Purpose: We aimed to evaluate the feasibility of using a novel device, the Smart Eye Camera (SEC), for assessing tear meniscus height (TMH) after fluorescein staining and the agreement of the results with measurements obtained using standard slit lamp examination. Methods: TMH was assessed using both SEC and conventional slit lamp examination. The images were analyzed using the software ImageJ 1.53t (National Institutes of Health, Bethesda, MD, USA). A common measurement unit scale was established based on a paper strip, which was used as a calibration marker to convert pixels into metric scale. A color threshold was applied using uniform parameters for brightness, saturation, and hue. The images were then binarized to black and white to enhance the representation of the tear menisci. A 2 mm area around the upper and lower meniscus in the central eye lid zone was selected and magnified 3200 times to facilitate manual measurement. The values obtained using SEC were compared with those obtained with a slit lamp. Results: The upper and lower TMH values measured using the SEC were not statistically different from those obtained with a slit lamp (0.209 ± 0.073 mm vs. 0.235 ± 0.085, p = 0.073, and 0.297 ± 0.168 vs. 0.260 ± 0.173, p = 0.275, respectively). The results of Bland–Altman analysis demonstrated strong agreement between the two instruments, with a mean bias of −0.016 mm (agreement limits: −0.117 to 0.145 mm) for upper TMH and 0.031 mm (agreement limits: −0.306 to 0.368 mm) for lower TMH. Conclusions: The SEC demonstrated sufficient validity and reliability for assessing TMH in healthy eyes in a clinical setting, demonstrating concordance with the conventional slit lamp examination.

Primary angle closure glaucoma (PACG) is a major cause of visual impairment, particularly in Asia. Although effective screening tools are necessary, the current gold standard is complex and time-consuming, requiring extensive expertise. Artificial intelligence has introduced new opportunities for innovation in ophthalmic imaging. Anterior chamber depth (ACD) is a key risk factor for angle closure and has been suggested as a quick screening parameter for PACG. This study aims to develop an AI algorithm to quantitatively predict ACD from anterior segment photographs captured using a portable smartphone slit-lamp microscope. We retrospectively collected 204,639 frames from 1586 eyes, with ACD values obtained by anterior-segment OCT. We developed two models, (Model 1) diagnosable frame extraction and (Model 2) ACD estimation, using SWSL ResNet as the machine learning model. Model 1 achieved an accuracy of 0.994. Model 2 achieved an MAE of 0.093 ± 0.082 mm, an MSE of 0.123 ± 0.170 mm, and a correlation of R = 0.953. Furthermore, our model’s estimation of the risk for angle closure showed a sensitivity of 0.943, specificity of 0.902, and an area under the curve (AUC) of 0.923 (95%CI: 0.878–0.968). We successfully developed a high-performance ACD estimation model, laying the groundwork for predicting other quantitative measurements relevant to PACG screening.

Ophthalmological services face global inadequacies, especially in low- and middle-income countries, which are marked by a shortage of practitioners and equipment. This study employed a portable slit lamp microscope with video capabilities and cloud storage for more equitable global diagnostic resource distribution. To enhance accessibility and quality of care, this study targets corneal opacity, which is a global cause of blindness. This study has two purposes. The first is to detect corneal opacity from videos in which the anterior segment of the eye is captured. The other is to develop an AI pipeline to detect corneal opacities. First, we extracted image frames from videos and processed them using a convolutional neural network (CNN) model. Second, we manually annotated the images to extract only the corneal margins, adjusted the contrast with CLAHE, and processed them using the CNN model. Finally, we performed semantic segmentation of the cornea using annotated data. The results showed an accuracy of 0.8 for image frames and 0.96 for corneal margins. Dice and IoU achieved a score of 0.94 for semantic segmentation of the corneal margins. Although corneal opacity detection from video frames seemed challenging in the early stages of this study, manual annotation, corneal extraction, and CLAHE contrast adjustment significantly improved accuracy. The incorporation of manual annotation into the AI pipeline, through semantic segmentation, facilitated high accuracy in detecting corneal opacity.

Ophthalmological services face global inadequacies, especially in low- and middle-income countries, which are marked by a shortage of both practitioners and equipment. This study employed a portable slit-lamp microscope with video capabilities and cloud storage for a more equitable global diagnostic resource distribution. To enhance accessibility and quality of care, this study targeted corneal opacity, a global cause of blindness. To boost the online diagnosis efficiency, an AI pipeline was developed using anterior segment videos to detect corneal opacity. First, we extracted image frames from videos and learned them using a Convolutional Neural Network(CNN) model. Second, we manually annotated to extract only the corneal margins, adjusted the contrast with CLAHE, and learned using the CNN model. Finally, we performed semantic segmentation of the cornea using the annotated data. The results showed an accuracy of 0.8 for image frames, and 0.96 for corneal margins. Dice and IoU were 0.94 for semantic segmentation of corneal margins. While corneal opacity detection from video frames seemed challenging in the early stages of this study, manual annotation, corneal extraction, and CLAHE contrast adjustment significantly improved accuracy. Integrating manual annotation into the AI pipeline through semantic segmentation achieved a high accuracy in detecting corneal opacity.

Background: Anemia is a common systemic condition associated with adverse maternal, perioperative, and cardiovascular outcomes. Although timely screening is clinically important, diagnosis still relies on invasive blood testing. Palpebral conjunctival pallor has traditionally been used as a noninvasive indicator of anemia, but its diagnostic accuracy remains limited. This study aimed to develop and validate a deep learning system to estimate hemoglobin (Hb) concentration and screen for anemia using palpebral conjunctiva images captured with a smartphone-compatible slit-lamp microscope. Methods: In this prospective observational study, 225 Japanese participants (20–92 years) underwent conjunctival imaging and blood testing. Palpebral conjunctiva videos were obtained using the Smart Eye Camera. Video frames were processed using automated anterior-segment segmentation and conjunctiva extraction. A ConvNeXt-based regression model was trained to predict Hb values. Anemia was defined using sex-specific Hb thresholds. Results: From 225 videos, 53,776 frames were extracted, yielding 9903 quality-filtered conjunctiva images (training: 8082; test: 1821). Video-level predicted Hb values moderately correlated with measured Hb (r = 0.42). For anemia screening, frame-level analysis achieved an AUC of 0.75, with accuracy of 0.76, sensitivity of 0.71, and specificity of 0.79. Video-level aggregation achieved 69% accuracy. Conclusions: Deep learning analysis of palpebral conjunctiva images acquired with a portable slit-lamp microscope demonstrated the feasibility of non-invasive hemoglobin estimation and anemia screening. Although the proposed approach achieved moderate performance, further improvements in model accuracy and prospective multi-center validation are required before clinical implementation.

Background: Narrowing of the trabecular–iris angle (TIA) is a key anatomical feature of primary angle-closure disease and contributes to impaired aqueous humor outflow; however, conventional angle assessment requires specialized expertise or costly imaging equipment. This study aimed to develop an artificial intelligence model for quantitative TIA estimation from anterior-segment images acquired with a smartphone-attached slit-lamp microscope. Methods: In this retrospective study, 127,512 candidate frames were extracted from anterior-segment videos obtained from 2654 eyes. After automated diagnosability filtering, 59,412 frames suitable for TIA500 estimation were retained and assigned to the training, validation, and test datasets using patient-level grouping. A ResNet50-based model was trained to estimate inferior, nasal, and temporal TIA500 values using the corresponding measurements obtained by anterior-segment optical coherence tomography (AS-OCT) as the ground-truth reference. Model performance was evaluated by comparing the AI-predicted values with the AS-OCT measurements using mean absolute error (MAE), root mean squared error (RMSE), Spearman’s rank correlation coefficient, and the intraclass correlation coefficient (ICC). Results: Per-eye MAEs were 7.01° ± 5.69°, 7.28° ± 5.80°, and 7.90° ± 6.37° for inferior, nasal, and temporal TIA, respectively. Corresponding correlations with reference measurements were 0.683, 0.696, and 0.646, while ICCs were 0.769, 0.764, and 0.743 (all p < 0.001). Conclusions: These findings demonstrate the feasibility of estimating TIA500 from smartphone-based slit-lamp images. However, the moderate correlations with AS-OCT measurements indicate that the present model is not yet sufficiently accurate for independent clinical diagnosis or screening, and further model improvement and external validation are required.