Artificial Neural Network for Predicting the Safe Temporary Artery Occlusion Time in Intracranial Aneurysmal Surgery

Overview

Journal J Clin Med

Specialty General Medicine

Date 2021 Apr 30

PMID 33918168

Citations 2

Authors

Shima Shahjouei

Seyed Mohammad Ghodsi

Morteza Zangeneh Soroush

Saeed Ansari

Shahab Kamali-Ardakani

Affiliations

Soon will be listed here.

Abstract

Background: Temporary artery clipping facilitates safe cerebral aneurysm management, besides a risk for cerebral ischemia. We developed an artificial neural network (ANN) to predict the safe clipping time of temporary artery occlusion (TAO) during intracranial aneurysm surgery.

Method: We devised a three-layer model to predict the safe clipping time for TAO. We considered age, the diameter of the right and left middle cerebral arteries (MCAs), the diameter of the right and left A1 segment of anterior cerebral arteries (ACAs), the diameter of the anterior communicating artery, mean velocity of flow at the right and left MCAs, and the mean velocity of flow at the right and left ACAs, as well as the Fisher grading scale of brain CT scans as the input values for the model.

Results: This study included 125 patients: 105 patients from a retrospective cohort for training the model and 20 patients from a prospective cohort for validating the model. The output of the neural network yielded up to 960 s overall safe clipping time for TAO. The input values with the greatest impact on safe TAO were mean velocity of blood at left MCA and left ACA, and Fisher grading scale of brain CT scan.

Conclusion: This study presents an axillary framework to improve the accuracy of the estimated safe clipping time interval of temporary artery occlusion in intracranial aneurysm surgery.

Citing Articles

A Machine Learning-Based Approach to Predict Prognosis and Length of Hospital Stay in Adults and Children With Traumatic Brain Injury: Retrospective Cohort Study.

Fang C, Pan Y, Zhao L, Niu Z, Guo Q, Zhao B J Med Internet Res. 2022; 24(12):e41819.

PMID: 36485032 PMC: 9789495. DOI: 10.2196/41819.

Clinical Applications of Artificial Intelligence-An Updated Overview.

Busnatu S, Niculescu A, Bolocan A, Petrescu G, Paduraru D, Nastasa I J Clin Med. 2022; 11(8).

PMID: 35456357 PMC: 9031863. DOI: 10.3390/jcm11082265.

References

Wang J, Peng X, Lassance-Soares R, Najafi A, Alderman L, Sood S . Aging-induced collateral dysfunction: impaired responsiveness of collaterals and susceptibility to apoptosis via dysfunctional eNOS signaling. J Cardiovasc Transl Res. 2011; 4(6):779-89. PMC: 3756560. DOI: 10.1007/s12265-011-9280-4. View

Azimi P, Mohammadi H, Benzel E, Shahzadi S, Azhari S . Use of artificial neural networks to predict recurrent lumbar disk herniation. J Spinal Disord Tech. 2014; 28(3):E161-5. DOI: 10.1097/BSD.0000000000000200. View

Hassan T, Hassan A, Ahmed Y . Influence of parent vessel dominancy on fluid dynamics of anterior communicating artery aneurysms. Acta Neurochir (Wien). 2010; 153(2):305-10. DOI: 10.1007/s00701-010-0824-1. View

Dai X, Huang L, Qian Y, Xia S, Chong W, Liu J . Deep learning for automated cerebral aneurysm detection on computed tomography images. Int J Comput Assist Radiol Surg. 2020; 15(4):715-723. DOI: 10.1007/s11548-020-02121-2. View

Rughani A, Dumont T, Lu Z, Bongard J, Horgan M, Penar P . Use of an artificial neural network to predict head injury outcome. J Neurosurg. 2009; 113(3):585-90. DOI: 10.3171/2009.11.JNS09857. View

Taylor C, Selman W . Temporary vascular occlusion during cerebral aneurysm surgery. Neurosurg Clin N Am. 1998; 9(4):673-9. View

Tanioka S, Ishida F, Yamamoto A, Shimizu S, Sakaida H, Toyoda M . Machine Learning Classification of Cerebral Aneurysm Rupture Status with Morphologic Variables and Hemodynamic Parameters. Radiol Artif Intell. 2021; 2(1):e190077. PMC: 8017392. DOI: 10.1148/ryai.2019190077. View

Vernieri F, Pasqualetti P, Matteis M, Passarelli F, Troisi E, Rossini P . Effect of collateral blood flow and cerebral vasomotor reactivity on the outcome of carotid artery occlusion. Stroke. 2001; 32(7):1552-8. DOI: 10.1161/01.str.32.7.1552. View

Staarmann B, ONeal K, Magner M, Zuccarello M . Sensitivity and Specificity of Intraoperative Neuromonitoring for Identifying Safety and Duration of Temporary Aneurysm Clipping Based on Vascular Territory, a Multimodal Strategy. World Neurosurg. 2017; 100:522-530. DOI: 10.1016/j.wneu.2017.01.009. View

10.

Jou L, Lee D, Mawad M . Cross-flow at the anterior communicating artery and its implication in cerebral aneurysm formation. J Biomech. 2010; 43(11):2189-95. DOI: 10.1016/j.jbiomech.2010.03.039. View

11.

Kameda M, Hishikawa T, Hiramatsu M, Yasuhara T, Kurozumi K, Date I . Precise MEP monitoring with a reduced interval is safe and useful for detecting permissive duration for temporary clipping. Sci Rep. 2020; 10(1):3507. PMC: 7044220. DOI: 10.1038/s41598-020-60377-9. View

12.

Azimi P, Mohammadi H . Predicting endoscopic third ventriculostomy success in childhood hydrocephalus: an artificial neural network analysis. J Neurosurg Pediatr. 2014; 13(4):426-32. DOI: 10.3171/2013.12.PEDS13423. View

13.

Nixon A, Gunel M, Sumpio B . The critical role of hemodynamics in the development of cerebral vascular disease. J Neurosurg. 2009; 112(6):1240-53. DOI: 10.3171/2009.10.JNS09759. View

14.

Valencia A, Morales H, Rivera R, Bravo E, Galvez M . Blood flow dynamics in patient-specific cerebral aneurysm models: the relationship between wall shear stress and aneurysm area index. Med Eng Phys. 2007; 30(3):329-40. DOI: 10.1016/j.medengphy.2007.04.011. View

15.

Schramm J, Cedzich C . Outcome and management of intraoperative aneurysm rupture. Surg Neurol. 1993; 40(1):26-30. DOI: 10.1016/0090-3019(93)90165-w. View

16.

Luo X, Yang Y, Cao T, Li Z . Differences in left and right carotid intima-media thickness and the associated risk factors. Clin Radiol. 2011; 66(5):393-8. DOI: 10.1016/j.crad.2010.12.002. View

17.

Dumont T, Rughani A, Tranmer B . Prediction of symptomatic cerebral vasospasm after aneurysmal subarachnoid hemorrhage with an artificial neural network: feasibility and comparison with logistic regression models. World Neurosurg. 2011; 75(1):57-63. DOI: 10.1016/j.wneu.2010.07.007. View

18.

Vanninen R, Koivisto K, Tulla H, Manninen H, Partanen K . Hemodynamic effects of carotid endarterectomy by magnetic resonance flow quantification. Stroke. 1995; 26(1):84-9. DOI: 10.1161/01.str.26.1.84. View

19.

Hsu M, Li Y, Chiu W, Yen J . Outcome prediction after moderate and severe head injury using an artificial neural network. Stud Health Technol Inform. 2005; 116:241-5. View

20.

Alnaes M, Isaksen J, Mardal K, Romner B, Morgan M, Ingebrigtsen T . Computation of hemodynamics in the circle of Willis. Stroke. 2007; 38(9):2500-5. DOI: 10.1161/STROKEAHA.107.482471. View