Purpose Cyto–histological discordance in thyroid nodules is common, highlighting the role of ultrasonography (USG) as an adjunct to preoperative diagnosis. We designed an experiment to extract and evaluate latent numerical USG vectors capturing patterns below human visual threshold as interpretable AI-derived diagnostic signatures. Methodology Thyroid USG images (n=1230) from the prospectively maintained institutional database were used to train an autoencoder to extract the latent vectors. Cyto-histo labels, defining 17 diagnostic groups based on Bethesda categories and surgical histopathology to capture concordant and discordant outcomes, were applied post hoc to each image’s latent vectors. Multivariate analysis of variance (MANOVA) quantified variance across the diagnostic groups. Principal component analysis was used to capture linear combinations of latent vectors into a 3D space enabling human visualisation. Hierarchical clustering of diagnostic group centroids was performed to generate a dendrogram of imaging phenotype relationships. Results Sixty-two continuous latent vectors were extracted per nodule. These differed significantly across the 17 cyto-histo diagnostic groups (MANOVA, Pillai’s trace = 1.38, p<0.0001). Principal component projection 3D graphs demonstrated: 1) Cyto-histo concordant benign nodules formed compact regions 2) Cyto-histo discordant malignant nodules were displaced and heterogeneous, similar to cyto-histo concordant malignant nodules 3) Indeterminate nodules (Bethesda3/4–benign Bethesda3/4–malignant) showed the greatest variance and overlap Hierarchical dendrogram analysis demonstrated two higher-order imaging phenotypes: a concordant benign/malignant group, and a cyto–histo discordance spectrum encompassing low-risk follicular and papillary tumours. Conclusion Vectors below the human visual threshold extracted from USG using advanced AI techniques has the potential to differentiate between nodules with cyto-histo concordance and discordance. We have demonstrated this using interpretable graphical projections.