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  • Systematic Review
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27 February 2023

Deep Learning Algorithms for Screening and Diagnosis of Systemic Diseases Based on Ophthalmic Manifestations: A Systematic Review

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1
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
2
Hainan Eye Hospital and Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Haikou 570311, China
3
Center for Precision Medicine and Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510060, China
*
Author to whom correspondence should be addressed.
Diagnostics2023, 13(5), 900;https://doi.org/10.3390/diagnostics13050900
This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics
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Abstract

Deep learning (DL) is the new high-profile technology in medical artificial intelligence (AI) for building screening and diagnosing algorithms for various diseases. The eye provides a window for observing neurovascular pathophysiological changes. Previous studies have proposed that ocular manifestations indicate systemic conditions, revealing a new route in disease screening and management. There have been multiple DL models developed for identifying systemic diseases based on ocular data. However, the methods and results varied immensely across studies. This systematic review aims to summarize the existing studies and provide an overview of the present and future aspects of DL-based algorithms for screening systemic diseases based on ophthalmic examinations. We performed a thorough search in PubMed®, Embase, and Web of Science for English-language articles published until August 2022. Among the 2873 articles collected, 62 were included for analysis and quality assessment. The selected studies mainly utilized eye appearance, retinal data, and eye movements as model input and covered a wide range of systemic diseases such as cardiovascular diseases, neurodegenerative diseases, and systemic health features. Despite the decent performance reported, most models lack disease specificity and public generalizability for real-world application. This review concludes the pros and cons and discusses the prospect of implementing AI based on ocular data in real-world clinical scenarios.

1. Introduction

Deep learning (DL) is a state-of-the-art subset of machine learning that allows computers to automatically learn the features from raw data. It comprises multiple processing layers that transform the data into stratified abstract levels to achieve specific tasks [1]. In recent years, DL has significantly advanced in various fields, such as visual recognition and natural language processing. With its ability to unveil intrinsic characteristics from high-dimensional data, DL has also been widely applied in medical AI to develop disease screening and diagnosis algorithms.
Ophthalmology is a pioneer in the field of medical artificial intelligence (AI). The eye is informative and accessible due to its inherent anatomical features. As an organ located on the body surface, most examinations can be done non-intrusively. Furthermore, the complex anatomy comprising various cells and tissues allows the generation of multimodal data through diverse exam methods. These features provide data scientists and ophthalmologists with easily collectible data of good quality and quantity, making the eye a prime candidate for medical AI.
With the FDA’s approval of IDx-DR [2] as the first autonomous AI system for marketing, the development of ophthalmic AI preemptively entered a new era. Various DL algorithms have been developed for ophthalmic purposes [3], such as for diagnosing diabetic retinopathy [4], glaucoma [5], and age-related macular degeneration [6] based on color fundus photographs. Other forms of ocular examinations were also studied, such as color-coded corneal maps for detecting keratoconus [7] and corneal confocal microscopy for diabetic corneal neuropathy [8]. Beyond ocular diseases, ophthalmic manifestations can also indicate underlying systemic conditions. The innate anatomical structure of the eye provides a window for observing neural and vascular systems in vivo, making it possible to intuitively record physiological and pathological changes in the body. As a result, systemic diseases that induce vascular and neural impairment could present as ophthalmic complications, such as diabetic retinopathy and anemic retinopathy. Recent studies have proven that non-specific pathologies, namely vessel narrowing and retinal thinning, were predictive of systemic disease status and long-term prognostics [9,10]. Aside from the static presentations, there was also evidence for diagnosing neurodegenerative diseases based on specific eye movements [11]. With the help of the ever-developing DL technology, these characteristics can now be identified and utilized for screening and diagnosing purposes.
DL algorithms based on ocular manifestations have opened up new opportunities for managing systemic diseases in rapid, non-invasive manners. Most common ailments have been studied for their correlation with the eye, resulting in a growing number of diverse DL algorithms. However, these AI models are still immature in various aspects to be applied in clinical scenarios. This review aims to conclude and summarize the current DL-based AI models for detecting systemic diseases based on ocular manifestations. We intend to provide a well-rounded overview of the previous works as a reference for future studies.

2. Materials and Methods

This systemic review is conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 guidelines. We searched PubMed®, Embase, and Web of Science for English-language articles published until August 2022. The keywords were designed based on three elements: (1) ocular characteristics, (2) systemic diseases, and (3) artificial intelligence methods. The detailed search strategies are listed in Supplementary Table S1.

2.1. Selection Criteria

Studies were deemed eligible if they (1) were retrospective, cross-sectional, or prospective studies; (2) applied deep learning algorithms to analyze ocular characteristics for identifying systemic diseases; (3) reported the performance of the algorithms with metrics such as accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for binary outcomes or mean absolute error (MAE) and R square for regression models; (4) included validation experiment. Reviews, case reports, letters, comments, editorials, meta-analyses, and animal studies were excluded from this systematic review. Full-text revision and reference screening were performed on all qualified articles by two authors (W.C.I. and W.Z.).

2.2. Data Extraction and Quality Assessment

Pertinent data were extracted according to a pre-designed table by W.Z. and W.C.I. Information including the author, publication year, ocular data, DL model, training dataset, testing/validation dataset, external validation, systemic health features/diseases, outcome, and model performance were collected from the selected studies. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) [12] was applied to evaluate the quality and the risk of bias of the included studies.

3. Results

3.1. Study Selection

The flowchart of the selection process is demonstrated in Figure 1. In the searching stage, 2873 articles were identified from the three target databases, with 817 from PubMed, 822 from Embase, and 1234 from Web of Science. After removing 812 duplicates, 1976 reports were excluded by screening the title and abstract. In the 85 studies entering the full-text screening stage, one was unavailable and 26 others were eliminated for various reasons. With the addition of the four studies extended from references, 62 studies were included in this systematic review. The included articles are summarized in Table 1, Table 2 and Table 3.
Figure 1. Flowchart of the study selection process.
The results of the risk of bias evaluation using QUADAS-2 is demonstrated in Figure 2. Most of the studies included had low risk in all categories, while larger proportions of high risk were found in patient selection and index test. The detailed results are shown in Supplementary Table S2.
Figure 2. Stacked bar chart of the QUADAS-2 analysis: (a) Risk of Bias; (b) Applicability Concerns.
Table 1. Summary of deep learning algorithms identifying systemic diseases from anterior segment of the eye.
Table 1. Summary of deep learning algorithms identifying systemic diseases from anterior segment of the eye.
Author, YearOcular DataDL ModelTraining DatasetTesting/Validation DatasetExternal ValidationSystemic Health Features/DiseasesOutcomePerformance 1
Babenko et al., 2022 [13]External eye imagesDLSEyePACS (CA): 126,066 patients, 290,642 images19,766 patients, 41,928 imagesSet A: EyePACS (non-CA): 27,415 patients, 53,861 images;
Set B: EyePACS (non-CA): 5058 patients, 9853 images;
Set C: 10,402 patients, 19,763 images;
Set D: 6266 patients, 12,751 images		HbA1c
Total cholesterol
Triglycerides		Binary
Binary
Binary		AUC: 73.4%
AUC: 62.3%
AUC: 67.1%
Li et al., 2022 [14]Conjunctival imagesHMT-Net68 patients, 405 images; 62 HC, 206 images5-fold cross-validationN/AT2DMBinarySensitivity: 78.70%
Specificity: 69.08%
Accuracy: 75.15%
AUC: 0.82
Preston et al., 2022 [15]CCM imagesResNet-5065 HC images, 63 T1DM, 89 T2DM, 28 prediabetesTest: 15 HC, 11 T1DM, 10 T2DM, 4 prediabetes;
Validation: 10 HC, 14 T1DM, 42 T2DM, 18 prediabetes		N/ADPNTernary1. Healthy: F1-score: 0.91
2. No DPN: F1-score: 0.88
3. DPN: F1-score: 0.91
Scarpa et al., 2020 [16]CCM imagesCNN40 patients, 240 images; 40 HC, 240 images10 patients, 60 images; 10 HC, 60 images; 5-fold cross-validationN/ADPNBinarySensitivity: 98%
Specificity: 94%
Accuracy: 96%
Althnian et al., 2021 [17]Scleral imagesVGG-1624 images of patients, 44 images of HCN/MN/ANeonatal jaundiceBinaryAccuracy: 79.03%
F1-score: 70.73%
AUC: 69.67%
Lv et al., 2021 [18]Scleral imagesU-Net,
Resnet-18, MIL model		576 participants, 4608 images145 participants, 1160 images; 5-fold cross-validation N/APCOSBinaryAUC: 0.979
Accuracy: 0.929
F1-score: 0.934
1 Only the best performance are presented when there was more than one model. Metadata-based models and hybrid models are not presented in this table. EyePACS, eye picture archive communication system; HbA1c, glycated hemoglobin; AUC, area under curve; HC, healthy control; N/A, not applicable; T2DM, type 2 diabetes mellitus; CCM, corneal confocal microscopy; T1DM, type 1 diabetes mellitus; DPN, diabetic peripheral neuropathy; CNN, convolutional neural network; N/M, not mentioned; PCOS, polycystic ovary syndrome.

3.2. Algorithms Based on the Anterior Segment of the Eye

Most abnormalities of the external eye can be observed intuitively. These manifestations could provide easy access to several systemic diseases and were proven accessible with deep learning models. Babenko et al. [13] developed the DL algorithms based on external eye images taken with fundus cameras to distinguish hemoglobin A1c (HbA1c) ≥ 9% and lipid levels. The former achieved the highest AUC of 0.67 to 0.73, though the latter lacked significance. The models mainly focused on the nasal and temporal scleral areas, indicating that the clue for diagnosis may be conjunctival vessels. The work of Li et al. [14] proved that diabetes could be identified from conjunctival images with an accuracy of 75.15%.
Jaundice is also a distinct symptom often observed from the external eye. Using slit lamp photos as input, Xiao et al. [19] achieved AUCs over 0.90 in diagnosing liver cirrhosis and liver cancer. Another study focusing on neonatal jaundice [17] also attained an accuracy of 79.03% based on smartphone-captured images. On the other hand, the model built by Lv et al. [18] detected polycystic ovary syndrome (PCOS) based on sectioned scleral images. The model attained an accuracy and AUC over 0.90 by focusing on thick and foggy blood vessels in the sclera, which could be caused by sex steroid changes in the patients.
The cornea is densely innervated by the ophthalmic branch of the trigeminal nerves. Corneal confocal microscopy (CCM) allows for non-invasive quantification of the small corneal nerve fibers, providing a rapid evaluation method for various diseases. With CCM images as input, two DL algorithms were developed for the early diagnosis of diabetic neuropathy, one achieving an accuracy of 96% [16] and the other having an F1-score of 0.91 [15]. The Grad-CAM highlighted the absence of nerve fibers in the CCM images, showing that the models are explainable despite the relatively small datasets.

3.3. Algorithms Based on the Posterior Segment of the Eye

The retina provides a window for directly observing neurovascular structures in vivo based on its natural anatomical features. The development of retinal imaging technologies such as color fundus photographs and ultra-widefield fundus (UWF) imaging enabled intuitive en-face records of retinal pathologies. Additionally, optical coherence tomography (OCT) with the interferometry technique allows cross-sectional imaging of the multiple layers of the retina. The multimodal retinal data generated from various imaging methods creates an ideal platform for building DL algorithms for diagnosing systemic diseases.
Table 2. Summary of deep learning algorithms identifying systemic diseases from posterior segment of the eye.
Table 2. Summary of deep learning algorithms identifying systemic diseases from posterior segment of the eye.
Author, YearOcular DataDL ModelTraining DatasetTesting/Validation DatasetExternal ValidationSystemic Health Features/DiseasesOutcomePerformance 1
Betzler et al., 2021 [20]Retinal imagesVGG-16SEED: 7969 participants, 137,511 images1987 participants, 34,659 imagesN/AGenderBinaryAUC: 0.94
Corbin et al., 2022 [21]Fundus images EfficientNet14,711 participants for all datasets; 18,000 images for trainingValidation: 3860 images;
Test: 3877 images		N/AAge
SBP
DBP
BMI
Sex (image)
Sex (individual)
APOE4 (image)
APOE4 (individual)		Regression
Regression
Regression
Regression
Binary
Binary
Binary
Binary		R2: 0.778, MAE: 3.24
R2: 0.229, MAE: 10.94
R2: 0.227, MAE: 6.80
R2: 0.032, MAE: 3.99
AUC: 0.84
AUC: 0.85
AUC: 0.47
AUC: 0.50
Gerrits et al., 2020 [22]Retinal imagesMobileNet-V2Qatar Biobank: 1800 participants, 7200 imagesValidation: 600 participants, 2400 images
Test: 600 participants, 2400 images		N/AAge
Sex
SBP
DBP
HbA1c
BMI
RFM
Glucose
Insulin
SHBG
Estradiol
Testosterone
Tch
HDL
LDL
Tg
Smoking status		Regression
Binary
Regression
Regression
Regression
Regression
Regression
Regression
Regression
Regression
Regression
Regression
Regression
Regression
Regression
Regression
Binary		MAE: 2.78, R2: 0.89
AUC: 0.97
MAE: 8.96, R2: 0.40
MAE: 6.84, R2: 0.24
MAE: 0.61, R2: 0.34
MAE: 4.31, R2: 0.13
MAE: 5.68, R2: 0.43
MAE: 1.06, R2: 0.12
MAE: 7.15, R2: −0.04
MAE: 21.09, R2: 0.06
MAE: 154.18, R2: −0.03
MAE: 3.76, R2: 0.54
MAE: 0.75, R2: 0.03
MAE: 0.31, R2: 0.05
MAE: 0.72, R2: −0.03
MAE: 0.49, R2: 0.03
AUC: 0.78
Hu et al., 2022 [23]Retinal imagesDLUK Biobank: 11,052 participants, 19,200 images35,834 participantsN/ARetinal ageRegressionMAE: 3.55
Khan et al., 2022 [24]Retinal imagesDenseNet-201760 participants for all datasets; 1021 images for training256 images for testingN/AGender
ARB
Smoking status
ACEi
LDL
Hypertension
HDL
Cardiac disease
HbA1c
Age
Aspirin
Ethnicity		Binary
Binary
Binary
Binary
Binary
Binary
Binary
Binary
Binary
Binary
Binary
Binary		AUC: 0.852
AUC: 0.783
AUC: 0.732
AUC: 0.815
AUC: 0.766
AUC: 0.687
AUC: 0.756
AUC: 0.7
AUC: 0.708
AUC: 0.902
AUC: 0.696
AUC: 0.926
Kim et al., 2020 [25]Retinal imagesResNet-152155,449 participants for all datasets; 216,866 HC images for trainingValidation: 2436 HC images;
Test: 24,366 HC images, 40,659 hypertension, 14,189 DM, 113,510 smoking		N/AAge
Sex		Regression
Binary		MAE: 3.06, R2: 0.92
AUC: 0.969
Korot et al., 2021 [26]Retinal imagesCFDLUK Biobank: 84,743 patients, 173,819 images728 patients, 1287 images252 patients, 252 imagesSexBinarySensitivity: 83.9%
Specificity: 72.2%
Accuracy: 78.6%
Mendoza et al., 2021 [27]OCTDL1772 patients, 52,552 circle B-scans; 730 patients, 111,456 radial B-scans; 85% for training5% for validation, 10% for testingN/AAge
Axial length
Sex
Race
Diabetes
Hypertension
CVD		Regression
Regression
Binary
Binary
Binary
Binary
Binary		MAE: 5.4, R2: 0.73
MAE: 0.7, R2: 0.3
AUC: 0.72
AUC: 0.96
AUC: 0.65
AUC: 0.71
AUC: 0.56
Munk et al., 2021 [28]Fundus images,
OCT 		ResNet-15216,196 participants, 135,667 fundus images; 5578 participants, 85,536 OCT scans; 80% for training10% for validation, 10% for testingN/AAge
Sex		Binary
Regression		1. Fundus images:
MAE: 6.328, AUC: 0.80
2. OCT cross sections:
MAE: 5.625, AUC: 0.84
3. OCT volumes:
MAE: 4.541, AUC: 0.90
Nusinovici et al., 2022 [29]Retinal imagesRetiAGE36,432 participants, 116,312 imagesValidation: 4048 participants, 12,924 images;
Test: 10,171 participants, 32,318 images		UK Biobank: 56,301 participantsAgeBinaryAUC: 0.756
Poplin et al., 2018 [30]Retinal imagesInception-v3UK Biobank: 48,101 patients, 96,082 images;
EyePACS: 236,234 patients, 1,682,938 images		UK Biobank: 12,026 patients, 24,008 images;
EyePACS-2K: 999 patients, 1958 images		N/AAge
Gender
Smoking status
HbA1c
BMI
SBP
DBP		Regression
Binary
Binary
Regression
Regression
Regression
Regression		MAE: 3.26, R2: 0.74
AUC: 0.97
AUC: 0.71
MAE: 1.39, R2: 0.09
MAE: 3.29, R2: 0.13
MAE: 11.35, R2: 0.36
MAE: 6.42, R2: 0.32
Rim et al., 2020 [31]Retinal imagesVGG-1627,516 participants, 86,994 images6879 participants, 21,698 imagesSet 1: 4343 participants, 9324 images;
Set 2: BES: 1060 participants, 4234 images;
Set 3: SEED: 7726 participants, 63,275 images;
Set 4: UK Biobank: 25,366 participants, 50,732 images		Sex
Age
BMM
Height
Bodyweight
PBF
BMI
Creatinine
DBP
SBP
Hematocrit
Hemoglobin
RBC count		Binary
Regression
Regression
Regression
Regression
Regression
Regression
Regression
Regression
Regression
Regression
Regression
Regression		AUC: 0.91, Accuracy: 0.85
MAE: 3.78, R2: 0.36
MAE: N/A, R2: N/A
MAE: 5.48, R2: 0.23
MAE: 8.28, R2: 0.17
MAE: N/A, R2: N/A
MAE: 2.90, R2: 0.06
MAE: 0.11, R2: 0.12
MAE: 8.09, R2: 0.2
3MAE: 13.20, R2: 0.19
MAE: N/A, R2: N/A
MAE: N/A, R2: N/A
MAE: N/A, R2: N/A
Tham et al., 2019 [32]Fundus imagesResNet,
DenseNet		13,937 participants, 25,637 images3485 participants, 6830 imagesN/AHbA1cRegressionMAE: 0.87%
Vaghefi et al., 2019 [33]Retinal imagesCNN81,711 participants, 165,104 images; 60% for training20% for validation, 20% for testingN/ASmoking BinaryAccuracy: 88.88%
Specificity: 93.87%
Sensitivity: 62.62%
AUC: 0.86
Yang et al., 2020 [34]Retinal imagesVGG-16SEED: 9748 participants, 110,099 images; 80% for training20% for testingN/ARaceTernaryAccuracy: 95.1%
Zhang et al., 2020 [35]Retinal imagesInception-v3625 participants, 1222 images; 80% for training10% for validation, 10% for testingN/AHyperglycemia
Hypertension
Dyslipidemia
Age
Gender
Drinking status
Salty taste
Smoking status
BMI
WHR
HCT
MCHC
T-BIL
D-BIL		Binary
Binary
Binary
Binary
Binary
Binary
Binary
Binary
Binary
Binary
Binary
Binary
Binary
Binary		AUC: 0.880
AUC: 0.766
AUC: 0.703
AUC: 0.850
AUC: 0.704
AUC: 0.948
AUC: 0.809
AUC: 0.794
AUC: 0.731
AUC: 0.704
AUC: 0.759
AUC: 0.686
AUC: 0.764
AUC: 0.703
Al-Absi et al., 2022 [36]Retinal imagesResNet-34Qatar Biobank: 233 patients, 874 images; 250 HC, 931 images5-fold cross-validationN/ACVDBinaryAccuracy: 75.6%
Mellor et al., 2019 [37]Fundus imagesResNet4782 participants5-fold cross-validationN/ACVDBinaryAUC: 0.77
Chang et al., 2019 [38]Retinal imagesNASNet-Large33,025 participants, 96,968 images6597 participants, 13,373 imagesN/AFADRegressionMAE: 2.74
Ng et al., 2022 [39]Retinal imagesDLPPC58 patients, 116 images; 80% for training20% for testingN/ASpO2
LICUS
PC
OT
CBT
ABT		Binary
Binary
Binary
Binary
Binary
Binary		AUC: 0.712
AUC: 0.731
AUC: 0.722
AUC: 0.581
AUC: 0.800
AUC: 0.767
Mueller et al., 2022 [40]Fundus imagesMIL97 patients, 34 HC; 83,126 images for training9237 images for validationN/APADBinaryAccuracy: 0.837
F1-score: 0.883
AUC: 0.89
Chang et al., 2020 [41]Retinal imagesDL-FAS5296 participants, 12,362 imagesValidation: 647 participants, 1526 images;
Test: 654 participants, 1520 images		N/AAtherosclerosisBinaryAUC: 0.713
Accuracy: 0.583
Sensitivity: 0.891
Specificity: 0.404
Barriada et al., 2022 [42]Retinal imagesVGG-1676 patients, 152 images5-fold cross-validationN/ACACSBinaryAccuracy: 0.72
F1-score: 0.62
Rim et al., 2021 [43]Retinal imagesRetiCAC15,911 participants, 36,034 images3965 participants, 8930 imagesSet 1: 8707 participants, 18,920 images;
Set 2: 527 participants, 1054 images		CACSBinaryAUC: 0.742
Son et al., 2020 [44]Retinal imagesInception-v320,130 participants, 44,184 images; 80% for training20% for training; 5-fold cross-validationN/ACACSBinaryAUC: 83.2%
Dai et al., 2020 [45]Retinal imagesCNN735 patients, 684 HC; 60% for training20% for validation, 20% for testing; 5-fold cross-validationN/AHypertensionBinaryAccuracy: 60.94%
Specificity: 63.80%
AUC: 0.6506
Lo et al., 2021 [46]Fundus imagesAML-Net200 patient images, 200 HC; 70% for training30% for validationN/AMild hypertensionBinaryAccuracy: 93.75%
Islam et al., 2021 [47]Retinal imagesDiaNetEyePACS: over 80,000 images; Qatar Biobank: 246 patients, 246 controls, total 1852 images5-fold cross-validationN/ADiabetesBinaryAccuracy: 84.47%
Specificity: 83.06%
AUC: 84.46%
Wang et al., 2022 [48]Retinal imagesCNN10,766 imagesN/MN/AShort-term readmission risk in diabetesBinarySpecificity: 0.79
Accuracy: 0.837
Zhang et al., 2018 [49]Fundus imagesResNet79 patients, 79 HC; 80% for training20% for testingN/ADiabetesBinaryAccuracy: 84.7%
Abbasi-Sureshjani et al., 2018 [50]Retinal imagesResNet5791 HC images, 3133 T2DM; 80% for training20% for validationN/AT2DMBinaryF1-score: 0.758
Heslinga et al., 2020 [51]Retinal imagesVGG-191376 participants, 5222 imagesValidation: 464 participants, 1802 images;
Test: 496 participants, 1900 images;		N/AT2DMBinaryAUC: 0.746
Yun et al., 2022 [52]Retinal imagesResNet-18UK Biobank: 37,904 patients, 69,639 imagesTest: 12,173 patients, 22,342 images;
Validation: 12,185 patients, 22,394 images		6575 imagesT2DMBinaryAUC: 0.731
Sensitivity: 0.662
Specificity: 0.662
Cervera et al., 2021 [53]Retinal imagesSqueezenet v1.01081 patients, 17,028 images121 patients, 1892 images; 5-fold cross-validationN/ADPNBinaryAUC: 0.8013
Mitani et al., 2020 [54]Retinal imagesInception-v4UK Biobank: 40,041 participants, 80,006 imagesValidation: 11,388 participants, 22,742 images;
Test: 5734 participants, 11,457 images		N/AHemoglobin
Anemia		Regression
Binary		MAE: 0.67
AUC: 0.87
Wei et al., 2021 [55]OCTAneNet17 patients, 221 images; 13 HC, 207 images5-fold cross-validationN/AAnemiaBinaryAccuracy: 0.9865
Sensitivity: 0.9838
Specificity: 0.9594
AUC: 0.9983
Zhao et al., 2022 [56]UWF Fundus imagesASModel_
UWF,
ASModel_
CroppedUWF		2445 participants, 9221 imagesValidation: 213 participants, 577 images;
Test: 565 participants, 1730 images		N/AHemoglobin
Anemia		Regression
Binary		MAE: 0.83
AUC: 0.93
Sensitivity: 91.2%
Specificity: 80.00%
Kang et al., 2020 [57]Retinal imagesVGG-194970 patients, 20,787 imagesValidation: 621 patients, 2189 images;
Test: 621 patients, 2730 images		N/AEarly renal function impairmentBinaryAUC: 0.81
Sensitivity: 0.83
Specificity: 0.62
Accuracy: 0.73
Sabanayagam et al., 2020 [58]Retinal imagesCondenseNetSEED: 5188 participants, 10,376 images1297 participants, 2594 images; 5-fold cross-validation1. 3735 participants, 7470 images;
2. BES: 1538 participants, 3076 images		CKDBinaryAUC: 0.835
Sensitivity: 0.75
Specificity: 0.75
Zhang et al., 2021 [59]Retinal imagesResNet-5030,122 participants, 60,244 imagesValidation: 4307 participants, 8614 images;
Test: 8727 participants, 17,454 images		1. 8059 participants, 16,118 images;
2. 3081 participants, 6162 images		CKD
Early CKD
T2DM		Binary
Binary
Binary		AUC: 0.885
AUC: 0.834
AUC: 0.854
Xiao et al., 2021 [19]Retinal imagesSlit-lamp imagesResNet-1011252 participants, 2481 slit-lamp images, 1989 retinal images; 75% for training25% for tuning537 participants, 1069 slit-lamp images, 800 retinal imagesLiver cancer
Liver cirrhosis
Chronic viral hepatitis
Non-alcoholic fatty liver disease
Cholelithiasis
Hepatic cyst		Binary
Binary
Binary
Binary
 
Binary
Binary		Slit-lamp; Retinal images:
AUC: 0.93; 0.84
AUC: 0.90; 0.83
AUC: 0.69; 0.62
AUC: 0.63; 0.70
AUC: 0.58; 0.68
AUC: 0.66; 0.69
Cho et al., 2022 [60]Retinal imagesDenseNet-201,
EfficientNet-B7		1703 patients, 3353 images189 patients, 373 images; 10-fold cross-validationN/AWMHBinarySensitivity: 66.1
Specificity: 71.3
AUC: 0.736
Appaji et al., 2022 [61]Retinal imagesCNN116 patients, 82 HCValidation: 33 patients, 23 HC;
Test: 17 patients, 13 HC;
Confirmatory: 21 patients, 22 HC		N/ASCZBinaryAccuracy: 95%
AUC: 0.98Sensitivity: 91.66%
Specificity: 95%
Lai et al., 2020 [62]Retinal imagesResNet-5046 patients, 24 HC10-fold cross-validationN/AASDBinarySensitivity: 82.6%
Specificity: 91.3%
AUC: 0.907
Wisely et al., 2019 [63]Retinal imagesResNet-1836 patients, 117 HC for all datasets; 57 patient eyes, 198 HC eyes for training6 patient eyes, 24 control eyes for testing; 9-fold cross validationN/AADBinaryAUC: 0.74
Accuracy: 0.79
Huang et al., 2020 [64]Retinal imagesEfficientNet-B1144 patients, 74 HC, total 342 images; Training and validation: 187 participants Training and validation: 187 participants
Testing: 31 participants		N/AAxial spondyloarthritisBinaryAUC: 0.735
Sensitivity: 87%
Specificity: 62.5%
1 Only the best performance is presented when there was more than one model. Metadata-based models and hybrid models are not presented in this table. SEED, Singapore Epidemiology of Eye Diseases; N/A, not applicable; AUC, area under curve; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; APOE4, apolipoprotein E4; MAE, mean absolute error; RFM, relative fat mass; SHBG, sex hormone binding globulin; Tch, total cholesterol; HDL, high density lipoprotein; LDL, low density lipoprotein; Tg, triglyceride; ARB, angiotensin receptor blocker; ACEi, angiotensin-converting enzyme inhibitor; HC, healthy control; DM, diabetes mellitus; OCT, optical coherence tomography; CVD, cardiovascular diseases; EyePACS, eye picture archive communication system; BES, Beijing Eye Study; BMM, body muscle mass; PBF, percentage body fat; RBC, red blood cell; WHR, waist–hip ratio; HCT, hematocrit; MCHC, mean corpuscular hemoglobin concentration; T-BIL, total bilirubin; D-BIL, direct bilirubin; FAD, fundus age difference; SpO2, oxygen saturation; LICUS, length of ICU stay; PC, perioperative complications; OT, operation time; CBT, cardiopulmonary bypass time; ABT, arterial blocking time; PAD, peripheral arterial disease; CACS, coronary artery calcium score; N/M, not mentioned; T2DM, type 2 diabetes mellitus; DPN, diabetic peripheral neuropathy; UWF, ultra-wide-field; CKD, chronic kidney disease; WMH, white matter hyperintensity; SCZ, schizophrenia; ASD, autistic spectrum disorder; AD, Alzheimer disease.

3.3.1. Systemic Health Features

Systemic health features, such as age, gender, smoking status, blood pressure, and glucose level, are indicative and predictive of various disorders. The pioneering work of Poplin et al. [30] unveiled the possibility of using deep learning algorithms based on fundus photographs to predict systemic risk factors, giving rise to a series of works with akin goals. These models successfully predicted age with the mean absolute error (MAE) ranging from 2.43 to 6.328 [22,24,25,28,30,31]. As for identifying gender, the models also achieved satisfying AUCs ranging from 0.85 to 0.97 [20,22,24,25,26,28,30,31,52], with a few studies highlighting the optic disc and the macula as regions of interest [20,26,30]. Ethnicities could also be categorized from fundus images with AUC and accuracy surpassing 0.90 [24,34].
Other than fundus images, OCT scans were also proven to be suggestive of patients’ age and gender. The result from Munk et al. [28] indicated that prediction performance with OCT C-Scans or B-Scans of the macular region outperformed fundus images, achieving AUCs of 0.90 and 0.84 in detecting gender and obtaining the MAEs of 5.625 and 4.541 in predicting age. Mendoza et al. [27] proposed that circle and radial scans of the optic nerve head incorporate the potential for predicting age, gender, and race, among which race prediction achieved the best performance with an AUC of 0.96. The authors also illustrated that circle scans have better predictive value in DL algorithms.
As a notorious risk factor for various systemic diseases, smoking is deleterious since it impacts systemic vascular structure and function [65]. Recent studies have demonstrated that DL diagnostic models based on retinal images could achieve AUCs between 0.71 and 0.78 by capturing the pathological changes, as retinal circulation characteristics were marked in the attention maps [22,24,30]. Contrast-enhanced photographs emphasizing the vessel structure could significantly boost the model’s performance and reach an accuracy of 88.88% [33].
Based on the parallel decrepitude of the body and the retina, several researchers proposed the idea of “retinal age” as a novel feature in disease monitoring. Chang et al. [38] suggested that a higher algorithm-predicted age than the chronological age translates into higher all-cause mortality. Nusinovici et al. [29] interpreted it with a different approach by defining “RetiAGE” as the probability of age being ≥65 years, and their study obtained similar results. The work of Hu et al. [23] extended the application of this method, proving that the model based on retinal age is predictive of the future risk of Parkinson’s disease with an AUC of 0.71.

3.3.2. CVD

Cardiovascular diseases (CVD) cause the most significant proportion of deaths among non-communicable diseases. Studies have proven that the presence and severity of CVD are associated with retinal vascular morphology [66], providing the theoretical basis for building AI diagnosing models with retinal images.
The coronary artery calcium score (CACS) is a non-invasive assessment system that quantifies the prognostic risks of CVD [67]. Two studies have applied DL to predict CAC cores based on retinal images. One of the algorithms attained the highest accuracy of 0.72 in predicting CACS >0 despite a small sample size [42], and both studies suggested that the AUC improved as the diagnosing threshold increased [44].
Alternatively, several researchers have developed unique retinal scoring systems to use as CVD indicators. The RetiCAC score, the probability score of the DL binary classification task, could predict the presence of coronary artery calcium with an AUC over 0.70 [43] and was comparable with the traditional CAC risk stratification in predicting disease prognosis. Another scoring system based on retinal images, namely the DL fundoscopic atherosclerosis score (DL-FAS), achieved akin results in predicting carotid artery atherosclerosis and all-cause mortality [41].
For direct classification of CVD, Al-Absi et al. [36] achieved an accuracy of 75.6% using only retinal images, and the region of interest of the model was mainly the central retinal area. Another study recruiting type 1 diabetes mellitus patients achieved an AUC of 0.77 in diagnosing CVD [37]. Peripheral artery disease (PAD), also attributed to atherosclerosis, was proven to be detectable from fundus images with an AUC reaching 0.89 [40]. Furthermore, there was evidence of applying retinal image-based AI in predicting perioperative parameters of congenital heart diseases [39], with the AUC of detecting cardiopulmonary bypass time reaching 0.80 and that of oxygen saturation, arterial blocking time, length of ICU stay, and perioperative complications surpassing 0.70.

3.3.3. Hypertension

Hypertension causes microvascular dysfunction. Morphological retinal vascular changes, such as narrower arteries and wider venules, could be observed in hypertensive patients [68]. In algorithms predicting biomarkers, the MAE was from 8.96 to 11.35 for systolic blood pressure (BP) and 6.42 to 7.20 for diastolic BP [22,30,31]. Interestingly, the studies applying DL to diagnose hypertension concomitantly preprocessed the input photographs to augment the vessel structures and erase background noise. The models based on processed images achieved AUC values of 0.65% and 0.77%, respectively [35,45], and the work predicting mild hypertension reached an accuracy of 93.75% [46] based on only 400 photographs.

3.3.4. Diabetes Mellitus

Diabetic retinopathy, with its rocketing prevalence and distinct fundus pathologies, has become the pilot field of ophthalmic AI. Aside from diagnosing typical retinopathy, there have been multiple attempts at employing DL to predict diabetic mellitus (DM) as a disease. Kang et al. reached the highest AUC of 0.92 [59], and the performance of other approaches was no worse than 0.73 [35,47,50,51,52]. When evaluated for accuracy, the models reached from 83.7% to 85.0% [48,49]. One study that applied Xception and dense neural network (DNN) achieved a training accuracy of 96.68% and a validation accuracy of 66.82% although only 220 images were put into model training.
Hemoglobin A1c (HbA1c) is an essential biomarker for long-term glucose monitoring [69]. Tham et al. have proven that retinal images contain information indicating HbA1c level by achieving an MAE of 0.87% with the DL algorithm [32]. Notably, it was suggested that diabetic neuropathy could also be detected from fundus photographs, with the AUC reaching 0.71 [53].

3.3.5. Anemia

Anemia is a common disease and a symptom of various systemic disorders. DL based on fundus images was proven sufficient in predicting hemoglobin concentration and diagnosing anemia [54,56], thus could be considered a novel non-invasive method for disease management. Explanation methods showed that the models focused on the optic disc and the retinal vessels, which is consistent with the typical ocular symptoms such as pale discs and narrower arteries in anemic patients.
Wei et al. [55] tackled the problem from a different perspective by using OCT images that displayed the cross-section of retinal vessels as the model input. Although the algorithm achieved excellent results, the dataset was diminutive and external validation was not applied.

3.3.6. Hepatobiliary Diseases and Kidney Diseases

The liver and the kidney share multiple essential physiological functions, including metabolism and maintaining homeostasis. Recent studies have suggested that diseases of both organs can be observed with deep learning algorithms based on fundus photos. Xiao et al. [19] proved that hepatobiliary diseases, especially liver cancer and liver cirrhosis, could be diagnosed with an AUC over 0.80 from fundus images. In the case of chronic kidney disease (CKD), the algorithms obtained excellent performance in predicting early CKD and CKD [57,58,59]. Color fundus images could provide intuitive observation of the systemic microvasculature, enabling the detection of vascular defects in CKD patients.

3.3.7. Neurological Disorders

A diversity of neurological diseases can be detected from the morphological changes of the retina. White matter hyperintensity, referring to the lesions caused by cerebral small vessel diseases, is predicted from fundus photos with an AUC of approximately 0.70 [60]. As for cognitive impairment, previous studies indicated that DL with retinal images alone was limited in predicting cognition status [21]; however, UWF combined with OCTA and autofluorescence (FAF) could achieve an AUC of 0.74 in detecting Alzheimer’s disease (AD) [63]. Likewise, autoimmune diseases such as axial spondyloarthritis [64] could also be distinguished with a fair AUC of 0.74. On the contrary, studies focusing on autism spectrum disorder (ASD) [62] and schizophrenia [61] obtained an AUC of over 0.97, possibly attributable to the fact that both models applied cross-validation methods for performance evaluation. These results have proven that several categories of neurological disorders demonstrate retinal changes, although the DL models based on fundus images are not yet sufficiently developed to perform diagnostic tasks individually.

3.4. Algorithms Based on the Movements of the Eye

Eye movements are coordinative actions dominated by cognitive processes and behavior mechanisms [70]. Previous studies have proven that the specific gaze patterns captured by eye-tracking devices could be predictive of neurodegenerative diseases, such as Parkinson’s disease (PD), dementia, and autism spectrum disorders (ASD). With the advancements in hardware and algorithms, the current eye-tracking methods have achieved explicit temporal resolutions and could provide additional information unattainable by traditional imaging techniques.

3.4.1. Dementia and Parkinson’s Disease

Dementia is a global health issue in the aging society. It was suggested that eye-tracking tests could provide a rapid and objective method for assessing patients’ cognitive functions, such as memory and attention [11]. Mengoudi et al. [71] designed a test to trace the participants’ sight while presenting images with different stimuli, and the model achieved an accuracy of 78.3% in classifying dementia. Alternatively, Biondi et al. [72] developed a resolution based on eye movement during reading tasks. Their result had a decent performance with an accuracy of 89.8%, and the severity scaled by model output was comparable with psychiatrists’ scoring.
PD is another neurodegenerative disease affecting a large population worldwide. As previous studies implied fixational defects in PD patients, Archila et al. [73] developed an algorithm based on fixational performances to distinguish and stage PD. Their model achieved relatively good specificities, and the performance advanced after combining gait data.
Table 3. Summary of deep learning algorithms identifying systemic diseases from eye movements.
Table 3. Summary of deep learning algorithms identifying systemic diseases from eye movements.
Author, YearOcular DataDL ModelTraining DatasetTesting/Validation DatasetExternal ValidationSystemic Health Features/DiseasesOutcomePerformance 1
Li et al., 2022 [74]Gaze estimation videosAttentionGazeNet,
LSTM		50 participants, 64,000 images1. 15 participants, about 1500 images;
2. 16 participants		405 participants, 405 videosASDBinaryAccuracy: 94.8%
Sensitivity: 91.1%
Specificity: 96.7%
Li et al., 2020 [75]Eye movement videosLSTM136 patients, 136 videos; 136 HC, 136 videos10-fold cross-validationN/AASDBinaryAccuracy: 92.7%
Sensitivity: 91.9%
Specificity: 93.4%
Varma et al., 2022 [76]Eye movement videosLSTM68 patients and 27 HC in all datasets; 324 videos for trainingValidation: 71 videos;
Test: 54 videos		N/AASDBinaryRecall: 0.656
Precision: 0.661
Xie et al., 2022 [77]Eye movement dataVGG-1620 patients, 19 HCLeave-one-out and 13-fold cross-validationN/AASDBinaryAccuracy: 0.95
Sensitivity: 1.00
Specificity: 0.89
AUC: 0.93
Jiang et al., 2017 [78]Eye movement dataVGG-1639 participants, 100 imagesLeave-one-subject-out cross-validationN/AASDBinaryAccuracy: 0.92
Sensitivity: 0.93
Specificity: 0.92
AUC: 0.92
Mengoudi et al., 2020 [71]Eye movement dataSelf-Supervised Learning,
SVM		432 HC30 patients, 144 HCN/ADementiaBinaryAccuracy: 78.3%
Sensitivity: 89.7%
Specificity: 67.6%
Biondi et al., 2018 [72]Eye movement dataSparse-Autoencoders22 patients, 39 HC, total 2922 trials4 patients, 4 HC, total 313 trialsN/AADBinaryAccuracy: 89.78%
Archila et al., 2021 [73]Eye movement videosLSTM12 patients, 144 videos; 13 HC, 156 videosLeave-one-patient-out cross-validationN/APDTernarySpecificity:
Control: 1,
Stage2: 0.87,
Stage3: 0.86
F1-score:
Control: 0.81,
Stage2: 0.57,
Stage3: 0.72
Mao et al., 2020 [79]Eye movement dataLSTM34 HC, 34 patients with brain injury, and 30 patients with vertigo; 64 subjects for training34 subjects for testingN/ABrain injury and vertigoTernaryAccuracy: 0.9412
Ahmadi et al., 2020 [80]Eye movement dataRF,
ANN,
SingleGMC,
MultiGMC		40 patients with vestibular stroke, 68 patients with peripheral AVS; 90% for training10% for testingN/AAVSBinaryAccuracy: 82%
AUC: 0.96
1 Only the best performance is presented when there was more than one model. Metadata-based models and hybrid models are not presented in this table. ASD, autism spectrum disorder; HC, healthy control; N/A, not applicable; AUC, area under curve; AD, Alzheimer disease; PD, Parkinson’s disease; AVS, acute vestibular syndrome.

3.4.2. Autism Spectrum Disorders

Visual attention characteristics are among the most specific traits obtained from eye movement data. Such hallmarks could be applied in ASD screening, which distinctively presents changes in attention patterns towards certain visual elements. Jiang et al. [78] discovered that ASD patients mainly focused on non-social subjects while presented with a variety of pictures, and their model achieved an AUC of 0.92. Xie et al. [77] further distinguished several categories of image features, such as outdoor objects and food and drinks, that poses importance in identifying ASD. The model based on the top features also performed excellently with an AUC of 0.92.
Li et al. adopted a different method by displaying the mother’s image and tracking the children’s gaze patterns [74,75]. By applying appearance-based gaze estimation, their models achieved high accuracies of over 90%. Besides the reaction to still images, Varma et al. [76] captured the gaze fixation and visual scanning methods in socially motivated gameplay. The developed algorithm showed mild predictive power in identifying ASD in children.

3.4.3. Other Disorders

Vestibular disorders could cause significant ocular presentations, namely abnormal nystagmus and saccade. It usually requires an experienced specialist for evaluation in clinical settings to help diagnose vestibular diseases. With the advancement of DL, a few studies utilized eye movement data for discrimination between systemic diseases. Ahmadi et al. [80] identified vestibular strokes and peripheral acute vestibular syndrome with an AUC of 0.96. Mao et al. [79], on the other hand, obtained eye motion during gazing tasks and achieved an AUC of 0.94 in differentiating controls, brain injury, and vertigo patients.

4. Discussion

This systematic review concludes the performances of deep learning algorithms based on ocular data in evaluating systemic health conditions. Overall, most systemic diseases proven to be detectable from static ocular manifestations impact neurovascular structures, which project changes to the eye in areas such as retinal vessels and corneal nerves. Most studies used colored photographs as input; however, depth-resoluted OCT images were also applicable. Alternatively, neurodegenerative disorders mainly present as defects in eye movements, and eye-tracking data in specific tasks or spontaneous abnormalities were obtained as model inputs. The reported algorithms achieved fair results, with AUCs and accuracies exceeding 0.7 in most studies despite small datasets. The saliency maps and heatmaps also showed that the models were built on rational reasoning despite the “black box” process of deep learning. Regardless of the outstanding performances presented in mostly retrospective datasets and with handpicked participants, several aspects should be advanced before putting the models in real-world application.

4.1. Present and Prospects

Systemic health features could have significant latent effects on the primeval ocular characteristics. Features such as age, sex, and ethnicity were proven to be credibly identified from ophthalmic data. While predicting age, the algorithms mainly focused on retinal vessels and the optic nerve head (ONH) areas [25,30], which are concordant with the aging of the retina [81,82]. Sex, on the other hand, was identified based on the ONH and the macular area, where innate gender differences in ONH blood supply [83] and FAZ area [84] exist. Besides being the baseline characteristics of the patients, these features could concurrently be risk factors for many systemic diseases. Former reports [7,8] proved that age and sex are interrelated with cardiometabolic risk factors and conditions in retinal image-based DL algorithms, possibly due to their mutual effect on fundus vessels. Therefore, studies targeting diseases with sex or age differences should control for these confounders to prevent overestimating the model’s performance.
Ethnicity was another critical factor proven to be distinguishable from ophthalmic presentations. Aside from affecting the retinal structure [85], ethnicity is also a determinant of the ocular disease spectrum. However, most algorithms were trained on datasets with little to no diversity, affecting the generalizability in real-world scenarios. We suggest that researchers consider data with racial diversity as external validation, and more multi-ethnic datasets should be established to produce generalizable DL models.
Regarding algorithms for diagnosing neurodegenerative diseases based on gazing patterns and eye movements, the communal issues are the limited datasets and the lack of external validation. With video data as input in most cases, these algorithms must be robust against significantly greater interferences to be applied in different real-world scenarios. A large-scale validation in the generalized public would be much preferred for further approval of the DL algorithms.
DL is known for its representation-learning nature. The ocular vasculature, including the conjunctival and the retinal vessels, were some of the most conspicuous and vulnerable structures and were often identified as the focused feature in saliency maps. For instance, metabolic syndrome [86] presented as hypertension, hyperglycemia, and dyslipidemia was found to cause retinal arteriolar narrowing [87]. Arterial defects in these conditions were reported to be caused by a few shared pathophysiology, such as oxidative stress, glutathione peroxidase, and impaired acetylcholine-mediated vasodilatation. As a result, algorithms predicting hypertension, diabetes, and CVD simultaneously highlighted the retinal vessels as the area of interest. On the other hand, CKD causes systemic atherosclerosis and vascular calcification [88], which could also present in the retina as arteriolar thinning and sparse capillaries. Since current studies mainly focused on discriminating the target disease from healthy controls, the algorithms were likely to identify universal pathologies instead of exclusive characteristics of each condition. Therefore, these DL models could lack specificity if applied in real-world scenarios where all systemic diseases coexist. Future studies aiming to distinguish between diseases with similar pathological characteristics would greatly favor the implementation of DL algorithms in real-world screening and diagnosis.

4.2. Advantages and Drawbacks of AI in Clinical Settings

The application of AI algorithms in clinical settings has been a controversial topic. AI models benefit disease screening, diagnosing, and management in several aspects: (a) improve efficiency compared with human graders and enable large-scale screening programs; (b) allow advanced medical technology to reach remote areas with algorithms deployed in portable devices; (c) reduce health-care expenses by saving human resources; and (d) discover preclinical changes for early disease screening. Implementing ophthalmic examinations in disease screening algorithms further provides several advantages. Ophthalmic examinations are non-invasive and rapid compared with other traditional tests; therefore, the screening procedure can be simplified to a great extent. Moreover, the neurovascular structures could be observed intuitively from the ocular anatomy, offering a window for analyzing the underlying morphological and pathological features.
Nonetheless, there are primary disputes about launching AI algorithms in clinical settings. First and foremost is the debate on AI ethics. Models should be thoroughly investigated before being assigned with allowance for real-world tasks. Secondly, the robustness of AI models is often questioned in actual practice. Despite data with varied baseline characteristics, the algorithms could also encounter a variety of low-quality inputs. Researchers should ensure the algorithm can adapt to widely-varied datasets to offer a generalizable and reliable program.

4.3. Strengths and Limitations

This systematic review is the first to conclude deep learning algorithms for systemic disease screening and diagnosing based on ocular data. It provides a comprehensive view of the current trend and methodology in observing various systemic conditions from eye manifestations. We believe this work could be a valuable reference for subsequent studies.
There are some limitations in the current study. According to our selection criteria, studies utilizing DL for feature extraction and statistical methods for condition diagnosis were excluded. This may lead to information loss, as several studies achieving decent results were eliminated. Secondly, our study included meeting abstracts to involve up-to-date research works that have not yet been published. However, the lack of detailed information in the study design translates into unknown risks of bias. Lastly, this review did not inspect the development of deep learning algorithms in detail. Future reviews focusing on AI techniques are preferred to provide further information for computer scientists and program developers.

5. Conclusions

Deep learning has been shown to be beneficial in identifying systemic diseases from ocular presentations. Despite presenting decent performance in the articles, the algorithms still have several shortcomings for clinical application. Future studies should aim at improving the disease specificity and generalizability of the DL models for implementation in real-world screening tasks.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/diagnostics13050900/s1; Table S1: The search strategy used for obtaining research articles in the three selected databases; Table S2: The detailed results of the QUADAS-2 analysis.

Author Contributions

Conceptualization, H.L. and D.L.; formal analysis, W.C.I., W.Z. and Y.W.; investigation, W.C.I. and W.Z.; data curation, W.C.I. and W.Z.; writing—original draft preparation, W.C.I.; writing—review and editing, H.L., D.L. and X.W.; visualization, W.Z. and W.C.I.; supervision, H.L.; project administration, D.L. and X.W.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (82171035), the National Natural Science Foundation of China (82000946), the Natural Science Foundation of Guangdong Province (2021A1515012238), the Science and Technology Program of Guangzhou (202201020337), the Guangzhou Science and Technology Project (202201020522), the Science and Technology Planning Projects of Guangdong Province (2021B1111610006), and the Key-Area Research and Development of Guangdong Province (2020B1111190001). The funders had no role in the study design, data collection, data analysis, data interpretation, or writing of the report.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

This project is supported by Hainan Province Clinical Medical Center.

Conflicts of Interest

The authors declare no conflict of interest.

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