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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.
- 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
- 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
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.
- 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)
- Ai model successfully learned the morphologies of all 6 cell types and was able to quantify total cell counts in each class
An artificial intelligence model that enumerates and classifies abnormal urothelial cells may improve urine cytology throughput, accuracy and reproducibility.