Timely detection and treatment are essential for maintaining eye health. Visual acuity (VA), which measures the clarity of vision at a distance, is a crucial metric for managing eye health. Machine learning (ML) techniques have been introduced to assist in VA measurement, potentially alleviating clinicians' workloads. However, the inherent uncertainties in ML models make relying solely on them for VA prediction less than ideal. The VA prediction task involves multiple sources of uncertainty, requiring more robust approaches. A promising method is to build prediction sets or intervals rather than point estimates, offering coverage guarantees through techniques like conformal prediction and Probably Approximately Correct (PAC) prediction sets. Despite the potential, to date, these approaches have not been applied to the VA prediction task.To address this, we propose a method for deriving prediction intervals for estimating visual acuity from fundus images with a PAC guarantee. Our experimental results demonstrate that the PAC guarantees are upheld, with performance comparable to or better than that of two prior works that do not provide such guarantees.

翻译：及时检测与治疗对于维持眼部健康至关重要。视觉敏锐度作为衡量远距离视物清晰度的关键指标，是眼健康管理的核心参数。机器学习技术已被引入以辅助VA测量，有望减轻临床医生的工作负担。然而，机器学习模型固有的不确定性使得单纯依赖其进行VA预测存在不足。VA预测任务涉及多种不确定性来源，需要采用更稳健的方法。构建预测集或预测区间而非点估计是一种前景广阔的方法，可通过保形预测和概率近似正确预测集等技术提供覆盖保证。尽管具有潜力，但迄今为止这些方法尚未应用于VA预测任务。为此，我们提出一种从眼底图像估计视觉敏锐度的预测区间推导方法，该方法具有PAC保证。实验结果表明，该方法能够维持PAC保证，其性能与两项未提供此类保证的已有工作相当或更优。