Publications

Keya Medical scientists have authored over 400 research papers published in top journals. The following list includes articles co-authored by our scientists and publications written by collaborating researchers using our technology. This information is for research, scientific, and educational purposes only.

Cardiovascular Publications

A stepwise strategy integrating dynamic stress CT myocardial perfusion and deep learning–based FFRCT in the work-up of stable coronary artery disease.
Lyu, L., Pan, J., Li, D. et al. Eur Radiol (2024). https://doi.org/10.1007/s00330-023-10562-x

Deep learning-based coronary artery calcium score to predict coronary artery disease in type 2 diabetes mellitus
Jingcheng Hu, Guangyu Hao, Jialiang Xu, Ximing Wang, Meng Chen, Heliyon, Volume 10, Issue 6, 2024

Incremental diagnostic value of perivascular fat attenuation index for identifying hemodynamically significant ischemia with severe calcification.
Shan, D., Ding, Y., Wang, X. et al.
Int J Cardiovasc Imaging 39, 2023.

Clinical value of perivascular fat attenuation index and computed tomography derived fractional flow reserve in identification of culprit lesion of subsequent acute coronary syndrome.
Huang Minggang, Han Tingting, Nie Xuan, Zhu Shunming, Yang Di, Mu Yue, Zhang Yan.
Frontiers in Cardiovascular Medicine. 2023.

On-Site Computed Tomography–Derived Fractional Flow Reserve to Guide Management of Patients With Stable Coronary Artery Disease: The TARGET Randomized Trial.
Yang J, et al. Circulation. 2023.

Effect of 320-Row Computed Tomography Acquisition Technology on Coronary Computed Tomography Angiography–Derived Fractional Flow Reserve Based on Machine Learning: Systolic and Diastolic Scan Acquisition.
Yang, Fengfeng MMed∗; Shi, Ke MMed†; Chen, Yuhuan PhD‡; Yin, Youbing PhD‡; Zhao, Yang MD∗; Zhang, Tong MD†. Journal of Computer Assisted Tomography. 47(2):p 205-211, 3/4 2023.

Deep Learning–based Prediction of Percutaneous Recanalization in Chronic Total Occlusion Using Coronary CT Angiography.
Zhen Zhou, Yifeng Gao, Weiwei Zhang, Nan Zhang, Hui Wang, Rui Wang, Zhifan Gao, Xiaomeng Huang, Shanshan Zhou, Xu Dai, Guang Yang, Heye Zhang, Koen Nieman, and Lei Xu
Radiology 2023.

Value of CT‑derived fractional flow reserve in identifying patients with acute myocardial infarction based on coronary computed tomography angiography.
Yang F, Pang Z, Yang Z, Yang Y, Wang Y, Jia P, Wang D, Cui S.
Exp Ther Med. 2023.

Impact of coronary computed tomography angiography-derived fractional flow reserve based on deep learning on clinical management.
Pan Y, Zhu T, Wang Y, Deng Y, Guan H.
Front Cardiovasc Med. 2023.

Effect of 320-row CT reconstruction technology on fractional flow reserve derived from coronary CT angiography based on machine learning: single- versus multiple-cardiac periodic images.
Shi K, Yang FF, Si N, Zhu CT, Li N, Dong XL, Guo Y, Zhang T. Quant Imaging Med Surg 2022.

Coronary Computed Tomography Angiography for the Assessment of Sirolimus-Eluting Resorbable Magnesium Scaffold.
Tonet, E.; Cossu, A.; Pompei, G.; Ruggiero, R.; Caglioni, S.; Mele, D.; Boccadoro, A.; Micillo, M.; Cocco, M.; De Raffele, M.; et al.
Life 2022.

Risk predicting for acute coronary syndrome based on machine learning model with kinetic plaque features from serial coronary computed tomography angiography.
Yabin Wang, Haiwei Chen, Ting Sun, et al.
European Heart Journal – Cardiovascular Imaging, Volume 23, Issue 6, June 2022.

Variation of computed tomographic angiography–based fractional flow reserve after transcatheter aortic valve implantation.
Zhang, Y., Xiong, TY., Li, YM. et al.
Eur Radiol 31, 6220–6229 (2021).

The ADAPT Study: Assessment of the DiAgnostic Performance of DeepVessel FFR in SuspecTed Coronary Artery Disease (ADAPT).
ClinicalTrials.Gov, 31 Dec. 2021, classic.clinicaltrials.gov/ct2/show/NCT04828590. Accessed 18 Feb. 2024.

A 2-year Investigation of the Impact of the Computed Tomography-derived Fractional Flow Reserve Calculated Using a Deep Learning Algorithm on Routine Decision-making for Coronary Artery Disease Management
Liu X, Xukai Mo, Zhang H, Yang G, et al.
European Radiology, 2021.

Additional Value of Deep Learning Computed Tomographic Angiography-based Fractional Flow Reserve in Detecting Coronary Stenosis and Predicting Outcomes
Yang L, Qiu H, Zheng J, Yin Y, et al.
Acta Radiologica, 2021.

Learning Physical Properties in Complex Visual Scenes: An Intelligent Machine for Perceiving Blood Flow Dynamics from Static CT Angiography Imaging
Gao Z., Wang, X., Sun, S., Wu, D., Bai, J., Yin, Y., et al.
Neural Networks, 2020.

Learning Tree-structured Representation for 3D Coronary Artery Segmentation
Kong, B., Wang, X., Bai, J., Lu, Y., Gao, F., Cao, K., et al.
Computerized Medical Imaging and Graphics, 2020.

DeepCenterline: A Multi-task Fully Convolutional Network for Centerline Extraction
Guo Z., Bai, J., Lu, Y., Wang X., Cao K., et al.,
International Conference on Information Processing in Medical Imaging (IPMI), 2019.

Evaluation of Fractional Flow Reserve in Patients with Stable Angina: Can CT Compete with Angiography?
Liu, X., Wang, Y., Zhang, H., Yin, Y., Cao, K., et al.
European Radiology, 2019.

Automated Anatomical Labeling of Coronary Arteries via Bidirectional Tree LSTMs
Wu, D., Wang, X., Bai, J., Xu, X., Ouyang B., et al.
International Journal of Computer Assisted Radiology and Surgery, 2019.14: 271.

Precise Diagnosis of Intracranial Hemorrhage and Subtypes Using a Three-dimensional Joint Convolutional and Recurrent Neural Network
Ye H*, Gao F* (*Equal contribution), Yin Y, Guo D, Zhao P, et al., European Radiology, March 2019.

Diagnostic Accuracy of a Deep Learning Approach to Calculate FFR from Coronary CT Angiography
Wang, Z., Zhou, Y., Zhao, Y., Shi, D., Liu, Y., et al.
Journal of Geriatric Cardiology, 2019.

Hemodynamics Analysis of the Serial Stenotic Coronary Arteries
Liu, X., Peng, C., Xia, Y., et al.
Biomedical Engineering Online.16(1).127, 2017.

A Study of Noninvasive Fractional Flow Reserve Derived from a Simplified Method based on Coronary Computed Tomography Angiography in Suspected Coronary Artery Disease
Shi, C., Zhang, D., Cao, K., et al.
Biomedical Engineering Online.16(1).43, 2017.

Other Publications

Simultaneous Classification and Segmentation of Intracranial Hemorrhage Using a Fully Convolutional Neural Network
Guo, D., Wei, H., Zhao, P., Pan, Y, et al.
International Symposium on Biomedical Imaging (ISBI), IEEE, April 2020.

Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT
Li L., Qin L., Xu Z. Yin, Y., Wang, X., et al.
Radiology, March 2020.

DeepCenterline: A Multi-task Fully Convolutional Network for Centerline Extraction
Guo Z., Bai, J., Lu, Y., Wang X., Cao K., et al.,
International Conference on Information Processing in Medical Imaging (IPMI), 2019.

Residual Attention Based Network for Hand Bone Age Assessment
Wu E, Kong B, Wang X, Bai J, Lu Y, et al.,
IEEE International Symposium on Biomedical Imaging (ISBI), 2019.

Dual Adversarial Auto-encoder for Dermoscopic Image Generative Modeling
Yang, HY, Staib, L.,
IEEE International Symposium on Biomedical Imaging (ISBI), 2019.

Automatic Brain Tumor Segmentation with Contour Aware Residual Network and Adversarial Training
Yang, HY. Yang, J.,
International MICCAI Brain Lesion Workshop, 2018.

Volumetric Adversarial Training for Ischemic Stroke Lesion Segmentation
Yang, HY.
International MICCAI Brain Lesion Workshop, 2018.

Learn to be Uncertain: Leveraging Uncertain Labels in Chest X-Rays with Bayesian Neural Networks
Yang HY, Yang J, Pan Y, Cao K, Song Q, et al., Uncertainty and Robustness in Deep Visual Learning Workshop, IEEE Conference on Computer Vision and Pattern Recognition, June, 2019.

Precise Diagnosis of Intracranial Hemorrhage and Subtypes Using a Three-dimensional Joint Convolutional and Recurrent Neural Network
Ye H*, Gao F* (*Equal contribution), Yin Y, Guo D, Zhao P, et al., European Radiology, March, 2019.

Train a 3D U-Net to Segment Cranial Vasculature in CTA Volume without Manual Annotation
Chen, X., Lu, Y., Bai, J., et al.
IEEE 15th International Symposium on Biomedical Imaging (ISBI), 2018.

Invasive Cancer Detection Utilizing Compressed Convolutional Neural Network and Transfer Learning
Kong, B., Sun, S., Wang, X., et al.
In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2018.

A Novel Method of Estimating Small Airway Disease Using Inspiratory-to-Expiratory Computed Tomography
Kirby M, Yin Y, et al.,
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2018.

Integrate Domain Knowledge in Training CNN for Ultrasonography Breast Cancer Diagnosis
Liu, J., Li, W., Zhao, N., Cao, K., Yin Y., et al.
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2018.

Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning
Hussein, S., Cao, K., Song, Q., et al.
International Conference on Information Processing in Medical Imaging (IPMI), 2017.

Cancer Metastasis Detection via Spatially Structured Deep Network
Kong B*, Wang X* (*Equal contribution), Li Z, Song Q, Zhang S.
International Conference on Information Processing in Medical Imaging (IPMI), 2017.

TumorNet: Lung Nodule Characterization using Multi-view Convolutional Neural Network with Gaussian Process
Hussein, R. Gillies, K. Cao, et al.
IEEE 14th International Symposium on Biomedical Imaging (ISBI), 2017.

Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method
Guo X, Dominick K, Minai, A, Li H, Erickson C, et al.,
Frontiers in Neuroscience, 11, 460. 2017.

A Computational Model for the Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Based on Functional Brain Volume
Tan L, Guo X, Ren S, Epstein, J, Lu L
Frontiers in Computational Neuroscience, 11, 75. 2017.

Towards Quantitative Assessment of Rheumatoid Arthritis Using Volumetric Ultrasound
Cao K, Mills DM, Thiele RG, Patwardhan KA
IEEE Transactions on Biomedical Engineering (TBME), 63(2): 449-458, 2016.

MASCG: Multi-Atlas Segmentation Constrained Graph Method for Accurate Segmentation of Hip CT image
Chu C, Bai J, Wu X, Zheng G.
Medical Image Analysis, 26(1):173, December 2015.

Multiple Surface Segmentation Using Truncated Convex Priors
Shah A, Bai J, Hu Z, Sadda S, Wu X
Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015.

Computed Tomography Predictors of Response to Endobronchial Valve Lung Reduction Treatment. Comparison with Chartis
Schuhmann M, RaffyP, Yin Y, Gompelmann D, Oguz I et al.,
American Journal of Respiratory and Critical Care Medicine (AJRCCM), Vol. 191 (7), 767-774, 2015.

Error-tolerant Scribbles Based Interactive Image Segmentation
Bai J, Wu X
Computer Vision and Pattern Recognition (CVPR), 2014 .

Globally Optimal Lung Tumor Co-segmentation of 4D CT and PET Images
Bai J, Song Q, Bhatia S, Wu X
Proceedings of SPIE Medical Imaging (oral presentation), 2013.

Optimal Co-segmentation of Tumor in PET-CT Images with Context Informations
Song Q,  Bai J, Han D, Bhatia S, Sun W, et al.,
IEEE Transactions on Medical Imaging, 32(9):1685-97, September 2013.

Intensity-based Registration for Lung Motion Estimation
Cao K, Ding K, Amelon R, Du K, Reinhardt J et al.,
In Springer Book on “4D Modeling and Estimation of Respiratory Motion for Radiation Therapy”, published in the Springer series Biological and Medical Physics, Biomedical Engineering 2013, pp 125-158.

Motion-Compensated Mega-Voltage Cone Beam CT Using the Deformation Derived Directly from 2D Projection Images
Chen M, Cao K,  Zheng Y, Siochi R
IEEE Transactions on Medical Imaging, 32(8): 1365-1375, 2013.

Fast Dynamic Programming for Labeling Problems with Ordering Constraints
Bai J, Song Q, Veksler O, Wu X
Computer Vision and Pattern Recognition (CVPR), 2012.

Registration-based Estimates of Local Lung Tissue Expansion Compared to Xenon CT Measures of Specific Ventilation
Reinhardt J, Ding K, Cao K, Christensen G, Hoffman E, et al.,
Medical Image Analysis, 12(6):752-763, 2008 .