Performance of AI-CAD in mammography
Literature | AI-CAD | AUROC | Sensitivity | Specificity |
---|---|---|---|---|
Rodriguez-Ruiz et al. (2019)25) | Deep CNN (Transpara 1.4.0; Screenpoint Medical) | 0.84 | 75%–86% at radiologists’ specificity | - |
Kim et al. (2020)27) | Deep CNN (Lunit INSIGHT MMG; Lunit Inc.) | 0.94 | 88.87% | 81.87% |
Kim et al. (2018)28) | Deep CNN | 0.903–0.906 | 75.6%–76.1% | 88.5%–90.2% |
Ribli et al. (2018)29) | Faster R-CNN | 0.95 | 90% | - |
Becker et al. (2017)30) | Deep ANN | 0.81–0.85 | 59.8%–73.7% | 69.6%–84.4% |
Kooi et al. (2017)31) | Deep CNN | 0.929 | - | - |
McKinney et al. (2020)32) | Deep learning AI system | 0.740 | 42%–100% | 92%–97% |
Lee et al. (2022)33) | Deep CNN (Lunit INSIGHT MMG 1.1.1.0; Lunit Inc.) | 0.915 | - | - |
Rodríguez-Ruiz et al. (2019)34) | Deep CNN (Transpara 1.3.0; Screenpoint Medical) | With AI: 0.89 (vs. Without AI: 0.87) | With AI: 86% (vs. Without AI: 83%) | With AI: 79% (vs. Without AI: 77%) |
Chen et al. (2023)35) | Deep CNN (Lunit INSIGHT MMG 1.1.7.1; Lunit Inc.) | 0.93 | 84%–91% | 77%–89% |
Salim et al. (2020)36) | Three commercialized AI-CADs | 0.920–0.956 | 67.0%–81.9% at radiologists’ specificity | - |
Hickman et al. (2022)37) | ML algorithms used in 14 studies | Pooled AUROC 0.89 | Pooled sensitivity 75.4% | Pooled specificity 90.6% |
Yoon et al. (2023)38) | AI algorithms used in 13 studies | Pooled AUROC 0.87–0.89 | Pooled sensitivity 75.8%–80.8% | Pooled specificity 76.9%–95.6% |
Akselrod-Ballin et al. (2019)40) | A ML-DL model | 0.91 | 87% | 77.3% |
Schaffter et al. (2020)42) | A custom neural network | 0.858–0.903 | - | 66.2%–81.2% at radiologists’ sensitivity |
Pacilè et al. (2020)43) | Deep CNN (MammoScreen V1; Therapixel) | With AI: 0.797 (vs. Without AI: 0.769) | With AI: 69.1% (vs. Without AI: 65.8%) | With AI: 73.5% (vs. Without AI: 72.5%) |
AI, artificial intelligence; AI-CAD, artificial intelligence-based computer-aided detection; AUROC, area under the receiver operating characteristics curve; CNN, convolutional neural network; ANN, artificial neural network; ML, machine learning; DL, deep learning.