Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect

Abstract

In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.

Keywords: artificial intelligence; bioinformatics analysis; colorectal cancer; deep learning; diagnosis; machine learning; screening; therapy.

Figure 1

(A) Estimated number of new cases in 2020, World, both sexes, all ages. (B) Estimated number of new cases in 2020, China, both sexes, all ages. (Data source: GLOBOCAN 2020).

Figure 2

(A) The concept and relationship of Artificial intelligence (AI), machine learning (ML), and deep learning (DL). (B) Common types of machine learning (ML): supervised learning (SL); deep learning (DL); semi-supervised learning (SSL); support vector machine (SVM); random forest (RF); and convolutional neural network (CNN).

Figure 3

Common types of CRC diagnostic images: (A) Endoscopy; (B) CT; (C) MRI; (D) pathology image (HE×100). The arrow indicates the location of the lesions. (Image source: The First Affiliated Hospital of Dalian Medical University.)

Figure 4

The basic workflow of CNN (15).

Figure 5

The fourth generation da Vinci. (Image source: The First Affiliated Hospital of Dalian Medical University.)