The main classes and the subclasses of the new system are presented in Table 1 and are as follows (Fig. 1): Table 1 Classification of uterine anomalies Main class Uterine anomaly Main sub-classes Class 0 Normal uterus Class I Dysmorphic uterus a. T-shaped b.
Patients with a class I anomaly have no reproductive potential, class III and IV hardly have obstetrical complications and class VI is considered a normal variant.
Even so, a class II anomaly can be easily missed on routine examination in the absence of an associated cervical or vaginal abnormality [ 6].
They are accompanied by various malformations, including disturbances in facial look as well as skeletal disorders that include malocclusions, most frequently crossbites and class III anomalies.
However, the very low incidence of class 2 anomalies in pregnant women results in a calculated risk of uterine rupture in medical termination of pregnancy on the basis of this anomaly of 1 in 300,000 pregnancies.
Class VI anomalies are often clinically insignificant, with the surface of the uterine contour being concave on the outside and resulting in a small uterine cavity, as in arcuate uterus.
For the Dental Health Component (DHC) of IOTN, 10 traits of malocclusion were assessed: overjet, reverse overjet, overbite, openbite, crossbite, crowding, impeded eruption, defects of cleft lip and palate as well as any craniofacial anomaly, Class II and Class III buccal occlusions, and hypodontia.
For the Dental Health Component DHCC) of IOTN, 10 traits of malocclusion are assessed: overjet, reverse overjet, overbite, open bite, cross bite, crowding, impeded eruption, defects of cleft lip and palate as well as any craniofacial anomaly, Class II and Class III buccal occlusions, and hypodontia.
Mr. Volpe is a working-class anomaly at the blue-blooded Met, the underdog who made good.
One-class-classification-based anomaly detection techniques assume that all training instances have only one class label.
Multi-class-classification-based anomaly detection techniques assume that the training data contains labeled instances belonging to multiple normal classes [11].
