Table 1.
AI system advances in cytology for cervical cancer
| Author and Ref. | Aim of study | Number of subjects | AI system performance | Key study outcomes |
|---|---|---|---|---|
| Du H44 | Feasibility and efficiency of cytology slide interpretation | 5,000 high-confidence slides (private dataset) |
Accuracy of NILM, HSIL, ASU, and LSIL prediction on single cells is 81.4%, 90%, 42.54%, and 68.23%, respectively | The interpretation time for each slide was reduced from 3 min to 30 seconds |
| Bai X45 | Identification and interpretation on CINII and above cervical smear pap | 32,451 cases (private dataset) |
Sensitivity of CIN2+ smear pap is 99.3% and specificity 9.87% by AI alone | The average reading time of pathologists with AI system was 22.23 seconds per case compared to a manual reading time of 180 seconds |
| Xue P15 | The performance of an AI-enabled liquid-based cytology as a screening triage approach | 489 cases (private dataset) |
The sensitivity of AI system at detecting CIN2+ is 86.49%, and the specificity is 51.33% | Compared to HPV16/18 typing the AI system sensitivity is substantially higher and specificity is lower. The AI system reduced referrals to colposcopy by approximately 10% |
| Xue P46 | The efficiency of abnormal cervical squamous cell detection in cervical cancer screening | 8,000 digitalized whole slide images (private dataset) |
The sensitivity of AI alone is 89.4% and the specificity is 66.4% | Reduced the cytology workload by more than one-third. The AI system had superior sensitivity and specificity compared to junior cytologists |
| Bao H14 | AI-assisted cytology system at different CIN levels of detection | 703,103 cases (private dataset) |
The sensitivity of the AI system on CIN1+, CIN2+, and CIN3+ is 88.9%, 90.1%, and 90.9%, respectively; specificity on CIN1+, CIN2+, and CIN3+ is > 90% | The agreement rate between AI and manual reading was 94.7%, which was a 5.8% increase in sensitivity compared to manual reading |
| Zhu X47 | Classified cervical liquid-based thin-layer cell smears on 5 classes | 34,403 smear samples (private dataset) |
The sensitivity of intraepithelial lesions is 92% and the specificity is 84.39% | Achieving a speed < 180s/slide with high sensitivity; the sensitivity of senior cytologists detection is lower than the AI system |
| Wentzensen N42 | Detection on dual-stain+ cells and performance of AI cytology in cervical cancer screening | Based on 3 epidemiologic studies, > 4,000 cases (private dataset) |
The sensitivity of CIN3+ cells on AI DS-cytology (single cell) is 91.8% | The AI system was developed using P16/Ki-67 dual-staining slides; AI-based cytology interpretation is more sensitive than manual; AI results reduced colposcopy referrals by one-third |
HSIL, high-grade squamous intraepithelial lesion; LSIL, low-grade squamous intraepithelial lesion; NILM, negative for intraepithelial lesion or malignancy; ASU, atypical squamous cells of undetermined significance; CIN, cervical intraepithelial neoplasia; AI, artificial intelligence.