316 Neurosurgical Federated Learning: A Multi-Center Collaboration to Detect Intracranial Hemorrhage Without Directly Sharing Data

Abstract

INTRODUCTION: The development of accurate and generalizable machine learning algorithms requires sufficient quantities of diverse data. This poses a challenge in healthcare due to the sensitive and siloed nature of biomedical information. Decentralized algorithms through a federated learning (FL) paradigm avoid the need for data aggregation by instead distributing algorithms to the data itself before centrally updating one global model. METHODS: Five academic neurosurgery departments across the US collaborated to establish a federated network using computed tomography (CT) scans for prototyping. We trained a convolutional neural network to detect the presence of ICH through FL and benchmarked this against a standard, centralized training approach. Models were validated on each site’s data and tested on a held-out dataset to determine the area under the ROC curve (AUROC). RESULTS: A federated network of practicing neurosurgeon-scientists was successfully initiated and used to train a model for predicting ICH across the US. The federated model achieved an average AUROC of 0.9487 at predicting all types of ICH compared to a benchmark non-FL model AUROC of 0.9753, although performance varied by hemorrhage subtype. Subdural bleeds had the lowest AUROC (0.9257) while intraventricular bleeds had the highest (0.9751) on hold-out data. The global FL model consistently achieved top-three performance (of five) when validated on data from each site, suggesting improved generalizability. A qualitative survey of participants revealed that a majority found the process of connecting to the federated network easy. CONCLUSIONS: This study demonstrates the feasibility of implementing a federated network for multi-institutional collaboration amongst clinicians and using FL to conduct machine learning research without the transfer of data between sites, thereby opening up a new paradigm for neurosurgical collaboration.

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