目的:深度学习(DL)模型已经实现了最先进的医学诊断分类准确性。当前的模型受到离散诊断标签的限制,但可以在连续规模的诊断中产生更多信息。通过将DL分类模型与均匀流形近似和投影(UMAP)相结合,我们开发了一种用于II型黄斑毛细血管扩张症(MacTel)的新型连续严重程度缩放系统。
方法:我们使用DL网络从离散严重性标签中学习MacTel严重性的特征表示,并应用UMAP将此特征表示嵌入到二维中,从而创建连续的MacTel严重程度量表。
方法:分析了1089名MacTel项目参与者的2003年OCT卷。
方法:我们使用来自OCT体积的多个B扫描来训练多视图DL分类器,以学习离散的7步Chew等人。MacTel严重程度量表。提取分类器的最后一个特征层作为UMAP的输入,将这些特征嵌入到连续的2D流形中。在测试准确性方面评估DL分类器。连续UMAP量表与Chew等人的等级相关性。已计算。此外,根据kappa协议,对5名临床专家对100对患者体积的UMAP量表进行了评估.对于每一对患者体积,临床专家被要求选择更严重的MacTel疾病的体积,并与UMAP量表进行了比较。
方法:DL分类器的分类精度,和Kappa协议与UMAP的临床专家。
结果:多视图DL分类器在保留的测试OCT体积上实现了63.3%(186/294)的前1准确度。UMAP指标显示了MacTel严重程度的清晰连续等级,与Chew等人的Spearman等级相关性为0.84。规模。此外,连续UMAP指标与5位临床专家达成了0.56-0.63的卡帕协议,这与观察者之间的kappas相当。
结论:我们的UMAP嵌入产生了连续的MacTel严重程度量表,不需要连续的培训标签。这种技术可以应用于其他疾病,并可能导致更准确的诊断,提高对疾病进展和病理关键影像学特征的认识。
OBJECTIVE: Deep learning (DL) models have achieved state-of-the-art medical diagnosis classification accuracy. Current models are limited by discrete diagnosis labels, but could yield more information with diagnosis in a continuous scale. We developed a novel continuous severity scaling system for macular telangiectasia (MacTel) type 2 by combining a DL classification model with uniform manifold approximation and projection (UMAP).
METHODS: We used a DL network to learn a feature representation of MacTel severity from discrete severity labels and applied UMAP to embed this feature representation into 2 dimensions, thereby creating a continuous MacTel severity scale.
METHODS: A total of 2003 OCT volumes were analyzed from 1089 MacTel Project participants.
METHODS: We trained a multiview DL classifier using multiple B-scans from OCT volumes to learn a previously published discrete 7-step MacTel severity scale. The classifiers\' last feature layer was extracted as input for UMAP, which embedded these features into a continuous 2-dimensional manifold. The DL classifier was assessed in terms of test accuracy. Rank correlation for the continuous UMAP scale against the previously published scale was calculated. Additionally, the UMAP scale was assessed in the κ agreement against 5 clinical experts on 100 pairs of patient volumes. For each pair of patient volumes, clinical experts were asked to select the volume with more severe MacTel disease and to compare them against the UMAP scale.
METHODS: Classification accuracy for the DL classifier and κ agreement versus clinical experts for UMAP.
RESULTS: The multiview DL classifier achieved top 1 accuracy of 63.3% (186/294) on held-out test OCT volumes. The UMAP metric showed a clear continuous gradation of MacTel severity with a Spearman rank correlation of 0.84 with the previously published scale. Furthermore, the continuous UMAP metric achieved κ agreements of 0.56 to 0.63 with 5 clinical experts, which was comparable with interobserver κ values.
CONCLUSIONS: Our UMAP embedding generated a continuous MacTel severity scale, without requiring continuous training labels. This technique can be applied to other diseases and may lead to more accurate diagnosis, improved understanding of disease progression, and key imaging features for pathologic characteristics.
BACKGROUND: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.