Search Results - "Haga, Akihiro"

A Deep Learning Approach for Assessment of Regional Wall Motion Abnormality From Echocardiographic Images by Kusunose, Kenya, Abe, Takashi, Haga, Akihiro, Fukuda, Daiju, Yamada, Hirotsugu, Harada, Masafumi, Sata, Masataka

“…This study investigated whether a deep convolutional neural network (DCNN) could provide improved detection of regional wall motion abnormalities (RWMAs) and…”
Get full text

Utilization of Artificial Intelligence in Echocardiography by Kusunose, Kenya, Haga, Akihiro, Abe, Takashi, Sata, Masataka

“…Echocardiography has a central role in the diagnosis and management of cardiovascular disease. Precise and reliable echocardiographic assessment is required…”
Get full text

Fine structural human phantom in dentistry and instance tooth segmentation by Takeya, Atsushi, Watanabe, Keiichiro, Haga, Akihiro

“…In this study, we present the development of a fine structural human phantom designed specifically for applications in dentistry. This research focused on…”
Get full text

Automatic contour segmentation of cervical cancer using artificial intelligence by Kano, Yosuke, Ikushima, Hitoshi, Sasaki, Motoharu, Haga, Akihiro

“…Abstract In cervical cancer treatment, radiation therapy is selected based on the degree of tumor progression, and radiation oncologists are required to…”
Get full text

Computed tomography image representation using the Legendre polynomial and spherical harmonics functions by Shimomura, Taisei, Haga, Akihiro

“…The representation of computed tomography (CT) images using the Legendre polynomial (LPF) and spherical harmonics (SHF) functions was investigated. We selected…”
Get full text

Standardization of imaging features for radiomics analysis by Haga, Akihiro, Takahashi, Wataru, Aoki, Shuri, Nawa, Kanabu, Yamashita, Hideomi, Abe, Osamu, Nakagawa, Keiichi

“…Radiomics has the potential to provide tumor characteristics with noninvasive and repeatable way. The purpose of this paper is to evaluate the standardization…”
Get full text

Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data by Seto, Hiroe, Oyama, Asuka, Kitora, Shuji, Toki, Hiroshi, Yamamoto, Ryohei, Kotoku, Jun’ichi, Haga, Akihiro, Shinzawa, Maki, Yamakawa, Miyae, Fukui, Sakiko, Moriyama, Toshiki

“…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…”
Get full text

Deep Learning for Assessment of Left Ventricular Ejection Fraction from Echocardiographic Images by Kusunose, Kenya, Haga, Akihiro, Yamaguchi, Natsumi, Abe, Takashi, Fukuda, Daiju, Yamada, Hirotsugu, Harada, Masafumi, Sata, Masataka

Get full text

Radiomics analysis of [18F]-fluoro-2-deoxyglucose positron emission tomography for the prediction of cervical lymph node metastasis in tongue squamous cell carcinoma by Kudoh, Takaharu, Haga, Akihiro, Kudoh, Keiko, Takahashi, Akira, Sasaki, Motoharu, Kudo, Yasusei, Ikushima, Hitoshi, Miyamoto, Youji

“…Objectives This study aimed to create a predictive model for cervical lymph node metastasis (CLNM) in patients with tongue squamous cell carcinoma (SCC) based…”
Get full text

Radiomics Analysis for Glioma Malignancy Evaluation Using Diffusion Kurtosis and Tensor Imaging by Takahashi, Satoshi, Takahashi, Wataru, Tanaka, Shota, Haga, Akihiro, Nakamoto, Takahiro, Suzuki, Yuichi, Mukasa, Akitake, Takayanagi, Shunsaku, Kitagawa, Yosuke, Hana, Taijun, Nejo, Takahide, Nomura, Masashi, Nakagawa, Keiichi, Saito, Nobuhito

“…A noninvasive diagnostic method to predict the degree of malignancy accurately would be of great help in glioma management. This study aimed to create a highly…”
Get full text

Evaluation of a commercial automatic treatment planning system for prostate cancers by Nawa, Kanabu, Ph.D, Haga, Akihiro, Ph.D, Nomoto, Akihiro, M.D, Sarmiento, Raniel A., M.Sc, Shiraishi, Kenshiro, M.D., Ph.D, Yamashita, Hideomi, M.D., Ph.D, Nakagawa, Keiichi, M.D., Ph.D

“…Abstract Recent developments in Radiation Oncology treatment planning have led to the development of software packages that facilitate automated…”
Get full text

Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network by Kida, Satoshi, Nakamoto, Takahiro, Nakano, Masahiro, Nawa, Kanabu, Haga, Akihiro, Kotoku, Jun'ichi, Yamashita, Hideomi, Nakagawa, Keiichi

“…Introduction Cone beam computed tomography (CBCT) plays an important role in image-guided radiation therapy (IGRT), while having disadvantages of severe…”
Get full text

A novel CT-based radiomics model for predicting response and prognosis of chemoradiotherapy in esophageal squamous cell carcinoma by Kasai, Akinari, Miyoshi, Jinsei, Sato, Yasushi, Okamoto, Koichi, Miyamoto, Hiroshi, Kawanaka, Takashi, Tonoiso, Chisato, Harada, Masafumi, Goto, Masakazu, Yoshida, Takahiro, Haga, Akihiro, Takayama, Tetsuji

“…No clinically relevant biomarker has been identified for predicting the response of esophageal squamous cell carcinoma (ESCC) to chemoradiotherapy (CRT)…”
Get full text

Estimation of X-ray Energy Spectrum of Cone-Beam Computed Tomography Scanner Using Percentage Depth Dose Measurements and Machine Learning Approach by Hasegawa, Yu, Haga, Akihiro, Sakata, Dousatsu, Kanazawa, Yuki, Tominaga, Masahide, Sasaki, Motoharu, Imae, Toshikazu, Nakagawa, Keiichi

“…This study presents, for the first time, a method to indirectly estimate the cone-beam computed tomography (CBCT) x-ray spectrum in the diagnostic energy range…”
Get full text

Author Correction: A novel CT-based radiomics model for predicting response and prognosis of chemoradiotherapy in esophageal squamous cell carcinoma by Kasai, Akinari, Miyoshi, Jinsei, Sato, Yasushi, Okamoto, Koichi, Miyamoto, Hiroshi, Kawanaka, Takashi, Tonoiso, Chisato, Harada, Masafumi, Goto, Masakazu, Yoshida, Takahiro, Haga, Akihiro, Takayama, Tetsuji

Get full text

Influence of distant scatterer on air kerma measurement in the evaluation of diagnostic X-rays using Monte Carlo simulation by Tominaga, Masahide, Nagayasu, Yukari, Sasaki, Motoharu, Furuta, Takuya, Hayashi, Hiroaki, Oita, Masataka, Nishiyama, Yuichi, Haga, Akihiro

“…The evaluation of the entrance surface dose (ESD) ensures safe radiation doses for X-ray imaging patients. The air kerma free-in-air value used to estimate ESD…”
Get full text

Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis by Nakamoto, Takahiro, Takahashi, Wataru, Haga, Akihiro, Takahashi, Satoshi, Kiryu, Shigeru, Nawa, Kanabu, Ohta, Takeshi, Ozaki, Sho, Nozawa, Yuki, Tanaka, Shota, Mukasa, Akitake, Nakagawa, Keiichi

“…We conducted a feasibility study to predict malignant glioma grades via radiomic analysis using contrast-enhanced T1-weighted magnetic resonance images…”
Get full text

Physical density estimations of single- and dual-energy CT using material-based forward projection algorithm: a simulation study by Li, Kai-Wen, Fujiwara, Daiyu, Haga, Akihiro, Liu, Huisheng, Geng, Li-Sheng

“…This study aims to evaluate the accuracy of physical density prediction in single-energy CT (SECT) and dual-energy CT (DECT) by adapting a fully…”
Get full text

Causal relations of health indices inferred statistically using the DirectLiNGAM algorithm from big data of Osaka prefecture health checkups by Kotoku, Jun'ichi, Oyama, Asuka, Kitazumi, Kanako, Toki, Hiroshi, Haga, Akihiro, Yamamoto, Ryohei, Shinzawa, Maki, Yamakawa, Miyae, Fukui, Sakiko, Yamamoto, Keiichi, Moriyama, Toshiki

“…Causal relations among many statistical variables have been assessed using a Linear non-Gaussian Acyclic Model (LiNGAM). Using access to large amounts of…”
Get full text

Author Correction: Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data by Seto, Hiroe, Oyama, Asuka, Kitora, Shuji, Toki, Hiroshi, Yamamoto, Ryohei, Kotoku, Jun’ichi, Haga, Akihiro, Shinzawa, Maki, Yamakawa, Miyae, Fukui, Sakiko, Moriyama, Toshiki

Get full text