The lens is a biconvex, transparent structure in the anterior segment of the eye, which focuses light to project images of objects at varying distances onto the retina. A cataract, defined as clouding of the lens, is primarily an age-related degenerative disease, and congenital and pediatric cataracts also occur [1]. Early cataracts are asymptomatic, but progressive clouding can lead to visual impairment, greatly reducing quality of life and productivity [2]. Cataracts are the major cause of visual loss worldwide. In 2020, the estimated number of the blind aged 50 years and older was 15 million, and moderate to severe visual disorders due to cataracts affected an estimated 79 million people aged 50 years and older [2]. These figures suggest the number of patients with blindness and moderate to severe visual disorders rose by 30% and 93%, respectively, versus the year 2000 [3]. Blinding cataracts refer to cataracts causing severe visual impairment (visual acuity ≤3/60) or blindness (visual acuity <3/60) according to the WHO (World Health Organization) and ICD-11 (International Classification of Diseases, 11th Revision) [4]. About 94% of blinding cataracts occur in low- and middle-income countries (LMICs), and cataract-related visual disorders are causally related to poverty in low-resource settings (LRS) [1]. As the conventional diagnosis of cataracts relies on ophthalmologists and complex equipment, it is subjective and requires health care resources, making it difficult to satisfy the need for large-scale screening. Moreover, due to the limitations of infrastructure and the lack of trained personnel in LMICs, inequalities are present in early cataract detection and severity classification.

Deep learning (DL) can process pixel-level information that cannot be recognized by the human eye to greatly contribute to the analysis of medical images, assist doctors in clinical decision-making, and enhance screening efficiency. As a branch of artificial intelligence (AI), DL models are inspired by the brain and specialized in pattern recognition [5]. Therefore, DL has been rapidly evolving with broad prospects in the field of medical image analysis involving disease detection, classification, segmentation, and image registration [6]. For example, convolutional neural networks (CNNs), as the primary technique of DL for image learning, perform excellently in image classification and feature extraction, making it a cornerstone in medical imaging [7]. Residual network (ResNet), one of the landmark architectures of CNNs, has contributed to the development of DL, especially with outstanding performance in benchmarking in image recognition and classification [8]. The superiority of the ResNet model in the medical field lies in its capability to efficiently train the deep network and raise the accuracy of image recognition [9].

The strategic importance of AI is to raise the quality of care and potentially reduce costs in high-income economies and to address critical health care issues and staffing shortages and provide access to specialized skills in LMICs [10]. Nowadays, DL is developed mostly based on data from high-income countries and regions and relies on high-resolution images and advanced electronic devices. In remote regions, however, fragmented health care systems are generally characterized by insufficient infrastructure, a shortage of professionals, and a lack of health care resources, so it is difficult to guarantee the quality of screening and diagnosis. The following problems are present in the available DL models: (1) lack of compatibility: mismatch with the low-cost equipment used in LRS [10]; (2) lack of generalization capability: significant decline in the model performance in real-world scenarios, especially under different lighting conditions or in different patient populations [11]; (3) lack of clinical validation: a recent systematic review of studies assessing the use of AI algorithms for medical image analysis found that only 6% of the included studies (n=516) conducted external validation, and the assessment of the diagnostic efficiency was lacking [12]; and (4) training data bias and lack of diversity in data (race, age, and disease subtypes) weaken the model’s generalization capability [13].

In the past 5 years, breakthroughs have been made in the use of DL for ophthalmic image analysis in diabetic retinopathy (DR), retinopathy of prematurity, and glaucoma. By automating the massive processing of ophthalmic images, DL can achieve a more accurate and rapid diagnosis of cataracts, reducing the doctors’ subjectivity and errors of conventional methods. Moreover, DL can fuse multiple image data (eg, slit lamp, fundus, and optical coherence tomography [OCT] images) to make a comprehensive and accurate diagnosis through multimodal image analysis. In addition, diagnostic results can be obtained quickly from the DL model with real-time diagnostic capability, greatly improving work efficiency and achieving large-scale screening and early diagnosis, especially in primary health care institutions and LRS [14]. Many DL-based diagnostic tools have been approved by the US Food and Drug Administration (FDA), but they require further evaluation and independent quality review [15]. For example, IDx-DR [16,17], the first AI system approved by the FDA, is a CNN-based system for automated screening for DR, with high sensitivity and specificity, which can contribute to the early diagnosis and lower the risk of visual loss in clinical practice, especially in LRS.

Image-based DL has exhibited greater potential for automatic cataract detection and classification using fundus and slit lamp images. However, the existing findings are still heterogeneous, and a systematic review of DL algorithms for cataract image analysis is still lacking. Therefore, this study was conducted to systematically assess the performance of different DL models in cataract detection and classification from sensitivity, specificity, and the area under the ROC curve (AUC), thereby revealing the methodological and reporting quality and contributing to the clinical translation of DL.

This meta-analysis included 63 studies and rigorously assessed the study quality using the QUADAS-2 to summarize the evidence to date on the performance of image-based DL in cataract diagnosis. The results revealed that DL algorithms might offer higher accuracy than traditional ML algorithms and fall within the range of reported accuracy of human experts in the detection of cataracts, demonstrating potential as tools for automated diagnosis. However, given the moderate quality and high heterogeneity of the current evidence base, these DL algorithms are considered primarily as adjuncts to cataract diagnosis.

DL has made tremendous progress in automated image analysis [6]. In clinical practice, a severe imbalance between supply and demand is present in ophthalmologic diagnosis. As AI advances, DL is expected to raise diagnostic efficiency and thus help alleviate health care resource inequality.

Four relevant systematic reviews and meta-analyses were identified. (1) Cheung et al [90] found high sensitivity, specificity, and reproducibility and great heterogeneity of the ML model for cataract diagnosis in children and adults, but the strength of evidence was limited since only 11 studies with 13 contingency tables were included. (2) Liu et al [91] found comparable performance of DL and experts in medical imaging, but only 2 out of 18 ophthalmology studies involved cataracts, necessitating in-depth research on DL in cataract diagnosis. (3) Aggarwal et al [92] included 82 ophthalmology studies, which did not involve cataracts, and verified that DL possesses good sensitivity, specificity, and AUC for the feature identification of other eye diseases, but the algorithmic evaluation criteria remain to be standardized due to high heterogeneity. (4) Islam et al [93] demonstrated that DL can be applied to retinal vessel segmentation, so it can be popularized in LMICs. Therefore, methodological limitations should be critically assessed during the clinical translation of DL to further improve its reliability.

This meta-analysis systematically evaluated the effectiveness of DL versus traditional ML in cataract detection and classification to provide a basis for clinical decision-making. For cataract detection, DL had a pooled sensitivity of 96% (95% CI 95%‐97%) and a pooled specificity of 98% (97%‐99%), with an AUC of 0.99 (0.98‐1.00); traditional ML had a pooled sensitivity of 90% (87%‐91%) and a pooled specificity of 94% (91%‐96%), with an AUC of 0.95 (0.93‐0.97). For cataract classification, the pooled sensitivity and specificity of DL were 94% (93%-96%) and 97% (96%‐98%), with an AUC of 0.99 (0.98‐1.00); the pooled sensitivity and specificity of traditional ML were 88% (85%‐90%) and 94% (90%‐96%), with an AUC of 0.94 (0.92‐0.96). Available evidence suggests that DL models exhibit high sensitivity and specificity in automated cataract diagnosis and that medical image-based DL still demonstrates superior robustness to ML despite the RoB in most studies. However, our conclusions were made partly based on studies of low quality due to a lack of external validation and nonstandardized reporting of performance metrics, which may overestimate algorithmic accuracy. The use of overlapping public repositories introduced potential clustering bias. To ensure robust generalizability, future research must prioritize validation on independent, multicenter, and nonpublic datasets. Furthermore, the lack of a sensitivity analysis excluding high-RoB studies implied that the high overall performance of DL models could be partially influenced by methodologically weaker studies, warranting validation in future high-quality trials. Finally, due to the limited number of eligible studies, we pooled smartphone-based data from varying modalities, including diffuse photography and slit-lamp adapters. While this aligned with our assessment of general mobile health accessibility, it introduced potential optical heterogeneity. Consequently, statistical quantification of performance differences between optical sectioning and diffuse lighting was not feasible, warranting future separate investigations.

We identified the potential of DL for clinical use from the included studies, but the number of studies directly comparing the performance of DL and ML in cataract diagnosis was limited, with only 2 in detection and 4 in classification. Traditional ML (eg, logistic regression, random forest, and support vector machine) is dependent on manual feature engineering and classifiers, whose performance is limited by feature quality and domain knowledge [94]. By contrast, DL (eg, CNN and transformer-based vision models) has higher accuracy in image recognition by automatic feature extraction but requires higher data volume and stronger arithmetic support [95].

The number of included studies was limited, so image segmentation performance was not meta-analyzed. Image segmentation is essentially a pixel-level prediction task, which relies on the ability to characterize discriminative features. However, it is often difficult for traditional ML to effectively capture such features. In contrast, DL demonstrates significantly better performance by automated learning of complex patterns in highly heterogeneous data [6]. The property of DL models is such that they contain millions of parameters that need to be optimized by data-driven training, while traditional ML models display higher stability in small-sample multiclassification [95]. These findings suggest that traditional ML models may have the potential to raise the accuracy in cataract classification, while DL is more advantageous in reducing misdiagnosis rates. A combination of the 2 may facilitate the clinical translation of AI in the future.

Despite the available evidence on the clinical translational potential of DL, only 7 studies compared the efficacy of DL and human experts in cataract detection, and no studies compared their performance in cataract classification. Notably, significant interrater variability was present in expert performance due to heterogeneity in the cumulative clinical experience and health care resource allocation, highlighting the need for more comprehensive comparative studies. These studies generally report positive results of DL, but an optimism bias in algorithm performance may be produced due to insufficient sample sizes and methodological heterogeneity. Therefore, it is urgently needed to adopt a standardized study design and transparent reporting norms for improving the quality of evidence and clinical translational value of DL. DL has been successfully applied to retinal fundus image analysis [14] for automated diagnosis of DR [96] and glaucoma [97]. Considering the technical feasibility of DL in ophthalmic disease screening, it is recommended that DL serve as an adjunct to clinical diagnosis to optimize the treatment process by human-machine collaboration. Additionally, no great publication bias was detected, but the findings need to be interpreted with caution. It is recommended that a clinician’s diagnostic efficiency be used as a core assessment metric and that a real-world validation be incorporated in subsequent studies.

In the field of AI methodology, several standardized guidelines have been recently issued [98,99]. However, no unified consensus has been reached on AI for cataract diagnosis. At the time of this writing, computer-aided diagnostic techniques mainly use a combination of medical image processing and AI, but research focuses mostly on eye diseases of the posterior segment (eg, DR, and age-related macular degeneration) and less on the anterior segment (eg, cataracts). Nowadays, cataract diagnosis relies on slit-lamp microscopy of lens morphology, and cataract classification is based on the LOCS III [87], the Oxford Clinical Cataract Classification and Grading System [100], and the American Cooperative Cataract Research Group method [101]. DL has made a preliminary breakthrough in automated cataract classification, such as the turbidity-density-location assessment system developed by Lin et al [19] based on anterior segment slit lamp images and the DL classification model developed by Zhou et al [102] using retinal fundus images, but a standardized severity scale adapted to DL is urgently needed for its clinical use. Therefore, it is recommended that future studies standardize the criteria for performance assessment of DL algorithms, with a focus on improving the transparency of methodology reporting and repeatability validation process.

Data scarcity and lack of generalization capability are key scientific challenges for DL. In this systematic review, retrospective designs were adopted in most of the included studies, while prospective designs and multicenter clinical trials accounted for less than 5%, and their annotation criteria were not optimized for the need of DL, restricting methodological rigor. We should recognize that most of the studies used double-blind, annotated, and quality-controlled image data for model training, which effectively improved diagnostic accuracy and reduced the RoB while balancing data size and quality. The included studies generally used data enhancement techniques, indicating the lack of high-quality annotated datasets and prospective validation studies.

The acquisition of representative data for clinical validation remains the major bottleneck. The model’s clinical generalization capability is severely restricted due to the long time consumption of pixel-level annotation of fundus images, domain shift resulting from cross-device and cross-race variations, and a lack of fine-grained pathology characterization. In the future, research should focus on multimodal data fusion for modeling (eg, OCT plus fundus images), deployment of edge computing architectures, and embedding of causal inference modules, thereby establishing intelligent diagnosis and treatment systems with both clinical credibility and engineering practicality.

In this study, great methodological heterogeneity was found in the reporting of DL performance metrics, including incomplete reporting of sensitivity or specificity, a lack of 2×2 contingency tables, and general overreliance on aggregated metrics such as AUC-ROC and F₁-score. Of particular note is that high AUC values (>0.90) may mask the risk of misjudgment of key positive events (eg, progressive cataract) in clinical scenarios with a severe imbalance in category distribution. Based on these findings, it is recommended that the confusion matrix serve as a core reporting metric. The above-mentioned problems can be gradually settled by high-quality studies.

Another key obstacle is no consensus on the interpretability of DL decision-making mechanisms. It is difficult to intuitively understand the decision-making logic of DL models due to their complex network structure and nonlinear feature extraction, which, as a “black-box” characteristic, has been deemed a challenge for clinical use. Recently, researchers have gradually revealed the intrinsic mechanism of DL models by gradient-weighted class activation mapping, adversarial testing, and causal inference [95]. For example, Chang et al [103] used adversarial samples to analyze the decision-making basis of DL models in glaucoma detection. Araújo et al [104] located the key region of DR in fundus images by multiple-instance learning. Explainable AI (XAI) is breaking through the limitations of traditional AI by synchronizing decision results with attributional explanations [105]. Abràmoff et al [106] systematically reviewed the interpretability framework for DL models in the medical field, laying a theoretical foundation for their clinical translation. Future research needs to further explore the innovative methods of XAI in medical image analysis to enhance clinical credibility.

This study showed that DL was applied primarily to the screening of eye diseases such as DR for which mature diagnostic guidelines have been established. Notably, differences in health care resource allocation should be considered when popularizing DL. In subsequent studies, the clinical effect and health economic benefits should be assessed across different DL algorithms, and the “black-box” problem of DL algorithms should be solved using interpretability methods to enhance clinical acceptance.