Academic Journal
Development and Clinical Validation of an Artificial Intelligence-Based Automated Visual Acuity Testing System.
| Τίτλος: | Development and Clinical Validation of an Artificial Intelligence-Based Automated Visual Acuity Testing System. |
|---|---|
| Συγγραφείς: | Li, Kelvin Zhenghao, Oo, Hnin Hnin, Liang, Kenneth Chee Wei, Ismail, Najah, Chua, Jasmine Ling Ling, Chng, Jackson Jie Sheng, Wu, Yang, Wong, Daryl Wei Ren, Khan, Sumaya Rani, Yap, Boon Peng, Tong, Rong, Kiew, Choon Meng, Huang, Yufei, Chua, Chun Hau, Lim, Alva Khai Shin, Fan, Xiuyi |
| Πηγή: | Life (2075-1729); Feb2026, Vol. 16 Issue 2, p357, 13p |
| Θεματικοί όροι: | Visual acuity, Artificial intelligence, Automatic speech recognition, Test systems, Health outcome assessment, Image recognition (Computer vision) |
| Περίληψη: | Background: To develop and validate an automated visual acuity (VA) testing system integrating artificial intelligence (AI)–driven speech and image recognition technologies, enabling self-administered, clinic-based VA assessment; Methods: The system incorporated a fine-tuned Whisper speech-recognition model with Silero voice activity detection and pose estimation through facial landmark and ArUco marker detection. A state-driven interface guided users through sequential testing with and without a pinhole. Speech recognition was enhanced using a local Singaporean English dataset. Laboratory validation assessed speech and pose recognition performance, while clinical validation compared automated and manual VA testing at a tertiary eye clinic; Results: The fine-tuned model reduced word error rates from 17.83% to 9.81% for letters and 2.76% to 1.97% for numbers. Pose detection accurately identified valid occluder states. Among 72 participants (144 eyes), automated unaided VA showed good agreement with manual VA (ICC = 0.77, 95% CI 0.62–0.85), while pinhole VA demonstrated moderate agreement (ICC = 0.63, 95% CI 0.25–0.83). Automated testing took longer (132.1 ± 47.5 s vs. 97.1 ± 47.8 s; p < 0.001), but user experience remained positive (mean Likert scale score 4.3 ± 0.8); Conclusions: The AI-based automated VA system delivered accurate, reliable, and user-friendly performance, supporting its feasibility for clinical implementation. [ABSTRACT FROM AUTHOR] |
| Copyright of Life (2075-1729) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=20751729&ISBN=&volume=16&issue=2&date=20260201&spage=357&pages=357-369&title=Life (2075-1729)&atitle=Development%20and%20Clinical%20Validation%20of%20an%20Artificial%20Intelligence-Based%20Automated%20Visual%20Acuity%20Testing%20System.&aulast=Li%2C%20Kelvin%20Zhenghao&id=DOI:10.3390/life16020357 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Header | DbId: edb DbLabel: Complementary Index An: 192038875 RelevancyScore: 1041 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1041.06896972656 |
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| Items | – Name: Title Label: Title Group: Ti Data: Development and Clinical Validation of an Artificial Intelligence-Based Automated Visual Acuity Testing System. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Kelvin+Zhenghao%22">Li, Kelvin Zhenghao</searchLink><br /><searchLink fieldCode="AR" term="%22Oo%2C+Hnin+Hnin%22">Oo, Hnin Hnin</searchLink><br /><searchLink fieldCode="AR" term="%22Liang%2C+Kenneth+Chee+Wei%22">Liang, Kenneth Chee Wei</searchLink><br /><searchLink fieldCode="AR" term="%22Ismail%2C+Najah%22">Ismail, Najah</searchLink><br /><searchLink fieldCode="AR" term="%22Chua%2C+Jasmine+Ling+Ling%22">Chua, Jasmine Ling Ling</searchLink><br /><searchLink fieldCode="AR" term="%22Chng%2C+Jackson+Jie+Sheng%22">Chng, Jackson Jie Sheng</searchLink><br /><searchLink fieldCode="AR" term="%22Wu%2C+Yang%22">Wu, Yang</searchLink><br /><searchLink fieldCode="AR" term="%22Wong%2C+Daryl+Wei+Ren%22">Wong, Daryl Wei Ren</searchLink><br /><searchLink fieldCode="AR" term="%22Khan%2C+Sumaya+Rani%22">Khan, Sumaya Rani</searchLink><br /><searchLink fieldCode="AR" term="%22Yap%2C+Boon+Peng%22">Yap, Boon Peng</searchLink><br /><searchLink fieldCode="AR" term="%22Tong%2C+Rong%22">Tong, Rong</searchLink><br /><searchLink fieldCode="AR" term="%22Kiew%2C+Choon+Meng%22">Kiew, Choon Meng</searchLink><br /><searchLink fieldCode="AR" term="%22Huang%2C+Yufei%22">Huang, Yufei</searchLink><br /><searchLink fieldCode="AR" term="%22Chua%2C+Chun+Hau%22">Chua, Chun Hau</searchLink><br /><searchLink fieldCode="AR" term="%22Lim%2C+Alva+Khai+Shin%22">Lim, Alva Khai Shin</searchLink><br /><searchLink fieldCode="AR" term="%22Fan%2C+Xiuyi%22">Fan, Xiuyi</searchLink> – Name: TitleSource Label: Source Group: Src Data: Life (2075-1729); Feb2026, Vol. 16 Issue 2, p357, 13p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Visual+acuity%22">Visual acuity</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+speech+recognition%22">Automatic speech recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Test+systems%22">Test systems</searchLink><br /><searchLink fieldCode="DE" term="%22Health+outcome+assessment%22">Health outcome assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Image+recognition+%28Computer+vision%29%22">Image recognition (Computer vision)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: To develop and validate an automated visual acuity (VA) testing system integrating artificial intelligence (AI)–driven speech and image recognition technologies, enabling self-administered, clinic-based VA assessment; Methods: The system incorporated a fine-tuned Whisper speech-recognition model with Silero voice activity detection and pose estimation through facial landmark and ArUco marker detection. A state-driven interface guided users through sequential testing with and without a pinhole. Speech recognition was enhanced using a local Singaporean English dataset. Laboratory validation assessed speech and pose recognition performance, while clinical validation compared automated and manual VA testing at a tertiary eye clinic; Results: The fine-tuned model reduced word error rates from 17.83% to 9.81% for letters and 2.76% to 1.97% for numbers. Pose detection accurately identified valid occluder states. Among 72 participants (144 eyes), automated unaided VA showed good agreement with manual VA (ICC = 0.77, 95% CI 0.62–0.85), while pinhole VA demonstrated moderate agreement (ICC = 0.63, 95% CI 0.25–0.83). Automated testing took longer (132.1 ± 47.5 s vs. 97.1 ± 47.8 s; p < 0.001), but user experience remained positive (mean Likert scale score 4.3 ± 0.8); Conclusions: The AI-based automated VA system delivered accurate, reliable, and user-friendly performance, supporting its feasibility for clinical implementation. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Life (2075-1729) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/life16020357 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 357 Subjects: – SubjectFull: Visual acuity Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Automatic speech recognition Type: general – SubjectFull: Test systems Type: general – SubjectFull: Health outcome assessment Type: general – SubjectFull: Image recognition (Computer vision) Type: general Titles: – TitleFull: Development and Clinical Validation of an Artificial Intelligence-Based Automated Visual Acuity Testing System. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Kelvin Zhenghao – PersonEntity: Name: NameFull: Oo, Hnin Hnin – PersonEntity: Name: NameFull: Liang, Kenneth Chee Wei – PersonEntity: Name: NameFull: Ismail, Najah – PersonEntity: Name: NameFull: Chua, Jasmine Ling Ling – PersonEntity: Name: NameFull: Chng, Jackson Jie Sheng – PersonEntity: Name: NameFull: Wu, Yang – PersonEntity: Name: NameFull: Wong, Daryl Wei Ren – PersonEntity: Name: NameFull: Khan, Sumaya Rani – PersonEntity: Name: NameFull: Yap, Boon Peng – PersonEntity: Name: NameFull: Tong, Rong – PersonEntity: Name: NameFull: Kiew, Choon Meng – PersonEntity: Name: NameFull: Huang, Yufei – PersonEntity: Name: NameFull: Chua, Chun Hau – PersonEntity: Name: NameFull: Lim, Alva Khai Shin – PersonEntity: Name: NameFull: Fan, Xiuyi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20751729 Numbering: – Type: volume Value: 16 – Type: issue Value: 2 Titles: – TitleFull: Life (2075-1729) Type: main |
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