Precise Image-level Localization of Intracranial Hemorrhage on Head CT Scans with Deep Learning Models Trained on Studylevel Labels

Yunan Wu, Michael Iorga, Suvarna Badhe, James Zhang, Donald R. Cantrell, Elaine J. Tanhehco, Nicholas Szrama, Andrew M. Naidech, Michael Drakopoulos, Shamis T. Hasan, Kunal M. Patel, Tarek A. Hijaz, Eric J. Russell, Shamal Lalvani, Amit Adate, Todd B. Parrish, Aggelos K. Katsaggelos, Virginia B. Hill*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Purpose: To develop a highly generalizable weakly supervised model to automatically detect and localize image-level intracranial hemorrhage (ICH) by using study-level labels. Materials and Methods: In this retrospective study, the proposed model was pretrained on the image-level Radiological Society of North America dataset and fine-tuned on a local dataset by using attention-based bidirectional long short-term memory networks. This local training dataset included 10 699 noncontrast head CT scans in 7469 patients, with ICH study-level labels extracted from radiology reports. Model performance was compared with that of two senior neuroradiologists on 100 random test scans using the McNemar test, and its generalizability was evaluated on an external independent dataset. Results: The model achieved a positive predictive value (PPV) of 85.7% (95% CI: 84.0, 87.4) and an area under the receiver operating characteristic curve of 0.96 (95% CI: 0.96, 0.97) on the held-out local test set (n = 7243, 3721 female) and 89.3% (95% CI: 87.8, 90.7) and 0.96 (95% CI: 0.96, 0.97), respectively, on the external test set (n = 491, 178 female). For 100 randomly selected samples, the model achieved performance on par with two neuroradiologists, but with a significantly faster (P < .05) diagnostic time of 5.04 seconds per scan (vs 86 seconds and 22.2 seconds for the two neuroradiologists, respectively). The model’s attention weights and heatmaps visually aligned with neuroradiologists’ interpretations. Conclusion: The proposed model demonstrated high generalizability and high PPVs, offering a valuable tool for expedited ICH detection and prioritization while reducing false-positive interruptions in radiologists’ workflows.

Original languageEnglish (US)
Article numbere230296
JournalRadiology: Artificial Intelligence
Volume6
Issue number6
DOIs
StatePublished - Nov 2024

Keywords

  • Brain/Brain Stem
  • Computer-Aided Diagnosis (CAD)
  • Convolutional Neural Network (CNN)
  • Hemorrhage
  • Transfer Learning

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Artificial Intelligence

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