Artificial Intelligence and Machine Learning in Head and Neck Oncology

摘要

INTRODUCTION Machine learning (ML) is an academic discipline that allows the computer to perform complex tasks that may not be humanly possible. It has elements of mathematics, statistics, and computer science. Recently, it has been employed in different fields, both academia and industry, to solve complex problems and develop products. It has tremendous potential in the field of medical science.[1] This editorial aims to give an overview and introduce the discipline and its applications in head-and-neck oncology. ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, AND DEEP LEARNING ML and artificial intelligence (AI) are not the same. However, they are closely linked. ML is regarded as a subset of AI.[2] The utilization of a computer system to imitate human cognitive processes, such as learning and problem-solving, is known as AI. It simulates human decision-making and learns from new knowledge using logic and mathematics. The science of creating computers and robots with intelligence that mimics and exceeds that of humans is known as AI. Programs with AI capabilities can contextualize and analyze data to deliver information or automatically initiate operations without human intervention. ML learns from data and applies mathematical models to it. In ML, algorithms are designed and used to draw conclusions from previous instances. If a behavior has occurred in the past, you can anticipate whether it will do so in the future. Historical data are used to provide accurate outcomes. To forecast sensible outputs, ML algorithms use computer science and statistics. This branch of AI uses algorithms to discover patterns automatically and acquire insights from data. Algorithms are collections of mathematical operations that describe the relationship between the variables.[3] Deep learning (DL) is a subset of ML using “neural” networks in layers. These neural networks attempt to simulate the human brain. DL drives many AI applications and services that improve automation, doing analytical tasks without human intervention. DL differs from classical ML in the type of data it uses and the learning methods. ML algorithms use structured labeled data to make predictions. The input data for the model are defined and organized into tables. DL algorithms can take unstructured data such as text and images. It automates feature extraction without the need for human experts.[4] SUPERVISED AND UNSUPERVISED MACHINE LEARNING Supervised ML refers to techniques in which a model is trained on a range of input variables (or features) associated with a known outcome. In the head and neck, training a model to relate a patient's characteristics (e.g., smoking status) or clinical factors (T stage, N stage) to predict a disease recurrence outcome (recurrence occurred or not) come under this category. The trained algorithm can make outcome predictions when applied to new data. It is called a classification model when the predictions are discrete or categorical (e.g., positive or negative, malignant or not). When the predictions are continuous (e.g., a score ranging from 0 to 100), it is referred to as a regression model.[3] Unsupervised ML does not involve a predefined outcome. Algorithms identify patterns without any prior inputs. Unsupervised learning does not require labeled data sets; instead, it detects patterns in the data. Unsupervised methods are so exploratory to find undefined patterns. They use dimension reduction techniques like clustering to identify clusters, leading to outcomes automatically. For example, genomic and precision medicine studies investigate outcomes without predefined outcome data.[3] Figure 1 shows the relationship between AI, ML, and DL.Figure 1: Relationship between artificial intelligence, machine learning, and deep learningSTEPS OF MACHINE LEARNING Cleaning and organizing the data (to identify and correct problems such as outliers, missing data, and restructuring) Exploratory data analysis (statistical analysis, univariate/bivariate an