Scholarlink

<p>Activated partial thromboplastin time (APTT) prolongation occurs due to coagulation factor deficiencies/inhibitors, lupus anticoagulant (LA), and anticoagulant-taking, necessitating discrimination through further testing. Clot waveform analysis (CWA) can discriminate causes while measuring APTT, but conventional CWA exhibits moderate accuracy due to visual judgement and limited parameter use. We applied deep learning (DL) techniques to huge numerical data constituting clot waveforms and their first- and second-derivative curves (CWA curves) to leverage hidden features for developing an accurate classification model. We utilized a multi-wavelength detection system embedded in modern coagulometers to obtain multi-wavelength CWA curves. A convolutional neural network-based DL model was trained on 683 samples (135 hemophilic, 95 LA-positive, 99 heparin-treated, 105 warfarin-treated, and 249 direct oral anticoagulant-treated) and evaluated using 10-fold cross-validation. Conventional CWA parameters showed limited discrimination abilities (area under the curve [AUC] 0.532–0.858). DL models using single-wavelength CWA curves achieved higher performance (AUC 0.943–0.988), and multi-wavelength CWA curves further improved it (AUC 0.961–0.993) with high sensitivity (≥ 88.0%), specificity (&gt; 92.0%), and overall accuracy (88.4%), although the performance may depend on reagents and/or analyzers. DL models using multi-wavelength CWA curves show promise as high-performance screening tools for classifying APTT prolongation causes and are best built in each laboratory.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Deep learning model for screening causes of activated partial thromboplastin time prolongation using clot waveform analysis at multiple wavelengths

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月2日

查看全文
  • Masato Matsuda,
  • Daiki Shimomura,
  • Takeshi Suzuki,
  • Yuka Tabuchi,
  • Hiroshi Kurono,
  • Keisuke Nishi,
  • Nobuo Arai,
  • Yoshiki Hoshiyama,
  • Mikio Kamioka,
  • Masato Moriyama,
  • Tomoko Matsumoto

摘要

Activated partial thromboplastin time (APTT) prolongation occurs due to coagulation factor deficiencies/inhibitors, lupus anticoagulant (LA), and anticoagulant-taking, necessitating discrimination through further testing. Clot waveform analysis (CWA) can discriminate causes while measuring APTT, but conventional CWA exhibits moderate accuracy due to visual judgement and limited parameter use. We applied deep learning (DL) techniques to huge numerical data constituting clot waveforms and their first- and second-derivative curves (CWA curves) to leverage hidden features for developing an accurate classification model. We utilized a multi-wavelength detection system embedded in modern coagulometers to obtain multi-wavelength CWA curves. A convolutional neural network-based DL model was trained on 683 samples (135 hemophilic, 95 LA-positive, 99 heparin-treated, 105 warfarin-treated, and 249 direct oral anticoagulant-treated) and evaluated using 10-fold cross-validation. Conventional CWA parameters showed limited discrimination abilities (area under the curve [AUC] 0.532–0.858). DL models using single-wavelength CWA curves achieved higher performance (AUC 0.943–0.988), and multi-wavelength CWA curves further improved it (AUC 0.961–0.993) with high sensitivity (≥ 88.0%), specificity (> 92.0%), and overall accuracy (88.4%), although the performance may depend on reagents and/or analyzers. DL models using multi-wavelength CWA curves show promise as high-performance screening tools for classifying APTT prolongation causes and are best built in each laboratory.