Evidence map›Paper›PMID 40689721›Full record

ArticleTranslational vision science & technology2025

Enhancing Orientation and Mobility Assessments: Integrating Visual and Auditory Factors Using Artificial Intelligence Tools.

Roberto Morollón Ruiz, Joel Alejandro Cueva Garcés, Leili Soo, Eduardo Fernández

Registry-linked trialAbstract read
In one paragraph

Article in Translational vision science & technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02983370 (Pilot Study for the Development of a Cortical Visual Neuroprosthesis for the Blind Based on Intracortical Microelectrodes), which is not on this map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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.

NCT02983370 narecruitingnot on this map

Pilot Study for the Development of a Cortical Visual Neuroprosthesis for the Blind Based on Intracortical Microelectrodes

TypeinterventionalSponsorUniversidad Miguel Hernandez de ElcheRan2019 to 2028Enrolled5ConditionsBlindnessArmsMinicraniotomy
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Roberto Morollón RuizMiguel Hernandez University, Elche, Spain.
Joel Alejandro Cueva GarcésMiguel Hernandez University, Elche, Spain.
Leili SooMiguel Hernandez University, Elche, Spain.
Eduardo FernándezMiguel Hernandez University, Elche and CIBER-BBN, Madrid, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Propose: Common orientation and mobility (O&M) assessments in individuals with visual impairments often overlook environmental and psychological factors, and report results in outcome measures that might not be sufficiently sensitive. The present study aims to extend previous assessments by comparing the O&M performance and arousal levels in different simulated vision impairment and auditory conditions while using artificial intelligence (AI) tools for body tracking in an O&M task within a StreetLab environment. Methods: We video-recorded the O&M performance in 32 volunteers under different auditory and visual conditions. In addition to common outcome metrics (speed and collisions), we measured path length and deviations based on AI tools. Physiological parameters and subjective experiences were also recorded. Results: Our results showed differences between various conditions when measured using AI-based path length and deviations that were not detected by common outcome measures. We found that both vision and auditory conditions influenced O&M performance and that performing the task with a lot of background sound increased arousal. Conclusions: Overall, the study shows that AI-based analysis can offer valuable insights into performance beyond traditional measures and that a comprehensive evaluation of O&M skills in visual impairment should include considerations for both visual and auditory factors as well as arousal level. Translational Relevance: The use of AI tools enhance O&M assessments for individuals with visual impairments provides insights into environmental and psychological factors influencing O&M performance and potentially improves the evaluation of and care for individuals with visual impairments.

Indexed as

Artificial IntelligenceOrientationOrientation, SpatialVision DisordersAdultFemaleHumansMaleMiddle AgedYoung Adult

Identifiers

PMID40689721
PMCPMC12302044

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Registered trials

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.