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» Authors » Asuka Oyama

Asuka Oyama

Explore the profile of Asuka Oyama including associated specialties, affiliations and a list of published articles. Areas
Snapshot
Articles 8
Citations 85
Followers 0
Related Specialties
Science
Radiology
Environmental Health
Top 10 Co-Authors
Junichi Kotoku
Ryohei Yamamoto
Hiroshi Toki
Kenshiro Shiraishi
Miyae Yamakawa
Toshiki Moriyama
Shinobu Kumagai
Akihiro Haga
Hiroe Seto
Sakiko Fukui
Published In
Sci Rep
J Radiat Res
PLoS One
Geriatr Gerontol Int
Environ Health Prev Med
Affiliations
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Recent Articles
1.
Related factors of postoperative half-year care utilization for health status and recovery in older adults with hip fracture: A retrospective study of Osaka National Health Insurance Data
Wang H, Yamakawa M, Suto S, Takeya Y, Oyama A, Toki H, et al.
Geriatr Gerontol Int . 2025 Jan; 25(2):190-205. PMID: 39822114
Aim: The aim of this study was to investigate the relationship between preoperative patient factors and postoperative half-year health care utilization reflecting recovery, common complications, comorbidities, and significant health concerns,...
2.
Cone Beam Computed Tomography Image-Quality Improvement Using "One-Shot" Super-resolution
Tsuji T, Yoshida S, Hommyo M, Oyama A, Kumagai S, Shiraishi K, et al.
J Imaging Inform Med . 2024 Dec; PMID: 39633213
Cone beam computed tomography (CBCT) images are convenient representations for obtaining information about patients' internal organs, but their lower image quality than those of treatment planning CT images constitutes an...
3.
Seasonal variations of the prevalence of metabolic syndrome and its markers using big-data of health check-ups
Seto H, Toki H, Kitora S, Oyama A, Yamamoto R
Environ Health Prev Med . 2024 Jan; 29:2. PMID: 38246652
Background: It is crucial to understand the seasonal variation of Metabolic Syndrome (MetS) for the detection and management of MetS. Previous studies have demonstrated the seasonal variations in MetS prevalence...
4.
Author Correction: Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data
Seto H, Oyama A, Kitora S, Toki H, Yamamoto R, Kotoku J, et al.
Sci Rep . 2022 Dec; 12(1):22599. PMID: 36585468
No abstract available.
5.
Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data
Seto H, Oyama A, Kitora S, Toki H, Yamamoto R, Kotoku J, et al.
Sci Rep . 2022 Oct; 12(1):15889. PMID: 36220875
We sought to verify the reliability of machine learning (ML) in developing diabetes prediction models by utilizing big data. To this end, we compared the reliability of gradient boosting decision...
6.
Causal relations of health indices inferred statistically using the DirectLiNGAM algorithm from big data of Osaka prefecture health checkups
Kotoku J, Oyama A, Kitazumi K, Toki H, Haga A, Yamamoto R, et al.
PLoS One . 2020 Dec; 15(12):e0243229. PMID: 33362207
Causal relations among many statistical variables have been assessed using a Linear non-Gaussian Acyclic Model (LiNGAM). Using access to large amounts of health checkup data from Osaka prefecture obtained during...
7.
Classification of optical coherence tomography images using a capsule network
Tsuji T, Hirose Y, Fujimori K, Hirose T, Oyama A, Saikawa Y, et al.
BMC Ophthalmol . 2020 Mar; 20(1):114. PMID: 32192460
Background: Classification of optical coherence tomography (OCT) images can be achieved with high accuracy using classical convolution neural networks (CNN), a commonly used deep learning network for computer-aided diagnosis. Classical...
8.
Hepatic tumor classification using texture and topology analysis of non-contrast-enhanced three-dimensional T1-weighted MR images with a radiomics approach
Oyama A, Hiraoka Y, Obayashi I, Saikawa Y, Furui S, Shiraishi K, et al.
Sci Rep . 2019 Jun; 9(1):8764. PMID: 31217445
The purpose of this study is to evaluate the accuracy for classification of hepatic tumors by characterization of T1-weighted magnetic resonance (MR) images using two radiomics approaches with machine learning...
9.
Image quality improvement in cone-beam CT using the super-resolution technique
Oyama A, Kumagai S, Arai N, Takata T, Saikawa Y, Shiraishi K, et al.
J Radiat Res . 2018 Apr; 59(4):501-510. PMID: 29659997
This study was conducted to improve cone-beam computed tomography (CBCT) image quality using the super-resolution technique, a method of inferring a high-resolution image from a low-resolution image. This technique is...