*Artificial Intelligence in Musculoskeletal Conditions DOI: http://dx.doi.org/10.5772/intechopen.110696*

changes in the environment. AI is capable of surpassing the speed of human analysis in many cases. Within this broad framework, there are different systems with their own characteristics.

Machine learning (ML) is a branch of artificial intelligence that refers to systems and algorithms capable of learning and improving through data analysis. Unsupervised ML does not require a labeled database to perform its training. Instead, it can identify nonapparent or hidden relationships in different data patterns. Supervised ML requires a labeled database in order to perform training. This database contains information in which the input and output are linked. This is what the computer uses to perform the correct matching.

Deep Learning (DL) is a type of ML capable of learning complex tasks through the analysis of large amounts of information with which it is trained [6]. An artificial neural network composed of nodes arranged in a hierarchy of levels is used in the DL. The network is able to process basic information at the initial level and forward it to the next level. There it is integrated with data from other nodes and passed to the next level. This process is done iteratively until the system learns the task, such as identifying a particular pattern. For example, DL techniques can be applied to radiologic studies to develop computer algorithms capable of analyzing images, classifying, and segmenting them [7].

Convolutional neural networks (CNN) are a subtype of DL especially used in image processing. They use learnable layers and filters through which data are passed and processed in a complex way, until they are completely transformed to the final layer or output layer. CNNs take advantage of the position of pixels in the images to reduce the processing complexity and parameter requirements per layer.

One of the great advantages of DL and CNN is their ability to be trained end-toend. This means that the training model only needs input data, for example, knee magnetic resonance imaging (MRI) and a set of gold standard labels, medial meniscal lesion, and no medial meniscal lesion. The algorithm is capable of self-learning, considering by itself which elements are most relevant to perform a process. Since training a CNN is an iterative process, a larger volume of information usually yields better performance of the algorithm. In addition, although the computational power required to train DL algorithms is high, subsequent analysis of new data is faster and easier than in other AI systems.
