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関連する概念動画

Knee Joint01:23

Knee Joint

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The knee joint is the most complicated joint in the body. It consists of three articulations– two tibiofemoral and one patellofemoral. As is characteristic of synovial joints, the knee joint has a thin articular capsule that partially surrounds this joint cavity. Additionally, several ligaments, muscles, and cartilaginous structures support the movement of the knee.
A total of seven ligaments support the knee joint. The patellar ligament, which is also attached to the quadriceps femoris...
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Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

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The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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Avoidance Learning and Learned Helplessness01:14

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Learning Disabilities01:25

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Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
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Associative Learning01:27

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Updated: Jan 23, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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膝X線写真における深層学習ベースのアライメント測定

Zhisen Hu1,2, Dominic Cullen1,3, Peter Thompson1

  • 1Division of Informatics, Imaging and Data Sciences, The University of Manchester, United Kingdom.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|January 22, 2026
PubMed
まとめ

本研究では、膝X線写真を用いた正確な膝アライメント（KA）測定のための深層学習ベースの方法を紹介します。自動化されたシステムは高い精度を達成し、関節の健康評価と手術計画のためのデジタルワークフローを改善します。

キーワード:
解剖学的脛骨大腿骨角度深層学習砂時計膝アライメントランドマーク局在化

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科学分野:

  • 整形外科;医用画像処理;人工知能

背景:

  • X線写真による膝アライメント（KA）は、関節の健康と人工膝関節全置換術の結果を予測するために重要です。現在の手動KA測定方法は時間がかかり、長下肢X線写真が必要です。

研究 の 目的:

  • 前後膝X線写真からの自動KA測定のための深層学習ベースの方法を開発および検証すること。膝の形状を包括的にアウトラインするために、多数の膝の解剖学的ランドマークを正確に局在化すること。

主な方法:

  • 堅牢なランドマーク局在化のために、アテンションゲート構造を持つ砂時計ネットワークを利用しました。術前および術後の画像における解剖学的脛骨大腿骨角度を用いたKA測定を統合する方法を開発しました。膝の形状を定義するために、100以上の膝の解剖学的ランドマークを局在化しました。

主要な成果:

  • 膝の内外反KAについて、臨床的真値測定と比較して約1°の平均絶対差を達成しました。術前（ICC = 0.97）で優れた一致、術後（ICC = 0.86）で良好な一致を示しました。膝の形状を完全にアウトラインし、100以上のランドマークを使用してKAを測定した最初の深層学習法です。

結論:

  • 深層学習を用いた自動KA評価は、非常に正確で信頼性が高いです。この技術は、整形外科におけるデジタル強化された臨床ワークフローの可能性を提供します。患者ケアと手術計画の改善のために、膝アライメントの正確な測定を容易にします。