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 consists of 1154 available CT scans from 491 patients.
“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]
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Measuring nominal scale agreement among many raters.
Fleiss, J. L. (1971) · Psychological Bulletin · doi:10.1037/h0031619 · Cited by 8,733 · see citing works
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 · see citing works
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 7,648 · see citing works
(2005) · PubMed · Cited by 7,011 · see citing works
Powers, W. J., Rabinstein, A. A., Ackerson, T., Adeoye, O. M., Bambakidis, N. C., Becker, K. J., Biller, J., Brown, M., Demaerschalk, B. M., Hoh, B. L., et al. (2018) · Stroke · doi:10.1161/str.0000000000000158 · Cited by 5,505 · see citing works
THE USE OF CONFIDENCE OR FIDUCIAL LIMITS ILLUSTRATED IN THE CASE OF THE BINOMIAL
Clopper, C. J., & Pearson, E. S. (1934) · Biometrika · doi:10.1093/biomet/26.4.404 · Cited by 5,106 · see citing works
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.369 · Cited by 4,616 · see citing works
Armato, S. G., McLennan, G., Bidaut, L., McNitt‐Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., et al. (2011) · Medical Physics · doi:10.1118/1.3528204 · Cited by 2,887 · see citing works
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep LearningPreprint
Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D. Y., Bagul, A., Langlotz, C. P., Shpanskaya, K. S., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.05225 · Cited by 2,283 · see citing works
The Unreasonable Effectiveness of Data
Halevy, A. Y., Norvig, P., & Pereira, F. M. B. (2009) · IEEE Intelligent Systems · doi:10.1109/mis.2009.36 · Cited by 1,776 · see citing works
Lung pattern classification for interstitial lung diseases using a deep convolutional neural network
Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., & Mougiakakou, S. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535865 · Cited by 1,343 · see citing works
Cheng, J.-Z., Ni, D., Chou, Y.-H., Qin, J., Tiu, C.-M., Chang, Y.-C., Huang, C.-S., Shen, D., & Chen, C.-M. (2016) · Scientific Reports · doi:10.1038/srep24454 · Cited by 755 · see citing works
Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network
Prasoon, A., Petersen, K., Igel, C., Lauze, F., Dam, E., & Nielsen, M. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_31 · Cited by 582 · see citing works
National Trends in CT Use in the Emergency Department: 1995–2007
Larson, D. B., Johnson, L. W., Schnell, B. M., Salisbury, S. R., & Forman, H. P. (2010) · Radiology · doi:10.1148/radiol.10100640 · Cited by 443 · see citing works
Classification of CT brain images based on deep learning networks
Gao, X. W., Hui, R., & Tian, Z. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.10.007 · Cited by 358 · see citing works
Prevedello, L. M. S., Erdal, B. S., Ryu, J., Little, K. J., Demirer, M., Qian, S., & White, R. (2017) · Radiology · doi:10.1148/radiol.2017162664 · Cited by 248 · see citing works
RADnet: Radiologist level accuracy using deep learning for hemorrhage detection in CT scans
Grewal, M., Srivastava, M. M., Kumar, P., & Varadarajan, S. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363574 · Cited by 225 · see citing works
Representation Learning: A Unified Deep Learning Framework for Automatic Prostate MR Segmentation
Liao, S., Gao, Y., Oto, A., & Shen, D. G. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40763-5_32 · Cited by 157 · see citing works
Papa, L., Stiell, I. G., Clement, C. M., Pawlowicz, A., Wolfram, A., Braga, C. F., Draviam, S., & Wells, G. A. (2012) · Academic Emergency Medicine · doi:10.1111/j.1553-2712.2011.01247.x · Cited by 145 · see citing works
2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation
Patravali, J., Jain, S., & Chilamkurthy, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-75541-0_14 · Cited by 130 · see citing works
Self-citationStatistical Methods for Rates and Proportions.
Marshall, T. F. D. C., & Fleiss, J. L. (1976) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2988144 · Cited by 130 · see citing works
Coles, J. (2007) · British Journal of Anaesthesia · doi:10.1093/bja/aem141 · Cited by 117 · see citing works
Radiology resident evaluation of head CT scan orders in the emergency department.
Erly, W. K., Berger, W. G., Krupinski, E. A., Seeger, J. F., & Guisto, J. A. (2002) · PubMed Central · Cited by 116 · see citing works
Visual Informatics: Bridging Research and Practice
Zaman, H. B., Schröder, H., Olivier, P. L., Robinson, P., Shih, T. K., & Πέτρου, Μ. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-05036-7 · Cited by 67 · see citing works
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
Chen, W., Belle, A., Cockrell, C., Ward, K. R., & Najarian, K. (2013) · Journal of Visualized Experiments · doi:10.3791/3871 · Cited by 37 · see citing works
Estimating age by assessing the ossification degree of cranial sutures with the aid of Flat-Panel-CT
Harth, S., Obert, M., Ramsthaler, F., Reuß, C., Traupe, H., & Verhoff, M. A. (2009) · Legal Medicine · doi:10.1016/j.legalmed.2009.01.091 · Cited by 37 · see citing works
Automated assessment of midline shift in head injury patients
Xiao, F., Liao, C.-C., Huang, K.-C., Chiang, I.-J., & Wong, J. (2010) · Clinical Neurology and Neurosurgery · doi:10.1016/j.clineuro.2010.06.020 · Cited by 31 · see citing works
A New Approach of Skull Fracture Detection in CT Brain Images
Zaki, W. M. D. W., Fauzi, M. F. A., & Besar, R. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-05036-7_16 · Cited by 14 · see citing works
Wang, H.-C., Ho, S.-H., Xiao, F., & Chou, J.-H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.00797 · Cited by 9 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works
Rajkomar, A., Dean, J., & Kohane, I. (2019) · New England Journal of Medicine · doi:10.1056/nejmra1814259 · Cited by 4,579 · see citing works
Key challenges for delivering clinical impact with artificial intelligence
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019) · BMC Medicine · doi:10.1186/s12916-019-1426-2 · Cited by 3,136 · see citing works
Deep learning-enabled medical computer vision
Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J., & Socher, R. (2021) · npj Digital Medicine · doi:10.1038/s41746-020-00376-2 · Cited by 1,406 · see citing works
A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT
Zheng, C., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., & Wang, X. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2995965 · Cited by 1,237 · see citing works
Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., Zha, Y., Liang, W., Wang, C., Wang, K., et al. (2020) · Cell · doi:10.1016/j.cell.2020.04.045 · Cited by 917 · see citing works
Antoniadi, A. M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B. A., & Mooney, C. (2021) · Applied Sciences · doi:10.3390/app11115088 · Cited by 629 · see citing works
Applications of machine learning to diagnosis and treatment of neurodegenerative diseases
Myszczynska, M. A., Ojamies, P. N., Lacoste, A. M. B., Neil, D., Saffari, A., Mead, R., Hautbergue, G. M., Holbrook, J. D., & Ferraiuolo, L. (2020) · Nature Reviews Neurology · doi:10.1038/s41582-020-0377-8 · Cited by 595 · see citing works
Sit, C., Srinivasan, R., Amlani, A., Muthuswamy, K., Azam, A., Monzon, L., & Poon, D. S. (2020) · Insights into Imaging · doi:10.1186/s13244-019-0830-7 · Cited by 507 · see citing works
Nagpal, K., Foote, D., Liu, Y., Chen, P.-H. C., Wulczyn, E., Tan, F., Olson, N., Smith, J. L., Mohtashamian, A., Wren, J. H., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0112-2 · Cited by 495 · see citing works
External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review
Yu, A. C., Mohajer, B., & Eng, J. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.210064 · Cited by 455 · see citing works
Lee, H., Yune, S., Mansouri, M., Kim, M., Tajmir, S. H., Guerrier, C. E., Ebert, S. A., Pomerantz, S. R., Romero, J. M., Kamalian, S., et al. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0324-9 · Cited by 441 · see citing works
Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
Oakden-Rayner, L., Dunnmon, J., Carneiro, G., & Re, C. (2020) · Proceedings of the ACM Conference on Health, Inference, and Learning · doi:10.1145/3368555.3384468 · Cited by 411 · see citing works
Medical imaging and nuclear medicine: a Lancet Oncology Commission
Hricak, H., Abdel–Wahab, M., Atun, R. A., Mikhail-Lette, M., Páez, D., Brink, J. A., Donoso, L., Frija, G. G., Hierath, M., Holmberg, O., et al. (2021) · The Lancet Oncology · doi:10.1016/s1470-2045(20)30751-8 · Cited by 382 · see citing works
Cen, L., Ji, J., Lin, J., Ju, S.-T., Lin, H.-J., Li, T.-P., Wang, Y., Yang, J.-F., Liu, Y.-F., Tan, S., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25138-w · Cited by 351 · see citing works
Armoundas, A. A., Narayan, S. M., Arnett, D. K., Spector-Bagdady, K., Bennett, D. A., Celi, L. A., Friedman, P. A., Gollob, M. H., Hall, J. L., Kwitek, A. E., et al. (2024) · Circulation · doi:10.1161/cir.0000000000001201 · Cited by 347 · see citing works
Intracerebral hemorrhage: an update on diagnosis and treatment
Hostettler, I. C., Seiffge, D. J., & Werring, D. J. (2019) · Expert Review of Neurotherapeutics · doi:10.1080/14737175.2019.1623671 · Cited by 345 · see citing works
Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing
Boecking, B., Usuyama, N., Bannur, S., Castro, D. C., Schwaighofer, A., Hyland, S., Wetscherek, M., Naumann, T., Nori, A., Alvarez-Valle, J., et al. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20059-5_1 · Cited by 317 · see citing works
“Such data is collected daily in routine clinical practice, and common examples are X-ray images [18, 32, 75] or computed tomography scans [9, 18, 20, 71] paired with reports written by qualified medical experts.”
BackgroundArnaout, R., Curran, L., Zhao, Y., Levine, J. C., Chinn, E., & Moon-Grady, A. J. (2020) · doi:10.1101/2020.06.22.20137786 · Cited by 315 · see citing works
Ullah, A., Anwar, S. M., Li, J., Nadeem, L., Mahmood, T., Rehman, A. U., & Saba, T. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01175-4 · Cited by 315 · see citing works
Artificial Intelligence and Acute Stroke Imaging
Soun, J. E., Chow, D. S., Nagamine, M., Takhtawala, R. S., Filippi, C. G., Yu, W., & Chang, P. D. (2020) · American Journal of Neuroradiology · doi:10.3174/ajnr.a6883 · Cited by 302 · see citing works
Deep neural network correlation learning mechanism for CT brain tumor detection
Woźniak, M., Siłka, J., & Wieczorek, M. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05841-x · Cited by 292 · see citing works
Kuo, W., Häne, C., Mukherjee, P., Malik, J., & Yuh, E. L. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1908021116 · Cited by 275 · see citing works
Fu, Q., Chen, Y., Li, Z., Jing, Q., Hu, C., Liu, H., Bao, J., Hong, Y., Shi, T., Li, K., et al. (2020) · EClinicalMedicine · doi:10.1016/j.eclinm.2020.100558 · Cited by 259 · see citing works
Deep Learning–Assisted Diagnosis of Cerebral Aneurysms Using the HeadXNet Model
Park, A., Chute, C., Rajpurkar, P., Lou, J., Ball, R. L., Shpanskaya, K., Jabarkheel, R., Kim, L. H., McKenna, E., Tseng, J., et al. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.5600 · Cited by 258 · see citing works
Exploring Large-scale Public Medical Image Datasets
Oakden-Rayner, L. (2020) · Academic Radiology · doi:10.1016/j.acra.2019.10.006 · Cited by 250 · see citing works
Ye, H., Gao, F., Yin, Y., Guo, D., Zhao, P., Lü, Y., Wang, X., Bai, J., Cao, K., Song, Q., et al. (2019) · European Radiology · doi:10.1007/s00330-019-06163-2 · Cited by 250 · see citing works
Han, S. S., Park, I., Eun Chang, S., Lim, W., Kim, M. S., Park, G. H., Chae, J. B., Huh, C. H., & Na, J.-I. (2020) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2020.01.019 · Cited by 248 · see citing works
Federated learning improves site performance in multicenter deep learning without data sharing
Sarma, K. V., Harmon, S., Sanford, T., Roth, H. R., Xu, Z., Tetreault, J., Xu, D., Flores, M. G., Raman, A. G., Kulkarni, R., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa341 · Cited by 242 · see citing works
“11,12 A major limitation of the DL approach is the need for a large volume of training data that captures the full breadth of inputs on which the model is likely to be subsequently used.”
BackgroundIntracranial Hemorrhage Segmentation Using a Deep Convolutional Model
Hssayeni, M. D., Croock, M. S., Salman, A. D., Al-khafaji, H. F., Yahya, Z. A., & Ghoraani, B. (2020) · Data · doi:10.3390/data5010014 · Cited by 239 · see citing works
How does artificial intelligence in radiology improve efficiency and health outcomes?
van Leeuwen, K. G., de Rooij, M., Schalekamp, S., van Ginneken, B., & Rutten, M. J. C. M. (2022) · Pediatric Radiology · doi:10.1007/s00247-021-05114-8 · Cited by 236 · see citing works
Recent advances of deep learning in bioinformatics and computational biology
Tang, B., Pan, Z., Yin, K., & Khateeb, A. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00214 · Cited by 233 · see citing works
Predicting treatment response from longitudinal images using multi-task deep learning
Jin, C., Yu, H., Ke, J., Ding, P., Yi, Y., Jiang, X., Duan, X., Tang, J., Chang, D. T., Wu, X., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22188-y · Cited by 228 · see citing works
Yuan, C., Yao, Y., Cheng, B., Cheng, Y., Li, Y., Li, Y., Liu, X., Cheng, X., Xie, X., Wu, J., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-68252-3 · Cited by 219 · see citing works
Flanders, A. E., Prevedello, L. M. S., Shih, G., Halabi, S. S., Kalpathy–Cramer, J., Ball, R. L., Mongan, J., Stein, A., Kitamura, F., Lungren, M. P., et al. (2020) · Radiology Artificial Intelligence · doi:10.1148/ryai.2020190211 · Cited by 217 · see citing works
Fralick, M., Colak, E., & Mamdani, M. (2019) · New England Journal of Medicine · doi:10.1056/nejmc1906060 · Cited by 206 · see citing works
Wang, X., Shen, T., Yang, S., Lan, J., Xu, Y., Wang, M., Zhang, J., & Han, X. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102785 · Cited by 203 · see citing works
Artificial Intelligence in Nuclear Medicine
Nensa, F., Demircioğlu, A., & Rischpler, C. (2019) · Journal of Nuclear Medicine · doi:10.2967/jnumed.118.220590 · Cited by 197 · see citing works
Jin, L., Yang, J., Kuang, K., Ni, B., Gao, Y., Sun, Y., Gao, P., Ma, W., Tan, M., Kang, H. F., et al. (2020) · EBioMedicine · doi:10.1016/j.ebiom.2020.103106 · Cited by 197 · see citing works
Huang, S.-C., Kothari, T., Banerjee, I., Chute, C., Ball, R. L., Borus, N., Huang, A., Patel, B. N., Rajpurkar, P., Irvin, J., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-020-0266-y · Cited by 192 · see citing works
Singh, M., Kumar, A., Khanna, N. N., Laird, J. R., Nicolaides, A. N., Faa, G., Johri, A. M., Mantella, L. E., Fernandes, J. F. E., Teji, J. S., et al. (2024) · EClinicalMedicine · doi:10.1016/j.eclinm.2024.102660 · Cited by 186 · see citing works
Application of artificial intelligence in surgery
Zhou, X.-Y., Guo, Y., Shen, M., & Yang, G.-Z. (2020) · Frontiers of Medicine · doi:10.1007/s11684-020-0770-0 · Cited by 181 · see citing works
Deep Learning and Neurology: A Systematic Review
Valliani, A. A.-A., Ranti, D. L., & Oermann, E. K. (2019) · Neurology and Therapy · doi:10.1007/s40120-019-00153-8 · Cited by 179 · see citing works
Alabi, R. O., Youssef, O., Pirinen, M., Elmusrati, M. S., Mäkitie, A. A., Leivo, I. V. V., & Almangush, A. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102060 · Cited by 178 · see citing works
Yu, Y., Xie, Y., Thamm, T., Gong, E., Ouyang, J., Huang, C. W., Christensen, S., Marks, M. P., Lansberg, M. G., Albers, G. W., et al. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.0772 · Cited by 176 · see citing works
Advances in Deep Learning-Based Medical Image Analysis
Liu, X., Gao, K., Liu, B., Pan, C., Liang, K., Yan, L., Ma, J., He, F., Zhang, S., Pan, S., et al. (2021) · Health Data Science · doi:10.34133/2021/8786793 · Cited by 175 · see citing works
Habuza, T., Navaz, A. N., Hashim, F., Alnajjar, F., Zaki, N., Serhani, M. A., & Statsenko, Y. A. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100596 · Cited by 173 · see citing works
Jiang, Y., Jin, C., Yu, H., Wu, J., Chen, C., Yuan, Q., Huang, W., Hu, Y., Xu, Y., Zhou, Z., et al. (2021) · Annals of Surgery · doi:10.1097/sla.0000000000003778 · Cited by 169 · see citing works
Monteiro, M., Newcombe, V. F. J., Mathieu, F., Adatia, K., Kamnitsas, K., Ferrante, E., Das, T., Whitehouse, D., Rueckert, D., Menon, D. K., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30085-6 · Cited by 162 · see citing works
Akazawa, M., & Hashimoto, K. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102164 · Cited by 162 · see citing works
AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning—A Comprehensive Review
Kazimierczak, N., Kazimierczak, W., Serafin, Z., Nowicki, P., Nożewski, J., & Janiszewska‐Olszowska, J. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13020344 · Cited by 160 · see citing works
Jiang, Y., Cao, S., Cao, S., Chen, J., Wang, G., Shi, W., Deng, Y.-N., Cheng, N., Ma, K., Zeng, K., et al. (2020) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-020-03366-9 · Cited by 155 · see citing works
Nishio, M., Noguchi, S., Matsuo, H., & Murakami, T. (2020) · Scientific Reports · doi:10.1038/s41598-020-74539-2 · Cited by 154 · see citing works
Notice of Retraction: AI Techniques for COVID-19
Hussain, A. A., Bouachir, O., Al-Turjman, F., & Aloqaily, M. (2020) · IEEE Access · doi:10.1109/access.2020.3007939 · Cited by 153 · see citing works
RetractedDíaz, O., Kushibar, K., Osuala, R. O., Linardos, A., Garrucho, L., Igual, L., Radeva, P. I., Prior, F., Gkontra, P., & Lekadir, K. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.007 · Cited by 153 · see citing works
Rauschecker, A. M., Rudie, J. D., Xie, L., Wang, J., Duong, M. T., Botzolakis, E. J., Kovalovich, A. M., Egan, J., Cook, T. C., Bryan, R. N., et al. (2020) · Radiology · doi:10.1148/radiol.2020190283 · Cited by 148 · see citing works
COVID-AL: The diagnosis of COVID-19 with deep active learning
Wu, X., Chen, C., Zhong, M., Wang, J., & Shi, J. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101913 · Cited by 148 · see citing works
Keratinocytic Skin Cancer Detection on the Face Using Region-Based Convolutional Neural Network
Han, S. S., Moon, I. J., Lim, W., Suh, I. S., Lee, S. Y., Na, J., Kim, S. H., & Chang, S. E. (2019) · JAMA Dermatology · doi:10.1001/jamadermatol.2019.3807 · Cited by 147 · see citing works
Shinde, R., Patil, S., Kotecha, K. V., Potdar, V. M., Selvachandran, G., & Abraham, A. (2023) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4884 · Cited by 147 · see citing works
Automated acquisition of explainable knowledge from unannotated histopathology images
Yamamoto, Y., Tsuzuki, T., Akatsuka, J., Ueki, M., Morikawa, H., Numata, Y., Takahara, T., Tsuyuki, T., Tsutsumi, K., Nakazawa, R., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13647-8 · Cited by 145 · see citing works
Deep learning for predicting COVID-19 malignant progression
Fang, C., Bai, S., Chen, Q., Zhou, Y., Xia, L., Qin, L., Gong, S., Xie, X., Zhou, C., Tu, D., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102096 · Cited by 139 · see citing works
Artificial Intelligence in Ovarian Cancer Diagnosis
Akazawa, M., & Hashimoto, K. (2020) · Anticancer Research · doi:10.21873/anticanres.14482 · Cited by 138 · see citing works
Cho, J., Park, K., Karki, M., Lee, E., Ko, S., Kim, J. K., Lee, D. E., Choe, J. Y., Son, J.-W., Kim, M., et al. (2019) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-018-00172-1 · Cited by 134 · see citing works
Shallow 3D CNN for Detecting Acute Brain Hemorrhage From Medical Imaging Sensors
SINGH, S. P., Wang, L., Gupta, S., Gulyás, B. Z., & Padmanabhan, P. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3023471 · Cited by 133 · see citing works
Kers, J., Bülow, R. D., Klinkhammer, B. M., Breimer, G. E., Fontana, F., Adefidipe, A. A., Hofstraat, R., Corthals, G. L., Sengers, H. P., Djudjaj, S., et al. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00211-9 · Cited by 131 · see citing works
Clinical applications of artificial intelligence in radiology
Mello-Thoms, C. R., & Mello, C. A. B. (2023) · British Journal of Radiology · doi:10.1259/bjr.20221031 · Cited by 131 · see citing works
Machine learning applications in stroke medicine: advancements, challenges, and future prospectives
Daidone, M., Ferrantelli, S., & Tuttolomondo, A. (2023) · Neural Regeneration Research · doi:10.4103/1673-5374.382228 · Cited by 129 · see citing works
Prakash, A. V., & Das, S. (2021) · Information & Management · doi:10.1016/j.im.2021.103524 · Cited by 128 · see citing works
Artificial intelligence in emergency medicine: A scoping review
Kirubarajan, A., Taher, A., Khan, S., & Masood, S. (2020) · JACEP Open · doi:10.1002/emp2.12277 · Cited by 127 · see citing works
Lee, J. Y., Kim, J. S., Kim, T. Y., & Kim, Y. S. (2020) · Scientific Reports · doi:10.1038/s41598-020-77441-z · Cited by 124 · see citing works
Jiang, Y., Zhou, K., Sun, Z., Wang, H., Xie, J., Zhang, T., Sang, S., Islam, M. T., Wang, J.-Y., Chen, C., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101146 · Cited by 120 · see citing works
Zhou, W., Yang, Y., Yu, C. J., Liu, J., Duan, X., Weng, Z., Chen, D., Liang, Q., Qin, F., Zhou, J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21466-z · Cited by 120 · see citing works
Artificial intelligence and machine learning in emergency medicine: a narrative review
Mueller, B., Kinoshita, T., Peebles, A., Graber, M. A., & Lee, S. (2022) · Acute Medicine & Surgery · doi:10.1002/ams2.740 · Cited by 118 · see citing works
Wei, J., Suriawinata, A. A., Vaickus, L. J., Ren, B., Liu, X., Lisovsky, M., Tomita, N., Abdollahi, B., Kim, A. S., Snover, D. C., et al. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.3398 · Cited by 112 · see citing works
Artificial Intelligence in Imaging: The Radiologist’s Role
Rubin, D. L. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.05.036 · Cited by 110 · see citing works
Analysis of head CT scans flagged by deep learning software for acute intracranial hemorrhage
Ginat, D. T. (2019) · Neuroradiology · doi:10.1007/s00234-019-02330-w · Cited by 110 · see citing works
Deep learning for identifying corneal diseases from ocular surface slit-lamp photographs
Gu, H., Guo, Y., Gu, L., Wei, A., Xie, S., Ye, Z., Xu, J., Zhou, X., Lü, Y., Liu, X., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-75027-3 · Cited by 109 · see citing works
Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century
Naqa, I. E., Haider, M. A., Giger, M. L., & Haken, R. K. T. (2020) · British Journal of Radiology · doi:10.1259/bjr.20190855 · Cited by 101 · see citing works
Implementation of artificial intelligence in medicine: status analysis and development suggestions
Xiang, Y., Zhao, L., Liu, Z., Wu, X., Chen, J., Long, E., Lin, D., Zhu, Y., Chen, C., Lin, Z., et al. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101780 · Cited by 100 · see citing works
Tembely, M., AlSumaiti, A. M., & AlAmeri, W. S. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.02.065 · Cited by 97 · see citing works
Predicting COVID-19 Malignant Progression with AI TechniquesPreprint
Bai, X., Fang, C., Zhou, Y., Bai, S., Liu, Z., Xia, L., Chen, Q., Xu, Y., Xia, T., Gong, S., et al. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3557984 · Cited by 97 · see citing works
The fate of medicine in the time of AI
Coiera, E. W. (2018) · The Lancet · doi:10.1016/s0140-6736(18)31925-1 · Cited by 96 · see citing works
A deep learning model for detection of cervical spinal cord compression in MRI scans
Merali, Z. A., Wang, J. Z., Badhiwala, J. H., Witiw, C. D., Wilson, J. R., & Fehlings, M. G. (2021) · Scientific Reports · doi:10.1038/s41598-021-89848-3 · Cited by 95 · see citing works
Knight, D. R. T., Aakre, C. A., Anstine, C. V., Munipalli, B., Biazar, P., Mitri, G., Valery, J. R., Brigham, T., Niazi, S. K., Perlman, A. I., et al. (2023) · Health Policy and Technology · doi:10.1016/j.hlpt.2023.100824 · Cited by 94 · see citing works
Artificial intelligence in food science and nutrition: A narrative review
Miyazawa, T., Hiratsuka, Y., Toda, M., Hatakeyama, N., Ozawa, H., Abe, C., Cheng, T.-Y., Matsushima, Y., Miyawaki, Y., Ashida, K., et al. (2022) · Nutrition Reviews · doi:10.1093/nutrit/nuac033 · Cited by 91 · see citing works
Arntfield, R. T., VanBerlo, B., Alaifan, T., Phelps, N., White, M. R., Chaudhary, R., Ho, J., & Wu, D. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-045120 · Cited by 91 · see citing works
Artificial intelligence for clinical decision support in neurology
Pedersen, M., Verspoor, K. M., Jenkinson, M., Law, M., Abbott, D. F., & Jackson, G. D. (2020) · Brain Communications · doi:10.1093/braincomms/fcaa096 · Cited by 89 · see citing works
Machine learning predictive models for acute pancreatitis: A systematic review
Zhou, Y. J., Ge, Y., Shi, X., Wu, K., Chen, W., Ding, Y., Xiao, W., Wang, D., Lu, G., & Hu, L. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104641 · Cited by 89 · see citing works
Kumari, S., & Singh, P. P. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107912 · Cited by 88 · see citing works
A Petri Dish for Histopathology Image Analysis
Wei, J., Suriawinata, A. A., Ren, B., Liu, X., Lisovsky, M., Vaickus, L. J., Brown, C. L., Baker, M. L., Tomita, N., Torresani, L., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77211-6_2 · Cited by 87 · see citing works
FairLens: Auditing black-box clinical decision support systems
Panigutti, C., Perotti, A., Panisson, A., Bajardi, P., & Pedreschi, D. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102657 · Cited by 86 · see citing works
Weikert, T., Noordtzij, L. A., Bremerich, J., Stieltjes, B., Parmar, V., Cyriac, J., Sommer, G., & Sauter, A. W. (2020) · Korean Journal of Radiology · doi:10.3348/kjr.2019.0653 · Cited by 86 · see citing works
Rao, B., Zohrabian, V. M., Cedeño, P. A., Saha, A., Pahade, J. K., & Davis, M. A. (2020) · Academic Radiology · doi:10.1016/j.acra.2020.01.035 · Cited by 85 · see citing works
Langerhuizen, D. W. G., Bulstra, A. E. J., Janssen, S. J., Ring, D., Kerkhoffs, G. M. M. J., Jaarsma, R. L., & Doornberg, J. N. (2020) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000001318 · Cited by 85 · see citing works
Wang, M., Chen, X., Huang, L., Xu, S., Qin, C., Liu, J., Cao, Y., Yu, P., Zhu, T., Zhu, H., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30199-0 · Cited by 85 · see citing works
Mansour, R. F., & Aljehane, N. O. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06020-8 · Cited by 85 · see citing works
Artificial Intelligence Applications for Workflow, Process Optimization and Predictive Analytics
Guillon, L. L., Camirand, D., Guilbert, F., & Forghani, R. (2020) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2020.08.008 · Cited by 84 · see citing works
Pre-existing and machine learning-based models for cardiovascular risk prediction
Cho, S.-Y., Kim, S., Kang, S., Lee, K. J., Choi, D., Kang, S., Park, S. J., Kim, T., Yoon, C., Youn, T., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-88257-w · Cited by 84 · see citing works
Qiu, W., Chen, H., Kaeberlein, M. R., & Lee, S. (2023) · The Lancet Healthy Longevity · doi:10.1016/s2666-7568(23)00189-7 · Cited by 85 · see citing works
Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning
Alwhbi, I. A., Zou, C. C., & Alharbi, R. N. (2024) · Sensors · doi:10.3390/s24113509 · Cited by 81 · see citing works
“Emerging IoT applications span various sectors, including healthcare, agriculture, and military, presenting new challenges in device management, data handling, and security [45,46].”
Background
- 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
- Life Sciences › Neuroscience › Neurology › 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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