Automatic real-time gait event detection in children using deep neural networks

af Kidziński, Łukasz, Delp, Scott, Schwartz, Michael
Udgivet i PLoS One (2019)

Session Intro: ARTIFICIAL INTELLIGENCE FOR ENHANCING CLINICAL MEDICINE

af Daneshjou, Roxana, Kidzinski, Lukasz, Afanasiev, Olga, Chen, Jonathan H.
Udgivet i Pac Symp Biocomput (2020)

Multimodal Teaching Analytics: Automated Extraction of Orchestration Graphs from Wearable Sensor Data

af Prieto, Luis P., Sharma, Kshitij, Kidzinski, Łukasz, Rodríguez-Triana, María Jesús, Dillenbourg, Pierre
Udgivet i J Comput Assist Learn (2018)

Reply: Limitations in the creation of an automatic diagnosis tool for dysgraphia

af Asselborn, Thibault, Gargot, Thomas, Kidziński, Łukasz, Johal, Wafa, Cohen, David, Jolly, Caroline, Dillenbourg, Pierre
Udgivet i NPJ Digit Med (2019)

Automated human-level diagnosis of dysgraphia using a consumer tablet

af Asselborn, Thibault, Gargot, Thomas, Kidziński, Łukasz, Johal, Wafa, Cohen, David, Jolly, Caroline, Dillenbourg, Pierre
Udgivet i NPJ Digit Med (2018)

The turning and barrier course reveals gait parameters for detecting freezing of gait and measuring the efficacy of deep brain stimulation

af O’Day, Johanna, Syrkin-Nikolau, Judy, Anidi, Chioma, Kidzinski, Lukasz, Delp, Scott, Bronte-Stewart, Helen
Udgivet i PLoS One (2020)

Deep neural networks enable quantitative movement analysis using single-camera videos

af Kidziński, Łukasz, Yang, Bryan, Hicks, Jennifer L., Rajagopal, Apoorva, Delp, Scott L., Schwartz, Michael H.
Udgivet i Nat Commun (2020)

Estimating the effect size of surgery to improve walking in children with cerebral palsy from retrospective observational clinical data

af Rajagopal, Apoorva, Kidziński, Łukasz, McGlaughlin, Alec S., Hicks, Jennifer L., Delp, Scott L., Schwartz, Michael H.
Udgivet i Sci Rep (2018)

Pre-operative gastrocnemius lengths in gait predict outcomes following gastrocnemius lengthening surgery in children with cerebral palsy

af Rajagopal, Apoorva, Kidziński, Łukasz, McGlaughlin, Alec S., Hicks, Jennifer L., Delp, Scott L., Schwartz, Michael H.
Udgivet i PLoS One (2020)

A neural network to predict the knee adduction moment in patients with osteoarthritis using anatomical landmarks obtainable from 2D video analysis

af Boswell, Melissa A., Uhlrich, Scott D., Kidziński, Łukasz, Thomas, Kevin, Kolesar, Julie A., Gold, Garry E., Beaupre, Gary S., Delp, Scott L.
Udgivet i Osteoarthritis Cartilage (2021)

Automated Classification of Radiographic Knee Osteoarthritis Severity Using Deep Neural Networks

af Thomas, Kevin A., Kidziński, Łukasz, Halilaj, Eni, Fleming, Scott L., Venkataraman, Guhan R., Oei, Edwin H. G., Gold, Garry E., Delp, Scott L.
Udgivet i Radiol Artif Intell (2020)

Wearable sensors enable personalized predictions of clinical laboratory measurements

af Dunn, Jessilyn, Kidzinski, Lukasz, Runge, Ryan, Witt, Daniel, Hicks, Jennifer L., Rose, Sophia Miryam Schüssler-Fiorenza, Li, Xiao, Bahmani, Amir, Delp, Scott L., Hastie, Trevor, Snyder, Michael P.
Udgivet i Nat Med (2021)

Deep longitudinal multiomics profiling reveals two biological seasonal patterns in California

af Sailani, M. Reza, Metwally, Ahmed A., Zhou, Wenyu, Rose, Sophia Miryam Schüssler-Fiorenza, Ahadi, Sara, Contrepois, Kevin, Mishra, Tejaswini, Zhang, Martin Jinye, Kidziński, Łukasz, Chu, Theodore J., Snyder, Michael P.
Udgivet i Nat Commun (2020)