summarizes different MLãnd DL models used for therapeutic intervention in CRC cancerãlong with the gapãnd challenges for AI.