CuraCloud™ is a venture-backed company formed to accelerate the use of AI to improve healthcare efficiency and precision through better diagnostics. Our founding team formed the company in early 2016, having previously designed AI features for some of the worlds most advanced diagnostic imaging equipment manufacturers.
Today our multidisciplinary R&D team includes more than 20 PhDs from leading US universities with field experience in machine learning, deep learning, computer vision, medical image analysis, genomic sequencing, and molecular biology. Our team members have published over 100 articles in peer-reviewed science journals, and have accumulated decades of experience working with healthcare delivery systems world-wide.
The future of smarter healthcare is artificial intelligence. CuraCloud has attracted strong financial backing because it leads the field in using the power of machine learning to enhance healthcare effectiveness, precision and efficiency worldwide.
CuraCloud investors bring an unmatched breadth and depth of experience in introducing disruptive technologies across global markets. They see outstanding growth potential in healthcare markets worldwide, and regard CuraCloud’s technology as a key ingredient for improving and expanding access to high-quality healthcare across the globe.
CuraCloud is actively seeking collaborators to achieve smarter healthcare through machine intelligence. Examples of our collaborations:
- Radiology workflow software company: Clario Medical is collaborating with CuraCloud to use AI to prioritize radiology worklists, so that the most urgent exams are seen first.
- Cancer center: A cancer research center is working with us to use Natural Language Processing to extract data from unstructured clinical documents in order to populate a cancer registry.
- Academic research: Multiple groups are collaborating with CuraCloud to better detect circulating tumor cell DNA to aid cancer detection.
Our multidisciplinary scientific team has published well over 100 articles in leading journals, textbooks, and proceedings of international scientific conferences, even though our major focus is product development.
A sample of our thought leadership in 2015-2017:
- Dennis, H, Ward, A, Balson, T, Yuwei Li, Henschel, R, Slavin, S, Simms, S, Brunst, H, “High Performance Computing Enabled Simulation of the Food-Water-Energy System: Simulation of Intensively Managed Landscapes”, PEARC17 Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact, Article no. 43
- Hussein, K. Cao,Q. Song, and U. Bagci, “Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning”, Information Processing in Medical Imaging (IPMI)2017
- Yuwei Li, Castro, A. M., Martin, J. E., Sinokrot, T., Prescott, W., Carrica, P. M. (2017). “Coupled computational fluid dynamics/multibody dynamics method for wind turbine aero-servo-elastic simulation including drivetrain dynamics”. Renewable Energy, 101, 1037-1051.
- Yujie Li*, Hanbo Chen*, Xi Jiang, Xiang Li, Jinglei Lv, Hanchuan Peng**, Joe Z. Tsien**, Tianming Liu**, Discover Mouse Gene Coexpression Landscape Using Dictionary Learning and Sparse Coding, MICCAI, LNCS, 2016, vol. 9900, pp. 63-71. *Co-first authors; **Joint corresponding authors.
- Bin Kong, Xin Wang, Zhongyu Li, Qi Song, Shaoting Zhang: Cancer Metastasis Detection via Spatially Structured Deep Network. The 25th Biennial International Conference on Information Processing in Medical Imaging, 2017.
- Gao, M., Bagci, U., Lu, L., Wu, A., Buty, M., Shin, H. C., … & Xu, Z. (2016). Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1-6
- Chu, J. Bai, L. Liu, X. Wu and G. Zheng. “Fully Automatic Segmentation of Hip CT Images”, Computational Radiology for Orthopaedic Interventions, page 91-110, 2016 (Book Chapter)
- Bai, Z. Hu, Y. Shi, S. Sadda and X. Wu. “Automatic Retinal Surface Segmentation in 3D OCT scans with Geographic Atrophy”, The Association for Research in Vision and Ophthalmology (ARVO), 2016
- Gao F* and Keinan A*. Inference of super-exponential human population growth via efficient computation of the site frequency spectrum for generalized models. Genetics 202(1), 235-245 (2016).
- Wang, X. Chang, S. Lyu. Co-regularized PLSA For Multi-Modal Learning. In: AAAI Conference on Artificial Intelligence (AAAI), Phoenix, Arizona USA, 2016.
- Nathan J. Kempema, Bin Ma, and Marshall B. Long “Investigation of In-Flame Soot Optical Properties in Laminar Coflow Diffusion Flames using Thermophoretic Particle Sampling and Spectral Light Extinction”, Applied Physics B, 122 (9) (2016), 232
- Cao, D. Mills, R. Thiele, and K. Padwardhan, “Towards Quantitative Assessment of Rheumatoid Arthritis Using Volumetric Ultrasound”, IEEE Transactions on Biomedical Engineering (TBME), 63(2): 449-458, 2016
- Zhou J, Wan J, Gao X, Zhang X, Jaffrey SR, Qian SB. Dynamic m(6)A mRNA methylation directs translational control of heat shock response. Nature. 2015 Oct 22;526(7574):591-4