SOFTWARE OPTIMIZATION & VALIDATION STUDIES
ASC Poster Presentation
Evaluating image quality and reproducibility of digital scanners for whole-slide imaging in urine cytology slides for artificial intelligence applications
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THE PARIS SYSTEM FOR URINE CYTOLOGY NUCLEAR TO CYTOPLASMIC RATIO
November 2025
ASC Oral Presentation
COMING SOON! The Paris Study: Multicenter Retrospective Analysis of Artificial Intelligence-Quantified Nuclear-to-Cytoplasmic Ratios and Nuclear Areas in Confirmed Bladder Cancer Urology Specimens
ASC Oral Presentation
COMING SOON! The Paris Study: Multicenter Retrospective Analysis of Artificial Intelligence-Quantified Nuclear-to-Cytoplasmic Ratios and Nuclear Areas in Confirmed Bladder Cancer Urology Specimens
STUDY DESIGN:
RESULTS:
CONCLUSIONS:
AIxURO URINE CYTOLOGY CLINICAL STUDIES
November 2025
ASC Poster Presentation
COMING SOON! PST 75: AI-Driven Logistic Regression Models for Bladder Cancer Prediction: A Pilot Study Quantifying Atypical Urothelial Cells with Cytomorphometric Features
ASC Poster Presentation
COMING SOON! PST 75: AI-Driven Logistic Regression Models for Bladder Cancer Prediction: A Pilot Study Quantifying Atypical Urothelial Cells with Cytomorphometric Features
STUDY DESIGN:
RESULTS:
CONCLUSIONS:
AIxTHY THYROID FINE NEEDLE ASPIRATION CLINICAL STUDIES
October 2025
Pathology Visions Oral Presentation
Redefining Thyroid Cytology with AIxTHY: Improved Accuracy and Efficiency in FNAC Interpretation
Pathology Visions Oral Presentation
Redefining Thyroid Cytology with AIxTHY: Improved Accuracy and Efficiency in FNAC Interpretation
STUDY DESIGN:
- 100 thyroid ThinPrep slides categorized forground truth diagnosis (The Bethesda System for Reporting Thyroid Cytopathology) using a consensus of the original cytology report interpretation and the surgical pathology findings. All cases had a surgical result except Nondiagnostic cases.
- Urine slides were digitized as single-layer (S-WSIs)and 7-Z-layer (7-WSIs) WSIs by the 3D-Histech scanner and analyzed with AI-assistance software (AIxTHY)
- 5 reviewers including 3 cytopathologist (CP) and5 cytologists (CT) evaluated slides with a 2-week washout period between research arms:
- Arm 1- Microscopy only
- Arm 2- S-WSI with AIxTHY-assistance
- Arm 3- 7-WSI with AIxTHY-assistance
- Performance Metrics:
Comparison of study diagnosis with ground truth cytology diagnosis for 3 Arms across 500 total reads.
- Binary diagnostic accuracy (positive: TBS-III and above; negative: TBS-II)
- TBS category concordance (TBS-I~VI)
- Mean diagnostic turnaround time (second)
RESULTS:
- Sensitivity: Improved with AIxTHY (Arm 2: 81.3%;Arm 3: 83.0%) compared with microscopy (Arm 1: 68.7%; p < 0.001); no difference between AI-assisted arms (p = 0.522).
- Specificity: Slightly lower with AIxTHY (Arm 2:68.0%; Arm 3: 65.7%) than microscopy (73.1%), significant for Arm 1 vs. Arm 3(p = 0.049).
- Overall Accuracy: Increased from 70.3%(microscopy) to 76.4–76.6% (AIxTHY; p = 0.004).
- Diagnostic Efficiency: Mean review time reduced by 32.8% (163.6 s → 109.9 s; p < 0.001).
- TBS Concordance: Overall comparable (Arm 1:52.4%; Arm 2: 51.4%; Arm 3: 53.5%).
- Higher agreement for indeterminate TBS-III with AIxTHY (44–53%) vs. microscopy (25%).
- Nondiagnostic (TBS-I) Rates: Decreased from10.8% to 6.4–5.6%, representing a 41–48% reduction.
- Several TBS-I cases were reclassified to TBS-II or ≥TBS-III under AI-assisted review.
CONCLUSIONS:
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
November 2023
Evaluating image quality and reproducibility of digital scanners for whole-slide imaging in urine cytology slides for artificial intelligence applications
ASC 2023 Poster Presentation / Abstract (JASC 2023; Vol12(5):S74-75). Yang WL, Hang JF, Ou YC et al.
STUDY DESIGN:
- 20 urine cytology slides, 5 types of preparation (cytospin, ThinPrep nonGYN, ThinPrep Urocyte, and BD CytoRich) were digitized using 3 digital scanners (Roch DP200, Roch DP600, and Hamamatsu Nanozoomer 360)
- Images were evaluated for quality, focus and color from the default, manual and advanced scanning modes by a senior cytologist
RESULTS:
- Roche DP200 and DP600 scanners achieved good quality WSI (30-50% in default mode and 40-65% in manual mode)
- Hamamatsu achieved 90% good quality imaging only in the manual mode, whereas the default quality dropped to 15%. It also had a lower coefficient of variation for average number of atypical cells across all preparation types
CONCLUSIONS:
Overall, Hamamatsu scanner showed better performance for image quality in the manual mode than Roche scanners, which performed better in the default mode. Hamamatsu scanners also had improved detection of atypical urothelial cells among triplicate images from the same samples compared to Roche scanners.
October 2023
AI SOFTWARE DEVELOPMENT
ASC Poster Presentation
Assessing an artificial intelligence-driven digital cytology tool for efficient diagnosis of upper tract urothelial carcinoma in upper urinary tract cytology
Assessing an artificial intelligence-driven digital cytology tool for efficient diagnosis of upper tract urothelial carcinoma in upper urinary tract cytology
ASC 2023 Poster Presentation / Abstract (JASC 2023; Vol 12(5):S73-75) Wei-Lei Yang; JF Hang et al.
STUDY DESIGN:
- 60 paired cytospin and CytoRich slides from 30 pts scanned into WSI and inferred using AIxURO AI-driven software, ranking into most top 24 suspicious cells/groups
- 3 senior cytologists with variable digital interpretive experience (A= over 1 year; B = 6 mo-1 yr; C = less than one month) evaluated interface gallery of images to render a diagnosis
RESULTS:
- Dx performance was consistent for the 2 preparation types, with cytologists A & B showing excellent performance
- All reviewers spent similar amounts of time on review per preparation type, with cytologist C taking the least amount of time.
CONCLUSIONS:
The AI-assisted tool allowed for excellent performance to aid interpretation of upper GU tract urothelial carcinoma
October 2023
AIxURO URINE CYTOLOGY CLINICAL STUDIES
USCAP Poster Presentation
Assessing the practicability of artificial intelligence-assisted digital urine cytology in diagnosing bladder cancer in clinical practice
Assessing the practicability of artificial intelligence-assisted digital urine cytology in diagnosing bladder cancer in clinical practice
DPA PathVysions 2023 Poster Presentation / Abstract (J Pathol Inform 2023) Liu TJ, Yang WC, Huang SM, et al. Show Chwan Memorial Hospital
STUDY DESIGN:
- 116 urine cytology slides digitized into WSI and analyzed by AI-assisted software to identify and categorize abnormal cells into suspicious (likely SHGUC or HGUC) or atypical (AUC) categories
- 1 cytopathologist (CP) and 2 cytologists (CP) reviewed all slides microscopically (Arm 1), as a WSI (Arm 2) and with AI-assistance presenting the top 24 most abnormal cells in a gallery display (Arm 3), with a 2-week wash-out period between reviews
- Performance Metrics: Comparison of expert panel consensus of the glass slide (86 negative, 30 positive) to the review diagnoses to calculate sensitivity, specificity, PPV and NPV, along with the time spent examining the slide for each arm per individual
RESULTS:
- AI-assisted software (AIxURO) improved the overall sensitivity (from 82.2% to 92.2%) and NPV (from 93.4% to 96.5%) but decreased specificity (from 87.2% to 75.6%), PPV (from 69.2% to 56.8%) and accuracy (from 85.9% to 79.9%) compared to microscopy. The WSI performance was the worst arm.
- Time for slide review was significantly reduced overall (from 159.9 min for microscopy to 106.3 minutes for AI-assisted), as well as per individual slide (from 1.38 min to 0.92 min)
CONCLUSIONS:
AI-assistance improves the sensitivity and NPV of urine cytology review, but at the expense of a decrease in specificity, PPV and accuracy. This may indicate that AI-assistance will facilitate the detection of abnormal urothelial cells while lowering the time required for slide review.
October 2023
AI SOFTWARE DEVELOPMENT
Pathology Visions Poster Presentation
Utilizing artificial intelligence-assisted heuristic scanning to enhancing efficiency of urine cytology slides scanning
Utilizing artificial intelligence-assisted heuristic scanning to enhancing efficiency of urine cytology slides scanning
DPA PathVysions 2023 Poster Presentation / Abstract (J Path Inform 2023) Liu TJ, Hang JF, Ou YC et al. TVGHTE and Tung’s Taichung MetroHospital
STUDY DESIGN:
- 52 urine cytology slides from bladder cancer patients (24 cytospin, 16 ThinPrep, 12 Cytorich) were digitally scanned with Leica AT2 using conventional Z-stacking (Z = 0 to Z = +10 layers above and below at 1 µm intervals) and compared with using a heuristic scanning simulation method with AI to determine regions of interest (ROI) for scanning, whereby the software determines the ideal number of layers to scan, from 3 layers (Z = 0+1) to 21 layers (Z = +10).
- Performance Metrics: Total number of suspicious cells; coverage rate (ratio of suspicious cells in single vs multiple Z-layers to the total suspicious cells in 21 Z-layers); scanning time (seconds); and image file size for storage (in gigabytes).
RESULTS:
- Heuristic scanning was comparable to Z-stacking with similar average numbers of suspicious cell coverage rates (79.3% cytospin; 85.9% TP; 78.3% CytoRich) to those of Z-stacking (81.9% in 5 layers for cytospin, 87.1% in 9 layers for TP, 82.9% in 7 layers for CytoRich).
- Heuristic scanning significantly reduced scanning time (65-72%) and image file size (by 46-64%).
- Heuristic scanning in low cellularity slides was more accurate (7/7 correct diagnoses) than using single Z-layer WSI (4/7 correct diagnoses)
CONCLUSIONS:
Heuristic scanning is an effective alternative to conventional Z-stacking to identify suspicious cells on urine cytology slides, with the advantage of reducing scanning time and image file size.
August 2023
SOFTWARE OPTIMIZATION & VALIDATION STUDIES
Japanese Society of Digital Pathology (JSDP) 21st Annual Meeting
Detection of urothelial carcinoma in upper urinary tract cytology using a digital artificial intelligence-assisted tool
Detection of urothelial carcinoma in upper urinary tract cytology using a digital artificial intelligence-assisted tool
2023 Japanese Society of Digital Pathology Oral Platform/ Abstract Liu TJ, Yang WL, Hsu SW, Yeh CH, Hang JF. Taipei Veterans General Hospital (VGHTE); Nat’l Yang Ming Chiao Tung University
STUDY DESIGN:
- 100 paired cytospin and CytoRich urine cytology slides from 50 bladder cancer patients digitized to create WSI and analyzed with AI-assisted software that identifies the most atypical urothelial cells and displays them in a gallery of the top 24
- 3 cytologists with variable digital pathology experience (A= over 12 months; B= 6-12 months; C= less than one month) reviewed the AI-assisted images and interpreted them using The Paris System categories
RESULTS:
- No significant difference in diagnostic performance of cytologists between preparations (cytospin vs CytoRich)
- Digital pathology experience improved performance, with C having the poorest performance
- Sensitivity = 84-96%; Specificity = 92- 96%; PPV = 91.3-95.7%; NPV = 85.2-95.8%; accuracy = 88-94%
- Cytologist C took the least time reviewing slides (median 29.5-30 sec) compared to the other 2 (median 63.5-71.5 sec)
CONCLUSIONS:
AI-assistance markedly improves efficiency for the interpretation of upper urinary tract cytology. Experience using the software system enhances overall performance and diagnostic concordance.
May 2023
AIxURO URINE CYTOLOGY CLINICAL STUDIES
iSMIT Conference Oral Presentation
A novel and effective deep-learning algorithm for risk stratification of urothelial carcinoma
A novel and effective deep-learning algorithm for risk stratification of urothelial carcinoma
International Society for Medical Innovation and Technology 2023 Oral Platform / Abstract (Min Inv Ther Allied Technol)
STUDY DESIGN:
- 116 urine cytology WSI (76 cytospin, 40 CytoRich) scanned with Leica AT2 at 20X and analyzed and ranked “top 24” by AI-deep learning algorithm for most abnormal urothelial cells
- 1 cytopathologist (CP) and 2 cytologists (CT) evaluated WSI with AI-assistance and diagnosed slides as NHGUC, AUC, SHGUC or HGUC according to The Paris System 2.0.
- Performance Metrics: Concordance with original cytologic diagnosis and time to diagnosis compared to conventional microscopy
RESULTS:
- AI-assistance increased sensitivity for all 2 reviewers (from 83.3-100%) and improved time efficiency from 159.9 mins to 106.3 min)
- Specificity was reduced for the CTs, but not the CP
CONCLUSIONS:
AI-assistance using a deep learning algorithm to identify and rank abnormal cells improves overall clinical sensitivity for the detection of urothelial carcinoma while improving clinical efficiency through time-savings.
March 2023
WHOLE SLIDE IMAGE SCANNER STUDIES
USCAP Poster Presentation
Optimization of the number of Z-stack layers for whole-slide images of urine cytology with different cellularity by an artificial intelligence algorithm
Optimization of the number of Z-stack layers for whole-slide images of urine cytology with different cellularity by an artificial intelligence algorithm
ASC/IAC 2022 Poster Presentation / Abstract Hang JF, Ou YC, Tsao TY et al. VGHTE, Tungs Taichung MetroHarbor Hospital
STUDY DESIGN:
- 14 positive (HGUC) urine cytology specimens (cytospin and ThinPrep) digitized with Leica Aperio AT2 scanner at 4 different scan modes:
- Mode A- Default automatic
- Mode B- Optimized automatic
- Mode C- Manually focused points of adjustment
- Mode D- Multilayer Z-stacking
- Performance metrics:
- Rate of successful scanning (total slides that obtain WSI files from scanner);
- Number of high risk cells detected by AI algorithm
- Coverage rate of high risk cells (detected high risk cells in one Z-layer divided by the total high risk cells detected in 21 Z-layer WSI per slide)
RESULTS:
- Advanced scan modes increase successful slide scanning, but does not increase the average high risk cell detection or coverage rates
- More high risk cells were detected with TP than cytospin but with comparable coverage rates
- More Z-layers increased average high risk cell detection and coverage rates
CONCLUSIONS:
Advanced scan modes improve the success rate of obtaining an optimal WSI and the number and coverage rate of high risk cells increases with multiple Z-layers. More high risk cells were detected on ThinPrep vs cytospin but this did not significantly affect the AI algorithm analytic results.
March 2023
SOFTWARE OPTIMIZATION & VALIDATION STUDIES
USCAP Poster Presentation
Optimization of snapshot gallery presentation of artificial intelligence-inferred diagnostic cell images in urine cytology
Optimization of snapshot gallery presentation of artificial intelligence-inferred diagnostic cell images in urine cytology
USCAP 2023 Poster Presentation / Abstract Liu TJ, Yang WL, Tung MC, Hang JF et al. Tungs Taichung MetroHarbor Hospital; VGHTE
STUDY DESIGN:
- 45 HGUC urine cytology slides and 45 negative (NHGUC) slides digitized into WSI and inferred by an AI-algorithm to identify potential cancer cells.
- 12, 24, 36, or 48 abnormal cell thumbnail images were order-ranked by AI and presented in a reviewer gallery
- 2 cytologists reviewed thumbnail images and rendered a diagnosis based on The Paris System for Reporting Urine Cytology 2.0 for each gallery set
- Concordance with microscopy performed for each gallery set, requiring an exact match (HGUC to HGUC, NHGUC to NHGUC)
RESULTS:
- Concordance for HGUC was significantly improved by increasing the number of diagnostic cells in the gallery from 38.9% to 91.1%, but were slightly decreased for NHGUC specimens from 92.2% to 90%.
- The highest concordance rate (95.6%) was established at 36 gallery cell images, but was only slightly decreased with 48 images
CONCLUSIONS:
36 thumbnail cell images provides the highest concordance for HGUC vs NHGUC diagnoses in urine cytology
November 2022
AIxURO URINE CYTOLOGY CLINICAL STUDIES
ASC/IAC Oral Presentation
Validation of a deep-learning-based algorithm for risk stratification and diagnosis of bladder cancer in digital urine cytology
Validation of a deep-learning-based algorithm for risk stratification and diagnosis of bladder cancer in digital urine cytology
ASC/IAC 2022 Oral Presentation / Abstract PLAT 15 Yang WL, Ou YC, Lin YS, Hang JF, Lui TJ. VGHTE, Taichung MetroHarbor Hospital
STUDY DESIGN:
- 104 urine cytology slides (70 NHGUC and 34 positive [AUC, SHGUC, HGUC]), with diagnostic confirmation by an expert panel
- Review of AI (deep learning algorithm)- inferred digital gallery images by 1 cytopathologist (CP) and 2 cytologists (CT), assigning cases to The Paris System (TPS 2.0) diagnostic categories (4 week washout period between Arm 1 and 3)
- Arm 1- Microscopic review of urine cytology glass slide by CP
- Arm 2- WSI review of slide by CP
- Arm 3- AI-assisted review of images by CP and CTs
- Performance Metrics:
- Comparison of diagnostic concordance with sensitivity, specificity, PPV, NPV and accuracy for CP and CTs against initial diagnosis
- Comparison of diagnostic concordance with sensitivity, specificity, PPV, NPV and accuracy between conventional microscopy and AI-assisted image review for the CP
RESULTS:
Microscopic Glass Slide Review versus AI-Assisted Image Review (CP only)
- Sensitivity: 85.2% vs 91.2% (+5.9%)
- Specificity: 100% vs 100%
- PPV: 100% vs 100%
- NPV: 93. vs 95.9% (+2.6%)
- Accuracy: 95.2% vs 97.1% (+1.9%)
AI-Assisted Review (CTA, CTB and CP)
- Sensitivity: 76.5%, 79.4%, 91.2%
- Specificity: 100%, 98.6%, 100%
- PPV: 100%, 96.4%, 100%
- NPV: 89.7%, 90.8%, 95.9%
- Accuracy: 92.3%, 92.3%, 97.1%
CONCLUSIONS:
AI-assisted diagnoses were comparable to expert panel consensus (sensitivity 76.5 – 91.2%) with high specificity (98.6-100%). AI-assisted interpretation showed better sensitivity, NPV, and accuracy than conventional microscopy.
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November 2024
American Society of Cytopathology (ASC)
Dr. Barbara Crothers is the recipient of the ASC’s highest award- 2024 Papanicolaou AwardApril 2024
