Evidence map›Paper›PMID 36607624›Full record

ArticleTranslational vision science & technology2023

Portable Anterior Eye Segment Imaging System for Teleophthalmology.

Ana Diego, Mohamed Abou Shousha

Abstract read
In one paragraph

Article in Translational vision science & technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Trial
  2. Article
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

2 authors.

Ana DiegoDepartment of Biomedical Engineering, University of Miami, Miami, FL, USA.
Mohamed Abou ShoushaDepartment of Biomedical Engineering, University of Miami, Miami, FL, USA.

Funding

Shared Equipment ModuleP30EY014801 · NEI · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI Victor L Perez · 2004 to 2026
$12.2M
NEI NIH HHS P30 EY014801
6 · The paper itself

Abstract

Objective: This study aims to compare a new prototype for a portable anterior eye segment imaging system with the standard method for ophthalmology examination. Methods: The new imaging system consisted of two IMX219 Arducam autofocus sensors (Arducam, China, Nanjing) for Raspberry Pi V2 camera module connected to a Raspberry Pi Zero W (Raspberry Pi Foundation, UK, Cambridge) that clips to a wearable headset. The 2D videos of the anterior eye segment were recorded with the new system and a 720p FaceTime HD camera (Apple, Cupertino, CA). Afterward, ophthalmologists evaluated the videos using a standard clinical eye examination form. These evaluations were compared with the standard slit-lamp clinical assessment performed during the patient's visit. Results: Thirty-five eyes were evaluated. The sensitivity and specificity percentages were statistically significant between the two imaging modalities (P ≤ 0.001). The evaluations performed from videos obtained with the new imaging system had better sensitivity and specificity percentages overall. However, statistically significant differences were only observed in cornea, anterior chamber, iris, and lens. Conclusions: Specificity percentages were higher than sensitivity percentages in both imaging modalities, indicating that video evaluations are less accurate for pathological screening. Nevertheless, the new system evaluations were significantly better than the webcam evaluations. Translational Relevance: This study presented an alternative system to assess eye conditions for telemedicine, one that provides more details than the current standard and uses new wearable headsets technologies.

Indexed as

Eye DiseasesOphthalmologyTelemedicineAnterior ChamberAnterior Eye SegmentHumans

Identifiers

PMID36607624
PMCPMC9836007

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.