Novel therapeutic approaches for treating inherited retinal degenerations (IRDs) prompt a need to understand which patients with impaired vision have the anatomical potential to gain from participation in a clinical trial. We used supervised machine learning to predict foveal function from foveal structure in blue cone monochromacy (BCM), an X-linked congenital cone photoreceptor dysfunction secondary to mutations in the OPN1LW/OPN1MW gene cluster. BCM patients with either disease-associated large deletion or missense mutations were studied and results compared with those from subjects with other forms of IRD and various degrees of preserved central structure and function. A machine learning technique was used to associate foveal sensitivities and best-corrected visual acuities to foveal structure in IRD patients. Two random forest (RF) models trained on IRD data were applied to predict foveal function in BCM. A curve fitting method was also used and results compared with those of the RF models. The BCM and IRD patients had a comparable range of foveal structure. IRD patients had peak sensitivity at the fovea. Machine learning could successfully predict foveal sensitivity (FS) results from segmented or un-segmented optical coherence tomography (OCT) input. Application of machine learning predictions to BCM at the fovea showed differences between predicted and measured sensitivities, thereby defining treatment potential. The curve fitting method provided similar results. Given a measure of visual acuity (VA) and foveal outer nuclear layer thickness, the question of how many lines of acuity would represent the best efficacious result for each BCM patient could be answered. We propose that foveal vision improvement potential in BCM is predictable from retinal structure using machine learning and curve fitting approaches. This should allow estimates of maximal efficacy in patients being considered for clinical trials and also guide decisions about dosing.

中文翻译：

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蓝锥体单色性的中心凹治疗：人工智能视觉潜力的预测


治疗遗传性视网膜变性（IRD）的新方法促使我们需要了解哪些视力受损的患者具有从参与临床试验中获益的解剖潜力。我们使用监督机器学习来根据蓝视锥单色性 (BCM) 的中心凹结构来预测中心凹功能，BCM 是一种继发于 OPN1LW/OPN1MW 基因簇突变的 X 连锁先天性视锥光感受器功能障碍。研究人员对患有疾病相关大缺失或错义突变的 BCM 患者进行了研究，并将结果与​​患有其他形式 IRD 和不同程度保留中枢结构和功能的受试者进行了比较。使用机器学习技术将 IRD 患者的中心凹敏感性和最佳矫正视力与中心凹结构相关联。应用两个根据 IRD 数据训练的随机森林 (RF) 模型来预测 BCM 中的中心凹功能。还使用了曲线拟合方法并将结果与​​ RF 模型的结果进行比较。 BCM 和 IRD 患者的中心凹结构范围相当。 IRD 患者的中央凹敏感度最高。机器学习可以成功地预测分段或未分段光学相干断层扫描 (OCT) 输入的中心凹敏感性 (FS) 结果。将机器学习预测应用于中央凹的 BCM 显示了预测和测量的敏感性之间的差异，从而确定了治疗潜力。曲线拟合方法提供了类似的结果。给定视力 (VA) 和中心凹外核层厚度的测量值，可以回答每个 BCM 患者多少行视力代表最佳有效结果的问题。 我们提出，使用机器学习和曲线拟合方法，可以根据视网膜结构预测 BCM 中黄斑中心凹视力改善的潜力。这应该可以估计考虑参加临床试验的患者的最大疗效，并指导剂量决策。