Artificial intelligence-assisted digital thyroid FNA cytology: Improved agreement and sensitivity for higher-risk Bethesda categories with enhanced screening efficiency.

BACKGROUND

Accurate cytologic classification of thyroid nodules is essential for clinical management, but interobserver variability and indeterminate interpretations remain persistent challenges. The clinical feasibility of AIxTHY, a disease-specific deep-learning algorithm integrated into a digital cytology platform, was evaluated for assisting thyroid fine-needle aspiration (FNA) diagnosis using whole-slide imaging (WSI).

METHODS

Two cytopathologists and one cytologist independently reviewed 100 archival ThinPrep FNA slides using three modalities: microscopy, artificial intelligence (AI)-assisted single-layer WSI (S-WSI), and AI-assisted seven-layer Z-stack WSI (7-WSI), with 2-week washout intervals. Reviewers assigned The Bethesda System (TBS) categories, and review times were recorded. Diagnoses were compared with expert cytologic consensus as ground truth.

RESULTS

AI-assisted review modalities improved overall agreement with consensus diagnoses, particularly for higher-risk categories (TBS III+: atypia of undetermined significance, follicular neoplasm, and malignant), and reduced downgrading compared to microscopy. For binary risk stratification (TBS III+ vs TBS II), AI assistance increased sensitivity from 60% to 78% to 79% and accuracy from 61% to approximately 71%, whereas specificity decreased because of increased false-positive classifications, particularly among indeterminate cases. AI-assisted review significantly reduced slide review time by 40% to 65% across diagnostic categories (p < 0.01), with the greatest efficiency gains in positive cases. Compared with S-WSI, 7-WSI did not further improve diagnostic concordance or binary classification performance but provided additional reductions in review time.

CONCLUSIONS

AIxTHY is a feasible adjunct for improving detection and workflow efficiency in digital thyroid cytopathology, although further refinement is needed to improve specificity in indeterminate lesions.