Evaluation of Automatic Monitoring of Instillation Adherence Using Eye Dropper Bottle Sensor and Deep Learning in Patients with Glaucoma

DOI

Bibliographic Information

Other Title
  • 点眼瓶センサーとDeep Learningによる緑内障患者点眼アドヒアランス自動把握能力の評価

Description

<p>Purpose: We developed and evaluated an eye dropper bottle sensor system comprising motion sensor with automatic motion waveform analysis using deep learning (DL) to accurately measure adherence of patients with antiglaucoma ophthalmic solution therapy. Results: The developed eye bottle sensor detected all 60 instillation events (100%). Mean (SD) difference between patient and eye bottle sensor recorded time was 1 (1.22) (range; 0–3) min. Additionally, mean (SD) instillation movement duration was 16.1 (14.4) (range; 4–43) s. Two-way ANOVA revealed a significant difference in instillation movement duration among patients (P<0.001) and across days (P<0.001). Conclusion: The eye dropper bottle sensor system developed by us can be used for automatic monitoring of instillation adherence in patients with glaucoma.</p>

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Details 詳細情報について

  • CRID
    1390845713074342656
  • NII Article ID
    130007658698
  • DOI
    10.11517/pjsai.jsai2019.0_3rin247
  • ISSN
    27587347
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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