Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Jakub Olczak"'
Autor:
Jakub Olczak, Jasper Prijs, Frank IJpma, Fredrik Wallin, Ehsan Akbarian, Job Doornberg, Max Gordon
Publikováno v:
BMC Musculoskeletal Disorders, Vol 25, Iss 1, Pp 1-13 (2024)
Abstract Background Advances in medical imaging have made it possible to classify ankle fractures using Artificial Intelligence (AI). Recent studies have demonstrated good internal validity for machine learning algorithms using the AO/OTA 2018 classi
Externí odkaz:
https://doaj.org/article/61aba385a4254302a8e4b136b10b995c
Autor:
Luisa Oliveira e Carmo, Anke van den Merkhof, Jakub Olczak, Max Gordon, Paul C. Jutte, Ruurd L. Jaarsma, Frank F. A. IJpma, Job N. Doornberg, Jasper Prijs, Machine Learning Consortium
Publikováno v:
Bone & Joint Open, Vol 2, Iss 10, Pp 879-885 (2021)
Aims: The number of convolutional neural networks (CNN) available for fracture detection and classification is rapidly increasing. External validation of a CNN on a temporally separate (separated by time) or geographically separate (separated by loca
Externí odkaz:
https://doaj.org/article/2a4623a5c87943efad356faa435f1d00
Autor:
Jakub Olczak, John Pavlopoulos, Jasper Prijs, Frank F A Ijpma, Job N Doornberg, Claes Lundström, Joel Hedlund, Max Gordon
Publikováno v:
Acta Orthopaedica, Vol 92, Iss 5, Pp 513-525 (2021)
Background and purpose — Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have become common research fields in orthopedics and medicine in general. Engineers perform much of the work. While they gear the results towards
Externí odkaz:
https://doaj.org/article/d5f89633677b492791e7c073220f9c52
Publikováno v:
Acta Orthopaedica, Vol 92, Iss 1, Pp 102-108 (2020)
Background and purpose — Classification of ankle fractures is crucial for guiding treatment but advanced classifications such as the AO Foundation/Orthopedic Trauma Association (AO/OTA) are often too complex for human observers to learn and use. We
Externí odkaz:
https://doaj.org/article/b6a49c2925c54bd6973ea961fdc3191c
Autor:
Jakub Olczak, Niklas Fahlberg, Atsuto Maki, Ali Sharif Razavian, Anthony Jilert, André Stark, Olof Sköldenberg, Max Gordon
Publikováno v:
Acta Orthopaedica, Vol 88, Iss 6, Pp 581-586 (2017)
Background and purpose — Recent advances in artificial intelligence (deep learning) have shown remarkable performance in classifying non-medical images, and the technology is believed to be the next technological revolution. So far it has never bee
Externí odkaz:
https://doaj.org/article/535a85f3c85244a987b3b0e7c5fcdeb1
Autor:
Jasper, Prijs, Zhibin, Liao, Minh-Son, To, Johan, Verjans, Paul C, Jutte, Vincent, Stirler, Jakub, Olczak, Max, Gordon, Daniel, Guss, Christopher W, DiGiovanni, Ruurd L, Jaarsma, Frank F A, IJpma, Job N, Doornberg, David, Worsley
Publikováno v:
European journal of trauma and emergency surgery : official publication of the European Trauma Society.
Convolutional neural networks (CNNs) are increasingly being developed for automated fracture detection in orthopaedic trauma surgery. Studies to date, however, are limited to providing classification based on the entire image-and only produce heatmap
Publikováno v:
Acta Orthopaedica, Vol 92, Iss 1, Pp 102-108 (2020)
Acta Orthopaedica
article-version (VoR) Version of Record
Acta Orthopaedica
article-version (VoR) Version of Record
Background and purpose — Classification of ankle fractures is crucial for guiding treatment but advanced classifications such as the AO Foundation/Orthopedic Trauma Association (AO/OTA) are often too complex for human observers to learn and use. We
Autor:
Jasper Prijs, Luisa Oliveira E Carmo, Frank F A IJpma, Job N. Doornberg, Paul C Jutte, Anke van den Merkhof, Ruurd L. Jaarsma, Max Gordon, Jakub Olczak
Publikováno v:
Bone and Joint Open, 2(10), 879-885
Bone & Joint Open, Vol 2, Iss 10, Pp 879-885 (2021)
Bone & Joint Open, 2, 10, pp. 879-885
Bone & Joint Open, 2, 879-885
Bone & Joint Open, Vol 2, Iss 10, Pp 879-885 (2021)
Bone & Joint Open, 2, 10, pp. 879-885
Bone & Joint Open, 2, 879-885
Aims The number of convolutional neural networks (CNN) available for fracture detection and classification is rapidly increasing. External validation of a CNN on a temporally separate (separated by time) or geographically separate (separated by locat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3680ef3c7245be7e349a119dd6528910
https://research.rug.nl/en/publications/1a57a04b-14cf-49ea-898f-4f384ad3ba59
https://research.rug.nl/en/publications/1a57a04b-14cf-49ea-898f-4f384ad3ba59
Autor:
Niklas Fahlberg, Olof Sköldenberg, Jakub Olczak, Anthony Jilert, André Stark, Atsuto Maki, Ali Sharif Razavian, Max Gordon
Publikováno v:
Acta Orthopaedica, Vol 88, Iss 6, Pp 581-586 (2017)
Background and purpose — Recent advances in artificial intelligence (deep learning) have shown remarkable performance in classifying non-medical images, and the technology is believed to be the next technological revolution. So far it has never bee
Publikováno v:
Seminars in Cell & Developmental Biology. 51:44-52
Network inference is a rapidly advancing field, with new methods being proposed on a regular basis. Understanding the advantages and limitations of different network inference methods is key to their effective application in different circumstances.