CC BY 4.0 · Indian Journal of Neurotrauma 2023; 20(02): 081-088
DOI: 10.1055/s-0043-1770770
Review Article

Automated Detection of Intracranial Hemorrhage from Head CT Scans Applying Deep Learning Techniques in Traumatic Brain Injuries: A Comparative Review

1   Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, India
2   Behavioral Sciences, In-Med Prognostics Inc, San Diego, California, United States
3   Department of Research, In-Med Prognostics Inc, Pune, Maharashtra, India
› Author Affiliations
Funding None.


Traumatic brain injury (TBI) is not only an acute condition but also a chronic disease with long-term consequences. Intracranial hematomas are considered the primary consequences that occur in TBI and may have devastating effects that may lead to mass effect on the brain and eventually cause secondary brain injury. Emergent detection of hematoma in computed tomography (CT) scans and assessment of three major determinants, namely, location, volume, and size, is crucial for prognosis and decision-making, and artificial intelligence (AI) using deep learning techniques, such as convolutional neural networks (CNN) has received extended attention after demonstrations that it could perform at least as well as humans in imaging classification tasks. This article conducts a comparative review of medical and technological literature to update and establish evidence as to how technology can be utilized rightly for increasing the efficiency of the clinical workflow in emergency cases. A systematic and comprehensive literature search was conducted in the electronic database of PubMed and Google Scholar from 2013 to 2023 to identify studies related to the automated detection of intracranial hemorrhage (ICH). Inclusion and exclusion criteria were set to filter out the most relevant articles. We identified 15 studies on the development and validation of computer-assisted screening and analysis algorithms that used head CT scans. Our review shows that AI algorithms can prioritize radiology worklists to reduce time to screen for ICH in the head scans sufficiently and may also identify subtle ICH overlooked by radiologists, and that automated ICH detection tool holds promise for introduction into routine clinical practice.

Publication History

Article published online:
10 July 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (

Thieme Medical and Scientific Publishers Pvt. Ltd.
A-12, 2nd Floor, Sector 2, Noida-201301 UP, India

  • References

  • 1 Maas AIR, Menon DK, Manley GT. et al; InTBIR Participants and Investigators. Traumatic brain injury: progress and challenges in prevention, clinical care, and research. Lancet Neurol 2022; 21 (11) 1004-1060
  • 2 Vidhya V, Gudigar A, Raghavendra U. et al. Automated detection, and screening of traumatic brain injury (TBI) using computed tomography images: a comprehensive review and future perspectives. Int J Environ Res Public Health 2021; 18 (12) 6499
  • 3 Teixeira PGR, Inaba K, Hadjizacharia P. et al. Preventable or potentially preventable mortality at a mature trauma center. J Trauma 2007; 63 (06) 1338-1346 , discussion 1346–1347
  • 4 Alouani AT, Elfouly T. Traumatic brain injury (TBI) detection: past, present, and future. Biomedicines 2022; 10 (10) 2472
  • 5 Howley IW, Bennett JD, Stein DM. Rapid detection of significant traumatic brain injury requiring emergency intervention. Am Surg 2021; 87 (09) 1504-1510
  • 6 Perel P, Roberts I, Bouamra O, Woodford M, Mooney J, Lecky F. Intracranial bleeding in patients with traumatic brain injury: a prognostic study. BMC Emerg Med 2009; 9: 15
  • 7 MedLink Neurology. Traumatic intracerebral hemorrhage. Accessed April 21, 2023 at:
  • 8 Majumdar A, Brattain L, Telfer B, Farris C, Scalera J. Detecting intracranial hemorrhage with deep learning. Annu Int Conf IEEE Eng Med Biol Soc 2018; 2018: 583-587
  • 9 Prevedello LM, Erdal BS, Ryu JL. et al. Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology 2017; 285 (03) 923-931
  • 10 Wintermark M, Sanelli PC, Anzai Y, Tsiouris AJ, Whitlow CT. ACR Head Injury Institute, ACR Head Injury Institute. Imaging evidence and recommendations for traumatic brain injury: conventional neuroimaging techniques. J Am Coll Radiol 2015; 12 (02) e1-e14
  • 11 InMed. About Neuroshield. Accessed March 27, 2023, at:
  • 12 Irene K, Masum MA, Yunus RE, Jatmiko W. Segmentation and Approximation of Blood Volume in Intracranial Hemorrhage Patients Based on Computed Tomography Scan Images Using Deep Learning Method. Paper presented at: 2020 International Workshop on Big Data and Information Security (IWBIS); October 17–18, 2020; Depok, Indonesia
  • 13 Desai V, Flanders AE, Lakhani P. Application of Deep Learning in Neuroradiology: Automated Detection of Basal Ganglia Hemorrhage Using 2D-Convolutional Neural Networks. Accessed June 10, 2023 at:
  • 14 Anupama CSS, Sivaram M, Lydia EL, Gupta D, Shankar K. Synergic deep learning model–based automated detection and classification of brain intracranial hemorrhage images in wearable networks. Pers Ubiquitous Comput 2022; 26 (01) 1-10
  • 15 Kuang H, Menon BK, Qiu W. Segmenting hemorrhagic and ischemic infarct simultaneously from follow-up non-contrast CT images in patients with acute ischemic stroke. IEEE Access 2019; 7: 39842-39851
  • 16 Dawud AM, Yurtkan K, Oztoprak H. Application of deep learning in neuroradiology: brain haemorrhage classification using transfer learning. Comput Intell Neurosci 2019; 2019: 4629859
  • 17 Cho J, Park KS, Karki M. et al. Improving sensitivity on identification and delineation of intracranial hemorrhage lesion using cascaded deep learning models. J Digit Imaging 2019; 32 (03) 450-461
  • 18 Cenek M, Hu M, York G, Dahl S. Survey of image processing techniques for brain pathology diagnosis: challenges and opportunities. Front Robot AI 2018; 5 (NOV): 120
  • 19 Ker J, Wang L, Rao J, Lim T. Deep learning applications in medical image analysis. IEEE Access 2017; 6: 9375-9379
  • 20 Suganyadevi S, Seethalakshmi V, Balasamy K. A review on deep learning in medical image analysis. Int J Multimed Inf Retr 2022; 11 (01) 19-38
  • 21 Abdel-Hamid O, Deng L, Yu D. Exploring Convolutional Neural Network Structures and Optimization Techniques for Speech Recognition. Paper presented at: Interspeech 2013; August 25–29, 2013; Lyon, France
  • 22 Grewal M, Srivastava MM, Kumar P, Varadarajan S. RADNET: radiologist level accuracy using deep learning for hemorrhage detection in CT scans. Paper presented at: 2018 IEEE 15th International Symposium on Biomedical Imaging; April 4–7, 2018; Washington, DC
  • 23 Watanabe Y, Tanaka T, Nishida A. et al. Improvement of the diagnostic accuracy for intracranial haemorrhage using deep learning-based computer-assisted detection. Neuroradiology 2021; 63 (05) 713-720
  • 24 Mansour RF, Aljehane NO. An optimal segmentation with deep learning based inception network model for intracranial hemorrhage diagnosis. Neural Comput Appl 2021; 33 (20) 13831-13843
  • 25 Hssayeni MD, Croock MS, Salman AD, Al-Khafaji HF, Yahya ZA, Ghoraani B. Intracranial hemorrhage segmentation using a deep convolutional model. Data (Basel) 2020; 5 (01) 14
  • 26 Titano JJ, Badgeley M, Schefflein J. et al. Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat Med 2018; 24 (09) 1337-1341
  • 27 Kuo W, Hӓne C, Mukherjee P, Malik J, Yuh EL. Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning. Proc Natl Acad Sci U S A 2019; 116 (45) 22737-22745
  • 28 Chilamkurthy S, Ghosh R, Tanamala S. et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet 2018; 392 (10162): 2388-2396
  • 29 Arbabshirani MR, Fornwalt BK, Mongelluzzo GJ. et al. Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ Digit Med 2018; 1 (01) 9
  • 30 Lee H, Yune S, Mansouri M. et al. An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nat Biomed Eng 2019; 3 (03) 173-182
  • 31 Ye H, Gao F, Yin Y. et al. Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network. Eur Radiol 2019; 29 (11) 6191-6201
  • 32 Chang PD, Kuoy E, Grinband J. et al. Hybrid 3D/2D convolutional neural network for hemorrhage evaluation on head CT. AJNR Am J Neuroradiol 2018; 39 (09) 1609-1616