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
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AIxURO URINE CYTOLOGY CLINICAL STUDIES
March 2026
USCAP 2026 Oral Presentation
AI-Powered Urine Cytology: Redefining Diagnostic Precision and Workflow at PathNet
USCAP 2026 Oral Presentation
AI-Powered Urine Cytology: Redefining Diagnostic Precision and Workflow at PathNet
STUDY DESIGN:
RESULTS:
The implementation of AIxURO™ at PathNet has delivered a comprehensive set of clinical and operational advantages by bridging the gap between digital pathology and automated analytical software. By integrating this tool into the daily workflow, the laboratory realized several key benefits:
Strategic Clinical Uses
- Cyto-Histo Correlation: The platform serves as a powerful additional tool for correlating cytology findings with surgical pathology results.
- Quality Control Review: AIxURO™ provides a robust QC layer, acting as atireless "second set of eyes" to confirm results and reduce human fatigue.
- Educational Advancement: The digitized data and statistical analysis offer a modernized platform for the training and education of cytologists, residents and fellows.
Operational and Patient Benefits
- Cost-Effectiveness: Patients benefit from a reduction in unnecessary and expensive ancillary testing, such as UroVysion FISH, as the AI's precision assists in increased diagnostic confidence.
- Workflow Efficiency: The laboratory observed an increase in both cytologist and pathologist efficiency, particularly through the use of superior digital imaging.
- Ergonomics and Reliability: Beyond technical accuracy, the transition to digital analysis improved the physical ergonomics for cytologists and ensured the consistent confirmation of negative cases.
CONCLUSIONS:
PathNet’s success proves that the future of pathology lies in the synergy between human expertise and machine learning. By embedding AI within the existing digital workflow, PathNet has empowered its staff to work more efficiently and comfortably while setting a new global standard for patient care.
AIxURO URINE CYTOLOGY CLINICAL STUDIES
March 2026
USCAP 2026 Oral Presentation
Objective Precision in the Caribbean: CorePlus and the Future of AI-Driven Pathology
USCAP 2026 Oral Presentation
Objective Precision in the Caribbean: CorePlus and the Future of AI-Driven Pathology
STUDY DESIGN:
RESULTS:
The laboratory conducted a comprehensive validation study involving 117 cases across a wide range of diagnostic categories (benign, AUC, HGUC). The results showed:
- High Concordance: A 93.7% concordance rate between manual microscopy and the AI-driven
- Statistical Robustness: High sensitivity (90.4%) and specificity (94.1%),demonstrating the AI's ability to accurately identify HGUC cases while maintaining low false-positive rates.evaluation.
CONCLUSIONS:
CorePlus has demonstrated that private laboratories can lead the way in adopting advanced AI technology. By prioritizing patient care and investing in digital tools, the lab has achieved unprecedented efficiency and diagnostic precision.
AIxURO URINE CYTOLOGY CLINICAL STUDIES
March 2026
USCAP 2026 Oral Presentation
Digital Cytopathology in the Heart of Europe: A Bioptická Laboratoř Experience
USCAP 2026 Oral Presentation
Digital Cytopathology in the Heart of Europe: A Bioptická Laboratoř Experience
STUDY DESIGN:
RESULTS:
The implementation of the AI-driven workflow resulted in significant improvements:
- Increased Speed: Pathologists reported a 30% to 50% increase in workflow speed, with the diagnosis of benign cases often reduced to mere seconds.
- Enhanced Sensitivity: The AI was slightly biased toward sensitivity, ensuring that atypical cells were prioritized for review.
- Objective Data: The system provides quantitative metrics for atypical and suspicious cells, removing subjectivity from the diagnostic process.
- Improved Efficiency: The reduction in screening time allowed the lab to free up capacity for growth without compromising accuracy.
CONCLUSIONS:
Clinical use of AIxURO resulted in a 30–50% faster overall read time, improved sensitivity with moreobjective data for atypical cases, and a noticeably better user experience. The system is easy to use,avoids unnecessary features, integrates well with glass slides, and supports increased efficiency andgrowth in routine clinical practice.
August 2023
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.
November 2022
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
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.
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March 2026
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Objective Precision in the Caribbean: CorePlus and the Future of AI-Driven Pathology March 2026
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Digital Cytopathology in the Heart of Europe: A Bioptická Laboratoř ExperienceFebruary 2026
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Dr. Barbara Crothers is the recipient of the ASC’s highest award- 2024 Papanicolaou AwardApril 2024
