Background: AIxTHY is an artificial intelligence platform designed to analyze whole-slide images (WSIs) and support cytopathologists in interpreting thyroid fine-needle aspiration cytology (FNAC) within The Bethesda System for Reporting Thyroid Cytopathology (TBS). Multi-Z-layer scanning may overcome the diagnostic limitations of single-layer digital cytopathology. This study evaluated AIxTHY’s performance on single-layer versus 7-Z-layer WSIs, using conventional microscopy as the benchmark, for thyroid FNAC interpretation in routine clinical practice.

Design: 100 ThinPrep FNAC slides with consensus cytologic diagnoses were selected (5 TBS-I, 35 TBS-II, 15 TBS-III, 15 TBS-IV, and 30 TBS-VI cases). Each slide was digitized using a 3DHISTECH scanner to produce paired single-layer (S-WSI) and 7-Z-layer (7-WSI) images. AIxTHY pre-analyzed all WSIs to flag atypical cells for reviewer evaluation. Five reviewers (2 cytopathologists and 3 cytotechnologists) assessed each case across three modalities with two-week washout periods: microscopy (Arm 1), AIxTHY-assisted S-WSIs (Arm 2), and AIxTHY-assisted 7-WSIs (Arm 3). Binary diagnostic accuracy (positive: TBS-III and above; negative: TBS-II), TBS category concordance, and diagnostic turnaround time were compared across 500 total reads.

Results: Sensitivity was higher with AIxTHY (Arm 2: 81.3%; Arm 3: 83.0%) than with microscopy (Arm 1: 68.7%; p<0.001), with no significant difference between Arms 2 and 3 (p=0.522). Specificity was slightly lower with AIxTHY (Arm 2: 68.0%; Arm 3: 65.7%) versus microscopy (Arm 1: 73.1%), reaching significance for Arm 1 versus Arm 3 (p=0.049). Overall accuracy improved with AIxTHY (Arm 2: 76.4%; Arm 3: 76.6%) compared with microscopy (70.3%; p=0.004). Diagnostic efficiency increased substantially, with mean review time reduced by 32.8% (Arm 1: 163.6 sec vs. Arm 2: 109.9 sec; p<0.001). TBS category concordance with consensus was comparable (Arm 1: 52.4%; Arm 2: 51.4%; Arm 3: 53.5%), but agreement for indeterminate TBS-III was higher with AIxTHY (Arm 2: 53.3%; Arm 3: 44.0%) than microscopy (25.3%). Conversely, agreement for benign TBS-II was slightly lower with AIxTHY (Arm 2: 68.0%; Arm 3: 65.7%) than microscopy (73.1%). Nondiagnostic (TBS-I) calls decreased from 10.8% (54/500) with microscopy to 6.4% (Arm 2) and 5.6% (Arm 3), representing a 41–48% relative reduction. Of Arm 1 TBS-I reads, Arms 2 and 3 reclassified 9 and 7 cases to TBS-II, and 4 and 4 cases to ≥TBS-III, respectively.

Conclusion: AIxTHY increased sensitivity, reduced review time, and improved agreement for indeterminate (TBS-III) FNAC, with a modest decrease in specificity. It lowered nondiagnostic rates and enhanced overall diagnostic accuracy compared with conventional microscopy. These findings support the potential of AI-assisted digital cytopathology to streamline thyroid FNAC workflows and improve diagnostic efficiency.