Combined treatment of submacular hemorrhage with low-dose subretinal recombinant tissue plasminogen activator and intravitreal conbercept.

BACKGROUND: Pars Plana Vitrectomy (PPV) combined with subretinal injection of low-dose recombinant tissue plasminogen activator (rt-PA) and intravitreal injection of Conbercept as a novel therapy for submacular hemorrhage (SMH) requires evaluation. METHODS: In a retrospective interventional clinical study, 14 eyes of 14 patients with SMH underwent PPV along with rt-PA (subretinal) and Conbercept (intravitreal) injections. The main outcomes included best-corrected visual acuities (BCVAs), degrees of blood displacement, and adverse events. All patients completed at least 6-month follow-up visits. RESULTS: Mean BCVAs significantly improved at 7 days (22.29 ± 15.35), 1 month (30.71 ± 16.42), 3 months (38.29 ± 13.72), 4 months (38.86 ± 14.15), and 6 months (41.21 ± 14.91) post-treatment compared to baseline (16.36 ± 13.97) (F = 12.89, P = 0.004). The peak improvement in BCVAs occurred at 6 months postoperatively. The procedure effectively eliminated subfoveal hemorrhages in all eyes, with clots removal and absorption occurring within one month and complete regression by 3-month follow-up visits. Postoperatively, two cases of AMD resulted in discoid scars on the fundus. No instances of rt-PA-related retinal toxicity were observed during the follow-up period. CONCLUSION: The combined approach of PPV with low-dose rt-PA and anti-VEGF shows promise in enhancing both vision and anatomical structure in SMH therapy. Individualized treatment plans tailored to the primary disease should be developed to optimize visual prognoses. TRIAL REGISTRATION: Retrospectively registered No.ChiCTR2100053034. Registration date: 10/11/2021.

Deep Learning-Based Vascular Aging Prediction From Retinal Fundus Images.

Purpose: The purpose of this study was to establish and validate a deep learning model to screen vascular aging using retinal fundus images. Although vascular aging is considered a novel cardiovascular risk factor, the assessment methods are currently limited and often only available in developed regions. Methods: We used 8865 retinal fundus images and clinical parameters of 4376 patients from two independent datasets for training a deep learning algorithm. The gold standard for vascular aging was defined as a pulse wave velocity ≥1400 cm/s. The probability of the presence of vascular aging was defined as deep learning retinal vascular aging score, the Reti-aging score. We compared the performance of the deep learning model and clinical parameters by calculating the area under the receiver operating characteristics curve (AUC). We recruited clinical specialists, including ophthalmologists and geriatricians, to assess vascular aging in patients using retinal fundus images, aiming to compare the diagnostic performance between deep learning models and clinical specialists. Finally, the potential of Reti-aging score for identifying new-onset hypertension (NH) and new-onset carotid artery plaque (NCP) in the subsequent three years was examined. Results: The Reti-aging score model achieved an AUC of 0.826 (95% confidence interval [CI] = 0.793-0.855) and 0.779 (95% CI = 0.765-0.794) in the internal and external dataset. It showed better performance in predicting vascular aging compared with the prediction with clinical parameters. The average accuracy of ophthalmologists (66.3%) was lower than that of the Reti-aging score model, whereas geriatricians were unable to make predictions based on retinal fundus images. The Reti-aging score was associated with the risk of NH and NCP (P < 0.05). Conclusions: The Reti-aging score model might serve as a novel method to predict vascular aging through analysis of retinal fundus images. Reti-aging score provides a novel indicator to predict new-onset cardiovascular diseases. Translational Relevance: Given the robust performance of our model, it provides a new and reliable method for screening vascular aging, especially in undeveloped areas.

Performance of deep learning for detection of chronic kidney disease from retinal fundus photographs: A systematic review and meta-analysis.

OBJECTIVE: Deep learning has been used to detect chronic kidney disease (CKD) from retinal fundus photographs. We aim to evaluate the performance of deep learning for CKD detection. METHODS: The original studies in CKD patients detected by deep learning from retinal fundus photographs were eligible for inclusion. PubMed, Embase, the Cochrane Library, and Web of Science were searched up to October 31, 2022. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess the risk of bias. RESULTS: Four studies enrolled 114,860 subjects were included. The pooled sensitivity and specificity were 87.8% (95% confidence interval (CI): 61.6% to 98.3%), and 62.4% (95% CI: 44.9% to 78.7%). The area under the curve (AUC) was 0.864 (95%CI: 0.769, 0.986). CONCLUSION: Deep learning based on retinal fundus photographs has the ability to detect CKD, but it currently has a lot of room for improvement. It is still a long way from clinical application.

Ocular images-based artificial intelligence on systemic diseases.

PURPOSE: To provide a summary of the research advances on ocular images-based artificial intelligence on systemic diseases. METHODS: Narrative literature review. RESULTS: Ocular images-based artificial intelligence has been used in a variety of systemic diseases, including endocrine, cardiovascular, neurological, renal, autoimmune, and hematological diseases, and many others. However, the studies are still at an early stage. The majority of studies have used AI only for diseases diagnosis, and the specific mechanisms linking systemic diseases to ocular images are still unclear. In addition, there are many limitations to the research, such as the number of images, the interpretability of artificial intelligence, rare diseases, and ethical and legal issues. CONCLUSION: While ocular images-based artificial intelligence is widely used, the relationship between the eye and the whole body should be more clearly elucidated.