WHOLE SLIDE IMAGE SCANNER STUDIES
ASC/IAC Poster Presentation
Optimization of digital urine cytology via focus and Z-layer scan settings of the whole-slide imager
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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 2022
Optimization of digital urine cytology via focus and Z-layer scan settings of the whole-slide imager
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.
June 2022
AIxURO URINE CYTOLOGY CLINICAL STUDIES
Academic Article: Cancer Cytopathology
Evaluation of an artificial intelligence algorithm for assisting the Paris System in reporting urinary cytology: A pilot study
Evaluation of an artificial intelligence algorithm for assisting the Paris System in reporting urinary cytology: A pilot study
Ou YC, Tsoa TY, Chang MC, et al. Cancer Cytopathology 2022; 130:872-880. Tung University Clinical Study
STUDY DESIGN:
- 131 urine cytology slides digitized and analyzed with AI-assisted algorithm for detecting and ranking abnormal urothelial cells using criteria from The Paris System (TPS2.0); ground truth diagnosis established by an expert panel
- Two arm study with 1 cytopathologist (CP) in both arms and 2 cytologists (CT) in the AI-assisted Arm 2
- Arm 1: CP review of urine cytology glass slide with diagnosis (4 week washout period)
- Arm 2: CP and CT review of AI-assisted inferred WSI of urine cytology slide with quantitative data statistics
- Performance Metrics: Comparison of research TPS diagnosis with expert diagnosis and calculation of sensitivity, specificity, PPV, NPV and accuracy
RESULTS:
- Conventional Microscopy vs AI-Assisted Interpretation
- Sensitivity: 87% vs 92.3% (+5.1)
- Specificity: 100% vs 100%
- PPV: 100% vs 100%
- NPV: 94.8% vs 96.8% (+2.0)
- AI-Assisted Microscopy, CTA vs CTB vs CP interpretation
- Sensitivity: 79.55 VS 82.1% VS 92.3%
- Specificity: 100% vs 98.9% vs 100%
- PPV: 100% vs 97% vs 100%
- NPV: 92% vs 92.9% vs 96.8%
- ĸ (95% CI): 0.845 vs 0.847 vs 0.994
CONCLUSIONS:
Ai-assisted interpretation was comparable to the expert consensus diagnosis, with superior sensitivity and NPV. AI-assisted interpretations showed near perfect agreement with the expert consensus diagnosis (ĸ = 0.944) and the microscopic diagnosis (ĸ = 0.862).
May 2021
AI SOFTWARE DEVELOPMENT
AACR Poster Presentation
Monitoring of stem cell differentiation to mature hepatocytes with a machine learning-based AI model
Monitoring of stem cell differentiation to mature hepatocytes with a machine learning-based AI model
AACR 2021 Poster Presentation / Abstract 185 Cancer Res (2021) 81 (13_Supplement): 185. https://doi.org/10.1158/1538-7445.AM2021-185 Yang WL, Huo Z, Chen SY, Zhu D, Liu TJ, Lee DF.
STUDY DESIGN:
- Develop a machine-learning-based artificial intelligence (AI) model to assist monitoring morphologic changes in human embryonic stem cells (hESC) in color, using bright field microscopy images
- Pilot Study: Train the model to estimate degree of stem cell differentiation at the Hepatic Progenitor Cell (HPC stage), the critical checkpoint for hepatocyte differentiation, based on cellular morphologic features
- Initial training set: Expert annotated images of 341 successful HPC differentiations and 366 failed HPC differentiations
- Cross-validation set: Images of 86 successful and 51 failed HPC results
- Test set: Images of 64 successful and 29 failed HPC results
- Failed differentiation = no differentiation or differentiation into non-hepatocyte tissue types
- Performance Metrics: Accuracy and F1 scores of test set
RESULTS:
The AI model showed excellent performance compared with the conventional method of determining degree of hepatocyte differentiation
- Accuracy = 0.978
- F1 score = 0.975
CONCLUSIONS:
AI-assisted models have the potential to improve the detection of degrees of hepatocyte differentiation, thereby improving the efficiency of a manual process that is very time-intensive.
March 2021
AI SOFTWARE DEVELOPMENT
AACR Virtual Special Conference on AI Poster Presentation
A deep learning model assists urine cytology reporting with computational estimates of the nuclear/cytoplasmic ratios of the urothelial cells based on the Paris System
A deep learning model assists urine cytology reporting with computational estimates of the nuclear/cytoplasmic ratios of the urothelial cells based on the Paris System
AACR Virtual Special Conference on AI 2021 Poster Presentation / Abstract PO-069 Yang WL, Li CB, Ou YC, Lin YS, Tsao TY, ChaAnnong MC, Hang JF, Liu TJ VGHTE, Taichung MetroHarbor Hospital
STUDY DESIGN:
- Descriptive study using deidentified urine cytology slides digitized into WSI
- Artificial intelligence model training on slides with “active learning” to improve results
- Annotated WSI initially used to train computational model, then expert review of results with feedback to the model to learn.
- Sequence was repeated until satisfactory results were achieved
RESULTS:
- AI deep-learning model was able to differentiate nucleus from cytoplasm to calculate N/C ratio using whole slide images (WSI)
- The model correctly provided statistical data (N/C ratio and nuclear size) on cells and successfully categorized them as atypical (NHGUC or AUC) or suspicious (SHGUC or HGUC) cells
CONCLUSIONS:
AI-assistance for interpreting urine cytology using The Paris System for Reporting Urine Cytology has the potential to enhance abnormal cell detection and diagnostic concordance.
June 2020
AI SOFTWARE DEVELOPMENT
AACR Special Conference on Advances in Liquid Biopsies
Artificial intelligence assists automation and high performance of circulating tumor cells enumeration and circulating tumor microemboli characterization in fluorescence microscopy images
Artificial intelligence assists automation and high performance of circulating tumor cells enumeration and circulating tumor microemboli characterization in fluorescence microscopy images
AACR Special Conference on Advances in Liquid Biopsies 2020 Poster Presentation / Abstract B14 Yang WL, Chen JY, Chen S, Liu TJ, Chang YC.
STUDY DESIGN:
- Development of an automated deep-learning AI model for circulating tumor cell (CTC) analysis and enumeration
- Fluorescent microscopy CTC images (CK+/CD45-/DAPI+) collected from blood samples of non-small cell lung carcinoma patient by CMx CTC capture platform
- AI model developed with active learning implemented to train after expert image annotation on 20 slides and validation with 4 extra images
- 18 new test images studied for performance
RESULTS:
- AI model predicted 34% more total CTC than current methods (1775 vs 1328)
- AI model recovered 45% more total CTCs absent from original human annotation (2507 vs 1732 events)
- AI model produced 90% time savings over conventional methods of enumeration (< 20 min vs approximately 4 hours)
- The model correctly characterized features of circulating tumor microthrombi (CTM), including CTC clusters and CTC-associated immune cells
CONCLUSIONS:
An AI model trained to detect and enumerate circulating tumor cells in nonsmall cell lung cancer patients outperformed semiautomated methods, with higher sensitivity and significantly reduced review time (less than 20 minutes) for CTC enumeration in lung cancer specimens.
June 2020
AI SOFTWARE DEVELOPMENT
AACR Poster Presentation
Applying machine learning for urine cytology—computational urothelial carcinoma analysis and diagnosis
Applying machine learning for urine cytology—computational urothelial carcinoma analysis and diagnosis
American Association for Cancer Research (AACR) 2020 Poster Presentation / Abstract B13 Yang WL, Lee CM, Wu ML, Peng YC, Liu TJ, Liu YH.
STUDY DESIGN:
- Development of a deep-learning based image analysis model for cell classification and enumeration in urine cytology
- De-identified whole slide images (WSI) digitized and “active learning” approach used to train the model
- 3 sub-images (3335 cells) annotated by 3 domain experts for initial training
- Cells classified into 7 categories: High grade urothelial carcinoma (HGUC), cluster HGUC, atypical neoplastic cell, atypical reactive cell, inflammatory cell, epithelial cell, and unidentified cell, with expert feedback to the model
- Pilot study after training involved 2 sub-images from 5 digital slides (10 total sub-images)
RESULTS:
- Ai model successfully learned the morphologies of all 6 cell types and was able to quantify total cell counts in each class
CONCLUSIONS:
An artificial intelligence model that enumerates and classifies abnormal urothelial cells may improve urine cytology throughput, accuracy and reproducibility.
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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
