Dataset Information

Comparison of image-based quantification methods in evaluating fixation stability using a remote eye tracker in abnormal phoria.

ABSTRACT:

Objective

This study was performed to establish a quantitative evaluation and comparison of fixation stability, as measured by an eye tracker, using image-based areas determined by the bivariate contour ellipse area (BCEA), kernel density estimation (KDE), and Scanpath methods.

Methods

This prospective cross-sectional study included 45 and 20 participants with abnormal and normal phoria, respectively. Eye movements were recorded using a remote eye tracker and were plotted using RStudio software. Image-based areas were evaluated using ImageJ software.

Results

The image-based areas used to evaluate fixation stability exhibited decreasing stability in the abnormal phoria group in the following order: KDE with ±1 standard deviation (SD), BCEA with ±1 SD, KDE with ±2 SD or Scanpath, and BCEA with ±2 SD. The BCEA tended to be overestimated, and the KDE tended to be underestimated at high density. The Scanpath method had a very high probability area because the area spans all gaze points.

Conclusions

Fixation stability could be quantified as image-based areas by the KDE, BCEA, and Scanpath methods. Our findings suggest that fixation stability may be evaluated using one or more methods.

SUBMITTER: Kim SY

PROVIDER: S-EPMC9112722 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Publications

Comparison of image-based quantification methods in evaluating fixation stability using a remote eye tracker in abnormal phoria.

Kim Sang-Yeob SY Moon Byeong-Yeon BY Cho Hyun Gug HG Yu Dong-Sik DS

The Journal of international medical research 20220501 5

<h4>Objective</h4>This study was performed to establish a quantitative evaluation and comparison of fixation stability, as measured by an eye tracker, using image-based areas determined by the bivariate contour ellipse area (BCEA), kernel density estimation (KDE), and Scanpath methods.<h4>Methods</h4>This prospective cross-sectional study included 45 and 20 participants with abnormal and normal phoria, respectively. Eye movements were recorded using a remote eye tracker and were plotted using RS ...[more]

PMID: 35546441

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Project description:BackgroundThe emergence of Large Language Model (LLM) chatbots, such as ChatGPT, has great promise for enhancing healthcare practice. Online consultation, accurate pre-diagnosis, and clinical efforts are of fundamental importance for the patient-oriented management system.ObjectiveThis cross-sectional study aims to evaluate the performance of ChatGPT in inquiries across ophthalmic domains and to focus on Thyroid Eye Disease (TED) consultation and image-based preliminary diagnosis in a non-English language.MethodsWe obtained frequently consulted clinical inquiries from a published reference based on patient consultation data, titled A Comprehensive Collection of Thyroid Eye Disease Knowledge. Additionally, we collected facial and Computed Tomography (CT) images from 16 patients with a definitive diagnosis of TED. From 18 to 30 May 2024, inquiries about the TED consultation and preliminary diagnosis were posed to ChatGPT using a new chat for each question. Responses to questions from ChatGPT-4, 4o, and an experienced ocular professor were compiled into three questionnaires, which were evaluated by patients and ophthalmologists on four dimensions: accuracy, comprehensiveness, conciseness, and satisfaction. The preliminary diagnosis of TED was deemed accurate, and the differences in the accuracy rates were further calculated.ResultsFor common TED consultation questions, ChatGPT-4o delivered more accurate information with logical consistency, adhering to a structured format of disease definition, detailed sections, and summarized conclusions. Notably, the answers generated by ChatGPT-4o were rated higher than those of ChatGPT-4 and the professor, with accuracy (4.33 [0.69]), comprehensiveness (4.17 [0.75]), conciseness (4.12 [0.77]), and satisfaction (4.28 [0.70]). The characteristics of the evaluators, the response variables, and other quality scores were all correlated with overall satisfaction levels. Based on several facial images, ChatGPT-4 twice failed to make diagnoses because of lacking characteristic symptoms or a complete medical history, whereas ChatGPT-4o accurately identified the pathologic conditions in 31.25% of cases (95% confidence interval, CI: 11.02-58.66%). Furthermore, in combination with CT images, ChatGPT-4o performed comparably to the professor in terms of diagnosis accuracy (87.5, 95% CI 61.65-98.45%).ConclusionChatGPT-4o excelled in comprehensive and satisfactory patient consultation and imaging interpretation, indicating the potential to improve clinical practice efficiency. However, limitations in disinformation management and legal permissions remain major concerns, which require further investigation in clinical practice.

Dataset Information

Comparison of image-based quantification methods in evaluating fixation stability using a remote eye tracker in abnormal phoria.

Objective

Methods

Results

Conclusions

Publications

Comparison of image-based quantification methods in evaluating fixation stability using a remote eye tracker in abnormal phoria.

Similar Datasets

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