Computer image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals

Abstract

Artificial intelligence tools, particularly convolutional neural networks (CNNs), are transforming healthcare by enhancing predictive, diagnostic, and decision-making capabilities. This review provides an accessible and practical explanation of CNNs for clinicians and highlights their relevance in medical image analysis. CNNs have shown themselves to be exceptionally useful in computer vision, a field that enables machines to 'see' and interpret visual data. Understanding how these models work can help clinicians leverage their full potential, especially as artificial intelligence continues to evolve and integrate into healthcare. CNNs have already demonstrated their efficacy in diverse medical fields, including radiology, histopathology, and medical photography. In radiology, CNNs have been used to automate the assessment of conditions such as pneumonia, pulmonary embolism, and rectal cancer. In histopathology, CNNs have been used to assess and classify colorectal polyps, gastric epithelial tumours, as well as assist in the assessment of multiple malignancies. In medical photography, CNNs have been used to assess retinal diseases and skin conditions, and to detect gastric and colorectal polyps during endoscopic procedures. In surgical laparoscopy, they may provide intraoperative assistance to surgeons, helping interpret surgical anatomy and demonstrate safe dissection zones. The integration of CNNs into medical image analysis promises to enhance diagnostic accuracy, streamline workflow efficiency, and expand access to expert-level image analysis, contributing to the ultimate goal of delivering further improvements in patient and healthcare outcomes.

Keywords: biotechnology & bioinformatics; education and training; radiology & imaging.

Figure 1

AI and its subsets leading to CNNs, with examples

Figure 2

Differences between supervised and unsupervised learning with ML

Figure 3

Illustration showing relationship between images, pixels, and computer image data stored in RGB format

Figure 4

Visual representation and real example of convolutional image transformation into activation map using a 3 × 3 filter/kernel; images from wikicommons used with permission [50]

Figure 5

A summary overview of the steps in CNNs