On Tuesday, November 10, 2020, the Swiss academic journal MDPI Biomolecules published "Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates" Ryosuke Goto and Asahi Kitamura from Search Space Inc. supported this research as co-authors.
Dr. Norio Yamamoto and Dr. Shintaro Sukegawa of Kagawa Central Hospital led a study on "Deep Learning for Osteoporosis Classification Using Hip X-ray Images and Patient Clinical Covariates". We have shown that adding patient factor data, rather than only hip x-ray images, to the deep learning algorithm improves diagnostic performance. Goto and Kitamura were in charge of algorithm design and implementation in this study.
- About MDPI Biomolecules
Based in Basel, Switzerland, MDPI is a publisher of 279 diverse peer-reviewed journals that operate on the principle of promoting open scientific exchange in all forms and in all disciplines. MDPI Biomolecules is a peer-reviewed, open access journal on biomolecules (including proteins, nucleic acids, polysaccharides, membranes, lipids and metabolites) with an impact factor of 4.082.
- About Paper Information
Name of Author:
Norio Yamamoto, Shintaro Sukegawa, Akira Kitamura, Ryosuke Goto, Tomoyuki Noda, Keisuke Nakano , Kiyofumi Takabatake, Hotaka Kawai, Hitoshi Nagatsuka, Keisuke Kawasaki, Yoshihiko Furuki and Toshifumi Ozaki
Title:
Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates
Magazine name:
MDPI Biomolecules
DOI:
10.3390/biom10111534
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