AIxURO URINE CYTOLOGY CLINICAL STUDIES
USCAP 2025 Poster Presentation
Enhancing Bladder Cancer Diagnosis Through the Integration of an AI-Driven Cytology System
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AIxURO URINE CYTOLOGY CLINICAL STUDIES
March 2025
USCAP 2025 Poster Presentation
Enhancing Bladder Cancer Diagnosis Through the Integration of an AI-Driven Cytology System
USCAP 2025 Poster Presentation
Enhancing Bladder Cancer Diagnosis Through the Integration of an AI-Driven Cytology System
STUDY DESIGN:
RESULTS:
CONCLUSIONS:
THE PARIS SYSTEM FOR URINE CYTOLOGY NUCLEAR TO CYTOPLASMIC RATIO
March 2025
USCAP 2025 Oral Presentation
Quantitative Analysis of the Paris System Cytomorphological Criteria in Digital Urinary Cytology Using Artificial Intelligence Algorithm
USCAP 2025 Oral Presentation
Quantitative Analysis of the Paris System Cytomorphological Criteria in Digital Urinary Cytology Using Artificial Intelligence Algorithm
STUDY DESIGN:
RESULTS:
CONCLUSIONS:
WHOLE SLIDE IMAGE SCANNER STUDIES
March 2025
USCAP 2025 Poster Presentation
Does Digital Scanner Selection Impact Urine Cytology Whole Slide Imaging? A Comparative Study of Five Commercial Digital Scanning Devices
USCAP 2025 Poster Presentation
Does Digital Scanner Selection Impact Urine Cytology Whole Slide Imaging? A Comparative Study of Five Commercial Digital Scanning Devices
STUDY DESIGN:
RESULTS:
CONCLUSIONS:
March 2025
Enhancing Bladder Cancer Diagnosis Through the Integration of an AI-Driven Cytology System
STUDY DESIGN:
RESULTS:
CONCLUSIONS:
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:
RESULTS:
CONCLUSIONS:
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:
RESULTS:
CONCLUSIONS:
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 Threshold1: 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 highestaccuracy and consensus with biopsy and cytology (Arm 1 = 100%; Arm 2 = 95.7%;Arm 3 = 100%)
· Threshold 3 (TBS III+ as positive) showedaccuracy of Arm 1 = 75.8%; Arm 2 = 72.7%; Arm 3 = 72.9%
· For AI-assistance (Arm 3), Threshold TBS iV+ hadhigher 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 todetermine accuracy yielded higher accuracy, compared to lower accuracy withcytology 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.
November 2024
THE PARIS SYSTEM FOR URINE CYTOLOGY NUCLEAR TO CYTOPLASMIC RATIO
ASC Poster Presentation
How does artificial intelligence redefine N:C ratio thresholds for diagnosing high-grade urothelial carcinoma?
How does artificial intelligence redefine N:C ratio thresholds for diagnosing high-grade urothelial carcinoma?
ASC 2024 Poster Presentation Hang JF, Yang WL, Yeh CH et al. TVGHTE, Natl Tung University, Show Chwan Mem Hospital, Tungs MetroHarbor Hospital N:C ratio pilot study_2024
STUDY DESIGN:
- 109 urine cytology cytospin cases with surgically-confirmed HGUC, HGUC-CIS, “positive” or CIS within a 6 month pre-or post-urine collection window
- Digitally analyzed by AIxURO AI-deep learning software for N/C ratio and nuclear area for the cytologic specimen diagnoses of AUC, SHGUC, and HGUC
- Comparison of the cytologic and surgical diagnoses by mean cell numbers per slide, mean N/C ratio, and mean nuclear area (µm2)
RESULTS:
- Cell Quantification: AIxURO categorized fewer suspicious cells (mean 160.0) compared to atypical cells (mean 929.6)
- N/C Ratio and Nuclear Area: Suspicious cells had higher mean N/C ratio (0.66 vs 0.58) and higher mean nuclear area (107.2 vs 66,9 µm2) than atypical cells
- Mean N/C for suspicious cells across all biopsies = 0.65 to 0.68
- Larger nuclear areas were also noted in SHGUC (13.9% difference) and HGUC (24.4% difference)
- Carcinoma in situ (CIS) had the largest mean nuclear area for both suspicious (117.3 µm2) and atypical (101.1 µm2) cells
CONCLUSIONS:
A lower mean N/C ratio for suspicious cells as an indicator of biopsy-proven HGUC using TPS should be considered, especially for AI-assisted urine cytology software programs. AIxURO categorized suspicious cells have a higher nuclear area than categorized atypical cells. Very large nuclear areas may be an indicator of CIS.
November 2024
WHOLE SLIDE IMAGE SCANNER STUDIES
Pathology Visions Poster Presentation
Do different digital imagers used with the AIxURO system impact performance in urine cytology?
Do different digital imagers used with the AIxURO system impact performance in urine cytology?
DPA 2024 Poster Presentation / Abstract Yang WL, Yang WC, Huang SM, et al. Show Chwan Hospital and AIxMED (pilot studies 2 & 3)
STUDY DESIGN:
- 183 urine cytology slides (140 CytoRich, 43 SurePath), with expert panel consensus diagnoses as NHGUC (83), AUC (45), SHGUC (27), and HGUC (28)
- Slides scanned by Leica AT2 and Hamamatsu S360 digital scanners with images inferred by the AI algorithm (AIxURO), using The Paris System (TPS) 2.0 criteria
- AIxURO separated abnormal cells into “suspicious” and “atypical” categories for investigator review
- 3-Arm Study:
- 6 reviewers (1 cytopathologist and 5 cytologists) interpreted the slides using conventional microscopy only (Arm 1), Leica whole slide image (WSI) with AIxURO AI-assistance (Arm 2), and Hamamatsu WSI with AIxURO AI-assistance (Arm 3) with a 2-week wash-out period between reviews
RESULTS:
3294 total reviews (microscopy + WSI + AIxURO); binary distribution as positive (AUC, SHGUC, or HGUC) or negative (NHGUC)
- Sensitivity and Specificity of AIxURO with Leica: 85.0% and 90.0%
- Sensitivity and Specificity of AIxURO with Hamamatsu: 82.9% and 89.6%
- Sensitivity and Specificity of microscopy only: 83.7% and 89.4%
- Total Time for review of AIxURO with Leica: 37.4 seconds
- Total Time for Review of AIxURO with Hamamatsu: 53.1 seconds
- Total Time for Review of microscopy only: 82.0 seconds
- Reviewers (3) experienced with the AIxURO system had higher sensitivity (91.3% vs 78.7%) but lower specificity (87.7% vs 92.3%) and spent less time (30.6 sec vs 44.4 sec) than the 3 without experience
CONCLUSIONS:
Use of AI-assisted software (AIxURO) to classify abnormal urothelial cells into atypical or suspicious categories improved the overall sensitivity and specificity for a binary interpretation over conventional microscopy. Additionally, AI-assistance significantly reduced reporting time by 52.4% (AIxURO + Leica AT2) and 35.2% (AIxURO + Hamamatsu) compared to microscopy. Experienced users of AIxURO showed better overall performance, suggesting that training and experience with AI-assistance will improve performance.
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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
