The accessory fallopian tube, a rare Mullerian duct anomaly: Two case reports.

Intrusive saccades during active visual fixation indicate deficits in inhibitory control which is crucial for cognitive control function. Research has shown that abnormalities in these mechanisms are linked to neurological disorders such as schizophrenia and obsessive-compulsive disorder (OCD), both involving dysfunctions in frontal-subcortical circuits. Eye movement studies and machine learning (ML) techniques have been used to differentiate clinical from neurotypical populations. This study aimed to classify healthy controls, patients with OCD and schizophrenia patients, based on oculomotor behavior during active fixation tasks and provide insights into related neurophysiological mechanisms. Data from three visual fixation tasks were analyzed using statistical tests to select saccade features to be used in the classification. A shallow Artificial Neural Network (ANN) was implemented for binary and three-class classification. Binary classification achieved 87% accuracy and 93% specificity in distinguishing controls from the patients with schizophrenia group, 84% accuracy and 90% sensitivity in distinguishing between controls and medicated patients with OCD not taking antipsychotics, while differentiation between patients with schizophrenia and medicated patients with OCD not taking antipsychotics reached 77% accuracy and 82% specificity. The findings provided indications that selected saccadic features can differentiate OCD and schizophrenia patients from healthy controls using shallow ANNs, while distinguishing between OCD and schizophrenia patients remains more challenging. Notably, tentative indications were provided that group differences were driven more by intrinsic saccadic generation properties than by fixation or inhibitory mechanisms, concerning unwanted saccades that are intrusive in nature in the context of fixation.