甲状腺結節の検出と悪性腫瘍の分類のための人工知能強化超音波画像:YOLOv11に関する研究
Jiaqi Yang1, Zhigang Luo2, Yanting Wen3
1Operating Room, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, China.
Quantitative imaging in medicine and surgery
|September 2, 2025
まとめ
You Only Look Once v. 11 (YOLOv11) モデルは,超音波画像から甲状腺結節の検出と分類に高い精度を示しています. この人工知能のツールは 臨床医の診断効率を高め 患者の治療結果を改善します
科学分野:
- 医療用イメージング
- 医療における人工知能
- 腫瘍学
背景:
- 甲状腺の結節は一般的であり,効果的な治療のために正確な診断が必要です.
- 伝統的な超音波診断は 医者の経験に依存し 変化を招きます
- 医療イメージングにおける人工知能 (AI) は,診断の正確性と効率性を向上させることができます.
研究 の 目的:
- 甲状腺ノドルの検出と分類のためのYou Only Look Once v. 11 (YOLOv11) モデルを評価する.
- 超音波画像解析におけるYOLOv11の有効性を評価する.
- リアルタイムでの臨床意思決定を支援し,診断作業の流れを改善します.
主な方法:
- 1,503件の甲状腺超音波画像 (742件の良性,778件の悪性) でYOLOv11モデルを利用した.
- モデルの最適化のためにデータ増強と転送学習を使用した.
- 他のYOLO変種とResNet50と比較したYOLOv11性能.
主要な成果:
- YOLOv11は他のYOLO変種とResNet50よりも優れた性能を示した.
- IOU 0.5で,YOLOv11は精度 (P) 0.841とリコール (R) 0.823を達成した.
- YOLOv11は良性および悪性ノドルの両方に対して高いPとRを示し,性能はIOU値によって変化した.
結論:
- YOLOv11は 甲状腺結節の検出と悪性腫瘍の分類のための強力なツールです.
- このモデルは,ダイナミックな超音波に不可欠な高い精度とリアルタイムの性能を提供します.
- 将来の研究はデータセットを拡張し,リアルタイムでの臨床的有用性を検証します.
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