Fig 3 - Big Data and Machine Learning in Plastic Surgery: A New Frontier in Surgical Innovation

Graphic representation of an artificial neural network. Modeled after biological neural networks, artificial neural networks use input nodes, representing data input into the model; hidden nodes, responsible for making the predictions); and output nodes, representative of the predictions being made (Oncologists partner with Watson on genomics. Cancer Discov. 2015;5:788). During training, artificial neural networks, in a fashion similar to biological neurons, take part in a process called back-propagation, whereby the weight of the connections between nodes is adjusted based on the difference between the artificial neural networks output values and known target values. This process ensures that the output of the artificial neural network is as close as possible to the desired target values. (adapted from Meyfroidt G, Güiza F, Ramon J, Bruynooghe M. Machine learning techniques to examine large patient databases. Best Pract Res Clin Anaesthesiol. 2009;23:127-143.)

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Big Data and Machine Learning in Plastic Surgery: A New Frontier in Surgical Innovation - Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/Graphic-representation-of-an-artificial-neural-network-Modeled-after-biological-neural_fig2_301688300 [accessed 19 Apr 2025]

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Fig. 3. Graphic representation of an artificial neural network. Modeled after biological neural networks, artificial neural networks use input nodes, representing data input into the model; hidden nodes, responsible for making the predictions); and output nodes, representative of the predictions being made (Oncologists partner with Watson on genomics. Cancer Discov. 2015;5:788). During training, artificial neural networks, in a fashion similar to biological neurons, take part in a process called back-propagation, whereby the weight of the connections between nodes is adjusted based on the difference between the artificial neural networks output values and known target values. This process ensures that the output of the artificial neural network is as close as possible to the desired target values. (adapted from Meyfroidt G, Güiza F, Ramon J, Bruynooghe M. Machine learning techniques to examine large patient databases. Best Pract Res Clin Anaesthesiol. 2009;23:127-143.)

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<a href="https://www.researchgate.net/figure/Graphic-representation-of-an-artificial-neural-network-Modeled-after-biological-neural_fig2_301688300"><img src="https://www.researchgate.net/profile/Jonathan-Kanevsky/publication/301688300/figure/fig2/AS:677399805038596@1538516107440/Graphic-representation-of-an-artificial-neural-network-Modeled-after-biological-neural.ppm" alt="Graphic representation of an artificial neural network. Modeled after biological neural networks, artificial neural networks use input nodes, representing data input into the model; hidden nodes, responsible for making the predictions); and output nodes, representative of the predictions being made (Oncologists partner with Watson on genomics. Cancer Discov. 2015;5:788). During training, artificial neural networks, in a fashion similar to biological neurons, take part in a process called back-propagation, whereby the weight of the connections between nodes is adjusted based on the difference between the artificial neural networks output values and known target values. This process ensures that the output of the artificial neural network is as close as possible to the desired target values. (adapted from Meyfroidt G, Güiza F, Ramon J, Bruynooghe M. Machine learning techniques to examine large patient databases. Best Pract Res Clin Anaesthesiol. 2009;23:127-143.)"/></a>