Evidence map›Paper›PMID 39092249›Full record

ArticleHeliyon2024

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

Hitoshi Tabuchi, Kazuaki Nishimura, Masahiro Akada, Tomohiro Ishikami, Tomoki Shirakami, Naotake Kamiura, Yoshiaki Kiuchi

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Trial
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Hitoshi TabuchiDepartment of Technology and Design Thinking for Medicine, Hiroshima University, Hiroshima, Japan.
Kazuaki NishimuraDepartment of Ophthalmology, Tsukazaki Hospital, Himeji, Japan.
Masahiro AkadaDepartment of Ophthalmology, Tsukazaki Hospital, Himeji, Japan.
Tomohiro IshikamiGraduate School of Engineering, University of Hyogo, Himeji, Japan.
Tomoki ShirakamiDepartment of Ophthalmology, Tsukazaki Hospital, Himeji, Japan.
Naotake KamiuraGraduate School of Engineering, University of Hyogo, Himeji, Japan.
Yoshiaki KiuchiDepartment of Ophthalmology, Hiroshima University, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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: 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 aid-type 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 self-medicating. This system has the potential to improve glaucoma patient outcomes by supporting adherence.

Identifiers

PMID39092249
PMCPMC11292234

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.