• Alexander T M Cheung, Mustafa Nasir-Moin, Young Joon Fred Kwon, Jiahui Guan, Chris Liu, Lavender Jiang, Christian Raimondo, Silky Chotai, Lola Chambless, Hasan S Ahmad, Daksh Chauhan, Jang W Yoon, Todd Hollon, Vivek Buch, Douglas Kondziolka, Dinah Chen, Lama A Al-Aswad, Yindalon Aphinyanaphongs, and Eric Karl Oermann.
• Department of Neurosurgery, NYU Langone Health, New York, New York, USA.
• Neurosurgery. 2023 Feb 1; 92 (2): 431438431-438.

BackgroundThe development of accurate machine learning algorithms requires sufficient quantities of diverse data. This poses a challenge in health care because of the sensitive and siloed nature of biomedical information. Decentralized algorithms through federated learning (FL) avoid data aggregation by instead distributing algorithms to the data before centrally updating one global model.ObjectiveTo establish a multicenter collaboration and assess the feasibility of using FL to train machine learning models for intracranial hemorrhage (ICH) detection without sharing data between sites.MethodsFive neurosurgery departments across the United States collaborated to establish a federated network and train a convolutional neural network to detect ICH on computed tomography scans. The global FL model was benchmarked against a standard, centrally trained model using a held-out data set and was compared against locally trained models using site data.ResultsA federated network of practicing neurosurgeon scientists was successfully initiated to train a model for predicting ICH. The FL model achieved an area under the ROC curve of 0.9487 (95% CI 0.9471-0.9503) when predicting all subtypes of ICH compared with a benchmark (non-FL) area under the ROC curve of 0.9753 (95% CI 0.9742-0.9764), although performance varied by subtype. The FL model consistently achieved top three performance when validated on any site's data, suggesting improved generalizability. A qualitative survey described the experience of participants in the federated network.ConclusionThis study demonstrates the feasibility of implementing a federated network for multi-institutional collaboration among clinicians and using FL to conduct machine learning research, thereby opening a new paradigm for neurosurgical collaboration.Copyright © Congress of Neurological Surgeons 2022. All rights reserved.

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