Table 1.
Summary of reviewed articles.
| Authors, Year | AI Method | Study Population | Outcome Measure | Imaging Modality | Number of Images | Number of Patients | Demographics Reported | Algorithm Results |
|---|---|---|---|---|---|---|---|---|
| Microbial Keratitis | ||||||||
| Li et al., 2021 [15] | CNN | MK, controls | MK detection | SLP, External Photography | 13,557 | 7,988 | Complete | AUC 0.998, Sens 98%, Spec 98%* |
| Tiwari et al., 2022 [16] | CNN | MK, controls | MK detection | External Photography | 1,445 | 1,445 | Partial | Internal set (India): AUC 0.973, Sens 94%, Spec 84% External set (US): AUC 0.947, Sens 78%, Spec 91% |
| Lv et al., 2020 [26] | CNN | FK, controls | FK detection | Confocal | 2,623 | NR | NR | AUC 0.988, Acc 96%, Sens 92%, Spec 98% |
| Xu et al., 2021 [28] | CNN | BK, FK | FK detection | Confocal | 1,089 | 35 | NR | AUROC 0.983, Acc 97%, Sens 94%, Spec 98% |
| Liu et al., 2020 [27] | CNN | FK, controls | FK detection | Confocal | 1,213 | NR | NR | Acc 99.9% |
| Xu et al., 2021 [17] | CNN | MK, controls | MK detection Differentiate MK subtypes |
SLP | 2,284 | 867 | NR | Acc 80%, 53%, 83%, and 93% for overall, BK, FK, and HSK |
| Wang et al., 2021 [18] | CNN | MK, controls | MK detection Differentiate MK subtypes |
SLP | 5,673 | 3,320 | Complete | AUC 0.959 |
| Koyama et al., 2021 [20] | CNN | MK | Differentiate MK subtypes | SLP | 4,306 | 362 | Complete | Acc 88% |
| Hung et al., 2021 [25] | CNN | BK, FK | Differentiate MK subtypes | SLP | 1,330 | 580 | Complete | AUC 0.85, Sens range 26 – 66%, Spec range 80 – 96%, BK Acc range 80 – 96%, FK Acc range 26 – 66% |
| Redd et al., 2022 [21] | CNN | BK, FK | Differentiate MK subtypes | External Photography | 980 | 980 | Complete | AUC 0.83 |
| Ghosh et al., 2021 [22] | CNN | BK, FK | Differentiate MK subtypes | SLP | 2,167 | 194 | NR | Sens 77%, F1 score 83% |
| Kuo et al., 2020 [24] | CNN | MK | Differentiate FK from other MK | SLP | 288 | 288 | NR | AUC 0.65 |
| Kuo et al., 2021 [23] | CNN | MK | Differentiate BK from other MK | External Photography | 1,512 | 1,512 | NR | Sens 74%, Spec 64% |
| Loo et al., 2021 [30] | CNN | MK | MK feature quantification | SLP | 266 | 133 | NR | DSC range 0.62 – 0.95 |
| Loo et al., 2021 [31] | CNN | MK | Visual Acuity | SLP | 152 | 76 | Complete | r = 0.84 |
| Keratoconus | ||||||||
| Kuo et al., 2020 [37] | CNN | KCN, ffKCN, controls | KCN detection | Corneal Topography | 354 | 206 | NR | AUROC 0.995, Acc 96%, Sens 94%, Spec 97% |
| Cao et al., 2020 [39] | ML | ffKCN, controls | KCN detection | Corneal Tomography | NR | 88 | Partial | AUC 0.96, Acc 87%, Sens 88%, Spec 85% |
| Castro-Luna et al., 2021 [38] | ML | ffKCN, controls | KCN detection | Corneal Tomography, Tonometry | NR | 81 | Partial | Acc 89%, Sens 86%, Spec 93% |
| Al-Timemy et al., 2021. [32] | Hybrid DL - CNN | KCN, controls | KCN detection | Corneal Tomography | 4,844 | 365 | Partial | Normal vs KCN: AUC 0.99, Acc 92% Normal vs KCN vs suspected KCN: AUC 0.81, Acc 69% |
| Zéboulon et al., 2020 [35] | Hybrid ML - CNN | KCN, controls | KCN detection | Corneal Topography | 3,000 | 3,000 | Complete | Overall Acc: 99.3% Detection of KCN: Sens 100%, Spec 100% |
| Aatila et al., 2021 [40] | ML | KCN, ffKCN, controls | KCN detection, staging | AS-OCT | 12,242 | NR | NR | Diagnostic Acc 98% Staging Acc 95% |
| Ghaderi et al., 2021 [42] | CNN | KCN, controls | KCN detection, staging | Corneal Tomography | NR | 450 eyes | Partial | Detection: Acc 98%, Sens 99%, Spec 96% Staging: Acc 98%, Sens 99%, Spec 99% |
| Feng et al., 2021 [34] | CNN | KCN, ffKCN, controls | KCN detection, staging | Corneal Tomography | 854 | 854 | Complete | Acc 95% |
| Abdelmotaal et al., 2020 [33] | CNN | KCN, ffKCN, controls | KCN detection, staging | Corneal Tomography | 3,218 | 1,619 | Partial | Normal: Acc 99%, Sens 99%, Spec 99% Subclinical KCN: Acc 99%, Sens 99%, Spec 99% KCN: Acc 100%, Sens 100%, Spec 100% |
| Shi et al., 2020 [36] | ML | KCN, ffKCN, controls | KCN detection, staging | Corneal Tomography, OCT | NR | 121 eyes | NR | Normal vs ffKCN: AUC 0.93, Sens 99%, Spec 95% Normal vs KCN: AUC 1.0, Sens 100%, Spec 100% |
| Kamiya et al., 2021 [43] | CNN | KCN, controls | KCN detection, staging | Corneal Topography | 519 | 519 eyes | Partial | Detection: Acc 97%, Sens 99%, Spec 94% Classification: AUC 0.888 – 0.997, Acc 79% |
| Chen et al., 2021 [44] | CNN | KCN, controls | KCN detection, staging | Corneal Tomography | 1,926 | 1,836 | NR | AUC range 0.82 – 0.91, Acc rage 85 – 99%, Sens range 69 – 99%, Spec range 80 – 94% |
| Malyugin et al., 2021 [41] | ML | KCN, controls | KCN detection, staging | Corneal Tomography | NR | 852 eyes | NR | Overall AUC: 0.97 AUC by KCN stage: Normal 0.98, preclinical KCN 0.95, Stage 1 0.96, Stage 2 0.97, Stage 3 0.97, Stage 4 1.0 |
| Kamiya et al., 2021 [45] | DL | KCN | KCN progression | AS-OCT | NR | 218 | NR | Acc 79% |
| Kato et al., 2021 [46] | CNN | KCN | KCN progression | Corneal Tomography | 274 | 158 | Complete | AUC 0.81, Sens 78%, Spec 70% |
| Yousefi et al., 2020 [47] | ML | KCN, controls | KCN progression | AS-OCT | 12,242 | 3,162 | Complete | Normalized likelihood of need for keratoplasty for clusters 1–5: 2%, 1%, 33%, 33%, 31% |
| Dry Eye Syndrome | ||||||||
| Chase et al., 2021 [48] | CNN | DES, control | DES detection | AS-OCT | 27,180 | 91 | Partial | Acc 85%, Sens 86%, Spec 82% |
| Su et al., 2020 [50] | CNN | SPK, control | SPK detection, grading | SLP | 10,468** | 101 | NR | SPK detection: Acc 97% Grading threshold: Sens 97%, Spec 79% |
| Qu et al., 2021 [51] | CNN | SPK, control | SPK detection, grading | SLP | 763 | NR | NR | AUROC 0.940, Acc 77% |
| Stegmann et al., 2020 [52] | CNN | control | Tear meniscus segmentation | Custom OCT | 6,658 | 10 | Complete | Sens 96%, Spec 99.9% |
| Deng et al., 2021 [53] | CNN | NR | Tear meniscus segmentation, quantification | Corneal Topography | 485 | 217 | Complete | Segmentation: Sens 90%, F1 score 90% Quantification: r = 0.97 (p < 0.001) |
| Wei et al., 2021 [54] | CNN | NR | Corneal nerve fiber segmentation | Confocal | 691 | 104 | NR | AUC 0.96, Sens 96%, Spec 75% |
| Maruoka et al., 2020 [49] | CNN | MGD, control | MGD detection | Confocal | 221 | 221 | Complete | Single model: AUC 0.966, Sens 94%, Spec 82% Ensemble model: AUC 0.981, Sens 92%, Spec 99% |
| Yeh et al., 2021 [56] | ML | MGD, control | MGD quantification, grading | Corneal Topography | 706 | 576 | Complete | Acc 81% |
| Wang et al., 2021 [57] | DL | NR | MGD segmentation | Corneal Topography | 1,443 | 475 | Complete | Segmenting MG (upper, lower): Sens 54%, Sens 74% Identifying ghost glands: Sens 84%, Spec 72% |
| Setu et al., 2021 [58] | CNN | NR | MGD segmentation | Corneal Topography | 728 | NR | NR | AUROC 0.96, Sens 81%, F1 score 84% |
| Khan et al., 2021 [60] | CGAN | MGD | MGD segmentation | Corneal Topography | 112 | 112 | Partial | MG segmentation: F1 score 83% MG dropout grading: r = 0.962, p < 0.001 |
| Prabhu et al., 2020 [59] | CNN | MGD, control | MGD segmentation, quantification | Corneal Topography, Prototype handheld camera | 800 | NR | NR | p - values > 0.005 for all metrics between CNN and manual |
| Fuchs Endothelial Dystrophy | ||||||||
| Eleiwa et al., 2020 [61] | CNN | FED, control | FED detection | AS-OCT | 18,720 | 81 | Complete | Early-stage FED: AUC 0.997, Sens 91%, Spec 97% Late-stage FED: AUC 0.974, Sens up to 100%, Spec 92% Healthy vs. all FED: AUC 0.998, Sens 99%, Spec 98% |
| Zéboulon et al., 2021 [62] | CNN | Edema, control | Edema detection | AS-OCT | 806 | 110 | Partial | AUROC 0.994, Acc 99%, Sens 96%, Spec 100% |
| Shilpashree et al., 2021 [63] | CNN | FED, control | FED segmentation, quantification | Specular Microscopy | 2,246 | 130 | Complete | AUROC 0.967, Acc of 88%, F1 score 82% |
| Vigueras-Guillén et al., 2020 [64] | CNN | FED, control | FED segmentation, quantification | Specular Microscopy | 783 | 141 | Partial | CNN: able to estimate parameters in 98% of images; percentage error 2.5% - 5.7% Specular Microscopy: able to estimate parameters in in 31 – 72% of images; percentage error 7.5% - 18.3% |
| Multiple Cornea Conditions | ||||||||
| Elsawy et al., 2021 [65] | DL | FED, KCN, controls | Multi-disease diagnosis | AS-OCT | 16,721 | 258 | NR | FED: AUC 1.0, Sens 94%, Spec 100% KCN: AUC 0.95, Sens 94%, Spec 94% Healthy: AUC 0.93, Sens 91%, Spec 95% |
| Elsawy et al., 2021 [66] | CNN | FED, KCN, DES, controls | Multi-disease diagnosis | AS-OCT | 158,220 | 478 | Complete | FED: AUC 1.0, F1 score 100% KCN: AUC 0.99, F1 score 98% DES: AUC 0.99, F1 score 90% Healthy: AUC 0.98, F1 score 93% |
| Gu et al., 2020 [67] | DL | MK, noninfectious keratitis, corneal dystrophy, surface neoplasm, cataract, controls | Multi-disease diagnosis | SLP | 5,835 | ≥ 510 *** | NR | Retrospective data set: AUC range 0.903 – 0.951 Prospective data set: AUC > 0.91 |
| Li et al., 2020 [68] | DL | Keratitis; pterygium; conjunctival hyperemia, hemorrhage, edema; cataract | Multi-disease diagnosis | SLP | 1,772 | NR | Partial | Acc range 79 – 99%, Sens range 53 – 99%, Spec range 85 – 99% |
Acc, accuracy; AK, acanthamoeba keratitis; ANN, artificial neural network; AS-OCT, Anterior segment optical coherence tomography; AUC, area under the curve; AUROC, area under the receiver operating characteristic curve; BK, bacterial keratitis; CGAN, conditional generative adversarial network; CNN, convolutional neural network; DES, dry eye syndrome; DL, deep learning; DSC, Dice similarity coefficient; ffKCN, forme fruste keratoconus; FK, fungal keratitis; HSV, herpes simplex virus keratitis; KCN, keratoconus; MGD meibomian glad dysfunction; MK, microbial keratitis; ML, machine learning; NR, not reported; Sens, sensitivity; SLP, slit lamp photography; Spec, specificity; SPK, superficial punctate keratitis; r, Pearson correlation coefficient, UHR-OCT, Ultra-high-resolution optical coherence tomography; VK, Viral keratitis.
*
Multiple algorithms testing different outcome measures and in different datasets.
**
Number of original images, study augmented images to increase number for final data set.
***
Number of patients partially reported.