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Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study
Abstract
Background Non-contrast head CT scan is the current standard for initial imaging of patients with head trauma or stroke symptoms. We aimed to develop and validate a set of deep learning algorithms for automated detection of the following key findings from these scans: intracranial haemorrhage and its types (ie, intraparenchymal, intraventricular, subdural, extradural, and subarachnoid); calvarial fractures; midline shift; and mass effect.Methods We retrospectively collected a dataset containing 313 318 head CT scans together with their clinical reports from around 20 centres in India between Jan 1, 2011, and June 1, 2017. A randomly selected part of this dataset (Qure25k dataset) was used for validation and the rest was used to develop algorithms. An additional validation dataset (CQ500 dataset) was collected in two batches from centres that were different from those used for the development and Qure25k datasets. We excluded postoperative scans and scans of patients younger than 7 years. The original clinical radiology report and consensus of three independent radiologists were considered as gold standard for the Qure25k and CQ500 datasets, respectively. Areas under the receiver operating characteristic curves (AUCs) were primarily used to assess the algorithms.Findings The Qure25k dataset contained 21 095 scans (mean age 43 years; 9030 [43%] female patients), and the CQ500 dataset consisted of 214 scans in the first batch (mean age 43 years; 94 [44%] female patients) and 277 scans in the second batch (mean age 52 years; 84 [30%] female patients). On the Qure25k dataset, the algorithms achieved an AUC of 0·92 (95% CI 0·91-0·93) for detecting intracranial haemorrhage (0·90 [0·89-0·91] for intraparenchymal, 0·96 [0·94-0·97] for intraventricular, 0·92 [0·90-0·93] for subdural, 0·93 [0·91-0·95] for extradural, and 0·90 [0·89-0·92] for subarachnoid). On the CQ500 dataset, AUC was 0·94 (0·92-0·97) for intracranial haemorrhage (0·95 [0·93-0·98], 0·93 [0·87-1·00], 0·95 [0·91-0·99], 0·97 [0·91-1·00], and 0·96 [0·92-0·99], respectively). AUCs on the Qure25k dataset were 0·92 (0·91-0·94) for calvarial fractures, 0·93 (0·91-0·94) for midline shift, and 0·86 (0·85-0·87) for mass effect, while AUCs on the CQ500 dataset were 0·96 (0·92-1·00), 0·97 (0·94-1·00), and 0·92 (0·89-0·95), respectively.Interpretation Our results show that deep learning algorithms can accurately identify head CT scan abnormalities requiring urgent attention, opening up the possibility to use these algorithms to automate the triage process.Funding Qure.ai.
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“The CQ500 dataset, consisting of 1154 available CT scans from 491 patients, was downloaded from the Qure.ai website51.”
From Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation (2025) · cites this paper as [51]
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Metadata
- Authors
- Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N. G., Venugopal, V. K., Mahajan, V., Rao, P., & Warier, P.
- Affiliations
- Qure.ai Technologies Private Limited (India) (IN)
- Mayo Clinic (US)
- Corresponding author
- Chilamkurthy, Sasank
- Venue
- The Lancet
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 392
- Issue
- 10162
- Pages
- 2388-2396
- ISSN
- 0099-5355 (ISSN-L)
- 0140-6736
- OpenAlex
- W2896817483
- Cited by
- 1,020
- References
- 33
- Citations by year
- 2026: 96 · 2025: 144 · 2024: 163 · 2023: 148 · 2022: 115 · 2021: 146 · 2020: 134 · 2019: 66 · 2018: 7
- FWCI
- 23.93
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Lancet (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- automated detection, head CT, intracranial hemorrhage, intraventricular hemorrhage, subdural hematoma, subarachnoid hemorrhage, epidural hematoma, intraparenchymal hemorrhage, calvarial fractures, midline shift, mass effect, deep learning
- MeSH terms
- Deep Learning, Algorithms, Brain Injuries, Head, Humans, Retrospective Studies, Skull Fractures, Tomography, X-Ray Computed, Triage, Trauma Severity Indices, Intracranial Hemorrhages, Datasets as Topic
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