Contact

  • Academic
    mottaghi@stanford.edu
    University - Student Department: Computer Science Position: Graduate
    University - Student Department: Electrical Engineering Position: Graduate

Additional Info

  • Mail Code: 9505

All Publications

  • AUTOMATED DETECTION OF ISOLATED REM SLEEP BEHAVIOR DISORDER (IRBD) DURING SINGLE NIGHT IN-LAB VIDEO-POLYSOMNOGRAPHY (PSG) USING COMPUTER VISION Adaimi, G., Gupta, N., Mottaghi, A., Yeung, S., Mignot, E., Alahi, A., During, E. OXFORD UNIV PRESS INC. 2022: A282

    View details for Web of Science ID 000838094800637

  • Adaptation of Surgical Activity Recognition Models Across Operating Rooms Mottaghi, A., Sharghi, A., Yeung, S., Mohareri, O., Wang, L., Dou, Q., Fletcher, P. T., Speidel, S., Li, S. SPRINGER INTERNATIONAL PUBLISHING AG. 2022: 530-540

    View details for DOI 10.1007/978-3-031-16449-1_51

    View details for Web of Science ID 000867568000050

  • Deep learning-enabled medical computer vision. NPJ digital medicine Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J., Socher, R. 2021; 4 (1): 5

    Abstract

    A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields-including medicine-to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques-powered by deep learning-for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit-including cardiology, pathology, dermatology, ophthalmology-and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

    View details for DOI 10.1038/s41746-020-00376-2

    View details for PubMedID 33420381