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Table 1 Summary of application of AI in the screening or diagnosis of cataract

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
  1. ADA= adaptive boost modelling; AI = artificial intelligence; ANN = artificial neural network; AUC = area under the curve; BPNN = back propagation neural network; CNN = convolutional recursive neural network; CRNN = convolutional recurrent neural network; D = diagnosis; DA = discriminant analysis; DN = deconvolutional network; Dp = diopters; ECPP = error of corneal power prediction error; EM = expectation–maximization; EN = ensemble model; FCNN = fully connected neural network; FP = fundus photography; G = grading; GBT = gradient boosted trees; LSF = line spread function; MAE = mean absolute error; MARS = multivariate adaptive regression spline; MCDA = multi-class discriminant analysis; MLNN = multilayer neural network; PE = percentage of eyes; RBF = radial basis function; RF = random forrest; RM = regression model; RNN = recurrent neural network; SLP = slit-lamp photography; SOP = second order polynomials; SRK = formula created by John Retzlaff, Kraff and Sanders; SVM = support vector machine; SVR = support vector regression; XGB = extreme gradient boost