Tomi Nissinen
Grant-funded Researcher
Department of Technical Physics, Faculty of Science, Forestry and Technology
I am researching machine learning based image analysis. My PhD project is about using deep learning methods to analyze dual-energy X-ray absorptiometry (DXA) images. I seek to discover how these methods could exploit all the information in DXA images to improve osteoporotic fracture risk prediction.
Research groups
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Publications
4/4 items-
DXA-based 3D finite element models predict hip fractures better than areal BMD in elderly women
Grassi, Lorenzo; Väänänen, Sami P; Voss, Antti; Nissinen, Tomi; Sund, Reijo; Kröger, Heikki; Isaksson, Hanna. 2025. Bone. 195: A1 Journal article (refereed), original research -
Identifying proximal humerus fractures: an algorithmic approach using registers and radiological visit data
Nissinen, Tomi; Sund, Reijo; Suoranta, Sanna; Kröger, Heikki; Väänänen, Sami P.. 2025. Osteoporosis international. [Published: 07 February 2025]: 1-7 A1 Journal article (refereed), original research -
Combining Register and Radiological Visits Data Allows to Reliably Identify Incident Wrist Fractures
Nissinen, Tomi; Sund, Reijo; Suoranta, Sanna; Kröger, Heikki; Väänänen, Sami. 2023. Clinical epidemiology. 15: 1001-1008 A1 Journal article (refereed), original research -
Detecting pathological features and predicting fracture risk from dual-energy X-ray absorptiometry images using deep learning
Nissinen, Tomi; Suoranta, Sanna; Saavalainen, Taavi; Sund, Reijo; Hurskainen, Ossi; Rikkonen, Toni; Kröger, Heikki; Lähivaara, Timo; Väänänen, Sami P. 2021. Bone reports