TY - CHAP M1 - Book, Section TI - Neural Networks and Deep Learning A1 - Alapatt*, Deepak A1 - Mascagni*, Pietro A1 - Srivastav, Vinkle A1 - Padoy, Nicolas A2 - Hashimoto, MD, MS, Daniel A. A2 - Meireles, MD, Ozanan R. A2 - Rosman, PhD, Guy PY - 2021 T2 - Artificial Intelligence in Surgery: Understanding the Role of AI in Surgical Practice AB - HIGHLIGHTSThe growing availability of digital data and computational power enables the training of deep neural networks useful for real-world applications.Deep neural networks are typically composed of multiple convolutional layers used to efficiently extract information from high-dimensional inputs, pooling layers to reduce dimensions, and fully connected layers to aggregate neuron activations into output values.Neural networks learn to approximate a function by forward propagating the input layer by layer, calculating a loss comparing the current output to the ground truth, and then updating the network’s weights and biases through backpropagation.Deep architectures are selected based on the nature of the input and desired output data to perform tasks such as classification, detection, semantic segmentation, and temporal recognition.Deep-learning strategies to guarantee the security of sensitive medical data, train networks using less supervision, increase model explainability, and deliver real-time predictions in the operating room are being developed to generate value in surgery. SN - PB - McGraw-Hill Education CY - New York, NY Y2 - 2024/03/29 UR - accesssurgery.mhmedical.com/content.aspx?aid=1180349956 ER -