All Publications

  • Deep Learning for the Assessment of Facial Nerve Palsy: Opportunities and Challenges. Facial plastic surgery : FPS Boochoon, K., Mottaghi, A., Aziz, A., Pepper, J. P. 2023

    Abstract

    Automated evaluation of facial palsy using machine learning offers a promising solution to the limitations of current assessment methods, which can be time-consuming, labor-intensive, and subject to clinician bias. Deep learning-driven systems have the potential to rapidly triage patients with varying levels of palsy severity and accurately track recovery over time. However, developing a clinically usable tool faces several challenges, such as data quality, inherent biases in machine learning algorithms, and explainability of decision-making processes. The development of the eFACE scale and its associated software has improved clinician scoring of facial palsy. Additionally, Emotrics is a semiautomated tool that provides quantitative data of facial landmarks on patient photographs. The ideal artificial intelligence (AI)-enabled system would analyze patient videos in real time, extracting anatomic landmark data to quantify symmetry and movement, and estimate clinical eFACE scores. This would not replace clinician eFACE scoring but would offer a rapid automated estimate of both anatomic data, similar to Emotrics, and clinical severity, similar to the eFACE. This review explores the current state of facial palsy assessment, recent advancements in AI, and the opportunities and challenges in developing an AI-driven solution.

    View details for DOI 10.1055/s-0043-1769805

    View details for PubMedID 37290452

  • 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