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
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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.
AIxTHY THYROID FINE NEEDLE ASPIRATION CLINICAL STUDIES
March 2026
USCAP 2026 Oral Presentation
AIxTHY Digital Cytopathology Platform Improves Diagnostic Accuracy and Reduces Nondiagnostic Interpretations in Thyroid Fine-Needle Aspiration Cytology
USCAP 2026 Oral Presentation
AIxTHY Digital Cytopathology Platform Improves Diagnostic Accuracy and Reduces Nondiagnostic Interpretations in Thyroid Fine-Needle Aspiration Cytology
STUDY DESIGN:
- A total of 100 ThinPrep thyroid FNAC cases with established ground truth cytologic diagnoses (The Bethesda System for Reporting Thyroid Cytopathology) using a consensus of the original cytology report interpretation and the surgical pathology findings were analyzed:
- 5 TBS-I, 35 TBS-II, 15 TBS-III, 15 TBS-IV, and 30 TBS-VI cases
- Each case was digitized into single-layer whole-slide images (WSIs) using Leica AT2 or 3DHISTECH scanners.
- The AIxTHY algorithm was applied to identify abnormal cells and quantify cytomorphologic features on each WSI.
- Eight independent reviewers (3 cytopathologists, 5 cytologists) assessed each case under two arms, separated by a two-week washout period:
- Arm 1- Microscopy only
- Arm 2- AI-assisted digital review
- Performance Metrics: Comparison of study diagnosis with ground truth cytology diagnosis for two arms across 800 total reads.
- Diagnostic agreement for TBS categorization
- Reclassification of TBS category
- Binary reclassification of the TBS-I reads from microscopy by AI-assisted review
RESULTS:
- In the Nondiagnostic (TBS-I) category, microscopy (Arm 1) achieved 80.0% (32/40) agreement with the ground truth, but to our surprise, AI-assisted review (Arm 2) reduced nondiagnostic calls to 57.5% (23/40).
- Agreement for indeterminate TBS-III cases increased from 29.2% (35/120) in the microscopy arm to 46.7% (56/120) in the AI-assisted review arm.
- Overall, TBS-I reads decreased from 10.0% (80/800) in microscopy to 6.8% (54/800) in AI-assisted review, representing a 32.5% relative reduction (26 fewer TBS-I interpretations).
- Binary reclassification of the 80 TBS-I reads from microscopy (TBS-II as negative; TBS-III+ as positive) showed that AI-assisted review 2 reclassified:
- 12 as negative (32.1% specificity gain) and
- 4 as positive (40.0% sensitivity gain),
- thereby improving overall diagnostic accuracy and interpretive confidence for previously indeterminate cases.
CONCLUSIONS:
- AI-assisted review reduced nondiagnostic (TBS-I) interpretations originally rendered by microscopy and improved diagnostic accuracy in thyroid FNAC.
- By reclassifying TBS-I cases into actionable Bethesda categories, AIxTHY enhanced both specificity and sensitivity.
- Reassignment to a benign or malignant diagnoses expedites definitive care.
- These results highlight the potential of AIxTHY as an effective adjunct for digital cytology workflows, supporting more reliable and efficient thyroid FNAC interpretation.
AIxURO URINE CYTOLOGY CLINICAL STUDIES
March 2026
USCAP 2026 Poster Preentation
Mitigating Visual Fatigue in Urine Cytology: AIxURO, an AI-Driven Review Platform, Improves Diagnostic Accuracy and Efficiency
USCAP 2026 Poster Preentation
Mitigating Visual Fatigue in Urine Cytology: AIxURO, an AI-Driven Review Platform, Improves Diagnostic Accuracy and Efficiency
STUDY DESIGN:
- 100 urine cytology cases were selected, comprising 60 positive and 40 negative diagnoses for bladder cancer.
- Each slide was digitized into a whole-slide image (WSI) using a Hamamatsu S360 scanner.
- AIxURO was applied to detect abnormal urothelial cells and quantify cytomorphologic features on each WSI.
- Four cytologists independently reviewed all cases under microscopy and AI-assisted review (AIxURO) arms, separated by a washout period, and data was pooled for 400 reads/arm.
- Visual fatigue was assessed using the Computer Vision Syndrome Questionnaire (CVS-Q), which rates 16 ocular and visual symptoms by frequency and intensity (total score of 0–32; scores ≥6 indicate CVS).
- Performance Metrics: Comparison of study diagnosis with ground truth cytology diagnosis for two arms across 400 total read.
- Binary diagnostic accuracy (positive: AUC and above; negative: NHGUC)
- Mean diagnostic turnaround time (second)
RESULTS:
- In CVS-Q evaluation, microscopy produced higher mean scores than AIxURO at both the first 50 (5.8 vs. 1.2) and last 50 (10.5 vs. 2.8) reads, indicating greater visual fatigue with microscopy.
- CVS-Q scores progressively increased during microscopy (the mean score: 1.8 to 10.5), reaching levels consistent with CVS, whereas only one reviewer exceeded the CVS threshold after 100 reads in AIxURO.
- These results indicate that AIxURO reduces both the likelihood and severity of visual fatigue.
- In diagnostic performance, AIxURO achieved higher sensitivity (0.975 vs. 0.867) and accuracy (0.960 vs. 0.890), with substantially shorter mean turnaround time (24.2 seconds vs. 80.3 seconds) compared with microscopy, indicating improved accuracy and efficiency with AI assistance.
CONCLUSIONS:
- Compared with microscopy, AIxURO was associated with lower CVS-Q scores, reflecting reduced visual fatigue, along with higher diagnostic accuracy and shorter reporting times.
- These findings support integrating the AI platform into routine cytology practice to enhance user comfort, reduce occupational visual strain, and improve diagnostic performance.
March 2023
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.
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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March 2026
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