A feasibility study for objective evaluation of visual acuity based on pattern-reversal visual evoked potentials and other related visual parameters with machine learning algorithm
Abstract Background To develop machine learning models for objectively evaluating visual acuity (VA) based on pattern-reversal visual evoked potentials (PRVEPs) and other related visual parameters. Methods Twenty-four volunteers were recruited and forty-eight eyes were divided into four groups of 1....
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| Format: | Article |
| Language: | English |
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BMC
2023-06-01
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| Series: | BMC Ophthalmology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12886-023-03044-7 |
| _version_ | 1797789852254601216 |
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| author | Jian Zheng Chen Cong Cong Li Shao Heng Li Yu Ting Su Tao Zhang Yu Sheng Wang Guo Rui Dou Tao Chen Xiao Cheng Wang Zuo Ming Zhang |
| author_facet | Jian Zheng Chen Cong Cong Li Shao Heng Li Yu Ting Su Tao Zhang Yu Sheng Wang Guo Rui Dou Tao Chen Xiao Cheng Wang Zuo Ming Zhang |
| author_sort | Jian Zheng Chen |
| collection | DOAJ |
| description | Abstract Background To develop machine learning models for objectively evaluating visual acuity (VA) based on pattern-reversal visual evoked potentials (PRVEPs) and other related visual parameters. Methods Twenty-four volunteers were recruited and forty-eight eyes were divided into four groups of 1.0, 0.8, 0.6, and 0.4 (decimal vision). The relationship between VA, peak time, or amplitude of P100 recorded at 5.7°, 2.6°, 1°, 34′, 15′, and 7′ check sizes were analyzed using repeated-measures analysis of variance. Correlations between VA and P100, contrast sensitivity (CS), refractive error, wavefront aberrations, and visual field were analyzed by rank correlation. Based on meaningful P100 peak time, P100 amplitude, and other related visual parameters, four machine learning algorithms and an ensemble classification algorithm were used to construct objective assessment models for VA. Receiver operating characteristic (ROC) curves were used to compare the efficacy of different models by repeated sampling comparisons and ten-fold cross-validation. Results The main effects of P100 peak time and amplitude between different VA and check sizes were statistically significant (all P < 0.05). Except amplitude at 2.6° and 5.7°, VA was negatively correlated with peak time and positively correlated with amplitude. The peak time initially shortened with increasing check size and gradually lengthened after the minimum value was reached at 1°. At the 1° check size, there were statistically significant differences when comparing the peak times between the vision groups with each other (all P < 0.05), and the amplitudes of the vision reduction groups were significantly lower than that of the 1.0 vision group (all P < 0.01). The correlations between peak time, amplitude, and visual acuity were all highest at 1° (r s = − 0.740, 0.438). VA positively correlated with CS and spherical equivalent (all P < 0.001). There was a negative correlation between VA and coma aberrations (P < 0.05). For different binarization classifications of VA, the classifier models with the best assessment efficacy all had the mean area under the ROC curves (AUC) above 0.95 for 500 replicate samples and above 0.84 for ten-fold cross-validation. Conclusions Machine learning models established by meaning visual parameters related to visual acuity can assist in the objective evaluation of VA. |
| first_indexed | 2024-03-13T01:56:26Z |
| format | Article |
| id | doaj.art-78a63561e6d14b28bab6eae206e7845d |
| institution | Directory Open Access Journal |
| issn | 1471-2415 |
| language | English |
| last_indexed | 2024-03-13T01:56:26Z |
| publishDate | 2023-06-01 |
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| series | BMC Ophthalmology |
| spelling | doaj.art-78a63561e6d14b28bab6eae206e7845d2023-07-02T11:10:48ZengBMCBMC Ophthalmology1471-24152023-06-0123111210.1186/s12886-023-03044-7A feasibility study for objective evaluation of visual acuity based on pattern-reversal visual evoked potentials and other related visual parameters with machine learning algorithmJian Zheng Chen0Cong Cong Li1Shao Heng Li2Yu Ting Su3Tao Zhang4Yu Sheng Wang5Guo Rui Dou6Tao Chen7Xiao Cheng Wang8Zuo Ming Zhang9Ministry-of-Education Key Laboratory of Aerospace Medicine, School of Aerospace Medicine, Air Force Medical UniversityMinistry-of-Education Key Laboratory of Aerospace Medicine, School of Aerospace Medicine, Air Force Medical UniversityMinistry-of-Education Key Laboratory of Aerospace Medicine, School of Aerospace Medicine, Air Force Medical UniversityMinistry-of-Education Key Laboratory of Aerospace Medicine, School of Aerospace Medicine, Air Force Medical UniversitySchool of Biomedical Engineering, Air Force Medical UniversityDepartment of Ophthalmology, The First Affiliated Hospital, Air Force Medical UniversityDepartment of Ophthalmology, The First Affiliated Hospital, Air Force Medical UniversityMinistry-of-Education Key Laboratory of Aerospace Medicine, School of Aerospace Medicine, Air Force Medical UniversityMinistry-of-Education Key Laboratory of Aerospace Medicine, School of Aerospace Medicine, Air Force Medical UniversityMinistry-of-Education Key Laboratory of Aerospace Medicine, School of Aerospace Medicine, Air Force Medical UniversityAbstract Background To develop machine learning models for objectively evaluating visual acuity (VA) based on pattern-reversal visual evoked potentials (PRVEPs) and other related visual parameters. Methods Twenty-four volunteers were recruited and forty-eight eyes were divided into four groups of 1.0, 0.8, 0.6, and 0.4 (decimal vision). The relationship between VA, peak time, or amplitude of P100 recorded at 5.7°, 2.6°, 1°, 34′, 15′, and 7′ check sizes were analyzed using repeated-measures analysis of variance. Correlations between VA and P100, contrast sensitivity (CS), refractive error, wavefront aberrations, and visual field were analyzed by rank correlation. Based on meaningful P100 peak time, P100 amplitude, and other related visual parameters, four machine learning algorithms and an ensemble classification algorithm were used to construct objective assessment models for VA. Receiver operating characteristic (ROC) curves were used to compare the efficacy of different models by repeated sampling comparisons and ten-fold cross-validation. Results The main effects of P100 peak time and amplitude between different VA and check sizes were statistically significant (all P < 0.05). Except amplitude at 2.6° and 5.7°, VA was negatively correlated with peak time and positively correlated with amplitude. The peak time initially shortened with increasing check size and gradually lengthened after the minimum value was reached at 1°. At the 1° check size, there were statistically significant differences when comparing the peak times between the vision groups with each other (all P < 0.05), and the amplitudes of the vision reduction groups were significantly lower than that of the 1.0 vision group (all P < 0.01). The correlations between peak time, amplitude, and visual acuity were all highest at 1° (r s = − 0.740, 0.438). VA positively correlated with CS and spherical equivalent (all P < 0.001). There was a negative correlation between VA and coma aberrations (P < 0.05). For different binarization classifications of VA, the classifier models with the best assessment efficacy all had the mean area under the ROC curves (AUC) above 0.95 for 500 replicate samples and above 0.84 for ten-fold cross-validation. Conclusions Machine learning models established by meaning visual parameters related to visual acuity can assist in the objective evaluation of VA.https://doi.org/10.1186/s12886-023-03044-7Visual acuityVisual-evoked potentialsRefractive errorAircrewMachine learning |
| spellingShingle | Jian Zheng Chen Cong Cong Li Shao Heng Li Yu Ting Su Tao Zhang Yu Sheng Wang Guo Rui Dou Tao Chen Xiao Cheng Wang Zuo Ming Zhang A feasibility study for objective evaluation of visual acuity based on pattern-reversal visual evoked potentials and other related visual parameters with machine learning algorithm BMC Ophthalmology Visual acuity Visual-evoked potentials Refractive error Aircrew Machine learning |
| title | A feasibility study for objective evaluation of visual acuity based on pattern-reversal visual evoked potentials and other related visual parameters with machine learning algorithm |
| title_full | A feasibility study for objective evaluation of visual acuity based on pattern-reversal visual evoked potentials and other related visual parameters with machine learning algorithm |
| title_fullStr | A feasibility study for objective evaluation of visual acuity based on pattern-reversal visual evoked potentials and other related visual parameters with machine learning algorithm |
| title_full_unstemmed | A feasibility study for objective evaluation of visual acuity based on pattern-reversal visual evoked potentials and other related visual parameters with machine learning algorithm |
| title_short | A feasibility study for objective evaluation of visual acuity based on pattern-reversal visual evoked potentials and other related visual parameters with machine learning algorithm |
| title_sort | feasibility study for objective evaluation of visual acuity based on pattern reversal visual evoked potentials and other related visual parameters with machine learning algorithm |
| topic | Visual acuity Visual-evoked potentials Refractive error Aircrew Machine learning |
| url | https://doi.org/10.1186/s12886-023-03044-7 |
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