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Comparison of study diagnosis with ground truth cytology diagnosis for 3 Arms across 500 total reads.
AIxTHY improved sensitivity, diagnostic accuracy, and efficiency while reducing review time and nondiagnostic rates, with minimal impact on specificity. These results support its role in streamlining thyroid FNAC workflows through AI-assisted digital cytopathology
Comparison of study diagnosis with ground truth cytology diagnosis for 3 Arms across 500 total reads.
AIxTHY improved sensitivity, diagnostic accuracy, and efficiency while reducing review time and nondiagnostic rates, with minimal impact on specificity. These results support its role in streamlining thyroid FNAC workflows through AI-assisted digital cytopathology
AIxURO consistently enhanced diagnostic sensitivity and maintained accuracy across multiple scanners, outperforming microscopy in detecting bladder cancer. It demonstrated strong predictive performance in high-risk subgroups, supporting its utility in real-world clinical workflows and digital cytology integration.
Seven-layer Z-stacked WSIs (7-WSI) enhanced AI-assisted thyroid cancer diagnosis and improved interobserver agreement among cytologists, especially in indeterminate (TBS-IV) and malignant (TBS-VI) cases, supporting their clinical value over single-layer WSIs.
AIxURO provides objective measurement of N/C ratio and nuclear area in urine cytology, challenging the current TPS threshold (>0.7) for diagnosing HGUC. The study suggests that a revised N/C ratio cutoff of 0.66 may be more appropriate for SHGUC/HGUC categorization using AI. Findings also support the use of consistent thresholds across upper and lower urinary tract cases. Nuclear area offers additional discriminative value, particularly for differentiating CIS from HGUC.
o Arm 1- Microscopy only
o Arm 2- AIxURO Mikroscan SLxCyto’s WSI review
o Arm 3- AIxURO customized Huron’s WSI review
o Comparison of study diagnosis with ground truth diagnosis for 3 Arms using following thresholds:
(1) AUC+ (AUC, SHGUC and HGUC) cases as positive; NHGUC cases as negative
(2) SHGUC+ (SHGUC and HGUC) cases as positive; NHGUC and AUC cases as negative
o The slide evaluation time (SET) in each arm was documented and made comparison.
AIxURO WSI review demonstrated higher sensitivity but lower specificity than microscopy for AUC+ and SHGUC+ thresholds. Notably, AIxURO WSI review reduced slide evaluation time by at least 64.5%, offering substantial efficiency gains. These findings highlight AIxURO’s potential to enhance workflow efficiency in settings withstaffing shortages while maintaining diagnostic performance.
o Top-24 cells- 84%-95%
o Suspicious cells- 81%-91%
o Atypical cells- 22%-39%
o 93% of suspicious cells
o 24%-40% of atypical cells.
o HGUC (95% / 91%)
o SHGUC (89% / 89%)
o AUC (84% / 81%)
HGUC showed the highest numbers of abnormal urothelial cells compared to SHGUC and AUC. N/C ratio and nuclear sizes were larger in top-24 and suspicious cell categories compared to the atypical cell category. Hyperchromasia was the most important distinguishing morphologic feature between suspicious and atypical cells. HGUC had the highest % of top-24 and suspicious cells with all 3 TPS morphologic features, indicating that the AI algorithm correctly selected features predicting HGUC. These findings highlight AI’s potential to enhance accuracy and efficiency in bladder cancer diagnosis.
o Leica Aperio AT2
o Hamamatsu S360
o 3DHistech P1000
o Customized Huron WSI model
o Mikroscan SLxCyto
o Arm 1- Microscopy only
o Arm 2- AI-assisted WSI review
o WSI conversion rate
o Comparison of binary (positive vs negative) diagnosis (AUC, SHGUC, and HGUC cases = positive) with ground truth diagnosis for 2 Arms
o Quantitative data analysis
First study to assess multiple digital pathology imagers for AI-assisted urine cytology. Results demonstrate that a disease-specific AI algorithm produces consistent diagnostic performance and quantitative analysis across different WSIs, supporting its potential to enhance diagnostic accuracy across imaging platforms.
