Výsledky vyhledávání - "Daniel, Haschtmann"

Akademický článek

In silico medical device testing of anatomically and mechanically conforming patient-specific spinal fusion cages designed by full-scale topology optimisation

Autor: Thijs Smit, Niels Aage, Daniel Haschtmann, Stephen J. Ferguson, Benedikt Helgason

Publikováno v: Frontiers in Bioengineering and Biotechnology, Vol 12 (2024)

A full-scale topology optimisation formulation has been developed to automate the design of cages used in instrumented transforaminal lumbar interbody fusion. The method incorporates the mechanical response of the adjacent bone structures in the opti

Externí odkaz: https://doaj.org/article/2c65ffded9f64d9095e8c56a839eedbd

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Akademický článek

Fat beyond muscle: Assessing epimuscular fat of the lumbar spine and its association with vertebral level, demographics, BMI, and low back pain

Autor: Jacopo A. Vitale, Anne F. Mannion, Daniel Haschtmann, Mario Ropelato, Tamás F. Fekete, Frank S. Kleinstück, Markus Loibl, Tina Haltiner, Fabio Galbusera

Publikováno v: Brain and Spine, Vol 4, Iss , Pp 103916- (2024)

Introduction: Epimuscular fat (EF) has rarely been studied in the context of low back pain (LBP). Research question: This study aims to assess the presence and extent of EF in the lumbar muscles and its association with vertebral level in patients wi

Externí odkaz: https://doaj.org/article/182ade11e0b14b84ae05e380c4f50161

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Akademický článek

Are large language models valid tools for patient information on lumbar disc herniation? The spine surgeons' perspective

Autor: Siegmund Lang, Jacopo Vitale, Tamás F. Fekete, Daniel Haschtmann, Raluca Reitmeir, Mario Ropelato, Jani Puhakka, Fabio Galbusera, Markus Loibl

Publikováno v: Brain and Spine, Vol 4, Iss , Pp 102804- (2024)

Introduction: Generative AI is revolutionizing patient education in healthcare, particularly through chatbots that offer personalized, clear medical information. Reliability and accuracy are vital in AI-driven patient education. Research question: Ho

Externí odkaz: https://doaj.org/article/bb486bf807d6429cb4e0f678e37ad945

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Akademický článek

Comparing image normalization techniques in an end-to-end model for automated modic changes classification from MRI images

Autor: Andrea Cina, Daniel Haschtmann, Dimitrios Damopoulos, Nicolas Gerber, Markus Loibl, Tamas Fekete, Frank Kleinstück, Fabio Galbusera

Publikováno v: Brain and Spine, Vol 4, Iss , Pp 102738- (2024)

Introduction: Modic Changes (MCs) are MRI alterations in spine vertebrae's signal intensity. This study introduces an end-to-end model to automatically detect and classify MCs in lumbar MRIs. The model's two-step process involves locating interverteb

Externí odkaz: https://doaj.org/article/abcb8301824d4d7ca3d5b44df079dfc4

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