Despite current screening models, enhanced imaging modalities, and treatment regimens, diabetic retinopathy (DR) remains one of the leading causes of vision loss in working age adults. DR can result in irreversible structural and functional retinal damage, leading to visual impairment and reduced quality of life. Given potentially irreversible photoreceptor damage, diagnosis and treatment at the earliest stages will provide the best opportunity to avoid visual disturbances or retinopathy progression. We will review herein the current structural imaging methods used for DR assessment and their capability of detecting DR in the first stages of disease. Imaging tools, such as fundus photography, optical coherence tomography, fundus fluorescein angiography, optical coherence tomography angiography and adaptive optics-assisted imaging will be reviewed. Finally, we describe the future of DR screening programmes and the introduction of artificial intelligence as an innovative approach to detecting subtle changes in the diabetic retina. CLINICAL TRIAL REGISTRATION NUMBER: N/A.

Retinal diseases recognition is still a challenging task. Many deep learning classification methods and their modifications have been developed for medical imaging. Recently, Vision Transformers (ViT) have been applied for classification of retinal diseases with great success. Therefore, in this study a novel method was proposed, the Residual Self-Attention Vision Transformer (RS-A ViT), for automatic detection of acquired vitelliform lesions (AVL), macular drusen as well as distinguishing them from healthy cases. The Residual Self-Attention module instead of Self-Attention was applied in order to improve model's performance. The new tool outperforms the classical deep learning methods, like EfficientNet, InceptionV3, ResNet50 and VGG16. The RS-A ViT method also exceeds the ViT algorithm, reaching 96.62%. For the purpose of this research a new dataset was created that combines AVL data gathered from two research centers and drusen as well as normal cases from the OCT dataset. The augmentation methods were applied in order to enlarge the samples. The Grad-CAM interpretability method indicated that this model analyses the appropriate areas in optical coherence tomography images in order to detect retinal diseases. The results proved that the presented RS-A ViT model has a great potential in classification retinal disorders with high accuracy and thus may be applied as a supportive tool for ophthalmologists.

Deep learning models hold significant promise for disease diagnosis but often lack transparency in their decision-making processes, limiting trust and hindering clinical adoption. This study introduces a novel multi-task learning framework to enhance the medical explainability of deep learning models for diagnosing age-related macular degeneration (AMD) using fundus images. The framework simultaneously performs AMD classification and lesion segmentation, allowing the model to support its diagnoses with AMD-associated lesions identified through segmentation. In addition, we perform an in-depth interpretability analysis of the model, proposing the Medical Explainability Index (MXI), a novel metric that quantifies the medical relevance of the generated heatmaps by comparing them with the model's lesion segmentation output. This metric provides a measurable basis to evaluate whether the model's decisions are grounded in clinically meaningful information. The proposed method was trained and evaluated on the Automatic Detection Challenge on Age-Related Macular Degeneration (ADAM) dataset. Experimental results demonstrate robust performance, achieving an area under the curve (AUC) of 0.96 for classification and a Dice similarity coefficient (DSC) of 0.59 for segmentation, outperforming single-task models. By offering interpretable and clinically relevant insights, our approach aims to foster greater trust in AI-driven disease diagnosis and facilitate its adoption in clinical practice.

Optical Coherence Tomography (OCT) has become an indispensable imaging modality in ophthalmology, providing high-resolution cross-sectional images of the retina. Accurate classification of OCT images is crucial for diagnosing retinal diseases such as Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME). This study explores the efficacy of various deep learning models, including convolutional neural networks (CNNs) and Vision Transformers (ViTs), in classifying OCT images. We also investigate the impact of integrating metadata (patient age, sex, eye laterality, and year) into the classification process, even when a significant portion of metadata is missing. Our results demonstrate that multimodal models leveraging both image and metadata inputs, such as the Multimodal ResNet18, can achieve competitive performance compared to image-only models, such as DenseNet121. Notably, DenseNet121 and Multimodal ResNet18 achieved the highest accuracy of 95.16%, with DenseNet121 showing a slightly higher F1-score of 0.9313. The multimodal ViT-based model also demonstrated promising results, achieving an accuracy of 93.22%, indicating the potential of Vision Transformers (ViTs) in medical image analysis, especially for handling complex multimodal data.

Osteoarthritis (OA) is a major cause of disability worldwide, with symptoms like joint pain, limited functionality, and decreased quality of life, potentially leading to deformity and irreversible damage. Chemical changes in joint tissues precede imaging alterations, making early diagnosis challenging for conventional methods like X-rays. Although Raman imaging provides detailed chemical information, it is time-consuming. This paper aims to achieve rapid osteoarthritis diagnosis and grading using a self-developed Raman imaging system combined with deep learning denoising and acceleration algorithms. Our self-developed aberration-corrected line-scanning confocal Raman imaging device acquires a line of Raman spectra (hundreds of points) per scan using a galvanometer or displacement stage, achieving spatial and spectral resolutions of 2 μm and 0.2 nm, respectively. Deep learning algorithms enhance the imaging speed by over 4 times through effective spectrum denoising and signal-to-noise ratio (SNR) improvement. By leveraging the denoising capabilities of deep learning, we are able to acquire high-quality Raman spectral data with a reduced integration time, thereby accelerating the imaging process. Experiments on the tibial plateau of osteoarthritis patients compared three excitation wavelengths (532, 671, and 785 nm), with 671 nm chosen for optimal SNR and minimal fluorescence. Machine learning algorithms achieved a 98 % accuracy in distinguishing articular from calcified cartilage and a 97 % accuracy in differentiating osteoarthritis grades I to IV. Our fast Raman imaging system, combining an aberration-corrected line-scanning confocal Raman imager with deep learning denoising, offers improved imaging speed and enhanced spectral and spatial resolutions. It enables rapid, label-free detection of osteoarthritis severity and can identify early compositional changes before clinical imaging, allowing precise grading and tailored treatment, thus advancing orthopedic diagnostics and improving patient outcomes.

This study aimed to establish and validate a prediction model based on machine learning methods and SHAP algorithm to predict response to anti-vascular endothelial growth factor (VEGF) therapy in neovascular age-related macular degeneration (AMD). In this retrospective study, we extracted data including demographic characteristics, laboratory test results, and imaging features from optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA). Eight machine learning methods, including Logistic Regression, Gradient Boosting Decision Tree, Random Forest, CatBoost, Support Vector Machine, XGboost, LightGBM, K Nearest Neighbors were employed to develop the predictive model. The machine learning method with optimal performance was selected for further interpretation. Finally, the SHAP algorithm was applied to explain the model's predictions. The study included 145 patients with neovascular AMD. Among the eight models developed, the Random Forest model demonstrated general optimal performance, achieving a high accuracy of 75.86% and the highest area under the receiver operating characteristic curve (AUC) value of 0.91. In this model, important features identified as significant contributors to the response to anti-VEGF therapy in neovascular AMD patients included fractal dimension, total number of end points, total number of junctions, total vessels length, vessels area, average lacunarity, choroidal neovascularization (CNV) type, age, duration and logMAR BCVA. SHAP analysis and visualization provided interpretation at both the factor level and individual level. The Random Forest model for predicting response to anti-VEGF therapy in neovascular AMD using SHAP algorithm proved to be feasible and effective. OCTA imaging features, such as fractal dimension, total number of end points et al, were the most effective predictive factors.

BackgroundRetinal fundus images provide a noninvasive window into systemic health, offering opportunities for early detection of metabolic disorders such as metabolic syndrome (METS). ObjectiveThis study aimed to develop a deep learning model to predict METS from fundus images obtained during routine health checkups, leveraging a multi-task learning approach. MethodsWe retrospectively analyzed 5,000 fundus images from Japanese health checkup participants. Convolutional neural network (CNN) models were trained to classify METS status, incorporating fundus-specific data augmentation strategies and auxiliary regression tasks targeting clinical parameters such as abdominal circumference (AC). Model performance was evaluated using validation accuracy, test accuracy, and the area under the receiver operating characteristic curve (AUC). ResultsModels employing fundus-specific augmentation demonstrated more stable convergence and superior validation accuracy compared to general-purpose augmentation. Incorporating AC as an auxiliary task further enhanced performance across architectures. The final ensemble model with test-time augmentation achieved a test accuracy of 0.696 and an AUC of 0.73178. ConclusionCombining multi-task learning, fundus-specific data augmentation, and ensemble prediction substantially improves deep learning-based METS classification from fundus images. This approach may offer a practical, noninvasive screening tool for metabolic syndrome in general health checkup settings.

The adoption of standardized imaging protocols in retinal imaging is critical to overcoming challenges posed by fragmented data formats across devices and manufacturers. The lack of standardization hinders clinical interoperability, collaborative research, and the development of artificial intelligence (AI) models that depend on large, high-quality datasets. The Digital Imaging and Communication in Medicine (DICOM) standard offers a robust solution for ensuring interoperability in medical imaging. Although DICOM is widely utilized in radiology and cardiology, its adoption in ophthalmology remains limited. Retinal imaging modalities such as optical coherence tomography (OCT), fundus photography, and OCT angiography (OCTA) have revolutionized retinal disease management but are constrained by proprietary and non-standardized formats. This review underscores the necessity for harmonized imaging standards in ophthalmology, detailing DICOM standards for retinal imaging including ophthalmic photography (OP), OCT, and OCTA, and their requisite metadata information. Additionally, the potential of DICOM standardization for advancing AI applications in ophthalmology is explored. A notable example is the Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI) dataset, the first publicly available standards-compliant DICOM retinal imaging dataset. This dataset encompasses diverse retinal imaging modalities, including color fundus photography, infrared, autofluorescence, OCT, and OCTA. By leveraging multimodal retinal imaging, AI-READI provides a transformative resource for studying diabetes and its complications, setting a blueprint for future datasets aimed at harmonizing imaging formats and enabling AI-driven breakthroughs in ophthalmology. Our manuscript also addresses challenges in retinal imaging for diabetic patients, retinal imaging-based AI applications for studying diabetes, and potential advancements in retinal imaging standardization.

Shortcut learning has been identified as a source of algorithmic unfairness in medical imaging artificial intelligence (AI), but its impact on photoacoustic tomography (PAT), particularly concerning sex bias, remains underexplored. This study investigates this issue using peripheral artery disease (PAD) diagnosis as a specific clinical application. To examine the potential for sex bias due to shortcut learning in convolutional neural network (CNNs) and assess how such biases might affect diagnostic predictions, we created training and test datasets with varying PAD prevalence between sexes. Using these datasets, we explored (1) whether CNNs can classify the sex from imaging data, (2) how sex-specific prevalence shifts impact PAD diagnosis performance and underdiagnosis disparity between sexes, and (3) how similarly CNNs encode sex and PAD features. Our study with 147 individuals demonstrates that CNNs can classify the sex from calf muscle PAT images, achieving an AUROC of 0.75. For PAD diagnosis, models trained on data with imbalanced sex-specific disease prevalence experienced significant performance drops (up to 0.21 AUROC) when applied to balanced test sets. Additionally, greater imbalances in sex-specific prevalence within the training data exacerbated underdiagnosis disparities between sexes. Finally, we identify evidence of shortcut learning by demonstrating the effective reuse of learned feature representations between PAD diagnosis and sex classification tasks. CNN-based models trained on PAT data may engage in shortcut learning by leveraging sex-related features, leading to biased and unreliable diagnostic predictions. Addressing demographic-specific prevalence imbalances and preventing shortcut learning is critical for developing models in the medical field that are both accurate and equitable across diverse patient populations.

Adaptive optics scanning light ophthalmoscopy (AOSLO) reveals individual retinal cells and their function, microvasculature, and micropathologies in vivo. As compared to the single-channel offset pinhole and two-channel split-detector nonconfocal AOSLO designs, by providing multidirectional imaging capabilities, a recent generation of multidetector and (multi-)offset aperture AOSLO modalities has been demonstrated to provide critical information about retinal microstructures. However, increasing detection channels requires expensive optical components and/or critically increases imaging time. To address this issue, we present an innovative combination of machine learning and optics as an integrated technology to compressively capture 12 nonconfocal channel AOSLO images simultaneously. Imaging of healthy participants and diseased subjects using the proposed deep compressed multichannel AOSLO showed enhanced visualization of rods, cones, and mural cells with over an order-of-magnitude improvement in imaging speed as compared to conventional offset aperture imaging. To facilitate the adaptation and integration with other in vivo microscopy systems, we made optical design, acquisition, and computational reconstruction codes open source.