Artificial Intelligence Empowered Traumatic Brain Injury Diagnosis: A Comprehensive Survey of Methods and Applications

Maas AIR, Menon DK, Manley GT, Abrams M, Åkerlund C, Andelic N, et al. Traumatic brain injury: progress and challenges in prevention, clinical care, and research. Lancet Neurol. 2022;21(11):1004–60.

Article  Google Scholar 

Vutakuri N. Detection of emotional and behavioural changes after traumatic brain injury: A comprehensive survey. Cogn Computat Syst. 2023;5(1):42–63.

Article  Google Scholar 

Yuh EL, Gean AD, Manley GT, Callen AL, Wintermark M. Computer-aided assessment of head computed tomography (CT) studies in patients with suspected traumatic brain injury. J Neurotrauma. 2008;25(10):1163–72.

Article  Google Scholar 

Foo YH, Wong JHD, Azman RR, Leong YL, Tan LK. Identification of acute intracranial bleed on computed tomography using computer aided detection. In: Journal of physics: Conference series, volume 1497. IOP Publishing; 2020. p. 012019.

Gillebert CR, Humphreys GW, Mantini D. Automated delineation of stroke lesions using brain CT images. NeuroImage: Clin. 2014;4:540–8.

Article  Google Scholar 

Phan A-C, Vo V-Q, Phan T-C. A Hounsfield value-based approach for automatic recognition of brain haemorrhage. J Inf Telecommun. 2019;3(2):196–209.

Google Scholar 

Raghavendra U, Pham T-H, Gudigar A, Vidhya V, Rao BN, Sabut S, et al. Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images. Compl Intell Syst. 2021;7:929–40.

Article  Google Scholar 

Shahangian B, Pourghassem H. Automatic brain hemorrhage segmentation and classification algorithm based on weighted grayscale histogram feature in a hierarchical classification structure. Biocybern Biomed Eng. 2016;36(1):217–32.

Article  Google Scholar 

Farzaneh N, Williamson CA, Jiang C, Srinivasan A, Bapuraj JR, Gryak J, et al. Automated segmentation and severity analysis of subdural hematoma for patients with traumatic brain injuries. Diagnostics. 2020;10(10):773.

Article  Google Scholar 

Al-Ayyoub M, Alawad D, Al-Darabsah K, Aljarrah I. Automatic detection and classification of brain hemorrhages. WSEAS Trans Comput. 2013;12(10):395–405.

Google Scholar 

Flanders AE, Prevedello LM, Shih G, Halabi SS, Kalpathy-Cramer J, Ball R, et al. Construction of a machine learning dataset through collaboration: the RSNA 2019 brain CT hemorrhage challenge. Radiol: Artif Intell. 2020;2(3):e190211.

Google Scholar 

Chilamkurthy S, Ghosh R, Tanamala S, Biviji M, Campeau NG, Venugopal VK, et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. The Lancet. 2018;392(10162):2388–96.

Article  Google Scholar 

Lewick T, Kumar M, Hong R, Wu W. Intracranial hemorrhage detection in CT scans using deep learning. In: 2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService). IEEE; 2020. p. 169–172.

Ammar M, Lamri MA, Mahmoudi S, Laidi A. Deep learning models for intracranial hemorrhage recognition: a comparative study. Procedia Comput Sci. 2022;196:418–25.

Article  Google Scholar 

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;7:39842–51.

Article  Google Scholar 

Kuo W, Häne C, Yuh E, Mukherjee P, Malik J. Cost-sensitive active learning for intracranial hemorrhage detection. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part III 11. Springer; 2018. p. 715–723.

Cho J, Park K-S, Karki M, Lee E, Ko S, Kim JK, et al. Improving sensitivity on identification and delineation of intracranial hemorrhage lesion using cascaded deep learning models. J Digit Imag. 2019;32:450–61.

Article  Google Scholar 

Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI. A survey on deep learning in medical image analysis. Medical Image Analysis. 2017;42:60–88.

Chang PD, Kuoy E, Grinband J, Weinberg BD, Thompson M, Homo R, et al. Hybrid 3D/2D convolutional neural network for hemorrhage evaluation on head CT. Am J Neuroradiol. 2018;39(9):1609–16.

Article  Google Scholar 

Arbabshirani MR, Fornwalt BK, Mongelluzzo GJ, Suever JD, Geise BD, Patel AA, 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(1):9.

Article  Google Scholar 

Titano JJ, Badgeley M, Schefflein J, Pain M, Su A, Cai M, et al. Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat Med. 2018;24(9):1337–41.

Article  Google Scholar 

Jnawali K, Arbabshirani MR, Rao N, Patel AA. Deep 3D convolution neural network for CT brain hemorrhage classification. In: Medical Imaging 2018: Computer-Aided Diagnosis, vol 10575. SPIE; 2018. p. 307–313.

Cai J, Lu L, Xie Y, Xing F, Yang L. Improving deep pancreas segmentation in CT and MRI images via recurrent neural contextual learning and direct loss function. 2017. ArXiv:1707.04912

Chen J, Yang L, Zhang Y, Alber M, Chen DZ. Combining fully convolutional and recurrent neural networks for 3D biomedical image segmentation. Adv Neural Inf Process Syst. 2016;29.

Ypsilantis P-P, Montana G. Recurrent convolutional networks for pulmonary nodule detection in CT imaging. 2016. ArXiv:1609.09143

Ye H, Gao F, Yin Y, Guo D, Zhao P, Lu Y, et al. Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network. European Radiol. 2019;29:6191–201.

Article  Google Scholar 

Grewal M, Srivastava MM, Kumar P, Varadarajan S. Radnet: Radiologist level accuracy using deep learning for hemorrhage detection in CT scans. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE; 2018. p. 281–284.

Wang X, Shen T, Yang S, Lan J, Xu Y, Wang M, et al. A deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans. NeuroImage: Clinical. 2021;32:102785.

Article  Google Scholar 

Chen J, Wang W, Fang B, Liu Y, Yu K, Leung VCM, et al. Digital twin empowered wireless healthcare monitoring for smart home. IEEE J Selec Areas Commun. 2023;41(11):3662–76.

Article  Google Scholar 

Stein AMD, Wu C, Carr C, Shih G, Kalpathy-Cramer J, Elliott J, Prevedello L, Kohli MMD, Lungren M, Culliton P, Ball R, Halabi SMD. Rsna intracranial hemorrhage detection. 2019.

Strub WM, Leach JL, Tomsick T, Vagal A. Overnight preliminary head CT interpretations provided by residents: locations of misidentified intracranial hemorrhage. Am J Neuroradiol. 2007;28(9):1679–82.

Article  Google Scholar 

Sindhura C, Al Fahim M, Yalavarthy PK, Gorthi S. Fully automated sinogram-based deep learning model for detection and classification of intracranial hemorrhage. Med Phys. 2023.

Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. 2014. ArXiv:1412.3555

Diwakar M, Kumar M. A review on CT image noise and its denoising. Biomed Signal Process Contr. 2018;42:73–88.

Article  Google Scholar 

Liu A, Guo Y, Lyu J, Xie J, Xu F, Lou X, Yong J-h, Dai Q. Automatic intracranial abnormality detection and localization in head CT scans by learning from free-text reports. Cell Reports Med. 2023;4(9).

Li J, Fu G, Chen Y, Li P, Liu B, Pei Y, et al. A multi-label classification model for full slice brain computerised tomography image. BMC Bioinf. 2020;21:1–18.

Article  Google Scholar 

Junxin C, XuXu ZG, Li-bo Z, Yue T, Yongyong C, Marcin W, et al. A robust deep learning framework based on spectrograms for heart sound classification. IEEE/ACM Trans Computat Biol Bioinf. 2024;21(4):936–47.

Article  Google Scholar 

Wang W, Yu X, Fang B, Zhao Y, Chen Y, Wei W, et al. Cross-modality LGE-CMR segmentation using image-to-image translation based data augmentation. IEEE/ACM Trans Computat Biol Bioinf. 2023;20(4):2367–75.

Article  Google Scholar 

Bardera A, Boada I, Feixas M, Remollo S, Blasco G, Silva Y, et al. Semi-automated method for brain hematoma and edema quantification using computed tomography. Computer Med Imag Graph. 2009;33(4):304–11.

Article  Google Scholar 

Liao C-C, Xiao F, Wong J-M, Chiang I-J. Computer-aided diagnosis of intracranial hematoma with brain deformation on computed tomography. Computer Med Imag Graph. 2010;34(7):563–71.

Article  Google Scholar 

Prakash KNB, Zhou S, Morgan TC, Hanley DF, Nowinski WL. Segmentation and quantification of intra-ventricular/cerebral hemorrhage in CT scans by modified distance regularized level set evolution technique. Intern J Comput Assist Radiol Surger. 2012;7:785–98.

Article  Google Scholar 

Ray S, Kumar V, Ahuja C, Khandelwal N. Intensity population based unsupervised hemorrhage segmentation from brain CT images. Expert Syst Appl. 2018;97:325–35.

Article  Google Scholar 

Chan T. Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computer Med Imag Graph. 2007;31(4–5):285–98.

Article  Google Scholar 

Xia Y, Hu Q, Aziz A, Nowinski WL. A knowledge-driven algorithm for a rapid and automatic extraction of the human cerebral ventricular system from MR neuroimages. NeuroImage. 2004;21(1):269–82.

Article  Google Scholar 

Schönmeyer R, Prvulovic D, Rotarska-Jagiela A, Haenschel C, Linden DEJ. Automated segmentation of lateral ventricles from human and primate magnetic resonance images using cognition network technology. Magnet Reson Imag. 2006;24(10):1377–87.

Article  Google Scholar 

Singh BH, Dewal ML. Intracranial hemorrhage detection using spatial fuzzy c-mean and region-based active contour on brain CT imaging. Sign Image Video Process. 2014;8:357–64.

Article  Google Scholar 

Gautam A, Raman B. Automatic segmentation of intracerebral hemorrhage from brain CT images. In: Machine intelligence and signal analysis. Springer; 2019. p. 753–764.

Chen W, Smith R, Ji S-Y, Ward KR, Najarian K. Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching. BMC Med Inf Decis Mak. 2009;9(1):1–14.

Google Scholar 

Rao A, Ledig C, Newcombe V, Menon D, Rueckert D. Contusion segmentation from subjects with traumatic brain injury: a random forest framework. In: 2014 IEEE 11th International Symposium on biomedical imaging (ISBI). IEEE; 2014. p. 333–336.

Muschelli J, Sweeney EM, Ullman NL, Vespa P, Hanley DF, Crainiceanu CM. PItcHPERFeCT: Primary intracranial hemorrhage probability estimation using random forests on CT. NeuroImage: Clin. 2017;14:379–90.

Article  Google Scholar 

Xu X, Cong F, Chen Y, Chen J. Sleep stage classification with multi-modal fusion and denoising diffusion model. IEEE J Biomed Health Inf. 2024.

Jin D, Zhang Z, Ma Y, Dai Z, Zhao L, Ma T, et al. Magnetic resonance imaging features to evaluate the neonatal hypoglycemia brain injury and investigation of related risk factors under the fuzzy c-means clustering intelligent algorithm. Expert Syst. 2024;41(7):e13491.

Article  Google Scholar 

Li C, Xu C, Gui C, Fox MD. Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Process. 2010;19(12):3243–54.

Article  Google Scholar 

Zaki W, Fauzi M, Besar R, Ahmad W. Abnormalities detection in serial computed tomography brain images using multi-level segmentation approach. Multimed Tools Appl. 2011;54:321–40.

Article  Google Scholar 

Hodgson R, Wilson J, Fewins H, Magennis R, Healeyet A. Cad system for detecting haemorrhage in CT of stroke. Proceedings of the RSNA 2004. 2004.

Yang GL, Lim C, Hou Z, Gan R, Poh K, Nowinski W. A practical computer-aided diagnosis system for intracranial hemorrhage detection in acute stroke. Proceed RSNA. 2005;2005:856–7.

Google Scholar 

Hao L, Yang N, Javier DS, Guang Y. Large-kernel attention for 3d medical image segmentation. Cogn Comput. 2024;16(4):2063–77.

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