Huge efforts have been paid lastly to study the application of Machine Learning techniques to optical transport networks. Applications include Quality of Transmission (QoT) estimation, failure and anomaly detection, and network automation, just to mention a few. In this regard, the development of Optical Layer Digital Twins able to accurately model the optical layer, reproduce scenarios, and generate expected signals are of paramount importance. In this paper, we introduce two of the applications of Optical Layer Digital Twins, namely misconfiguration detection and QoT estimation. Illustrative results show the accuracy and usefulness of the proposed aplications. 1