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Detection of Patients with Congenital and Often Concealed Long-QT Syndrome by Novel Deep Learning Models.

Florian DoldiLucas PlagwitzLea Philine HoffmannBenjamin RathGerrit FrommeyerFlorian ReinkePatrick LeitzAntonius BüscherFatih GünerTobias Johannes BrixFelix Konrad WegnerKevin WillyYvonne HanelSven DittmannWilhelm HaverkampEric Schulze-BahrJulian VargheseLars Eckardt
Published in: Journal of personalized medicine (2022)
In this study, the XceptionTime model outperformed the FCN model for LQTS patients with even better results than in prior studies. Even when a patient cohort with cardiovascular comorbidities is used. AI-based ECG analysis is a promising step for correct LQTS patient identification, especially if common diagnostic measures might be misleading.
Keyphrases
  • case report
  • deep learning
  • artificial intelligence
  • heart rate variability
  • loop mediated isothermal amplification
  • label free
  • real time pcr
  • bioinformatics analysis