Cited by 1,020Artificial Intelligence in Healthcare and EducationBrain Tumor Detection and ClassificationMedical Imaging and Analysis

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Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study

Chilamkurthy, Sasank, Ghosh, Rohit, Tanamala, Swetha, Biviji, Mustafa, Campeau, Norbert G, Venugopal, Vasantha Kumar, Mahajan, Vidur, Rao, Pooja, Warier, Prashant·2018·The Lancet

DOI 10.1016/s0140-6736(18)31645-3

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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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Random Forests
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856
Statistical Methods for Rates and Proportions
Fleiss, J. L., Levin, B., & Paik, M. C. (2003) · Wiley Series in Probability and Statistics · doi:10.1002/0471445428 · Cited by 18,646
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
Measuring nominal scale agreement among many raters.
Fleiss, J. L. (1971) · Psychological Bulletin · doi:10.1037/h0031619 · Cited by 8,733
Guidelines for the Early Management of Patients With Acute Ischemic Stroke
Jauch, E. C., Saver, J. L., Adams, H. P., Bruno, A., Connors, J. J. “., Demaerschalk, B. M., Khatri, P., McMullan, P. W., Qureshi, A. I., Rosenfield, K., et al. (2013) · Stroke · doi:10.1161/str.0b013e318284056a · Cited by 7,698
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697
Untitled work
(2005) · PubMed · Cited by 7,011

Show all 33 references

Research works printed but not linked: 1

Yamada A, Teramoto A, Otsuka T, Kudo K, Anno H, Fujita H. Preliminary study on the automated skull fracture detection in CT images using black-hat transform. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Orlando, FL; Aug 17–20, 2016. 6437–40. · DOI

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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
ORCID: Campeau, Norbert G
Venugopal, Vasantha Kumar
Rao, Pooja
Venue: The Lancet
Publisher: Elsevier BV
Published: 2018-12
Volume: 392
Issue: 10162
Pages: 2388-2396
ISSN: 0099-5355 (ISSN-L)
0140-6736
DOI: 10.1016/s0140-6736(18)31645-3
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
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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Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study

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