Celebrating 30 yearsVirtual poster abstracts (414 videos), validation (139), and testing (196) maintaining similar positive/negative proportions.When applied to the test dataset using a threshold of 0.9, the model achieved: AUC 96.5% (95%CI, 90.8-100.0%),an accuracy of 88.8% (95%CI, 83.5-92.8%),sensitivity of 88.6% (95%CI, 83.0-92.9%),specificity of 90.0% (95%CI, 68.3-98.8%), a PPV of 98.7% (95%CI, 95.4-99.7%),and a NPV of 47.7% (95%CI, 36.8-58.2%). Conclusions:We developed an ML model for the prediction of the TVS-based sliding sign classification with an AUC comparable to that of two expert sonologists.