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関連する概念動画

Phase Diagrams02:39

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A phase diagram combines plots of pressure versus temperature for the liquid-gas, solid-liquid, and solid-gas phase-transition equilibria of a substance. These diagrams indicate the physical states that exist under specific conditions of pressure and temperature and also provide the pressure dependence of the phase-transition temperatures (melting points, sublimation points, boiling points). Regions or areas labeled solid, liquid, and gas represent single phases, while lines or curves represent...
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Phase Transitions02:31

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Whether solid, liquid, or gas, a substance's state depends on the order and arrangement of its particles (atoms, molecules, or ions). Particles in the solid pack closely together, generally in a pattern. The particles vibrate about their fixed positions but do not move or squeeze past their neighbors. In liquids, although the particles are closely spaced, they are randomly arranged. The position of the particles are not fixed—that is, they are free to move past their neighbors to...
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Force Classification01:22

Force Classification

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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,...
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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...
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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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AI-FLEET：乳腺葉状腫瘍分類のためのマルチモーダル深層学習モデルの第I相

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まとめ

超音波と臨床データを統合した人工知能（AI）モデルは、良性および悪性の線維上皮性乳腺病変を正確に鑑別します。このAI支援アプローチは、葉状腫瘍（PT）の診断精度を向上させ、誤分類のリスクを低減します。

キーワード:
人工知能乳房超音波深層学習線維上皮性病変葉状腫瘍

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科学分野:

  • 放射線学および医用画像処理; 医療における人工知能; 腫瘍学

背景:

  • 線維腺腫および葉状腫瘍（PT）を含む線維上皮性乳腺病変は、針生検において診断上の課題をもたらします。誤分類は、良性病変に対する不要な手術や、悪性PTに対する治療の遅れにつながる可能性があります。AI-FLEETプログラムは、様々なデータ型を統合することで診断精度を向上させることを目的としています。

研究 の 目的:

  • 超音波および臨床データを用いて、良性葉状腫瘍（PT）と境界悪性/悪性PTを鑑別するためのAIモデルを開発・評価すること。線維上皮性病変の分類における異なる深層学習アーキテクチャのパフォーマンスを評価すること。

主な方法:

  • 組織学的に確認されたPT（良性65例、境界悪性/悪性16例）を有する81例の患者の後ろ向き解析。超音波画像と臨床変数（年齢、BMI、人種、閉経状態、エコー源性、腫瘍サイズ）を用いたマルチモーダル深層学習モデル（ConvNeXt、ResNet18）のトレーニング。被験者層別5分割交差検証による評価。

主要な成果:

  • マルチモーダルConvNeXtおよびResNet18モデルは、高い精度（0.91-0.92）とAUC（0.94）を達成しました。超音波のみ、または臨床データのみのモデルは、より低いパフォーマンス（それぞれAUC 0.89および0.78）を示しました。サルエンシー分析により、腫瘍内不均一性が重要な予測因子であることが特定されました。

結論:

  • マルチモーダル深層学習モデルは、良性PTと境界悪性/悪性PTを効果的に鑑別します。線維上皮性病変のAI支援評価は実現可能であり、高い診断精度を示します。今後の研究（フェーズII）では、病理組織学的所見と良性線維腺腫の症例を組み込み、統合を強化する予定です。