CUA-Net self-reports 91.52% accuracy classifying uterine malformations on external 3D ultrasound set
CUA-Net, built on 3D ResNet-18, automatically classifies congenital uterine malformations in 3D ultrasound without coronal plane reconstruction, self-reporting 91.52% accuracy on the external test set.
ImportanceLocalEvidenceE2 unreplicated
CUA-Net can automatically classify congenital uterine malformations in 3D ultrasound without coronal plane reconstruction, self-reporting 93.88% accuracy on the internal test set and 91.52% on the external test set.
Previously this classification relied on manual reading by sonographers, requiring coronal plane reconstruction, with junior physicians performing below the model.
Self-tested by Yueyue Xu, Yuhao Huang and 12 others: built on 3D ResNet-18, self-reported as outperforming junior sonographers on all metrics and matching senior sonographers on most.
The results are not peer-reviewed or independently verified; it is an arXiv preprint (arXiv:2609.15225), submitted September 14 and revised as v2 on September 15.