Artificial Intelligence for Staging Glaucoma Severity: A Comparison With an Eye Specialist Using Visual Field Tests (AI-GSS2)
Reliability of Artificial Intelligence in Glaucoma Severity Staging Using the Brusini GSS2 System: A Longitudinal Comparative Study Against Expert Assessment
The study titled "Reliability of Artificial Intelligence in Glaucoma Severity Staging Using the Brusini GSS2 System: A Longitudinal Comparative Study against Expert Assessment" aims to evaluate the diagnostic reliability and clinical utility of an Artificial Intelligence (AI) system in staging glaucoma severity using the Brusini Glaucoma Staging System 2 (GSS2) compared to expert ophthalmologist assessment.
The study protocol involves the longitudinal analysis of visual fields (VFs) collected at baseline (T0) and at a 1-year follow-up (T1). Statistical methodology will utilize quadratic weighted Cohen's kappa to assess diagnostic agreement between the AI system and expert assessment, Wilcoxon signed-rank tests to evaluate potential systematic bias, and Spearman's rank correlation to investigate the relationship between longitudinal progression and changes in the clinical Visual Field Index.
研究概览
研究类型
注册 (实际的)
联系人和位置
学习地点
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Salerno、意大利、84084
- University of Salerno
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参与标准
资格标准
适合学习的年龄
- 孩子
- 成人
- 年长者
接受健康志愿者
取样方法
研究人群
描述
Inclusion Criteria:
- Adults (≥18 years of age).
- Diagnosis of glaucoma or suspected glaucoma undergoing routine clinical follow-up.
- Availability of reliable standard automated visual field (VF) examinations at baseline and approximately 1-year follow-up.
- Visual field tests suitable for staging using the Brusini Glaucoma Staging System 2 (GSS2).
- Availability of all clinical data required for study analysis.
Exclusion Criteria:
- Unreliable visual field examinations (e.g., excessive fixation losses, false-positive or false-negative responses according to institutional reliability criteria).
- Visual field defects attributable to ocular or neurological diseases other than glaucoma.
- Missing baseline or follow-up visual field examinations.
- Incomplete clinical data preventing GSS2 staging or statistical analysis.
- Previous ocular conditions or interventions that could significantly affect visual field interpretation, if judged by the investigator.
学习计划
研究是如何设计的？
设计细节
队列和干预
团体/队列 |
干预/治疗 |
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Glaucoma Patients
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An artificial intelligence system used to automatically stage glaucoma severity from standard automated visual field tests according to the Brusini Glaucoma Staging System 2 (GSS2).
AI classifications were compared with those of an expert ophthalmologist at baseline and 1-year follow-up.
The AI was used for assessment only and did not influence patient management or treatment.
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研究衡量的是什么？
主要结果指标
结果测量 |
措施说明 |
大体时间 |
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Quadratic weighted Cohen's kappa coefficient for diagnostic agreement in glaucoma severity staging between AI and expert assessment
大体时间：Baseline (T0) and 1-year follow-up (T1)
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Glaucoma severity will be staged (from Stage 0 to Stage 5) using the Brusini Glaucoma Staging System 2 (GSS2) based on visual field parameters.
Staging will be performed independently by the AI system and an expert ophthalmologist at each time point.
Inter-rater agreement between the AI and expert staging will be quantified using the quadratic weighted Cohen's kappa ($\kappa$), where values range from -1 (complete disagreement) to +1 (perfect agreement).
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Baseline (T0) and 1-year follow-up (T1)
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次要结果测量
结果测量 |
措施说明 |
大体时间 |
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Median difference in Brusini GSS2 staging scores between AI and expert assessment
大体时间：Baseline (T0) and 1-year follow-up (T1)
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Differences in Brusini Glaucoma Staging System 2 (GSS2) staging scores (ranging from Stage 0 to Stage 5) assigned by the AI system versus the expert ophthalmologist will be evaluated to detect systematic overestimation or underestimation of disease severity.
Paired score differences (AI minus Expert) at each time point will be reported and statistically tested using the Wilcoxon signed-rank test.
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Baseline (T0) and 1-year follow-up (T1)
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合作者和调查者
研究记录日期
研究主要日期
学习开始 (实际的)
初级完成 (实际的)
研究完成 (实际的)
研究注册日期
首次提交
首先提交符合 QC 标准的
首次发布 (实际的)
研究记录更新
最后更新发布 (实际的)
上次提交的符合 QC 标准的更新
最后验证
更多信息
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