頭部CTスキャンにおける重要な発見の検出のためのディープラーニングアルゴリズム:遡及的研究
Sasank Chilamkurthy1, Rohit Ghosh1, Swetha Tanamala1
1Qure.ai, Goregaon East, Mumbai, India.
Lancet (London, England)
|October 16, 2018
まとめ
ディープラーニングアルゴリズムは 頭部CTスキャンで 脳内出血や骨折などの 重要な発見を正確に検出します この技術は緊急の症例の特定を自動化し 緊急の状況での患者の分類を改善する見込みです
科学分野:
- 放射線科
- 人工知能
- 医療用イメージング
背景:
- 非コントラストの頭部CTスキャンが 頭部外傷と脳卒中の評価の標準です
- 効率を高めるために重要な異常を自動で検出する必要がある.
研究 の 目的:
- 頭蓋内出血,頭骨骨折,ミッドラインシフト,頭部CTスキャンにおける質量効果の自動検出のためのディープラーニングアルゴリズムの開発と検証.
主な方法:
- インドのセンターから31万3,318頭のCTスキャンを遡って収集 (2011年−2017年).
- Qure25kとCQ500のデータセットを用いた開発と検証
- 受信器の動作特性曲線 (AUC) 以下の面積を用いて性能を評価した.
主要な成果:
- Qure25kで0. 92,CQ500で0. 94) であった.
- 頭蓋内出血,頭蓋骨骨折 (AUC 0. 92),ミッドラインシフト (AUC 0. 93) および質量効果 (AUC 0. 86) の準確な検出
結論:
- ディープラーニングアルゴリズムは 緊急の頭部CT異常を 特定するのに高い精度を示しています
- 頭部トラウマや脳卒中症候群の 自動トリアージの可能性
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