TY - JOUR
T1 - Artificial neural networks can be effectively used to model changes of intracranial pressure (ICP) during spinal surgery using different noninvasive ICP surrogate estimators
AU - Watad, Abdulla
AU - Bragazzi, Nicola L.
AU - Bacigaluppi, Susanna
AU - Amital, Howard
AU - Watad, Samaa
AU - Sharif, Kassem
AU - Bisharat, Bishara
AU - Siri, Anna
AU - Mahamid, Ala
AU - Ras, Hakim ABU
AU - Nasr, Ahmed
AU - Bilotta, Federico
AU - Robba, Chiara
AU - Adawi, Mohammad
N1 - Publisher Copyright:
C 2018 EDIZIONI MINERVA MEDICA.
PY - 2023/6
Y1 - 2023/6
N2 - BACKGROUND: Artificial intelligence (AI) techniques play a major role in anesthesiology, even though their importance is often overlooked. In the extant literature, AI approaches, such as artificial neural networks (ANNs), have been underutilized, being used mainly to model patient’s consciousness state, to predict the precise number of anesthetic gases, the level of analgesia, or the need of anesthesiological blocks, among others. In the field of neurosurgery, ANNs have been effectively applied to the diagnosis and prognosis of cerebral tumors, seizures, low back pain, and also to the monitoring of intracranial pressure (ICP). METHODS: A multilayer perceptron (MLP), which is a feedforward ANN, with hyperbolic tangent as activation function in the input/hidden layers, softmax as activation function in the output layer, and cross-entropy as error function, was used to model the impact of prone versus supine position and the use of positive end expiratory pressure (PEEP) on ICP in a sample of 30 patients undergoing spinal surgery. Different noninvasive surrogate estimations of ICP have been used and compared: namely, mean optic nerve sheath diameter (ONSD), noninvasive estimated cerebral perfusion pressure (NCPP), Pulsatility Index (PI), ICP derived from PI (ICP-PI), and flow velocity diastolic formula (FVDICP). RESULTS: ONSD proved to be a more robust surrogate estimation of ICP, with a predictive power of 75%, whilst the power of NCPP, ICP-PI, PI, and FVDICP were 60.5%, 54.8%, 53.1%, and 47.7%, respectively. CONCLUSIONS: Our MLP analysis confirmed our findings previously obtained with regression, correlation, multivariate receiving operator curve (multi-ROC) analyses. ANNs can be successfully used to predict the effects of prone versus supine position and PEEP on ICP in patients undergoing spinal surgery using different noninvasive surrogate estimators of ICP.
AB - BACKGROUND: Artificial intelligence (AI) techniques play a major role in anesthesiology, even though their importance is often overlooked. In the extant literature, AI approaches, such as artificial neural networks (ANNs), have been underutilized, being used mainly to model patient’s consciousness state, to predict the precise number of anesthetic gases, the level of analgesia, or the need of anesthesiological blocks, among others. In the field of neurosurgery, ANNs have been effectively applied to the diagnosis and prognosis of cerebral tumors, seizures, low back pain, and also to the monitoring of intracranial pressure (ICP). METHODS: A multilayer perceptron (MLP), which is a feedforward ANN, with hyperbolic tangent as activation function in the input/hidden layers, softmax as activation function in the output layer, and cross-entropy as error function, was used to model the impact of prone versus supine position and the use of positive end expiratory pressure (PEEP) on ICP in a sample of 30 patients undergoing spinal surgery. Different noninvasive surrogate estimations of ICP have been used and compared: namely, mean optic nerve sheath diameter (ONSD), noninvasive estimated cerebral perfusion pressure (NCPP), Pulsatility Index (PI), ICP derived from PI (ICP-PI), and flow velocity diastolic formula (FVDICP). RESULTS: ONSD proved to be a more robust surrogate estimation of ICP, with a predictive power of 75%, whilst the power of NCPP, ICP-PI, PI, and FVDICP were 60.5%, 54.8%, 53.1%, and 47.7%, respectively. CONCLUSIONS: Our MLP analysis confirmed our findings previously obtained with regression, correlation, multivariate receiving operator curve (multi-ROC) analyses. ANNs can be successfully used to predict the effects of prone versus supine position and PEEP on ICP in patients undergoing spinal surgery using different noninvasive surrogate estimators of ICP.
KW - Artificial intelligence
KW - Intracranial pressure
KW - Neurosurgery
UR - http://www.scopus.com/inward/record.url?scp=85160875991&partnerID=8YFLogxK
U2 - 10.23736/s0390-5616.18.04299-6
DO - 10.23736/s0390-5616.18.04299-6
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C2 - 29480684
AN - SCOPUS:85160875991
SN - 0390-5616
VL - 67
SP - 288
EP - 296
JO - Journal of Neurosurgical Sciences
JF - Journal of Neurosurgical Sciences
IS - 3
ER -