[3Rin2] Interactive Session 1

[3Rin2-47] Evaluation of Automatic Monitoring of Instillation Adherence Using Eye Dropper Bottle Sensor and Deep Learning in Patients with Glaucoma

〇Hitoshi Tabuchi Tabuchi^1,2, Kazuaki Nishimura¹, Shunsuke Nakakura¹, Hiroki Masumoto¹, Hirotaka Tanabe¹, Asuka Noguchi¹, Ryota Aoki¹, Yoshiaki Kiuchi² (1. Tsukazaki Hospital, 2. Hiroshima University)

Keywords: Medication information, IoT, Deep Learning

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.