From: Application of artificial intelligence in cataract management: current and future directions
Year | Authors | Imaging | Sample size | AI algorithms | AUC | Accuracy (%) | Sensitivity (%) | Specificity (%) |
---|---|---|---|---|---|---|---|---|
Adult cataract diagnosis (screening and grading) | ||||||||
2020 | Li et al. [10] | SLP | 1772 | ResNet-CNN | – | D = 98.4–99.8 | D = 99.4 | D = 99.1 |
2019 | Wu et al. [11] | SLP | 37,638 | ResNet | D = 0.90–1.00 G = 0.86–0.97 | D = 84.2–99.5 G = 73.2–94.9 | D 60.1–99.5 G = 63.2–92.1 | D = 76.4–99.6 G = 63.2–92.1 |
2019 | Xu et al. [12] | FP | 8030 | AlexNet + VisualDN | – | D + G = 86.2 | D + G = 79.8–95.0 | D + G = 83.3–88.4 |
2019 | Zhang et al. [75] | FP | 1352 | SVM + FCNN | – | G = 93.0 | D = 99.4 G = 82.4–96.4 | – |
2017 | Xiong et al. [76] | FP | 1355 | BPNN + MCDA | – | D = 92.8 G = 81.1–83.8 | D = 93.1 | D = 92.1 |
2016 | Yang et al. [77] | FP | 1239 | Ensemble learning (SVM + BPNN) | – | D = 92.0–93.2 G = 83.9–84.5 | D = 91.4–94.2 G = 62.5–79.5 | D = 91.5–92.5 G = 87.9–98.9 |
2015 | Guo et al. [78] | FP | 445 | MCDA | – | D = 90.9 G = 77.1 | – | – |
2015 | Gao et al. [9] | SLP | 5378 | CRNN | – | G = 70.7 | – | – |
2013 | Xu et al. [79] | SLP | 5378 | SVR | – | G = 69.0 (with up to 98.9 for within 1-step error) | – | – |
2012 | Gao et al. [80] | SLP | 434 | – | – | D = 62.0 | – | – |
2011 | Cheung et al. [81] | SLP | 5547 | SVM | D = 0.88–0.90 | – | D = 79.7–83.7 | D = 79.5–81.9 |
2010 | Acharya et al. [82] | SLP | 140 | BPNN | – | D = 93.3 | D = 98.0 | D = 100.0 |
Intraocular lens power calculation methods and biometry | ||||||||
2021 | Ladas et al. [38] | Data | 1391 | SVR, XGB, ANN | – | PE within 0.5 D = 80.0 (SRK + SVR) 81.0 (SRK + XGB) 67.0 (SRK + ANN) 82.0 (Holladay I + SVR) 82.0 (Holladay I + XGB) 80.0 (Holladay I + ANN) 82.0 (LSF + SVR) 81.0 (LSF + XGB) 81.0 (LSF + ANN) MAE = 0.325 (SRK + SVR) 0.314 (SRK + XGB) 0.439 (SRK + ANN) 0.307 (Holladay I + SVR) 0.309 (Holladay I + XGB) 0.326 (Holladay I + ANN) 0.311 (LSF + SVR) 0.310 (LSF + XGB) 0.319 (LSF + ANN) | – | – |
2021 | Debellemanière et al. (PEARL-DGS) [39] | Data | 6120 | SVR, GBT, RM | – | PE within 0.50 D = 87.4 MAE = 0.443 (short); 0.240 (long) | – | – |
2020 | Carmona et al. (Karmona) [23] | Data | 260 | SVM-RBF MARS-SOP | – | PE within 0.50 D = 90.4 MAE = 0.240 | – | – |
2019 | Connell et al. (Kane) [83] | Data | 846 | RM | – | PE within 0.50 D = 77.9 MAE = 0.441 (short); 0.322 (medium); 0.326 (long) | – | – |
2019 | Wan et al. (Hill-RBF 2.0) [19] | Data | 127 | RM | – | PE within 0.50 D = 86.6 | – | – |
2019 | Sramka et al. [17] | Data | 2194 | SVM-RM and MLNN-EM | – | PE within 0.50 D = 82.3–82.7 | – | – |
2016 | Koprowski et al. [48] | Data | 173 | CNN | – | ECPP 0.16 ± 0.14 Dp | – | – |
Intraoperative tools | ||||||||
2020 | Lanza et al. [28] | Surgery factors | 1229 | DA | – | 68.4 | – | – |
2020 | Lecuyer et al. [26] | Cataract surgery videos | 50 | CNN (VGG19, InceptionV3, ResNet50) | – | 70.0–84.4 | – | – |
2019 | Yu et al. [24] | Cataract surgery videos | 100 | SVM, RNN, CNN (SqueezeNet), CNN-RNN | 0.71–0.77 | 91.5–95.9 | 0.5–97.4 | 87.7–99.9 |
Postoperative assessment of posterior capsule opacification | ||||||||
2018 | Jiang et al. [29] | SLP | 6090 | TempSeq-Net | 0.97 | 92.2 | 81.0 | 91.4 |
2012 | Mohammadi et al. [30] | SLP | 352 | ANN | 0.71 | – | 25 | 97 |
Pediatric cataract assessment | ||||||||
2020 | Lin et al. [51] | SLP | 1738 | RF ADA | 0.86 (RF) 0.85 (ADA) | 86.0 (RF) 85.0 (ADA) | 80.0 (RF) 77.0 (ADA) | 91.0 (RF) 90.0 (ADA) |
2019 | Lin et al. [52] | SLP | 350 | CNN | – | D = 87.4 G = 70.8–90.6 | D = 89.7 G = 84.2–91.3 | D = 86.4 G = 44.4–88.9 |
2017 | Liu et al. [53] | SLP | 886 | CNN | D = 0.97 G = 0.96–0.99 | D = 97.1 G = 89.0–92.7 | D = 96.8 G = 90.8–93.9 | D = 97.3 G = 82.7–91.1 |
2017 | Long et al. [54] | SLP | 1349 | CNN | D = 0.92–1.00 G = 0.96–1.00 | D = 92.5–98.9 G = 84.6–100 | D = 98.8–100 G = 85.7–100 | D = 71.4–99.0 G = 90.5–100 |
2020 | Long et al. [55] | HR | 594 | RF | D = 0.94 | D = 89.4–98.1 | D = 84.9–98.9 | D = 86.9–99.0 |