Artificial Intelligence in GI endoscopy: what to expect

摘要

From the candle-lit specula used by Hippocrates to visualize the rectum, to Bozzini's illuminated tubes; from the development of semi-flexible endoscopes during the interwar period to the creation of the first fully flexible endoscope by Basil Hirschowitz in the late 1950s, evolution in endoscopy has always been present. In fact, its primary objective remains the same: to achieve a clinical diagnosis with minimal invasiveness, later advancing towards the interventional endoscopy, therefore accomplishing a level of healing previously reserved to surgeons. (1,2) Nowadays, modern 4K video endoscopes provide exceptional image quality, along with the refinement of scoping materials and tools enable the application of advanced diagnostic and therapeutic techniques, directly impacting on patients care and quality of life.Merging these already existing technologies with artificial intelligence (AI) represents yet another step in its rapid evolution.Artificial intelligence lays in the field of computer science. Its main purpose consists of creating systems with the capacity of accomplishing tasks that would usually require human intelligence, such as voice recognition, problem-solving or decision-making (3).In medicine, AI made its entry through research, however, it is becoming increasingly present in the day-to-day clinical practice. As for the type of AI mostly used in our area of expertise, the Machine Learning (ML) subfield would be where to draw the attention. Based on the production of different algorithms, it allows the computer to learn from a dataset and enhance its own performance without explicitly programming it to do so. Two primary methodologies are encompassed: supervised learning and unsupervised learning. The former is trained on a labeled datasets, learning progressively from examples provided beforehand. Monitoring this learning and delivering the labeled information is where human scientists and physicians are included. The latter, on the other hand, searches and identifies patterns from raw data, autonomously. (4,5,6) The algorithms used for Deep Learning are inspired in the human brain functioning. Composed by "nodes" (similar to neurons) that intertwine. These are called Artificial Neuronal Networks (ANN). Regarding image detection and identification, a more efficient type of ANN was developed, known as Convolutional Neural Networks (CNN). The architecture of these networks is based on different layers of nodes and connections, some of them controlled by the developer, and some others considered as "hidden", in which connections and nodes distribution are unknown. Hidden layers look for prompts, smaller pieces of data or patterns existing in the information given, allowing it to reason and apply that reasoning for future scenarios. (4,5,6) The following diagram (Figure 1) aims to simplify the previous information:Convolutional Neural Networks (CNN) can be trained to identify and classify colorectal lesions from an image repository collected from previous colonoscopies. An example on how supervised learning is being applied in colonoscopy could be the identification of polyps in real time during the procedure. For that purpose, the AI has had to be trained beforehand, given multiple images (dataset) in which polyps, or other lesions, could and could not be seen, being told which of the images showcased a polyp and which ones did not (labeling) in a second phase. This information is the one provided by endoscopists or professionals involved and working on AI training.The detection and potential treatment of premalignant lesions in the digestive track derives in the decrease of cancer incidence. The detection of malignant lesions in early stages improves long-term prognosis or complete remission. (7,8) Given that colorectal cancer is known to be the second most frequent cause of death according to the Worlds Health Organization (WHO), and the already existing screening programs, optimizing i