Background: Previous studies have proposed an automated customized program named MATLAB used in the foveal avascular zone (FAZ) measurements in Triton optical coherence tomography angiography (OCTA) images. But it is not open-source and not easy to obtain, which will largely restrict its application in clinical practice and medical research. In this study, we aimed to investigate the feasibility of the Smooth Level Sets macro (SLSM), a free and open-source program, and compared with the manual measurements and MATLAB in the FAZ quantification in Triton OCTA. Methods: Thirty-five eyes of 35 healthy subjects were scanned four times continuously using Triton OCTA. Manual and automated methods including the SLSM and MATLAB were used in the FAZ metrics (area, perimeter, and circularity) of the superficial capillary plexus. The accuracy, repeatability of all methods, and agreement between automated and manual methods were analyzed. Results: The SLSM presented higher accuracy with a higher average Dice coefficient (0.9506) than MATLAB (0.9483), which was just second to the manual method (0.9568). Both the SLSM [intraclass correlation coefficient (ICC) =0.987; coefficient of variation (CoV) =3.935%] and MATLAB (ICC =0.983; CoV =4.165%) showed excellent repeatability for the FAZ area. They also had excellent agreement with manual measurement (SLSM, ICC =0.973; MATLAB, ICC =0.968). Conclusion: The SLSM exhibits better accuracy than MATLAB in the automated FAZ measurement in Triton OCTA, the results of which were comparable to those obtained by manual measurement. This free and open-source program may be an accessible and feasible option for automated FAZ segmentation on Triton OCTA images.
With the rapid development of artificial intelligence (AI) technology, the application of AI technology based on deep learning (DL) and machine learning (ML) in the medical field has received widespread attention. The application of AI in ophthalmology is gradually being shifted to a more comprehensive and in-depth level. Trained on corneal tomography, optical coherence tomography (OCT), slit-lamp images, and other techniques. AI can achieve robust performance in the diagnosis and treatment of corneal lesions, conjunctival lesions, cataract, glaucoma and other ophthalmic diseases. However, there are also some challenges in the application of AI in ophthalmology, including the lack of interpretability of results, lack of standardization of data sets, uneven quality of data sets, insufficient applicability of models and ethical issues. In the era of 5G and telemedicine, there are also many new opportunities for ophthalmic AI. In this review, we provided a summary of the state-of-the-art AI application in anterior segment ophthalmic diseases, potential challenges in clinical implementation and its development prospects, and provides reference information for the further development of artificial intelligence in the field of ophthalmology.
Optic nerves are a part of the central nervous system, which is difficult to regenerate after injury. Optic nerve injury is usually accompanied by continuous apoptosis of retinal ganglion cells (RGCs) and degeneration or necrosis of optic nerves, resulting in visual impairment or even complete blindness. At present, the basic research on optic nerve regeneration mainly focuses on protecting and maintaining the survival of RGCs after optic nerve injury, promoting RGCs axon regeneration, and reconstructing optic nerve function. In this paper, RGCs protection,axon regeneration, and optic nerve function reconstruction are used as key words to collect the latest domestic and foreign literatures on optic nerve regeneration. The research progress of optic nerve regeneration in recent years was reviewed from the aspects of antioxidant stress, provision of exogenous cytokines, inflammatory stimulation, anti-glial scar, gene regulation and so on, in order to help the follow-up basic research and clinical translation.
Artificial intelligence (AI) has been widely used in cataract surgery. The combination of the two can play a great role in improving preoperative diagnosis, grading management of cataract surgery, intraoperative intraocular lens selection and location prediction, postoperative management (vision prediction, complication prediction and follow-up), surgical training and teaching. It is true that AI still faces many problems in the management, analysis and research related to cataract surgery, but its broad application prospects cannot be ignored. This review summarizes the application of AI in cataract surgery and teaching, and the future prospects of AI.
Objective: To elucidate the expression of long non-coding RNAs (lncRNAs) and their roles as competing endogenous RNAs (ceRNAs) in uveal melanoma (UM) metastasis. Methods: RNA sequencing data and clinical information of 80 patients with UM were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed (DE) mRNAs, microRNAs (miR), and lncRNAs between metastatic and non-metastatic individuals with UM were screened using the edgeR algorithm. Gene enrichment analysis was conducted for the DE mRNAs. LncRNA-miR-mRNA regulatory triples and a ceRNA network were constructed. Betweenness centrality was used to screen hub genes and lncRNAs for subnetwork analysis. Kaplan-Meier survival analysis was conducted to explore correlations between the expression of hub RNAs and overall survival in the TCGA UM cohort. Results: A total of 346 upregulated mRNAs, 118 downregulated miRs, and 45 upregulated lncRNAs were identified in samples with systemic metastasis. Among them, 67 mRNAs, 7 miRs, and 30 lncRNAs mapped to 616 ceRNA triples, thus forming an interconnected ceRNA network with 181 edges. Gene enrichment analysis revealed that mRNAs in the network were enriched in multiple gene ontology terms and pathways associated with carcinogenesis and metastasis. Topological analysis identified 6 hub lncRNAs (LINC00861, LINC02421, BHLHE40-AS1, LINC01252, LINC00513, and LINC02389) and 3 hub mRNAs (UNC5D, BCL11B, and MTDH). The expression levels of all hub genes and 5 DEmiRs (miR-221, miR-222, miR-506, miR-507, miR-876) were significantly associated with the overall survival probability. Conclusion: This bioinformatic study revealed the functions of several lncRNAs and their associated ceRNA network in UM metastasis. It provides a novel in silicon evidence for future experimental study on the pathogenesis of systemic metastasis in uveal melanoma, especially from the perspective of non-coding RNA.
Objective: To investigate the predictive accuracy and effect of capsular tension ring (CTR) implantation with five new generation intraocular lens (IOL) calculation formulas [Barrett Universal Ⅱ (BU Ⅱ), Emmetropia Verifying Optical(EVO), Kane, Pearl-DGS and Hill-RBF 2.0] in high myopia patients. Methods: This is a prospective case-control study. The patients were enrolled with an axial length (AL)≥27.00 mm, and underwent cataract surgery with AR40E IOL implantation at the Shaanxi Eye Hospital from December 2020 to September 2021. The patients were randomly assigned to the CTR implantation group (group A) and the non-CTR implantation group (group B). With the ocular parameters measured by the IOLMaster700, the IOL power was calculated with the BUⅡformula before surgery. The postoperative actual equivalent spherical diopter (SE) were recorded,and the predicted error (PE) and absolute error (AE) using the five formulas were recorded and compared at 1 week, 1 month, and 3 months, repsectively. Group A was divided to A1 (27.00 mm ≤ AL ≤ 30.00 mm) and A2 (AL>30.00 mm), and group B was divided to B1 (27.00 mm ≤ AL ≤ 30.00 mm) and B2 (AL>30.00 mm). The effects of CTR implantation and the accuracy of the formulas were analyzed with different AL ranges. Results: A total of 63 patients (89 eyes) were included, aged (55.93±10.17) years old, with preoperative AL (30.30± 2.18)mm. There was no statistically significant difference in SE between groups A, A1, and A2 (P>0.05) at different postoperative times. While there was a statistically significant difference in SE between groups B, B1, and B2 (P < 0.05) at 1 week and 1 month after surgery, and between 1 week and 3 months after surgery. There was no statistically significant difference between 1 month and 3 months after suergery (P>0.05). There was no significant difference in the AE using the five formulas among groups A, B, A1, A2, B1, and B2 (P>0.05). There was no statistically significant difference in prediction error changes among the five formulas after CTR implantation (P>0.05). Conclusion: For cataract patients with AL ≥ 27.00 mm, the refractionvalue in the CTR implantation group tended to stabilizeafter one week of surgery. While in the non-CTR implantation group, the refractionvalue tended to stabilize after one month. CTR implantation had no effect on the accuracy and selection of the five formula, and the five IOL calculation formulas can be normally selected.
In recent years, artificial intelligence (AI) in ophthalmology has developed rapidly. Fundus image has become a research hotspot due to its easy access and rich biological information. The application of AI analysis in fundus image is under continuous development and exploration. At present, there have been many AI studies on clinical screening, diagnosis and prediction of common fundus diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma, and related achievements have been gradually applied in clinical practice. In addition to ophthalmic diseases, exploring the relationship between fundus features and various diseases and developing AI diagnostic systems based on this has become another popular research field. The application of AI in fundus image analysis will improve the shortage of medical resources and low diagnostic efficiency, and open up a “new track” for screening and diagnosis of various diseases. In the future, research on AI analysis of fundus image should focus on the intelligent and comprehensive diagnosis of multiple fundus diseases, and comprehensive auxiliary diagnosis of complex diseases, and lays emphasis on the integration of standardized and high-quality data resources, improve algorithm performance, and design clinically appropriate research program.
Objective: To compare the effective optical zone (EOZ) and the changes in corneal high order aberrations (HOAs) after small incision lenticule extraction (SMILE) with those after femtosecond laser-assisted in situ keratomileuses (FS-LASIK). Methods: This study included 80 subjects who underwent laser refractive surgery at the Second People’s Hospital of Foshan between February 2019 and May 2020. Only data from the right eye of each subject were analyzed. A total of 43 eyes underwent SMILE while 37 eyes received FS-LASIK. The eyes were further stratified into subgroups based on different programmed optical zones: the 6.5 mm group and the 6.0 mm group. EOZ, coma, and spherical aberration were measured with Pentacam 3D anterior segment analysis system preoperatively and one month postoperatively. In addition, the relationship between EOZ and corneal HOAs was analyzed and compared between different optical-zone groups after SMILE and FS-LASIK. Results: For the same programmed optical zone, the SMILE group achieved a significantly greater EOZ than the FS-LASIK group who was measured 1-month postoperatively did (P<0.05). Meanwhile, corneal HOAs, spherical aberration, and coma in the SMILE group are significantly lower than those in the FS-LASIK group (P<0.05). For the same procedure (SMILE or FS-LASIK), the 6.0 mm group demonstrated significantly higher corneal total HOAs, spherical aberration, and coma than the 6.5 mm group did 1-month after the surgery (P<0.05). Conclusion: In both the SMILE and the FS-LASIK groups, 1-month postoperative EOZ was smaller than the programmed optical zone. EOZ in the SMILE group was larger than that in the FS-LASIK group. The larger the 1-month postoperative EOZ was, the lower corneal HOAs were. For the same programmed optical zone, 1-month postoperative corneal HOAs in the SMILE group is lower than that in the FS-LASIK group.
Objective: To evaluate the accuracy of new generation artificial intelligence (AI)-based intraocular lens (IOL)power calculation formulas. Methods: This retrospective study included a total of 262 eyes from 262 patients with cataract who underwent uneventful phacoemulsification combined with IOL implantation. Keratometry, corneal white-to-white, central corneal thickness, anterior chamber depth, lens thickness, and axial length were measured by the IOL Master 700 before surgery. Predicted refractive errors were calculated by the third-generation formulas (SRK/T, Holladay 1, and Hoffer Q), Barrett UniversalⅡ (BUⅡ), and the newer-generation AI formulas (Kane, Pearl-DGS, Hill-RBF 3.0, Hoffer QST, and Jin-AI), and were compared with the actual postoperative refractive value. After adjusting the prediction error (PE) to zero, the standard deviation (SD), mean absolute error (MAE), median absolute error (MedAE), and the percentage of a PE within the range of ±0.25 diopter (D), ±0.50 D, ±1.00 D, and ±2.00 D were analyzed. Results: The SD, MAE, and MedAE of the AI-based formulas ranged from 0.37 D (Kane and Jin-AI) to 0.39 D (Hoffer QST), 0.28 D (Hill-RBF 3.0 and Jin-AI) to 0.31 D (Hoffer QST), and 0.21 D (Hill-RBF 3.0 and Jin-AI) to 0.24 D (Hoffer QST), respectively. These values were all lower than those of the third-generation formula (SD: 0.43 D to 0.45 D; MAE: 0.34 D; MedAE: 0.25 D to 0.28 D). Among all the formulas, the Jin-AI formula had the highest proportion of a PE within ±0.50 D (84.73%), followed by Kane (84.35%) and BUⅡ (83.97%) formulas. Conclusion: The new AI-based IOL formulas show higher accuracy compared with the traditional third-generation ones in predicting IOL power. thereby enabling more patients to achieve the expected refractive outcomes after surgery
The retina is a part of the central nervous system. Developmentally, both retina and brain are derived from the neural tube. Therefore, many neurodegenerative diseases that occur in the brain tend to involve both the retina. In the process of neurodegenerative diseases, related characteristic pathological changes, such as pathological protein aggregation, neurovascular unit impairment can often be detected in retinal tissue. In some neurodegenerative diseases, pathological changes in the eye occur even before clinical symptoms appear. In addition, the retina are easy to observe and local treatments are convenient. In recent years, the manifestations of the retina have attracted much attention in the study of pathogenesis, early diagnosis, and new treatments of systemic central neurodegenerative diseases. In this way, this article reviews the ocular pathological changes of common neurodegenerative diseases, aiming to provide new insights into the pathogenesis, diagnosis, and treatment of brain and retinal neurodegenerative diseases.
