Table 2.
Application of AI model in colposcopy to detect cervical cancer
| Author and Ref. | Aim of study | Study type | Number of subjects | Key study outcomes |
|---|---|---|---|---|
| Yuan C60 | Detection on LSIL+ colposcopy | Model development and validation study | 22,330 cases for AI model training and evaluation; 5384 cases for validation (private dataset) |
The AI model was able to segment and classify LSIL and HSIL cervical lesions. The accuracy of the AI model on LSIL is 84.1% and the sensitivity of the AI model on HSIL is 88.47%. In the validation study, 84.67% HSIL cases were detected, which was better than the colposcopist |
| Yan L61 | Detection on LSIL/HSIL colposcopy | Model development study | 7,530 patients, 15,276 images (private dataset) |
Significant 95% accuracy on normal/LSIL classification, and 90% accuracy on HSIL-and HSIL+; a stronger diagnostic performance than the junior colposcopist in 300 samples from the test set |
| Xue P69 | Detection on LSIL/HSIL colposcopy | Model development and validation study | Total 19,435 patients and 101,267 images (private dataset) |
Accuracy of AI model on LSIL/HSIL classification is 80.7% compared to the colposcopist interpretation on the validation dataset. The AI model showed slightly higher sensitivity: 65.8% vs. 60.4% |
| Wu A71 | Performance of CAIADS69 on CIN2+/CIN3+ detection | Hospital-based retrospective study (AI model external study) | 1,146 patients (private dataset) |
The average sensitivity of CAIADS on CIN2+/CIN3+ is 80%, which was not lower than a senior colposcopist. The sensitivity of the junior colposcopist with CAIADS is significantly improved. Number of biopsies recommended by CAIADS per case was less than the colposcopist |
| Kim S72 | Evaluation on the feasibility of interpreting colposcopy images with the AI-assist | Observation study (AI system application study) | 234 patients (private dataset) |
The final diagnostic accuracy of Physician 1 with AI-assist on colposcopy images increased from 76% to 80%, and the accuracy of Physician 2 increased from 71% to 77% |
| Fu L13 | Improved colposcopy DL-base model with HPV test result and cytology test result | Model developmet and validation study | 2,160 cases (private dataset) |
The diagnostic performance with AUC was improved to 0.921 as a multimodal integrated model from AUC 0.84 as a colposcopy-based DL model |
| Mukku J73 | CIN detection on colposcopy images with clinical outcomes by multimodal strategy | Model development study | 900 images from IARC image bank11 | Impressive 89.32% sensitivity and 91.6% specificity on diagnosing CIN with fusion strategy on various clinical findings (including age, HPV test, biopsy result, and transformation zone) |
HSIL, high-grade squamous intraepithelial lesion; LSIL, low-grade squamous intraepithelial lesion; CAIADS, colposcopic artificial intelligence auxiliary diagnostic system; CIN, cervical intraepithelial neoplasia; AI, artificial intelligence; DL, deep learning.