Methods and Impact for Using Federated Learning to Collaborate on Clinical Research

Alexander T.M. Cheung, Mustafa Nasir-Moin, Young Joon 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, Eric Karl Oermann

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

BACKGROUND: The 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. OBJECTIVE: To 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. METHODS: Five 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. RESULTS: A 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. CONCLUSION: This 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.

Original languageEnglish
Pages (from-to)431-438
Number of pages8
JournalNeurosurgery
Volume92
Issue number2
DOIs
StatePublished - 1 Feb 2023
Externally publishedYes

Keywords

  • Artificial intelligence
  • Federated learning
  • Intracranial hemorrhage
  • Machine learning

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