Bayesian deep learning outperforms clinical trial estimators of intracerebral and intraventricular hemorrhage volume

Matthew F. Sharrock, W. Andrew Mould, Meghan Hildreth, E. Paul Ryu, Nathan Walborn, Issam A. Awad, Daniel F. Hanley, John Muschelli

Research output: Contribution to journalArticlepeer-review

Abstract

Background and Purpose: Intracerebral hemorrhage (ICH) and intraventricular hemorrhage (IVH) clinical trials rely on manual linear and semi-quantitative (LSQ) estimators like the ABC/2, modified Graeb and IVH scores for timely volumetric estimation from CT. Deep learning (DL) volumetrics of ICH have recently approached the accuracy of gold-standard planimetry. However, DL and LSQ strategies have been limited by unquantified uncertainty, in particular when ICH and IVH estimates intersect. Bayesian deep learning methods can be used to approximate uncertainty, presenting an opportunity to improve quality assurance in clinical trials. Methods: A DL model was trained to simultaneously segment ICH and IVH using diagnostic CT data from the Minimally Invasive Surgery Plus Alteplase for ICH Evacuation (MISTIE) III and Clot Lysis: Evaluating Accelerated Resolution of IVH (CLEAR) III clinical trials. Bayesian uncertainty approximation was performed using Monte-Carlo dropout. We compared the performance of our model with estimators used in the CLEAR IVH and MISTIE II trials. The reliability of planimetry, DL, and LSQ volumetrics in the setting of high ICH and IVH intersection is quantified using consensus estimates. Results: Our DL model produced volume correlations and median Dice scores of.994 and.946 for ICH in MISTIE II, and.980 and.863 for IVH in CLEAR IVH, respectively, outperforming LSQ estimates from the clinical trials. We found significant linear relationships between ICH uncertainty, Dice scores (r = –.849), and relative volume difference (r =.735). Conclusion: In our validation clinical trial dataset, DL models with Bayesian uncertainty approximation provided superior volumetric estimates to LSQ methods with real-time estimates of model uncertainty.

Original languageEnglish (US)
Pages (from-to)968-976
Number of pages9
JournalJournal of Neuroimaging
Volume32
Issue number5
DOIs
StatePublished - Sep 1 2022

Keywords

  • clinical trials
  • intracerebral hemorrhage
  • neural networks
  • neuroimaging

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Clinical Neurology

Fingerprint

Dive into the research topics of 'Bayesian deep learning outperforms clinical trial estimators of intracerebral and intraventricular hemorrhage volume'. Together they form a unique fingerprint.

Cite this