Delving into the Latest Updates on Tottori University Hospital with Synapse

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1

项与 Tottori University Hospital 相关的药物

SR-16234

靶点

ERα

作用机制

ERα拮抗剂

在研机构-

原研机构

SRI International

在研适应症-

非在研适应症

乳腺癌 [+3]

最高研发阶段终止

首次获批国家/地区-

首次获批日期-

39

项与 Tottori University Hospital 相关的临床试验

JPRN-UMIN000053386 / RecruitingIIT

Predictor of A4 amplitude and atrio-ventricular synchrony in patients with leadless pacemaker - Predictors associated with leadless pacemakers

开始日期2022-09-02

申办/合作机构

Tottori University Hospital

JPRN-UMIN000045922 / RecruitingIIT

Effect of combined hyperbaric oxygen therapy on anterior cruciate ligament reconstruction - Effect of combined hyperbaric oxygen therapy on anterior cruciate ligament reconstruction

开始日期2021-11-01

申办/合作机构

Tottori University Hospital

JPRN-UMIN000036114 / RecruitingIIT

Exploratory study of hereditary porphyria diagnosis by porphyrin intermediate metabolite specific spectrum detection using liquid crystal tunable filter (LCTF) - Exploratory study of hereditary porphyria diagnosis by liquid crystal tunable filter (LCTF)

开始日期2019-12-19

申办/合作机构

Tottori University Hospital

100 项与 Tottori University Hospital 相关的临床结果

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0 项与 Tottori University Hospital 相关的专利（医药）

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605

项与 Tottori University Hospital 相关的文献（医药）

2024-10-25·Circulation Journal

Ruptured Huge Complex Aortic Arch Aneurysm Associated With Aortic Coarctation

Article

作者: Kumagai, Kunitaka ; Kishimoto, Yuichiro ; Yoshikawa, Yasushi ; Onohara, Takeshi ; Sumi, Naoki

2024-08-24·Circulation Journal

Regional Collaboration for Heart Failure Patients Certified as Needing Support or Care in Long-Term Care Insurance System

作者: Nozaka, Yoshihito ; Tanaka, Hiroaki ; Hirai, Masayuki ; Yamamoto, Kazuhiro ; Sota, Takeshi ; Hirota, Yutaka ; Nakamura, Kensuke ; Ota, Tomoki ; Kinugasa, Yoshiharu ; Tanaka, Yasunori ; Omodani, Hiroki ; Watanabe, Izuru ; Adachi, Toshiaki ; Ishiga, Natsuko ; Mizuta, Einosuke ; Mikami, Masaaki ; Kato, Masahiko ; Mura, Emiko ; Fukuki, Masaharu ; Nakayama, Natsuko ; Manba, Midori

2024-08-01·Journal of Tissue Viability

An ingenious deep learning approach for pressure injury depth evaluation with limited data

Article

作者: Fukuoka, Kohei ; Yagi, Shunjiro ; Morita, Maki ; Konishi, Mamoru ; Ikuta, Kento ; Nakagaki, Makoto ; Kimura, Yuka ; Nishikawa, Hiroyuki ; Suyama, Yoshiko ; Umeda, Ryunosuke ; Ohga, Makoto

BACKGROUNDThe development of models using deep learning (DL) to assess pressure injuries from wound images has recently gained attention. Creating enough supervised data is important for improving performance but is time-consuming. Therefore, the development of models that can achieve high performance with limited supervised data is desirable.MATERIALS AND METHODSThis retrospective observational study utilized DL and included patients who received medical examinations for sacral pressure injuries between February 2017 and December 2021. Images were labeled according to the DESIGN-R® classification. Three artificial intelligence (AI) models for assessing pressure injury depth were created with a convolutional neural network (Categorical, Binary, and Combined classification models) and performance was compared among the models.RESULTSA set of 414 pressure injury images in five depth stages (d0 to D4) were analyzed. The Combined classification model showed superior performance (F1-score, 0.868). The Categorical classification model frequently misclassified d1 and d2 as d0 (d0 Precision, 0.503), but showed high performance for D3 and D4 (F1-score, 0.986 and 0.966, respectively). The Binary classification model showed high performance in differentiating between d0 and d1-D4 (F1-score, 0.895); however, performance decreased with increasing number of evaluation steps.CONCLUSIONThe Combined classification model displayed superior performance without increasing the supervised data, which can be attributed to use of the high-performance Binary classification model for initial d0 evaluation and subsequent use of the Categorical classification model with fewer evaluation steps. Understanding the unique characteristics of classification methods and deploying them appropriately can enhance AI model performance.

100 项与 Tottori University Hospital 相关的药物交易

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100 项与 Tottori University Hospital 相关的转化医学

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