Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

429
Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
429
Classification of Titrimetric Analysis Based on Reaction Types01:01

Classification of Titrimetric Analysis Based on Reaction Types

1.9K
Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
Titrations between an acid and a base lead to neutralization reactions that form...
1.9K
Cardiovascular Drugs: Classification based on Therapeutic Indications01:18

Cardiovascular Drugs: Classification based on Therapeutic Indications

4.3K
Cardiovascular diseases, encompassing a range of conditions, can significantly affect the heart's operations and the overall circulatory system. These conditions impair the heart's ability to pump blood, leading to a deficit in oxygen supply to crucial organs. Anomalies in the heart's electrical system, known as arrhythmias, can cause heartbeats to accelerate or slow down. Usually, heart rates increase during physical activity and decrease while resting or sleeping. However,...
4.3K
Classification of Elements and Compounds02:54

Classification of Elements and Compounds

73.7K
Pure substances consist of only one type of matter. A pure substance can be an element or a compound. An element consists of only one type of atom, while a compound consists of two or more types of atoms held together by a chemical bond. Elements are classified as atomic or molecular based on the nature of their basic units.
Compounds are pure substances composed of two or more elements in fixed, definite proportions. Compounds are classified as ionic or molecular (covalent) based on the bonds...
73.7K
Force Classification01:22

Force Classification

2.5K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.5K
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

5.4K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
5.4K

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Bone Mineral Density Loss in Relapsing-remitting Multiple Sclerosis and Neuromyelitis Optica Spectrum Disorder: A Matched Case-control Study.

International journal of preventive medicine·2026
Same author

Vision-related quality of life in non-neovascular age-related macular degeneration: a protocol for systematic review and meta-analysis.

Eye (London, England)·2026
Same author

Cognitive Impairment and Quality of Life in AQP4-IgG Seropositive Neuromyelitis Optica Spectrum Disorder: A Cross-Sectional Study in Iranian Patients.

Neurology research international·2026
Same author

Surgical Management and Outcomes of Large High Myopic Macular Holes: Global Macular Hole Multicenter Study 3.

Retina (Philadelphia, Pa.)·2026
Same author

A Functional Shape Framework for the Detection of Multiple Sclerosis Using Optical Coherence Tomography Images.

Sensors (Basel, Switzerland)·2026
Same author

OphthoEvidence: a model for rapid and trustworthy ophthalmology guidelines.

Eye (London, England)·2026
Same journal

Inhibition of STAT6 Signaling by Rebamipide Attenuates IL-4- and IL-13-Induced Eotaxin Expression in Corneal Fibroblasts.

Experimental eye research·2026
Same journal

Translational animal model for genetic predisposition to anophthalmia/microphthalmia.

Experimental eye research·2026
Same journal

Inhibitory effect of the water-soluble retinoid X receptor partial agonist CAt-PMN on allergic conjunctivitis-like responses in mice.

Experimental eye research·2026
Same journal

Rheology of tear lipid components and their influence on surface properties.

Experimental eye research·2026
Same journal

Imidapril activates CD26/dipeptidyl peptidase IV to alleviate murine choroidal neovascularization by promoting bone marrow cell mobilization and differentiation.

Experimental eye research·2026
Same journal

NR_045396/MicroRNA761/FADD axis regulates necroptosis and survival of retinal ganglion cells.

Experimental eye research·2026
関連記事をすべて見る

関連する実験動画

Updated: Feb 14, 2026

In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses
09:31

In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses

Published on: March 30, 2015

9.3K

多発性硬化症のディープラーニングベースの診断分類 マルチセンター光学相関トモグラフィーデータによる多発性硬化症の診断分類

Zahra Khodabandeh1, Hossein Rabbani1, Neda Shirani Bidabadi2

  • 1Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan 817467346, Iran.

Experimental eye research
|February 12, 2026
PubMed
まとめ
この要約は機械生成です。

光学一貫性トモグラフィー (OCT) の人工知能 (AI) 分析による網膜スキャンは,多発性硬化症 (MS) を正確に検出します. このAIアプローチは,MSの早期診断と管理のための有望で非侵襲的なバイオマーカーを提供します.

キーワード:
人工知能 (AI) とは,人工知能 (AI) のことです.多発性硬化症 (MS) を患っている.視神経病理学 視神経病理学光学相関トモグラフィー網膜層分析 網膜層分析

さらに関連する動画

Doppler Optical Coherence Tomography of Retinal Circulation
10:46

Doppler Optical Coherence Tomography of Retinal Circulation

Published on: September 18, 2012

19.3K
The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool
11:35

The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool

Published on: June 30, 2014

58.8K

関連する実験動画

Last Updated: Feb 14, 2026

In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses
09:31

In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses

Published on: March 30, 2015

9.3K
Doppler Optical Coherence Tomography of Retinal Circulation
10:46

Doppler Optical Coherence Tomography of Retinal Circulation

Published on: September 18, 2012

19.3K
The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool
11:35

The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool

Published on: June 30, 2014

58.8K

科学分野:

  • オフタルモロジック (眼科)
  • 神経学 神経学とは
  • 人工知能 (AI) とは,人工知能 (AI) のことです.

背景:

  • 多発性硬化症 (MS) は,正確な診断を必要とする中枢神経系の炎症性疾患である.
  • オプティカルコヒーレンストモグラフィー (OCT) は網膜の変化,潜在的なMSバイオマーカーを検出します.
  • 加盟国におけるOCTの微妙な変化は,原始画像検査を超える高度な分析方法を必要とします.

研究 の 目的:

  • 多発性硬化症 (MS) を分類するための人工知能 (AI) モデルを評価するために,光学相干性トモグラフィ (OCT) を用いて網膜の特徴を導出.
  • MS検出のための最も有益な網膜層の厚さと表面特性を決定する.
  • MS分類におけるAIモデルの解釈性と一般化性を評価する.

主な方法:

  • AIモデルの3つのカテゴリーを調査しました:自動エンコーダー (AE) と浅いネットワークによる機能抽出,カスタム深層ネットワーク,および精密に調整された事前訓練されたネットワーク.
  • OCTの網膜層の厚みと表面の地図を分析し,チャンネルによる組み合わせとモザイクによる特徴を統合しました.
  • 38人の健康な対照 (HC) と78人のMSの目のデータセットで,オクラージュン感度とGrad-CAMをモデル解釈性のために利用しました.

主要な成果:

  • 網膜神経繊維層 (RNFL),ギャングリオン細胞と内側状層 (GCIPL),内側核層 (INL) の厚さマップを組み合わせた深層ネットワークは97.3%のバランス精度を達成しました.
  • 内部クロス検証のために公的データセットとローカルデータセットを組み合わせるときに,高いパフォーマンスを観察しました.
  • クロスデータセットの評価ではパフォーマンスが著しく低下し,特に公開データに関するトレーニングでは,外部の一般化が限られていることを強調しました.

結論:

  • OCTから派生した網膜の特徴のAIベースの分析は,正確で解釈可能なMS分類を提供します.
  • このアプローチは,MS診断のためのOCT由来網膜バイオマーカーの可能性を支持しています.
  • 異なるデータセットにおけるAIモデルの一般化性を改善するために,さらなる研究が必要である.