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関連する概念動画

Bonding in Metals02:32

Bonding in Metals

52.4K
Metallic bonds are formed between two metal atoms. A simplified model to describe metallic bonding has been developed by Paul Drüde called the “Electron Sea Model”. 
52.4K
Metallic Solids02:37

Metallic Solids

20.6K
Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
All metallic solids exhibit high thermal and electrical conductivity, metallic luster, and malleability....
20.6K
Alkali Metals03:06

Alkali Metals

24.6K
Group 1 elements are soft and shiny metallic solids. They are malleable, ductile, and good conductors of heat and electricity. The melting points of the alkali metals are unusually low for metals and decrease going down the group, while the density increases going down the group with the exception of potassium (Table 1).
Table 1: Properties of the alkali metals
24.6K
Machines01:19

Machines

577
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
577
Metal-Ligand Bonds02:51

Metal-Ligand Bonds

24.3K
The hemoglobin in the blood, the chlorophyll in green plants, vitamin B-12, and the catalyst used in the manufacture of polyethylene all contain coordination compounds. Ions of the metals, especially the transition metals, are likely to form complexes.
In these complexes, transition metals form coordinate covalent bonds, a kind of Lewis acid-base interaction in which both of the electrons in the bond are contributed by a donor (Lewis base) to an electron acceptor (Lewis acid). The Lewis acid in...
24.3K
Machines: Problem Solving II01:30

Machines: Problem Solving II

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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関連する実験動画

Updated: Jan 31, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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金属インプラントを有するKUBX線撮影におけるmAs最適化のための機械学習

Wen-Xuan Chen1, Jen-Pei Su2, Shih-Hua Huang3

  • 1Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan.

Journal of applied clinical medical physics
|January 30, 2026
PubMed
まとめ

機械学習は、金属インプラントを有する患者の腎尿管膀胱(KUB)X線撮影における放射線曝露(mAs)を正確に予測します。このアプローチは、過剰曝露とそれに関連するがんのリスクを低減します。

キーワード:
KUBX線撮影mAs機械学習目標露出インジケータ

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科学分野:

  • 放射線物理学
  • 医用画像
  • ヘルスケアにおける機械学習

背景:

  • 腎尿管膀胱(KUB)X線撮影は、一般的な診断ツールです。
  • 金属インプラントを有する患者は、放射線量とがんのリスクの増加により、課題をもたらします。
  • 曝露因子の最適化は、患者の安全性にとって重要です。

主な方法:

  • 自動露出制御(AEC)を用いた放射線曝露に対する金属インプラントの影響を評価するために、ファントム研究を利用しました。
  • 2つの病院にわたる942人の患者(金属インプラントを有する145人)のデータの後ろ向き分析を行いました。
  • 10倍クロスバリデーションと転移学習を使用して、人工ニューラルネットワーク(ANN)を含む5つのMLアルゴリズムをトレーニングおよび検証しました。

結論:

  • 機械学習は、KUBX線撮影における適切なmAsを予測するための実行可能なアプローチです。
  • 開発されたMLモデルは、金属インプラントを有する患者における過剰曝露を効果的に低減します。
  • この研究は、診断画像における放射線線量を最適化するためのMLの可能性を強調しています。