Dataset Information

A novel clinical dynamic stereopsis assessment based on autostereoscopic display system.

ABSTRACT:

Background

Although it is recognized that dynamic stereopsis is vital in daily life, there is still room for improvement in assessment methods. A novel clinical dynamic stereopsis assessment method was created based on an autostereoscopic display system that did not require additional auxiliary glasses. This study evaluated the optical parameters and clinical performance of the autostereoscopic display system for clinical dynamic stereopsis assessment.

Methods

The autostereoscopic dynamic stereopsis assessment device was based on a directional backlight technology. Experiment 1 was performed under the same environmental conditions to compare luminance, crosstalk, and spectrum between the autostereoscopic dynamic stereopsis assessment device and the conventional dynamic random-dot stereopsis measuring instrument. Experiment 2 was an observational, analytic, cross-sectional study involving 135 healthy participants, each of whom was asked to complete measurements on both the autostereoscopic and conventional devices in random order. Stereo acuity, operating time, acceptance, and visual fatigue scores were recorded for clinical evaluation.

Results

The autostereoscopic device had brighter luminance (139 and 140 cd/m² for 2 eyes, respectively), lower crosstalk (4.50% for both eyes), and higher color restoration degree than those of the conventional instrument. Clinically, the novel dynamic stereopsis assessment was as accurate as the traditional method [170" (0.00") and 170" (0.00") respectively; P=0.317], and with more efficiency (166±58.9 and 298±116 s, respectively; P<0.001), higher acceptance (3.36±0.93 and 2.02±0.59 points, respectively; P<0.001), lesser fatigue (0.27±0.46 and 0.73±0.66 points, respectively; P<0.001). The autostereoscopic dynamic stereopsis assessment device with brighter luminance, lower crosstalk, and higher color restoration degree was more effective than the traditional instrument at displaying dynamic clues for clinical dynamic stereopsis assessment. Furthermore, its high-quality image and user-friendly interface provided accurate assessment results in all 3 dynamic stereopsis assessment task conditions, with a higher level of acceptance and lesser visual fatigue, than the traditional assessment method.

Conclusions

The autostereoscopic device has excellent functions in both optical parameters and clinical performance, and therefore has the potential to be applied and popularized in future assessments.

SUBMITTER: Wang Y

PROVIDER: S-EPMC9279808 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Publications

A novel clinical dynamic stereopsis assessment based on autostereoscopic display system.

Wang Yiyao Y Zhong Jing J Cheng Mengyi M Li Jijing J Ma Ke K Hu Xiaoqing X Li Naiyang N Liang Haowen H Zhu Zhengyuan Z Zhou Jianying J Yuan Jin J

Annals of translational medicine 20220601 12

<h4>Background</h4>Although it is recognized that dynamic stereopsis is vital in daily life, there is still room for improvement in assessment methods. A novel clinical dynamic stereopsis assessment method was created based on an autostereoscopic display system that did not require additional auxiliary glasses. This study evaluated the optical parameters and clinical performance of the autostereoscopic display system for clinical dynamic stereopsis assessment.<h4>Methods</h4>The autostereoscopic ...[more]

PMID: 35845544

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Project description:PurposeStereopsis, the ability of humans to perceive depth through distinct visual stimuli in each eye, is foundational to autostereoscopic technology in computing. However, ensuring stable head position during assessments has been challenging. This study evaluated the utility of artificial intelligence (AI)-enhanced face tracking technology in overcoming this challenge by ensuring that each eye consistently receives its intended image.MethodsThe Lume Pad 2, an autostereoscopic tablet with AI-enhanced face tracking, was utilized to simulate quantitative parts of the Stereo Fly test and TNO Stereotests for contour and random dot stereopsis. The study recruited 30 children (14 males and 16 females, mean age of 9.2 ± 0.3 years, age range of 6-12 years) and 30 adults (10 males and 20 females, mean age of 29.4 ± 1.0 years, age range of 21-42 years) to assess the tablet's inter-session reliability. Agreement between conventional and the autostereoscopic tablet-simulated stereotests was tested in a larger group of 181 children (91 males and 90 females, mean age of 9.1 ± 0.4 years, age range of 6-12 years) and 160 adults (69 males and 91 females, mean age of 38.6 ± 2.1 years, age range of 21-65 years). Inter-session reliability and agreement were analyzed using weighted Kappa coefficient and non-parametric Bland-Altman analysis.ResultsThe autostereoscopic tablet demonstrated high inter-session reliability (κ all > 0.80), except for the simulated TNO Stereotest in adults, which demonstrated moderate inter-session reliability (κ = 0.571). Non-parametric Bland-Altman analysis revealed zero median differences, confirming consistent inter-session reliability. Similar patterns were observed in comparing AI-based and conventional methods, with both the weighted Kappa coefficient (κ all > 0.80) and non-parametric Bland-Altman analysis indicating significant agreement. The agreement between methodologies was confirmed by permissible differences, which were smaller than the minimum step range.ConclusionThe integration of AI-based autostereoscopic technology with sub-pixel precision demonstrates significant potential for clinical stereopsis measurements.

Project description:BackgroundTraditional clinical assessments often lack individualization, relying on standardized procedures that may not accommodate the diverse needs of patients, especially in early stages where personalized diagnosis could offer significant benefits. We aim to provide a machine-learning framework that addresses the individualized feature addition problem and enhances diagnostic accuracy for clinical assessments.MethodsIndividualized Clinical Assessment Recommendation System (iCARE) employs locally weighted logistic regression and Shapley Additive Explanations (SHAP) value analysis to tailor feature selection to individual patient characteristics. Evaluations were conducted on synthetic and real-world datasets, including early-stage diabetes risk prediction and heart failure clinical records from the UCI Machine Learning Repository. We compared the performance of iCARE with a Global approach using statistical analysis on accuracy and area under the ROC curve (AUC) to select the best additional features.FindingsThe iCARE framework enhances predictive accuracy and AUC metrics when additional features exhibit distinct predictive capabilities, as evidenced by synthetic datasets 1-3 and the early diabetes dataset. Specifically, in synthetic dataset 1, iCARE achieved an accuracy of 0·999 and an AUC of 1·000, outperforming the Global approach with an accuracy of 0·689 and an AUC of 0·639. In the early diabetes dataset, iCARE shows improvements of 1·5-3·5% in accuracy and AUC across different numbers of initial features. Conversely, in synthetic datasets 4-5 and the heart failure dataset, where features lack discernible predictive distinctions, iCARE shows no significant advantage over global approaches on accuracy and AUC metrics.InterpretationiCARE provides personalized feature recommendations that enhance diagnostic accuracy in scenarios where individualized approaches are critical, improving the precision and effectiveness of medical diagnoses.FundingThis work was supported by startup funding from the Department of Psychology at the University of Kansas provided to A.A., and the R01MH125740 award from NIH partially supported J.M.G.'s work.

Dataset Information

A novel clinical dynamic stereopsis assessment based on autostereoscopic display system.

Background

Methods

Results

Conclusions

Publications

A novel clinical dynamic stereopsis assessment based on autostereoscopic display system.

Similar Datasets

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