Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Jaakko Sahlsten"'
Autor:
Jaakko Sahlsten, Joel Jaskari, Kareem A. Wahid, Sara Ahmed, Enrico Glerean, Renjie He, Benjamin H. Kann, Antti Mäkitie, Clifton D. Fuller, Mohamed A. Naser, Kimmo Kaski
Publikováno v:
Communications Medicine, Vol 4, Iss 1, Pp 1-12 (2024)
Abstract Background Radiotherapy is a core treatment modality for oropharyngeal cancer (OPC), where the primary gross tumor volume (GTVp) is manually segmented with high interobserver variability. This calls for reliable and trustworthy automated too
Externí odkaz:
https://doaj.org/article/61b9b1e9e9514cf8a8cc114244e44457
Autor:
Jorma Järnstedt, Jaakko Sahlsten, Joel Jaskari, Kimmo Kaski, Helena Mehtonen, Ari Hietanen, Osku Sundqvist, Vesa Varjonen, Vesa Mattila, Sangsom Prapayasatok, Sakarat Nalampang
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-10 (2023)
Abstract Preoperative radiological identification of mandibular canals is essential for maxillofacial surgery. This study demonstrates the reproducibility of a deep learning system (DLS) by evaluating its localisation performance on 165 heterogeneous
Externí odkaz:
https://doaj.org/article/86f31ac2e51347da99caa9d41d58e5e5
Autor:
Jaakko Sahlsten, Jorma Järnstedt, Joel Jaskari, Hanna Naukkarinen, Phattaranant Mahasantipiya, Arnon Charuakkra, Krista Vasankari, Ari Hietanen, Osku Sundqvist, Antti Lehtinen, Kimmo Kaski
Publikováno v:
PLoS ONE, Vol 19, Iss 6, p e0305947 (2024)
Cephalometric analysis is critically important and common procedure prior to orthodontic treatment and orthognathic surgery. Recently, deep learning approaches have been proposed for automatic 3D cephalometric analysis based on landmarking from CBCT
Externí odkaz:
https://doaj.org/article/0413c2b47f8d483a9c27c62398a787fa
Autor:
Kareem A. Wahid, Jaakko Sahlsten, Joel Jaskari, Michael J. Dohopolski, Kimmo Kaski, Renjie He, Enrico Glerean, Benjamin H. Kann, Antti Mäkitie, Clifton D. Fuller, Mohamed A. Naser, David Fuentes
Publikováno v:
Physics and Imaging in Radiation Oncology, Vol 29, Iss , Pp 100526- (2024)
Externí odkaz:
https://doaj.org/article/17a952691b8d46579bdadb425d879a49
Autor:
Jorma Järnstedt, Jaakko Sahlsten, Joel Jaskari, Kimmo Kaski, Helena Mehtonen, Ziyuan Lin, Ari Hietanen, Osku Sundqvist, Vesa Varjonen, Vesa Mattila, Sangsom Prapayasotok, Sakarat Nalampang
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-11 (2022)
Abstract Deep learning approach has been demonstrated to automatically segment the bilateral mandibular canals from CBCT scans, yet systematic studies of its clinical and technical validation are scarce. To validate the mandibular canal localization
Externí odkaz:
https://doaj.org/article/7ecb1f5148cb4646921a19cdc311a766
Autor:
Nicolette Taku, Kareem A. Wahid, Lisanne V. van Dijk, Jaakko Sahlsten, Joel Jaskari, Kimmo Kaski, Clifton D. Fuller, Mohamed A. Naser
Publikováno v:
Clinical and Translational Radiation Oncology, Vol 36, Iss , Pp 47-55 (2022)
Purpose: Segmentation of involved lymph nodes on head and neck computed tomography (HN-CT) scans is necessary for the radiotherapy planning of early-stage human papilloma virus (HPV) associated oropharynx cancers (OPC). We aimed to train a deep learn
Externí odkaz:
https://doaj.org/article/acef4a8ea97a4eaea5d9d9663a1f7715
Autor:
Kareem A. Wahid, Brennan Olson, Rishab Jain, Aaron J. Grossberg, Dina El-Habashy, Cem Dede, Vivian Salama, Moamen Abobakr, Abdallah S. R. Mohamed, Renjie He, Joel Jaskari, Jaakko Sahlsten, Kimmo Kaski, Clifton D. Fuller, Mohamed A. Naser
Publikováno v:
Scientific Data, Vol 9, Iss 1, Pp 1-6 (2022)
Measurement(s) skeletal muscle • adipose tissue Technology Type(s) computed tomography
Externí odkaz:
https://doaj.org/article/1f6c545f5d4442dcb0df6172c10eca09
Autor:
Joel Jaskari, Jaakko Sahlsten, Theodoros Damoulas, Jeremias Knoblauch, Simo Sarkka, Leo Karkkainen, Kustaa Hietala, Kimmo K. Kaski
Publikováno v:
IEEE Access, Vol 10, Pp 76669-76681 (2022)
Automatic classification of diabetic retinopathy from retinal images has been increasingly studied using deep neural networks with impressive results. However, there is clinical need for estimating uncertainty in the classifications, a shortcoming of
Externí odkaz:
https://doaj.org/article/9a11f64f8d9b44a8a3e58a3351a5100e
Autor:
Jaakko Sahlsten, Kareem A. Wahid, Enrico Glerean, Joel Jaskari, Mohamed A. Naser, Renjie He, Benjamin H. Kann, Antti Mäkitie, Clifton D. Fuller, Kimmo Kaski
Publikováno v:
Frontiers in Oncology, Vol 13 (2023)
BackgroundDemand for head and neck cancer (HNC) radiotherapy data in algorithmic development has prompted increased image dataset sharing. Medical images must comply with data protection requirements so that re-use is enabled without disclosing patie
Externí odkaz:
https://doaj.org/article/bb8d117c69bf4d12ae7127714a0e1fad
Autor:
Mohamed A. Naser, Kareem A. Wahid, Aaron J. Grossberg, Brennan Olson, Rishab Jain, Dina El-Habashy, Cem Dede, Vivian Salama, Moamen Abobakr, Abdallah S. R. Mohamed, Renjie He, Joel Jaskari, Jaakko Sahlsten, Kimmo Kaski, Clifton D. Fuller
Publikováno v:
Frontiers in Oncology, Vol 12 (2022)
Background/PurposeSarcopenia is a prognostic factor in patients with head and neck cancer (HNC). Sarcopenia can be determined using the skeletal muscle index (SMI) calculated from cervical neck skeletal muscle (SM) segmentations. However, SM segmenta
Externí odkaz:
https://doaj.org/article/7cf49e7de1814fe293faa325bea8d53f