AI SOFTWARE DEVELOPMENT
ICC 2025 - Oral Presentation
Does AI performance in Thyroid Cancer Diagnosis Differ Between Single-Layer and Multi-Z-Stacked Imaging?
Resources and News
Educational resources and news from AIxMed to keep you informed
and up-to-date on our AI empowered digital cytology solutions.
and up-to-date on our AI empowered digital cytology solutions.
.png)
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
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.
May 2025
Does AI performance in Thyroid Cancer Diagnosis Differ Between Single-Layer and Multi-Z-Stacked Imaging?
STUDY DESIGN:
- 71 ThinPrep thyroid FNAC slides selected by consensus cytology diagnosis
- Cases included: TBS-II (n=35), TBS-IV (n=6), TBS-VI (n=30), confirmed by biopsy
- Slides digitized using 3DHistech scanner to create paired whole-slide images:
- S-WSI: single-layer WSI
- 7-WSI: seven-layer Z-stacked WSI
- AI-assisted review used an AIxTHY model to detect cancer cells and guide interpretation
- Three cytologists independently reviewed both S-WSI and 7-WSI sets
- Total of 213 reads (71 cases × 3 reviewers) analyzed
- Two diagnostic threshold:
- Threshold 1: Positive = TBS-V/VI; Negative = TBS-II
- Threshold 2: Positive = TBS-IV/V/VI; Negative = TBS-II
- Outcomes measured:
- Binary diagnostic sensitivity and specificity (under both thresholds)
- Agreement with consensus diagnoses
- Interobserver agreement (Cohen’s κ)
RESULTS:
- Threshold 1 (TBS-V/VI vs. TBS-II):
- Sensitivity: 7-WSI 88.9% vs. S-WSI 81.1%
- Specificity: 7-WSI 94.3% vs. S-WSI 96.2% (not significant)
- Threshold 2 (TBS-IV/V/VI vs. TBS-II):
- Sensitivity: 7-WSI 86.1% vs. S-WSI 80.6% (p < 0.05)
- Specificity: 7-WSI (%): All cases: 7-WSI 88.6% vs. S-WSI 91.4%
- Consensus Agreement (%): All cases: 7-WSI 74.6%vs. S-WSI 61.5%
- TBS-VI: 65.6% vs. 36.7%
- TBS-IV: 44.4% vs. 27.8%
- TBS-II: 87.6% vs. 88.6%
- Interobserver Agreement (Cohen’s κ): Overall: 0.366 vs. 0.351
- TBS-IV: 7-WSI 0.189 vs. S-WSI 0.075
- TBS-VI: 0.420 vs. 0.319
- TBS-II: 0.549 vs. 0.604
CONCLUSIONS:
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.
May 2025
THE PARIS SYSTEM FOR URINE CYTOLOGY NUCLEAR TO CYTOPLASMIC RATIO
Academic Article: Cancer Cytopathology
Does Artificial Intelligence Redefine Nuclear-to-Cytoplasmic Ratio Threshold for Diagnosing High-Grade Urothelial Carcinoma?
Does Artificial Intelligence Redefine Nuclear-to-Cytoplasmic Ratio Threshold for Diagnosing High-Grade Urothelial Carcinoma?
STUDY DESIGN:
- 106 urine cytology slides (from 46 lower and 60 upper urinary tract HGUC/CIS cases) digitized and analyzed using AIxURO, an AI-based WSI tool.
- AI quantified atypical and suspicious urothelial cells, their nuclear-to-cytoplasmic(N/C) ratios, and nuclear areas.
- Morphologic data were correlated with biopsy-confirmed HGUC/CIS diagnoses. Statistical comparisons were conducted using Kruskal–Wallis tests.
RESULTS:
- Suspicious vs Atypical Cells (Total):
- Fewer suspicious cells detected (median 20.5 vs 242.0, p<.001)
- Higher median N/C ratio: 0.66 vs 0.58 (p<.001)
- Larger nuclear area: 102.3 µm² vs 85.7 µm² (p<.001)
- By Cytology Category (AUC, SHGUC, HGUC):
- Suspicious cells consistently had higher N/C ratios and nuclear areas than atypical cells.
- Median N/C ratio of suspicious cells: 0.66~0.67 and atypical cells: 0.58~0.59 across groups
- Median nuclear areas of suspicious cells: AUC 108.9 µm², SHGUC 99.2 µm², HGUC 101.6 µm² and atypical cells: AUC 88.5 µm², SHGUC 86.8 µm², HGUC 83.5 µm²
- Upper vs Lower Tract:
- No significant difference in N/C ratios
- Nuclear areas were smaller in UUT vs LUT cases (e.g., suspicious: 98.0 µm² vs 108.0 µm², p<.001)
- No significant difference in N/C ratios
- Biopsy Correlation:
- CIS had the largest nuclear areas among suspicious (116.3 µm²) and atypical (101.5 µm²) cells
- Cell number and N/C ratio did not differ significantly across CIS, CIS-HGUC, and HGUC biopsy categories
CONCLUSIONS:
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.
March 2025
AIxURO URINE CYTOLOGY CLINICAL STUDIES
USCAP 2025 Poster Presentation
Enhancing Bladder Cancer Diagnosis Through the Integration of an AI-Driven Cytology System
Enhancing Bladder Cancer Diagnosis Through the Integration of an AI-Driven Cytology System
STUDY DESIGN:
- De-identified ThinPrep urine cytology slides (200) were retrospectively selected. Two cytopathologists (CP) provided consensus diagnoses (ground truth, GT) for all cases: 100 Negative for High-Grade Urothelial Carcinoma (NHGUC), 35 Atypical Urothelial Cells (AUC), 32 Suspicious for HGUC (SHGUC), and 33 HGUC.
- Slides were digitized into WSIs utilizing Mikroscan SLxCyto and customized Huron WSI imagers and examined using AI-assisted WSI review (AIxURO)
- 1 cytopathologist (CP) and 2 cytologists (CT) blindly reviewed slides with a 2-week washout period between research arms:
o Arm 1- Microscopy only
o Arm 2- AIxURO Mikroscan SLxCyto’s WSI review
o Arm 3- AIxURO customized Huron’s WSI review
- Performance Metrics:
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.
RESULTS:
- Diagnostic performance using the AUC+ threshold, AIxURO WSI review (Arm 2 and Arm 3) demonstrated higher sensitivity than microscopy (85.0% and 88.3% vs 79.3% overall). However, AIxURO WSI review exhibited lower specificity than microscopy (85.7% and 82.7% vs. 94.3%).
- When using the SHGUC+ threshold, AIxURO WSI review demonstrated higher overall sensitivity (74.9% and 86.2 vs 76.9%) and slightly lower specificity (96.0% and 92.3% vs 97.5% overall) compared to microscopy.
- AIxURO WSI review markedly reduced the SET versus microscopy (35.9s and 36.4s vs 102.6s). SETs for AUC+ are 45s and 43.6s vs 116.4s, while SET for negative cases are 26.5s and 29.1s vs 88.9s.
CONCLUSIONS:
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.
March 2025
THE PARIS SYSTEM FOR URINE CYTOLOGY NUCLEAR TO CYTOPLASMIC RATIO
USCAP 2025 Oral Presentation
Quantitative Analysis of the Paris System Cytomorphological Criteria in Digital Urinary Cytology Using Artificial Intelligence Algorithm
Quantitative Analysis of the Paris System Cytomorphological Criteria in Digital Urinary Cytology Using Artificial Intelligence Algorithm
STUDY DESIGN:
- 296 urine cytology slides from confirmed biopsy-positive cases within six months (70 AUC, 65 SHGUC,161 HGUC)
- Slides digitized into WSIs with Leica AT2 scanner
- AI algorithm identified and categorized cells with high (suspicious) or low (atypical) malignancy risk based on The Paris System for Reporting Urinary Cytology (TPS) features; Top-24 category order-ranks 24 most abnormal cells
- Performance Metrics: Mean values of suspicious cells, atypical cells, and the top-24 cells (cells ranked by highest probability of malignancy) with TPS features compared across the three categories (HGUC, SHGUC, and AUC).
RESULTS:
- HGUC cases had significantly higher mean number of abnormal cells for top-24 cells (12.2 vs. 5.5 vs. 2.5), suspicious cells (91.6 vs. 13.1 vs. 2.7), and atypical cells (885.1 vs. 108.9 vs. 30.8) compared to SHGUC and AUC, respectively.
- Top-24 and suspicious cells displayed higher N:C ratio (0.65-0.66) and larger nuclear area (106-113 mm 2 ) than atypical cells (0.57 and 86-91 mm 2 ) across three categories.
- Percentage of cells with all 3 key TPS morphological features across 3 categories:
o Top-24 cells- 84%-95%
o Suspicious cells- 81%-91%
o Atypical cells- 22%-39%
- Critical distinguishing feature between suspicious and atypical was hyperchromasia
o 93% of suspicious cells
o 24%-40% of atypical cells.
- Percentage of top-24 / suspicious cells with all 3 TPS features:
o HGUC (95% / 91%)
o SHGUC (89% / 89%)
o AUC (84% / 81%)
CONCLUSIONS:
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.
March 2025
WHOLE SLIDE IMAGE SCANNER STUDIES
USCAP 2025 Poster Presentation
Does Digital Scanner Selection Impact Urine Cytology Whole Slide Imaging? A Comparative Study of Five Commercial Digital Scanning Devices
Does Digital Scanner Selection Impact Urine Cytology Whole Slide Imaging? A Comparative Study of Five Commercial Digital Scanning Devices
STUDY DESIGN:
- Ten de-identified ThinPrep Urocyte slides with surgical concordance as gold standard/ground truth (4 NHGUC, 2 AUC, 1 SHGUC, 3 HGUC) digitized into WSIs using five digital imagers:
o Leica Aperio AT2
o Hamamatsu S360
o 3DHistech P1000
o Customized Huron WSI model
o Mikroscan SLxCyto
- AI algorithm was applied to the WSIs to detect suspicious cells (high-malignant risk) and atypical urothelial cells (low-malignant risk).
- 3 cytologists (CT) reported diagnosis on slides with 2-week washout period between arms:
o Arm 1- Microscopy only
o Arm 2- AI-assisted WSI review
- Performance Metrics:
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
RESULTS:
- All digital imagers achieved a 100% WSI conversion rate.
- In the binary diagnosis, AI-assisted WSI review demonstrated performance comparable to microscopy across all imagers
- Quantitative analysis revealed slight variations in key metrics, including suspicious/atypical cell numbers, nuclear-to-cytoplasmic (N:C) ratio, and nuclear area, among WSIs produced by different imagers
CONCLUSIONS:
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.
March 2025
AIxTHY THYROID FINE NEEDLE ASPIRATION CLINICAL STUDIES
USCAP 2025 Oral Presentation
Assessing Role of Artificial Intelligence in Thyroid Cytology: A Feasibility Study in Clinical Practice
Assessing Role of Artificial Intelligence in Thyroid Cytology: A Feasibility Study in Clinical Practice
STUDY DESIGN:
• 100 thyroid ThinPrep slides categorized for ground 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 WSIs and analyzed with AI-assistance software (AIxTHY)
• 1 cytopathologist (CP) and 2 cytologists (CT) evaluated slides with a 2-week washout period between research arms:
o Arm 1- Microscopy only
o Arm 2- WSI only
o Arm 3- WSI with AI-assistance
• Performance Metrics: Comparison of study diagnosis with ground truth diagnosis for 3 Arms, using following cut-off values:
o Threshold 1: Negative = TBS II; Positive = V or VI (III and IV are indeterminate)
o Threshold 2: Negative = TBS II; Positive = IV, V, VI (III excluded)
o Threshold 3: Negative = TBS II (Benign); Positive = III, IV, V, VI (Atypia of undetermined significance, Follicular neoplasm, Suspicious for malignancy, Malignant)
o All TBS I (Nondiagnostic) cases were excluded from the study
RESULTS:
- Threshold 1 (TBS V+ as positive) showed highest accuracy and consensus with biopsy and cytology (Arm 1 = 100%; Arm 2 = 95.7%;Arm 3 = 100%)
- Threshold 3 (TBS III+ as positive) showed accuracy of Arm 1 = 75.8%; Arm 2 = 72.7%; Arm 3 = 72.9%
- For AI-assistance (Arm 3), Threshold TBS iV+ had higher Sensitivity (93.5%), Specificity (96.2%), PPV (96.6% and NPV (92.6%)than including atypia (TBS III+)(Sensitivity = 94.3%; Specificity = 78.9%; PPV= 83.2%; NPV = 92.6%; accuracy 87.0%)
- Using consensus cytology+biopsy reports to determine accuracy yielded higher accuracy, compared to lower accuracy with cytology diagnosis alone
CONCLUSIONS:
The accuracy of AI-assisted thyroid cytology diagnosis closelyapproximates microscopic accuracy, but dependent on the cut-off point for“negative” and “positive” results. The best diagnostic review concordance occurswhen both cytology and biopsy results serve as the “ground truth” for thestudy.
November 2024
THE PARIS SYSTEM FOR URINE CYTOLOGY NUCLEAR TO CYTOPLASMIC RATIO
Pathology Visions Oral Presentation
AI-Driven Comparative Study of Cell Characteristics in Urine Cytology with Biopsy Correlation
AI-Driven Comparative Study of Cell Characteristics in Urine Cytology with Biopsy Correlation
DPA 2024 Oral Presentation Crothers BA et al. Tungs’ Taichung MetroHarbor Hospital, Show Chwan Memorial Hospital, Taipei Veteran’s General Hospital, Taichung Veteran’s General Hospital
STUDY DESIGN:
- 242 urine cytology slides (74 AUC, 56 SHGUC, 112 HGUC) from patients with biopsy-confirmed HGUC within a 6-month window
- 162 (67%) Cytospin, 60 (25%) TP-UroCyte, 20 (8%) BD CytoRich
- 124,980 abnormal cells inferred by AI-assisted digital software system (AIxURO)
- 54% of biopsy proven CIS or HGUC cases had a cytology interpretation of AUC or SHGUC; 46% cytology interpretation of HGUC
- Evaluation of N/C ratios and nuclear sizes for the top 24 most abnormal cells, and for the categories of suspicious cells vs atypical cells, with statistical significance
RESULTS:
- Average N/C ratios:some text
- Top 24 abnormal cells = 0.66 (95% CI; 0.65-0.66)
- Suspicious cell category = 0.65 (95% CI; 0.64-0.65)
- Atypical cell category = 0.57 (95% CI; 0.57-0.57)(p < 0.0001)
- Average nuclear size:some text
- Top 24 abnormal cells = 108.1-116.8 µm2
- Atypical cell category = 86.3-87.7 µm2
- Significantly larger nuclear sizes occurred in the top 24 abnormal and suspicious cell categories than in atypical cells category
- Nuclear size of biopsy:CIS cells was significantly larger than those of biopsy:HGUC cells for all AI-categories
- Slightly more HGUC biopsy cases were cytologically interpreted as AUC or SHGUC than as HGUC
CONCLUSIONS:
AI-assistance demonstrates a predictable quantitative advantage for assessment of nuclear size and N/C ratio when assessing atypical and suspicious cells using The Paris System. The average N/C ratio is lower (0.66) than that suggested for HGUC/SHGUC (0.70) in TPS.
November 2024
AIxURO URINE CYTOLOGY CLINICAL STUDIES
Academic Article: International Urology and Nephrology
Artificial Intelligence Algorithms Enhance Urine Cytology Reporting Confidence In Postoperative Follow-up For Upper Urinary Tract Urothelial Carcinoma
Artificial Intelligence Algorithms Enhance Urine Cytology Reporting Confidence In Postoperative Follow-up For Upper Urinary Tract Urothelial Carcinoma
International Urology and Nephrology, 2024: https://doi.org/10.1007/s11255-024-04270-8 Chen CC, Yen TH, Li JR, et al.
STUDY DESIGN:
- 296 urine cytology slides from 113 patients with upper tract urothelial carcinoma, with matching pre- and post-operative cytology, pathology, and follow up for recurrence
- Comparison of cytology slides digitized and independently assessed by AIxURO to original cytology report
RESULTS:
- 88/113 (77.8%) patients had 1-2 cytology specimens preoperatively
- 44/204 (21.5%) positive cytology slides with 34/113 patients diagnosed with upper tract carcinoma (UTUC)
- Postop recurrence detected in 27/113 patients (23%) at average 190 days
- 34/56 slides (60.7%) were negative for UTUC
- 8/27 patients (29.6%) met criteria for early diagnosis of intravesical recurrence
- AIxURO identified 2 more patients (10/27; 37%) with early intravesical recurrence
CONCLUSIONS:
AI-assisted diagnostic imaging (AIxURO) enhanced detection of underdiagnosed urine cytology slides to capture early recurrence in UTUC.
November 2024
SOFTWARE OPTIMIZATION & VALIDATION STUDIES
ASC Oral Presentation
Total Abnormal Cells in Bladder Cancer Screening: The Role of Artificial Intelligence-Based Logistic Models in Urine Cytology
Total Abnormal Cells in Bladder Cancer Screening: The Role of Artificial Intelligence-Based Logistic Models in Urine Cytology
ASC 2024 Oral Presentation / Hwang YT et al. / National Taipei University
STUDY DESIGN:
An AI-based logistic regression model was developed to determine the optimal number of abnormal urothelial cells (HGUC, SGHUC, or AUC) required to accurately predict the presence of bladder cancer. The study goal was to evaluate the accuracy and effectiveness of the logistic models in predicting bladder cancer from clinical urine cytology test datasets.
- 2025 cytology slide images (471 positives, 1334 negatives) were analyzed by the AI algorithm
- Abnormal cells were categorized as suspicious (SHGUC+) or atypical (AUC)
- Preparation types: Cytospin, ThinPrep, BD CytoRich, and TP-Urocyte
- Cell numbers were standardized by preparation sample area (mm2)
- Calculations:
- Suspicious cell numbers
- Log of suspicious cell numbers
- Atypical cell numbers
- Log of atypical cell numbers
- Performance Metrics: Accuracy, sensitivity, specificity, ROC curve
RESULTS:
- Logistic model- suspicious cells: 91.69 Accuracy, 64.29 Sensitivity, 95.44 Specificity
- Log Suspicious cells: 65.33 Accuracy, 95.24 Sensitivity, 61.24 Specificity
- Logistic model-atypical cells: 94.27 Accuracy, 69.05 Sensitivity, 97.72 Specificity
- Log Atypical cells: 76.22 Accuracy, 88.1 Sensitivity, 74.59 Specificity
- Cell cut-off value per preparation type:some text
- Suspicious cutoff value: 0.189 (Cytospin 5 cells, TP 59, CytoRich 25, UroCyte 10
- Atypical cutoff value: 1.823 (Cytospin 52 cells, TP 573, 242 CytoRich, 49 UroCyte
CONCLUSIONS:
Suspicious cell cut-off values support TPS criteria, with more than 5 suspicious cells categorized as SHGUC or higher indicating high likelihood of HGUC
Atypical cell cut-off values can aid in identifying potential false-negative cases when suspicious cell numbers are below cut-off values but atypical numbers are higher.
Upcoming Events
Upcoming event
American Urological Association -Visit us at the Lumea Booth (#547)
15
-
18
May 2026
Washington, DC
May
15
-
18
,
2026
No items found.
Latest news
March 2026
USCAP 2026 Video Recording
Objective Precision in the Caribbean: CorePlus and the Future of AI-Driven Pathology March 2026
USCAP 2026 Video Recording
Digital Cytopathology in the Heart of Europe: A Bioptická Laboratoř ExperienceFebruary 2026
January 2026
November 2025
EINPresswire
AIxMed and ASCT PartnershipNovember 2025
November 2024
American Society of Cytopathology (ASC)
Dr. Barbara Crothers is the recipient of the ASC’s highest award- 2024 Papanicolaou AwardApril 2024
