Connie Jung OTR/L (she/her/hers)
Instructor of Clinical Occupational Therapy
Montrose
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Connie Jung OTR/L earned her master’s degree in Occupational Therapy from New York University. She developed a passion for upper extremity rehabilitation, healthcare accessibility for older adults, and patient advocacy while working with underserved and medically complex populations at NYC Health + Hospitals/Bellevue Hospital, a Level I Trauma Center and the nation’s oldest public hospital. She has experience across a variety of clinical settings, including acute care, subacute rehabilitation, inpatient rehabilitation, outpatient hand therapy, and brain injury clinics.
Connie holds the CBOT Advanced Practice Hand Therapy Certification and serves as a clinical faculty member at USC Faculty Practice in Montrose. She provides specialized hand therapy services for patients with upper extremity conditions including tendon and nerve repairs, fractures, ligament injuries, burns, cumulative trauma disorders, and autoimmune diseases.
Education
Master of Science (MS)
in Occupational Therapy
New York University
Bachelor of Arts (BA)
in Health and Humanity; Minor in Occupational Science
University of Southern California
Selected Publications
Liew, S.-L., Anglin, J. M., Banks, N. W., Sondag, M., Ito, K. L., Kim, H., Chan, J., Ito, J., Jung, C., Khoshab, N., Lefebvre, S., Nakamura, W., Saldana, D., Schmiesing, A., Tran, C., Vo, D., Ard, T., Heydari, P., Kim, B., Aziz-Zadeh, L., Cramer, S. C., Liu, J., Soekadar, S., Nordvik, J.-E., Westlye, L. T., Wang, J., Winstein, C., Yu, C., Ai, L., Koo, B., Craddock, R. C., Milham, M., Lakich, M., Pienta, A., & Stroud, A. (2018). A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific Data, 5, 180011. https://doi.org/10.1038/sdata.2018.11 Show abstract
Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e.g., measures of brain structure) of long-term stroke recovery following rehabilitation. However, analyzing large rehabilitation-related datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but are labor intensive and require anatomical expertise. While algorithms have been developed to automate this process, the results often lack accuracy. Newer algorithms that employ machine-learning techniques are promising, yet these require large training datasets to optimize performance. Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation methods. We hope ATLAS release 1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods.
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