AIxURO URINE CYTOLOGY CLINICAL STUDIES
Pathology Visions Poster Presentation
Transforming bladder cancer diagnosis via AI-aided digital cytology integration in clinical workflow
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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.
November 2024
Transforming bladder cancer diagnosis via AI-aided digital cytology integration in clinical workflow
DPA 2024 Poster Presentation / Abstract Sigfred Lajara, MD; Samer Khader, MD; Rajiv Dhir, MD; Daniel Geisler, MD; Jacqueline Cuda, SCT; Caroline Horne, SCT UPMC pilot study
STUDY DESIGN:
- 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
RESULTS:
- 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).
CONCLUSIONS:
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.
November 2024
AIxURO URINE CYTOLOGY CLINICAL STUDIES
Pathology Visions Poster Presentation
Revolutionizing bladder cancer diagnosis: Insights on urine cytology from a digital pathology lab
Revolutionizing bladder cancer diagnosis: Insights on urine cytology from a digital pathology lab
DPA 2024 Poster Presentation / Abstract CorePlus pilot study
STUDY DESIGN:
- 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
RESULTS:
- 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
CONCLUSIONS:
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.
November 2024
AI SOFTWARE DEVELOPMENT
Academic Article: In Vivo
An artificial intelligence-assisted diagnostic system improves upper urine tract cytology diagnosis
An artificial intelligence-assisted diagnostic system improves upper urine tract cytology diagnosis
In Vivo 2024; 38(x): published ahead of print; doi:10.21873/invivo.11xxx Chang KY, Yang CS, Lai HY et al. VGHTC; Nat’l Chung Hsing University; Chung Shan Medical University
STUDY DESIGN:
- 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
RESULTS:
- 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:
CONCLUSIONS:
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).
July 2024
AIxURO URINE CYTOLOGY CLINICAL STUDIES
Academic Article: Cancer Cytopathology
Evaluating artificial intelligence–enhanced digital urine cytology for bladder cancer diagnosis
Evaluating artificial intelligence–enhanced digital urine cytology for bladder cancer diagnosis
STUDY DESIGN:
- 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
RESULTS:
- 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
CONCLUSIONS:
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.
June 2024
AIxURO URINE CYTOLOGY CLINICAL STUDIES
ECDP Poster Presentation
A comparative study of artificial intelligence integration in bladder cancer screening: Enhancing accuracy and efficiency
A comparative study of artificial intelligence integration in bladder cancer screening: Enhancing accuracy and efficiency
ECDP 2024 Poster Presentation / Abstract Show Chwan Hospital, clinical pilot study 2
STUDY DESIGN:
- 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
RESULTS:
- 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.
CONCLUSIONS:
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.
June 2024
SOFTWARE OPTIMIZATION & VALIDATION STUDIES
ECDP Poster Presentation
Developing an optimal artificial intelligence-based logistic model for automated bladder cancer screening in digital urine cytology
Developing an optimal artificial intelligence-based logistic model for automated bladder cancer screening in digital urine cytology
ECDP 2024 Poster Presentation Receiver-Operator Curve (ROC) study to assess AIxURO logistic model’s accuracy to predict bladder cancer
STUDY DESIGN:
- 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
RESULTS:
- 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%
CONCLUSIONS:
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.
April 2024
SOFTWARE OPTIMIZATION & VALIDATION STUDIES
Academic Article: Acta Cytologica
Evaluating urine cytology slide digitization efficiency: A comparative study using an artificial intelligence-based heuristic scanning simulation and multiple Z-plane scanning
Evaluating urine cytology slide digitization efficiency: A comparative study using an artificial intelligence-based heuristic scanning simulation and multiple Z-plane scanning
Hang JF, Ou YC, Yang WL et al. Acta Cytol. 2024;68(4):342-350. doi: 10.1159/000538985. Epub 2024 Apr 22. PMID: 38648759. AIxMED Technical Review Study, Stage II (Nat’l Yang Ming Chiao Tung Univ, VGHTPE, Tung Univ, Tung’s Taichung MetroHarbor)
STUDY DESIGN:
- 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
RESULTS:
- 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%)
CONCLUSIONS:
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.
March 2024
AIxURO URINE CYTOLOGY CLINICAL STUDIES
USCAP Poster Presentation
Evaluating the feasibility of a two-step workflow in artificial intelligence-assisted digital urine cytology
Evaluating the feasibility of a two-step workflow in artificial intelligence-assisted digital urine cytology
USCAP 2024 Poster Presentation / Abstract Wei-Lei Yang, PhD Show Chwan Hospital, clinical pilot study
STUDY DESIGN:
- 116 urine cytology slides (76 Cytospin, 40 CytoRich)
- Consensus diagnosis by two senior cytopathologists for ground truth, using TPS2.0, resulting in
- 30 positive slides (AUC/SHGUC/HGUC)
- 86 negative slides (NHGUC)
- Consensus diagnosis by two senior cytopathologists for ground truth, using TPS2.0, resulting in
- 3-Arm study with 1 cytopathologist (CP) and 2 cytologist (CT) reviewers and a 2-week washout period between each arm; and analysis of the cytopathologist paired with one of the cytologists to mimic clinical practice of cytologist review and referral to cytopathologist
- Arm 1: Microscopy
- Arm 2: Digital whole slide image review
- Arm 3: Digital image review using artificial intelligence software (AIxURO)
- Performances Metrics: Sensitivity, specificity, PPV, NPV, accuracy and total diagnostic (review) time
RESULTS:
- Sensitivity: Improved with AI-assistance for CP+CTA (90% vs 76.7%) and CP+CTB (76.7% vs 76.7%) compared with microscopy (but not with WSI review alone)
- Negative Predictive Value: Improved with AI-assistance for CP+CTA (96.4% vs 92.2%) and CP+CTB (92% vs 92.3%) compared with microscopy (but not with WSI review alone)
- Specificity: Decreased in AI-assistance for CP+CTA (93% vs 96.5%) and CP+CTB (92% vs 92.3%) compared to microscopy alone
- Positive Predictive Value: Decreased in AI-assistance for CP+CTA (81.8% vs 88.5%) and CP+CTB (79.3% vs 92%) compared to microscopy alone
- Overall, Arm 2 (WSI review) showed no improvement compared to microscopy for either CP+CTA or CP+CTB
- Overall Review Time: AI-assisted review decreased the total review time (72.2 min and 110.4 min), compared to microscopy (210.2 min and 244.7 min), whereas WSI review took as long or longer (227.1 min and 243.8 min) than microscopy
CONCLUSIONS:
AI-assisted urine cytology review markedly reduces review time while increasing sensitivity and NPV using TPS2.0 compared to conventional microscopy. However, specificity, PPV and accuracy are slightly diminished. Pairing a cytopathologist (CP) with different cytologists (CT) also influences the diagnostic outcomes and metrics.
March 2024
AIxURO URINE CYTOLOGY CLINICAL STUDIES
USCAP Oral Platform l Presentation
Assessing the clinical practicability of artificial intelligence assisted reporting using the Paris System for urinary cytology
Assessing the clinical practicability of artificial intelligence assisted reporting using the Paris System for urinary cytology
USCAP 2024 Oral Platform l Presentation- Tien-Jen Liu, MD Show Chwan Hospital clinical pilot study
STUDY DESIGN:
- 116 urine cytology slides with consensus diagnosis (ground truth) by a panel of experts to 86 NHGUC, 12 AUC, 11 SHGUC, and 7 HGUC, scanned with Leica Aperio AT2 to create a whole slide image (WSI)
- 1 Cytopathologist and 2 cytologists reviewed all slides/images in each arm independently, recording the time required to diagnosis and The Paris System (TPS) cytologic diagnosis, with a 2-week washout period between each review
- Arm 1: Microscopic review of the cytology slide
- Arm 2: Review of the scanned whole slide image (WSI)
- Arm 3: Review of images with AI-assistance software (AIxURO)
- Metrics: TPS diagnostic category compared with the expert panel and Total time spent on review
RESULTS:
- NHGUC, SHGUC and HGUC: AIxURO showed higher specificity than microscopy, but lower sensitivity
- AUC: AIxURO showed higher sensitivity but lower specificity than microscopy
- The performance of Arm 2 (using the whole slide digital image only for analysis) was poorest overall, compared to the other 2 arms
- There were performance inconsistencies between reviewers. For example, sensitivity for SHGUC was decreased for the pathologist and one cytologist, whereas the other cytologist noted an increase in sensitivity. One cytologist had a decrease in sensitivity for HGUC compared to the other 2 reviewers.
- AIxURO showed the largest reduction in time spent on review (32-45% less for the pathologist and 10-62% for the cytologists). The cytopathologist took the longest time to review AUC and the shortest for HGUC, whereas the cytologists took the most time to review SHGUC and the least on NHGUC.
CONCLUSIONS:
AI-assisted AIxURO outperformed microscopy in diagnostic accuracy for AUC while maintaining comparable accuracy across other TPS categories and significantly reducing total review time for all reviewers. WSI review alone did not improve diagnostic accuracy or efficiency.
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