Objectives: Thinning of retinal layers has been documented in patients with chronic schizophrenia using standard metrics of optical coherence tomography (OCT) devices. We demonstrate the effectiveness of machine learning (ML) techniques to differentiate between schizophrenia patients and healthy controls using OCT images. Methods: Features extracted from a convolutional neural network (CNN) designed to segment retinal layers from OCT images represented abstracted data from the OCT images of 14 first episode (FEP) and 18 chronic schizophrenia patients, and their respective 20 and 18 age-matched controls. The abstracted data and OCT machine metrics were used separately to train support vector classification (SVC) models to differentiate between control and schizophrenia samples and test them. Results: SVCs operating on OCT machine metrics did not classify unseen samples of FEP schizophrenia patients and controls with performance better than chance, while those looking at chronic schizophrenia did, paralleling results obtained using parametric statistics. In contrast, SVCs operating on OCT image data extracted from the CNN classified unseen samples from both populations with performance greater than chance. Conclusion: These results suggest that ML techniques can detect patterns in patients with FEP schizophrenia with greater performance using features extracted from OCT images than metrics provided by OCT machines.

中文翻译：

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使用机器学习基于视网膜图像对精神分裂症进行分类

目的：使用光学相干断层扫描（OCT）设备的标准指标，已证明慢性精神分裂症患者的视网膜层变薄。我们展示了使用OCT图像来区分精神分裂症患者和健康对照者的机器学习（ML）技术的有效性。方法：从卷积神经网络（CNN）提取的特征（用于从OCT图像中分割视网膜层）中提取的特征表示来自14例首发（​​FEP）和18例慢性精神分裂症患者及其20例和18例年龄相匹配的对照的OCT图像的抽象数据。分别使用抽象数据和OCT机器指标来训练支持向量分类（SVC）模型，以区分对照样本和精神分裂症样本并对其进行测试。结果：使用OCT机器指标进行操作的SVC并未对FEP精神分裂症患者和对照的未见样本进行分类，其性能优于偶然性，而那些研究慢性精神分裂症的患者则进行了分类，与使用参数统计获得的结果相类似。相反，对从CNN提取的OCT图像数据进行操作的SVC对来自两个种群的未见样本进行分类，其性能要高于偶然性。结论：这些结果表明，与从OCT机器提供的指标相比，使用从OCT图像中提取的特征，机器学习技术可以更好地检测FEP精神分裂症患者的模式。对从CNN提取的OCT图像数据进行操作的SVC将来自两个种群的未见样本分类为性能大于偶然性的样本。结论：这些结果表明，与从OCT机器提供的指标相比，使用从OCT图像中提取的特征，机器学习技术可以更有效地检测FEP精神分裂症患者的模式。对从CNN提取的OCT图像数据进行操作的SVC将来自两个种群的未见样本分类为性能大于偶然性的样本。结论：这些结果表明，与从OCT机器提供的指标相比，使用从OCT图像中提取的特征，机器学习技术可以更有效地检测FEP精神分裂症患者的模式。