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- 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.
- 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.
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.
- 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)
- 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
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.
- 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
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
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.
- 200 ThinPrep urine cytology slides (100 NHGUC, 35 AUC, 32 SHGUC, 33 HGUC) with diagnostic confirmation by 3 expert pathologists were digitized to WSIs using Mikroscan SLxCyto and analyzed by an AI-assisted program, AIxURO
- 1 cytopathologist (CP) and 2 cytologists (CT) reviewed urine slides or AI-assisted images with a 2-week washout period between reviews
- Arm 1: Microscopic review
- Arm 2: AI-assisted software (AIxURO) review
- Performance Metrics: Comparison of 2 diagnostic positive thresholds: AUC+ (AUC, SHGUC, and HGUC) and SHGUC+ (SHGUC and HGUC) and total time for interpretation
- AI-assisted AIxURO showed higher sensitivity (85% vs 79.3%) but lower specificity (92% vs 98%) than microscopy overall, using AUC+ as the positive threshold
- AIxURO showed lower sensitivity (74.9% vs 76.9%) and specificity (96% vs 97.5%) than microscopy using SHGUC+ as the positive threshold.
- Review Time: AIxURO markedly reduced review time (37.4 s) compared to microscopy (102.6 s) overall. CTs spent almost double the time for microscopy (121.6 s) compared to the CP (64.6 s).
AIxURO showed a 5.7% increase in sensitivity and 8.6% decrease in specificity compared to microscopy, suggesting that while AIxURO helps to ID more positive cases, it results in higher false positives. At SHGUC+ threshold, AIxURO showed a 2% decrease in sensitivity and 1.5% decrease in specificity, suggesting that reviewers diagnose more cases as AUC than with microscopy. AIxURO saves reviewers 63.5% of evaluation time compared to microscopy, at more consistent evaluation times. CTs spend substantially less evaluation time than CP using AIxURO.
- 100 CytoRich urine cytology slides with 3 expert consensus interpretations as ground truth (68 NHGUC, 11 AUC, 7 SHGUC, 14 HGUC) scanned with Mikroscan SLxCyto to form WSI, which was in turn analyzed with deep learning AI model (AIxURO) trained on The Paris System criteria for urine cytology reporting
- 1 cytopathologist and 2 cytologists microscopically reviewed the glass slide and digitally reviewed the WSI and AIxURO images, with a 2-week washout between each, resulting in 300 diagnostic pairs per observer
- Diagnostic pairs were compared with the ground truth; total time to report recorded; and sensitivity, specificity recorded per observer
- Sensitivity: For a binary diagnosis (negative = NHGUC; positive = AUC, SHGUC, HGUC), AIxURO showed higher sensitivity overall (88.5% vs 86.5%) than microscopy for all observers
- CT1: 84.4% vs. 87.5%
- CT2: 90.6% vs. 90.6%
- CP: 90.6% vs. 81.3%
- Specificity was lower overall for AIxURO (93.6% vs. 97.6%) compared to microscopy
- CT1: 91.2% vs. 94.1%
- CT2: 98.5% vs. 98.5%
- CP: 91.2% vs. 100.0%
- Reporting time: AIxURO substantially reduced mean reporting time for all observers compared to microscopy (13.6 seconds vs 83.3 seconds); this was especially pronounced for negative cases (7.7 seconds vs 72.3 seconds
AI-assisted software (AIxURO) substantially reduces overall interpretation and reporting time by 83.3% compared to conventional microscopy. AIxURO also improves sensitivity for a binary (positive vs negative) diagnosis but is less specific than microscopic interpretation.
- Retrospective cohort study
- 185 upper tract urine cytology slides (168 NHGUC, 14 AUC, 2 SHGUC, 1 HGUC) with one expert cytopathologist (CP) and one experienced cytologist (CT) confirmation of interpretation; discrepancies in diagnosis were resolved by multiheaded microscopy review by expert panel
- Digitized using Aperio AT2 scanner (Leica Biosystems) at 40X and single Z-plane
- Deep-learning training
- Cases ranked by AI-driven software into low risk (N/C 0.5 to 0.7) or high risk (N/C > 0.7)
- 37 discrepant results after AI analysis (AIxURO)
- Discrepancies (AIxURO vs conventional):
- Cytopathologist:
- Overcalled 1 NHGUC as SHGUC
- Undercalled 2 AUC as NHGUC
- Cytologist:
- Overcalled 3 NHGUC as AUC and 2 AUC as SHGUC
- Undercalled 9 AUC as NHGUC, and 1 SHGUC
- o NHGUC (20 of 168; 11.9% discrepancy rate)
- Cytopathologist:
Diagnostic accuracy is achieved in at least 85.7% for atypical and suspicious cells in the AUC and above categories, with AUC showing the least concordance (21.4% accuracy). The use of AI-assistance markedly reduced the miscall rate for the CP but not the CT compared to reported misdiagnosis rates as high as 27.6% (57 million cases in China).
- 116 urine (76 cytospin; 40 CytoRich) cytology slides with 3-armed microscopy, corresponding WSI and AI-digital (AIxURO) review by 1 experienced cytopathologist and 2 cytologists
- Performance metrics calculated for each arm included binary (negative vs positive) diagnosis, inter-and intra-observer agreement, and screening time
- Atypical Urothelial Cells (AUC): AIxURO improved diagnostic sensitivity (from 25-30.6% to 63.9%), PPV (from 21.6-24.3% to 31.1%), and NPV (91.3-19.6%) to 95.3%)
- Suspicious for High-Grade Urothelial Carcinoma (SHGUC): AIxURO improved sensitivity (from 15.2-27.3% to 33.3%), PPV (from 31.3-47.4% to 61.1%), and NPV (from 91.6%-92.7% to 93.3%).
- Binary Diagnosis (Negative vs Positive [AUC, SHGUC, or HGUC]): AIxURO improved sensitivity (from 77.8-82.2% to 90.0%) and NPV (from 91.7-93.4% to 95.8%)
- Interobserver agreement: Moderate concurrence across all methods of evaluation (ĸ = 0.57-0.61); cytopathologist showed the highest intraobserver agreement (ĸ = 0.75-0.88)
- Screening time: AIxURO significantly reduced screening time compared to conventional microscopy for all observers (by 52.3% to 83.2%); AUC case
The most significant finding is the marked reduction in screening time for AI-enhancement (AIxURO) compared with conventional microscopy (up to 83% less time required). Implementation of AI enhancement (AIxURO) for urine cytology interpretation improves diagnostic sensitivity, PPV and NPV for AUC and SHGUC, but not HGUC.
AIxURO improves the sensitivity and NPV of a binary interpretation of negative or positive. The interobserver agreement across all methods of review (microscopy, WSI and AIxURO) is moderate (ĸ = 0.57-0.61) with the cytopathologist showing the highest intra-observer agreement.
- 200 urine cytology slides (100 positive, 100 negative) were scanned to create whole slide images (WSI) that were analyzed by an artificial intelligence (AI)-assisted software program (AIxURO) to detect and quantify characteristics of abnormal urothelial cells
- Three study arms, each performed by 3 reviewers (1 cytopathologist, 2 cytologists) rendering the Paris System (TPS) 2.0 interpretation (2-week washout period between each arm):
- ARM 1- Glass slide microscopic interpretation
- ARM 2- Whole slide image interpretation
- ARM 3- WSI with AI-assisted interpretation (AIxURO)
- Performance Metrics: Total screening/reporting time, sensitivity and specificity compared to the ground truth diagnosis
- Average screening and reporting time was significantly reduced by 25.8%-58.7% (p < 0.05)
- Microscopy only and AI-assisted (AIxURO) outperformed WSI-only review in both sensitivity and specificity
- AIxURO was slightly less sensitive than microscopy (66.0 - 87.0% vs. 86.0 -89.0%) but more specific (89.0 – 95.0% vs. 81.0 – 88.0%).
- Use of AIxURO reclassified some ground-truth diagnoses from HGUC or SHGUC to AUC or NHGUC.
The use of an AI-assisted software platform (AIxURO) for detection of bladder carcinoma improves overall specificity in comparison with microscopic glass slide or whole slide imaging review, while significantly reducing screening and reporting time.
- 1856 urine cytology cases (1466 negative and 390 positive)- AI training set
- 169 urine cytology cases (88 negative, 81 positive)- Validation set
- AIxURO classifies abnormal urothelial cells into 2 categories based on The Paris System 2.0: “Suspicious” (SHGUC or HGUC) and “Atypical “(AUC), with the final interpretation deferred to a pathologist
- Logistic regression performed to predict presence of cancer, including variables such as total # suspicious cells, total # atypical cells, and predictive accuracy using sensitivity and specificity
- Optimal performance of the training set (based on the total number of atypical cells) was 10 cells (cytospin) and 49 cells (CytoRich)
- Training Set Sensitivity and Specificity: 75.9% and 73.0%
- Validation Set Sensitivity and Specificity: 75.3% and 87.5%
The logistic model supports the optimal cut-off values of at least 10 cells (cytospin) and 49 cells (CytoRich) for the number of atypical cells required for a high concordance with bladder cancer as the final outcome.
- 52 urine cytology slides (cytospin, ThinPrep, and CytoRIch) scanned with 21 Z-plane and a heuristic scan simulation method to generate whole slide images (WSI) using a Leica Aperio AT2 scanner
- An AI algorithm inferred the WSI from 21 Z-planes to quantitate total number of cells suspicious for high grade urothelial carcinoma (SHGUC) / high-grade urothelial carcinoma (HGUC)=[SHGUC+]
- The heuristic scan simulation calculated the total number of SHGUC+ using the 21 Z-plane scan data
- Performance metrics evaluated were SHGUC+ cell coverage rates, scanning times, file size, and AI-aided interpretation of WSI compared to the original cytology diagnosis for the 21 Z-plane scan and the heuristic scan
- SHGUC+ Coverage Rates: Heuristic scanning coverage rates were similar to 5 Z-plane scans for all 3 preparation types (0.78 to 0.91 vs 0.75 to 0.88; p = 0.451 to 0.578)
- Scanning Time: Heuristic scanning significantly reduced scanning time (137.2 to 635.0 seconds vs 332.6 to 1,278.8 seconds; p < 0.05)
- Image File Size: Heuristic scanning significantly reduced image file size (0.51 to 2.10 GB vs. 1.16 to 3.10 GB; p < 0.05)
- AI-aided Interpretation: Heuristic scanning had higher rates of accurate interpretation compared to single Z-plan scanning (62.5% vs. 37.5%)
Heuristic scanning showed improved scanning times and AI-aided cytologic interpretation while reducing overall image file size and maintaining similar scanning coverage for SHGUC+ cells for 3 urine cytology preparation types.