Real-world evaluation of novel eye drop bottle sensors: Cloud-based AI support for eye drop adherence

Purpose: To understand real-world eye drop adherence among glaucoma patients and evaluate the performance of our proposed cloud-based support for eye drop adherence (CASEA). Design: Prospective, observational case series. Methods: Setting: The Department of Ophthalmology at Tsukazaki Hospital. Patient or study population: Glaucoma patients treated at the hospital from May 2021 to September 2022, with 61 patients initially enrolled. Intervention or observation procedures: Pharmacists guided eye drop administration before the study. Changes in bottle orientation were detected using an accelerometer attached to the container, and acceleration waveforms and date/time data were recorded. Patients visited the clinic during the 4th and 8th weeks to report their eye drop administration, and the data were uploaded to the cloud. Main outcome measures: Two AI models (B-LSTM) were created to analyze the eye drop bottle movement time-series data for patients treating one or both eyes. The models were evaluated by comparing the true administration status with the AI model judgment. Results: Four of the 61 study subjects dropped out. The remaining 57 patients achieved recall, precision, and accuracy values of 98.6 %, 98.6 %, and 95.9 %, respectively, for the two-eyes model and 95.8 %, 98.8 %, and 95.6 % for the one-eye model. Three low-accuracy participants (77.1 %, 71.0 %, and 81.0 %) improved to 100 %, 99.1 %, and 100 %, respectively, after undergoing an additional 8-week performance validation using an aidtype container designed to ensure that the bottle was fully inverted during instillation. Conclusions: CASEA precisely monitored daily eye drop adherence and enhanced treatment efficacy by identifying patients with difficulty selfmedicating. This system has the potential to improve glaucoma patient outcomes by supporting adherence.