aceArtificial 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 ThinP…
AIxTHY-assisted digital cytology increased sensitivity for higher-risk Bethesda categories and improved agreement with expert consensus while cutting slide review time.
AI assistance reduced specificity because of increased false-positive classifications, particularly among indeterminate thyroid cases.
Specificity decreased with AI assistance due to false positives in indeterminate lesions, indicating need for further refinement before routine clinical use.
Evidence
- Peer-reviewedCancer Cytopathology2026-10-01
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Truvace Impact Record TRV-2026-1235, v1: “Artificial intelligence-assisted digital thyroid FNA cytology: Improved agreement and sensitivity for higher-risk Bethesda categories with enhanced screening efficiency.” Truvace, 2026-10-01. /record/TRV-2026-1235 (accessed at citation time). sha256 f5505f9586a7b2a4…
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