Department of Psychology, Columbia University, New York, NY, 10027, USA; Bernard and Shirlee Brown Glaucoma Research Laboratory, Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY, USA, 10032.
Department of Psychology, Columbia University, New York, NY, 10027, USA.
Prog Retin Eye Res. 2022 Sep;90:101052. doi: 10.1016/j.preteyeres.2022.101052. Epub 2022 Feb 23.
A method for detecting glaucoma based only on optical coherence tomography (OCT) is of potential value for routine clinical decisions, for inclusion criteria for research studies and trials, for large-scale clinical screening, as well as for the development of artificial intelligence (AI) decision models. Recent work suggests that the OCT probability (p-) maps, also known as deviation maps, can play a key role in an OCT-based method. However, artifacts seen on the p-maps of healthy control eyes can resemble patterns of damage due to glaucoma. We document in section 2 that these glaucoma-like artifacts are relatively common and are probably due to normal anatomical variations in healthy eyes. We also introduce a simple anatomical artifact model based upon known anatomical variations to help distinguish these artifacts from actual glaucomatous damage. In section 3, we apply this model to an OCT-based method for detecting glaucoma that starts with an examination of the retinal nerve fiber layer (RNFL) p-map. While this method requires a judgment by the clinician, sections 4 and 5 describe automated methods that do not. In section 4, the simple model helps explain the relatively poor performance of commonly employed summary statistics, including circumpapillary RNFL thickness. In section 5, the model helps account for the success of an AI deep learning model, which in turn validates our focus on the RNFL p-map. Finally, in section 6 we consider the implications of OCT-based methods for the clinic, research, screening, and the development of AI models.
仅基于光学相干断层扫描（OCT）的青光眼检测方法对于常规临床决策、研究和试验的纳入标准、大规模临床筛查以及人工智能（AI）决策模型的开发具有潜在价值。最近的工作表明，OCT 概率（p-）图，也称为偏差图，可以在基于 OCT 的方法中发挥关键作用。然而，健康对照组的 p 图上出现的伪影可能类似于青光眼引起的损伤模式。我们在第 2 节中记录了这些类似青光眼的伪影相对常见，并且可能是由于健康眼中正常的解剖变异引起的。我们还引入了一个简单的解剖伪影模型，基于已知的解剖变异，以帮助区分这些伪影和实际的青光眼损伤。在第 3 节中，我们将该模型应用于基于 OCT 的青光眼检测方法，该方法从视网膜神经纤维层（RNFL）p 图的检查开始。虽然该方法需要临床医生进行判断，但第 4 节和第 5 节描述了不需要判断的自动方法。在第 4 节中，简单模型有助于解释常用汇总统计量（包括周边视网膜神经纤维层厚度）的相对较差性能。在第 5 节中，该模型有助于解释 AI 深度学习模型的成功，这反过来又验证了我们对 RNFL p 图的关注。最后，在第 6 节中，我们考虑了基于 OCT 的方法对临床、研究、筛查和 AI 模型开发的影响。
