Abstract / Summary
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.