Progress and challenges of in vivo flow cytometry and its applications in circulating cells of eyes.

Circulating inflammatory cells in eyes have emerged as early indicators of numerous major diseases, yet the monitoring of these cells remains an underdeveloped field. In vivo flow cytometry (IVFC), a noninvasive technique, offers the promise of real-time, dynamic quantification of circulating cells. However, IVFC has not seen extensive applications in the detection of circulating cells in eyes, possibly due to the eye's unique physiological structure and fundus imaging limitations. This study reviews the current research progress in retinal flow cytometry and other fundus examination techniques, such as adaptive optics, ultra-widefield retinal imaging, multispectral imaging, and optical coherence tomography, to propose novel ideas for circulating cell monitoring.

Ultra-Widefield-Guided Swept-Source OCT Findings of Peripheral Vitreoretinal Abnormality in Young Myopes.

PURPOSE: To evaluate the ultra-widefield fundus photography (UWF-FP)-guided swept-source OCT (SS-OCT) images of peripheral vitreoretinal abnormality (PVRA) in young asymptomatic myopes. DESIGN: Cross-sectional, single-center study. PARTICIPANTS: A total of 1966 eyes of 983 consecutive patients aged 18 to 42 years with refractive error in the spherical equivalent of < 0 diopters (D) who visited KEYE Eye Center, Seoul, Republic of Korea, for refractive surgery. METHODS: The prevalence of PVRA and their characteristics, including shape, location, presence of pigmentation, membrane, retinal breaks, and detachment, were evaluated. A logistic regression analysis was done to evaluate the risk factors of PVRA and the risk of retinal breaks or detachment among eyes with PVRA. RESULTS: Among 1966 eyes, 317 (16.1%) had PVRA, and 182 (57.4%) and 135 (42.6%) had focal and linear lesions, respectively. The risk of PVRA was increased with axial length of the eyes (odds ratio [OR], 1.238, 95% confidence interval [CI], 1.112-1.379, P < 0.001), corneal astigmatism (OR, 1.320, 95% CI, 1.148-1.519, P < 0.001), presence of discrete margins of different retinal reflectivity (DMDRR; indicating outer retinal disruption from abnormal vitreoretinal traction) (OR, 1.751, 95% CI, 1.334-2.300, P < 0.001), and posterior vitreous detachment (PVD) at the posterior pole of the retina (OR, 1.833, 95% CI, 1.352-2.485, P < 0.001). Among eyes with PVRA, patient age (OR, 1.063, 95% CI, 1.008-1.121, P = 0.025), linear lesions (OR, 15.234, 95% CI, 7.891-29.407, P < 0.001), and multiple lesions (OR, 3.432, 95% CI, 1.780-6.620, P < 0.001) were independently associated with the presence of retinal breaks or detachment. CONCLUSIONS: The follow-up for PVRA among young myopes should be personalized on the basis of their ocular characteristics, including asymmetric ocular expansion (axial length and astigmatism) and vitreoretinal interface status. The treatment plan for PVRA eyes should emphasize the greater risk of retinal breaks and detachment with increasing age and the presence of linear and multiple lesions. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Predictive Performance of an Updated Polygenic Risk Score for Age-Related Macular Degeneration.

PURPOSE: A recent genome-wide association study of age-related macular degeneration (AMD) identified new AMD-associated risk variants. These variants now can be incorporated into an updated polygenic risk score (PRS). This study aimed to assess the performance of an updated PRS, PRS2023, in an independent cohort of older individuals with retinal imaging data and to compare performance with an older PRS, PRS2016. DESIGN: Cross-sectional study. PARTICIPANTS: A total of 4175 participants of European ancestry, 70 years of age or older, with genotype and retinal imaging data. METHODS: We used logistic regression models and area under the receiver operating characteristic curve (AUC) to assess the performance of PRS2023 compared with PRS2016. AMD status and severity were graded using color fundus photography. MAIN OUTCOME MEASURES: Association of PRS2023 and PRS2016 with AMD risk at baseline. RESULTS: At enrollment among 4175 participants, 2605 participants (62.4%) had no AMD and 853 participants (20.4%), 671 participants (16.1%), and 46 participants (1.1%) had early, intermediate, and late-stage AMD, respectively. More than 27% of the participants with a high PRS2023 (top quartile) had intermediate or late-stage AMD, compared with < 15% for those in the middle 2 quartiles and less than 13% for those in the lowest quartile. Both PRS2023 and PRS2016 were associated significantly with AMD after adjustment for age, sex, smoking status, and lipid levels, with increasing odds ratios (ORs) for worsening AMD grades. PRS2023 outperformed PRS2016 (P = 0.03 for all AMD and P = 0.03 for late AMD, DeLong test comparing AUC). PRS2023 was associated with late-stage AMD with an adjusted OR of 5.05 (95% confidence interval [CI], 3.41-7.47) per standard deviation. The AUC of a model containing conventional or nongenetic risk factors and PRS2023 was 91% (95% CI, 87%-95%) for predicting late-stage AMD, which improved 12% over the model without the PRS (AUC, 79%; P < 0.001 for difference). CONCLUSIONS: A new PRS, PRS2023, for AMD outperforms a previous PRS and predicts increasing risk for late-stage AMD (with stronger association for more severe imaging-confirmed AMD grades). Our findings have clinical implications for the improved prediction and risk stratification of AMD. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Prediction of cardiovascular risk factors from retinal fundus photographs: Validation of a deep learning algorithm in a prospective non-interventional study in Kenya.

AIM: Hypertension and diabetes mellitus (DM) are major causes of morbidity and mortality, with growing burdens in low-income countries where they are underdiagnosed and undertreated. Advances in machine learning may provide opportunities to enhance diagnostics in settings with limited medical infrastructure. MATERIALS AND METHODS: A non-interventional study was conducted to develop and validate a machine learning algorithm to estimate cardiovascular clinical and laboratory parameters. At two sites in Kenya, digital retinal fundus photographs were collected alongside blood pressure (BP), laboratory measures and medical history. The performance of machine learning models, originally trained using data from the UK Biobank, were evaluated for their ability to estimate BP, glycated haemoglobin, estimated glomerular filtration rate and diagnoses from fundus images. RESULTS: In total, 301 participants were enrolled. Compared with the UK Biobank population used for algorithm development, participants from Kenya were younger and would probably report Black/African ethnicity, with a higher body mass index and prevalence of DM and hypertension. The mean absolute error was comparable or slightly greater for systolic BP, diastolic BP, glycated haemoglobin and estimated glomerular filtration rate. The model trained to identify DM had an area under the receiver operating curve of 0.762 (0.818 in the UK Biobank) and the hypertension model had an area under the receiver operating curve of 0.765 (0.738 in the UK Biobank). CONCLUSIONS: In a Kenyan population, machine learning models estimated cardiovascular parameters with comparable or slightly lower accuracy than in the population where they were trained, suggesting model recalibration may be appropriate. This study represents an incremental step toward leveraging machine learning to make early cardiovascular screening more accessible, particularly in resource-limited settings.

Role of noninvasive ocular imaging as a biomarker in peripheral artery disease (PAD): A systematic review.

This study aimed to review the current literature exploring the utility of noninvasive ocular imaging for the diagnosis of peripheral artery disease (PAD). Our search was conducted in early April 2022 and included the databases Medline, Scopus, Embase, Cochrane, and others. Five articles were included in the final review. Of the five studies that used ocular imaging in PAD, two studies used retinal color fundus photography, one used optical coherence tomography (OCT), and two used optical coherence tomography angiography (OCTA) to assess the ocular changes in PAD. PAD was associated with both structural and functional changes in the retina. Structural alterations around the optic disc and temporal retinal vascular arcades were seen in color fundus photography of patients with PAD compared to healthy individuals. The presence of retinal hemorrhages, exudates, and microaneurysms in color fundus photography was associated with an increased future risk of PAD, especially the severe form of the disease. The retinal nerve fiber layer (RNFL) was significantly thinner in the nasal quadrant in patients with PAD compared to age-matched healthy individuals in OCT. Similarly, the choroidal thickness in the subfoveal region was significantly thinner in patients with PAD compared to controls. Patients with PAD also had a significant reduction in the retinal and choroidal circulation in OCTA compared to healthy controls. As PAD causes thinning and ischemic changes in retinal vessels, examination of the retinal vessels using retinal imaging techniques can provide useful information about early microvascular damage in PAD. Ocular imaging could potentially serve as a biomarker for PAD. PROSPERO ID: CRD42022310637.

Antacids and reflux esophagitis as a risk factor for gastric neoplasm of fundic-gland type: A retrospective, matched case-control study.

BACKGROUND AND AIM: Since the first report of gastric adenocarcinoma of the fundic-gland type in 2010, the clinicopathological characteristics of gastric neoplasm of the fundic-gland type (GNFG) have become clearer; however, their risk factors remain unclear. This exploratory study aimed to identify the risk factors for GNFG. METHODS: We conducted a single-center, retrospective, matched case-control study using medical information recorded at our health management center from January 2014 to July 2023. During this period, 39 240 people underwent upper gastrointestinal endoscopy. GNFG were extracted as cases and matched to controls, according to age and sex, in a 1:8 ratio, excluding those with a history of gastrointestinal surgery and those with a history or comorbidity of cancer. Univariate analysis was used to compare patient background and endoscopic findings. Multivariable analysis was performed, adjusting for factors with P values < 0.1 and antacid use. RESULTS: A total of 20 GNFG cases and 160 matched healthy controls were included. In the univariate analysis, only reflux esophagitis was significantly more common in GNFG (40.0% vs 18.1%; P = 0.036). Factors antacids and duodenitis had P values < 0.1. Logistic regression analysis was performed, adjusting for antacids, reflux esophagitis, and duodenitis. Antacids and reflux esophagitis were the independent risk factors for GNFG (odds ratio = 3.68 [95% confidence interval: 1.04-11.91] and 3.25 [95% confidence interval: 1.11-9.35]). CONCLUSIONS: Although the sample of patients with GNFG was small, antacids and reflux esophagitis were identified as a risk factor. The pathogenesis of antacids and reflux esophagitis may be involved in the development of GNFG.

Evaluating imaging repeatability of fully self-service fundus photography within a community-based eye disease screening setting.

PURPOSE: This study aimed to investigate the imaging repeatability of self-service fundus photography compared to traditional fundus photography performed by experienced operators. DESIGN: Prospective cross-sectional study. METHODS: In a community-based eye diseases screening site, we recruited 65 eyes (65 participants) from the resident population of Shanghai, China. All participants were devoid of cataract or any other conditions that could potentially compromise the quality of fundus imaging. Participants were categorized into fully self-service fundus photography or traditional fundus photography group. Image quantitative analysis software was used to extract clinically relevant indicators from the fundus images. Finally, a statistical analysis was performed to depict the imaging repeatability of fully self-service fundus photography. RESULTS: There was no statistical difference in the absolute differences, or the extents of variation of the indicators between the two groups. The extents of variation of all the measurement indicators, with the exception of the optic cup area, were below 10% in both groups. The Bland-Altman plots and multivariate analysis results were consistent with results mentioned above. CONCLUSIONS: The image repeatability of fully self-service fundus photography is comparable to that of traditional fundus photography performed by professionals, demonstrating promise in large-scale eye disease screening programs.

M2AI-CVD: Multi-modal AI approach cardiovascular risk prediction system using fundus images.

Cardiovascular diseases (CVD) represent a significant global health challenge, often remaining undetected until severe cardiac events, such as heart attacks or strokes, occur. In regions like Qatar, research focused on non-invasive CVD identification methods, such as retinal imaging and dual-energy X-ray absorptiometry (DXA), is limited. This study presents a groundbreaking system known as Multi-Modal Artificial Intelligence for Cardiovascular Disease (M2AI-CVD), designed to provide highly accurate predictions of CVD. The M2AI-CVD framework employs a four-fold methodology: First, it rigorously evaluates image quality and processes lower-quality images for further analysis. Subsequently, it uses the Entropy-based Fuzzy C Means (EnFCM) algorithm for precise image segmentation. The Multi-Modal Boltzmann Machine (MMBM) is then employed to extract relevant features from various data modalities, while the Genetic Algorithm (GA) selects the most informative features. Finally, a ZFNet Convolutional Neural Network (ZFNetCNN) classifies images, effectively distinguishing between CVD and Non-CVD cases. The research's culmination, tested across five distinct datasets, yields outstanding results, with an accuracy of 95.89%, sensitivity of 96.89%, and specificity of 98.7%. This multi-modal AI approach offers a promising solution for the accurate and early detection of cardiovascular diseases, significantly improving the prospects of timely intervention and improved patient outcomes in the realm of cardiovascular health.

Haemorrhage diagnosis in colour fundus images using a fast-convolutional neural network based on a modified U-Net.

Retinal haemorrhage stands as an early indicator of diabetic retinopathy, necessitating accurate detection for timely diagnosis. Addressing this need, this study proposes an enhanced machine-based diagnostic test for diabetic retinopathy through an updated UNet framework, adept at scrutinizing fundus images for signs of retinal haemorrhages. The customized UNet underwent GPU training using the IDRiD database, validated against the publicly available DIARETDB1 and IDRiD datasets. Emphasizing the complexity of segmentation, the study employed preprocessing techniques, augmenting image quality and data integrity. Subsequently, the trained neural network showcased a remarkable performance boost, accurately identifying haemorrhage regions with 80% sensitivity, 99.6% specificity, and 98.6% accuracy. The experimental findings solidify the network's reliability, showcasing potential to alleviate ophthalmologists' workload significantly. Notably, achieving an Intersection over Union (IoU) of 76.61% and a Dice coefficient of 86.51% underscores the system's competence. The study's outcomes signify substantial enhancements in diagnosing critical diabetic retinal conditions, promising profound improvements in diagnostic accuracy and efficiency, thereby marking a significant advancement in automated retinal haemorrhage detection for diabetic retinopathy.

Case of persistent corneal epithelial damage after cataract surgery leading to diagnosis of vitamin A deficiency.

PURPOSE: To report our findings of reduced full-field electroretinograms (ff-ERGs) and abnormal optical coherence tomographic (OCT) images in a patient with poor visual acuity after cataract surgery who was eventually diagnosed with vitamin A deficiency (VAD). METHODS: This was a clinical study of a patient who complained of blurred vision after cataract surgery. To determine the cause of the reduced vision, we recorded full-field electroretinograms (ff-ERGs) to determine the scotopic and photopic status of the retina. We also performed optical coherence tomography to assess the changes in the retinal structure. Serological tests were performed. RESULTS: A 74-year-old man presented with persistent corneal epithelial damages and reduced vision that developed after conventional cataract surgery. OCT showed an interrupted ellipsoid zone, and fundus autofluorescence (FAF) showed a severe hypofluorescence in the retina of the left eye. The scotopic ff-ERGs were severely reduced, and the photopic ff-ERGs were mildly reduced. Serological examinations revealed a vitamin A concentration < 7 IU/dL (normal, 97-316 IU/dL). Based on these findings, we diagnosed the patient with VAD and started treatment with oral vitamin A supplements. After three months, his visual acuity, ff-ERGs, and OCT findings recovered to normal levels. The amplitudes and implicit times of the RETeval flicker ERGs increased to be within the normal range, and the hypofluorescence of the left eye disappeared. The length of the photoreceptor outer segments increased after the vitamin A supplementation. CONCLUSION: Our findings indicate that the ERGs are helpful for diagnosing patients with VAD associated with persistent corneal epithelial damages.

Multifunctional nano-in-micro delivery systems for targeted therapy in fundus neovascularization diseases.

Fundus neovascularization diseases are a series of blinding eye diseases that seriously impair vision worldwide. Currently, the means of treating these diseases in clinical practice are continuously evolving and have rapidly revolutionized treatment opinions. However, key issues such as inadequate treatment effectiveness, high rates of recurrence, and poor patient compliance still need to be urgently addressed. Multifunctional nanomedicine can specifically respond to both endogenous and exogenous microenvironments, effectively deliver drugs to specific targets and participate in activities such as biological imaging and the detection of small molecules. Nano-in-micro (NIM) delivery systems such as metal, metal oxide and up-conversion nanoparticles (NPs), quantum dots, and carbon materials, have shown certain advantages in overcoming the presence of physiological barriers within the eyeball and are widely used in the treatment of ophthalmic diseases. Few studies, however, have evaluated the efficacy of NIM delivery systems in treating fundus neovascular diseases (FNDs). The present study describes the main clinical treatment strategies and the adverse events associated with the treatment of FNDs with NIM delivery systems and summarizes the anatomical obstacles that must be overcome. In this review, we wish to highlight the principle of intraocular microenvironment normalization, aiming to provide a more rational approach for designing new NIM delivery systems to treat specific FNDs.

Age prediction using fundus parameters of normal eyes from the Kumejima population study.

PURPOSE: Artificial intelligence can predict the age of an individual using color fundus photographs (CFPs). This study aimed to investigate the accuracy of age prediction in the Kumejima study using fundus parameters and to clarify age-related changes in the fundus. METHODS: We used nonmydriatic CFPs obtained from the Kumejima population study, including 1,646 right eyes of healthy participants with reliable fundus parameter measurements. The tessellation fundus index was calculated as R/(R + G + B) using the mean value of the red-green-blue intensity in eight locations around the optic disc and foveal region. The optic disc ovality ratio, papillomacular angle, and retinal vessel angle were quantified as previously described. Least absolute shrinkage and selection operator regression with leave-one-out cross-validation was used to predict age. The relationship between the actual and predicted ages was investigated using Pearson's correlation coefficient. RESULTS: The mean age of included participants (834 males and 812 females) was 53.4 ± 10.1 years. The mean predicted age based on fundus parameters was 53.4 ± 8.9 years, with a mean absolute error of 3.64 years, and the correlation coefficient between actual and predicted age was 0.88 (p < 0.001). Older patients had greater red and green intensities and weaker blue intensities in the peripapillary area (p < 0.001). CONCLUSIONS: Age could be predicted using the CFP parameters, and there were notable age-related changes in the peripapillary color intensity. The age-related changes in the fundus may aid the understanding of the mechanism of fundus diseases such as age-related macular degeneration.

An auxiliary diagnostic tool for common fundus diseases based on fundus color photography and light-weight classification models.

OBJECTIVE: To evaluate the performance of two lightweight neural network models in the diagnosis of common fundus diseases and make comparison to another two classical models. METHODS: A total of 16,000 color fundus photography were collected, including 2000 each of glaucoma, diabetic retinopathy (DR), high myopia, central retinal vein occlusion (CRVO), age-related macular degeneration (AMD), optic neuropathy, and central serous chorioretinopathy (CSC), in addition to 2000 normal fundus. Fundus photography was obtained from patients or physical examiners who visited the Ophthalmology Department of Beijing Tongren Hospital, Capital Medical University. Each fundus photography has been diagnosed and labeled by two professional ophthalmologists. Two classical classification models (ResNet152 and DenseNet121), and two lightweight classification models (MobileNetV3 and ShufflenetV2), were trained. Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were used to evaluate the performance of the four models. RESULTS: Compared with the classical classification model, the total size and number of parameters of the two lightweight classification models were significantly reduced, and the classification speed was sharply improved. Compared with the DenseNet121 model, the ShufflenetV2 model took 50.7% less time to make a diagnosis on a fundus photography. The classical models performed better than lightweight classification models, and Densenet121 showed highest AUC in five out of the seven common fundus diseases. However, the performance of lightweight classification models is satisfying. The AUCs using MobileNetV3 model to diagnose AMD, diabetic retinopathy, glaucoma, CRVO, high myopia, optic atrophy, and CSC were 0.805, 0.892, 0.866, 0.812, 0.887, 0.868, and 0.803, respectively. For ShufflenetV2model, the AUCs for the above seven diseases were 0.856, 0.893, 0.855, 0.884, 0.891, 0.867, and 0.844, respectively. CONCLUSION: The training of light-weight neural network models based on color fundus photography for the diagnosis of common fundus diseases is not only fast but also has a significant reduction in storage size and parameter number compared with the classical classification model, and can achieve satisfactory accuracy.

Fundus imaging features of congenital rubella retinopathy.

PURPOSE: To evaluate the clinical characteristics of congenital rubella retinopathy (CRR) with modern fundus imaging. METHODS: Single-center case series. Eleven patients (2005-2020) at the Emory Eye Center with known or presumed CRR. Trained image readers reviewed fundus imaging (color fundus photography, widefield pseudocolor imaging, near-infrared reflectance imaging, autofluorescence imaging, and spectral-domain optical coherence tomography) for pre-specified features suggestive of CRR. RESULTS: Eleven patients with confirmed (63.6%) or presumed (36.3%) CRR were identified. All were female with median (range) age of 53 (35-67) years. Six (54.5%) were born during the 1964-1965 United States rubella epidemic. All had congenital hearing loss. Two (18.2%) had a congenital heart defect. Eleven (50.0%) eyes had salt-and-pepper retinal pigmentary changes. Twenty-two eyes (100.0%) had irregularly distributed regions of speckled hypoautofluorescence. One eye (4.5%) had a presumed macular neovascularization. CONCLUSION: Modern fundus imaging demonstrates characteristic features of CRR, even when pigmentary changes are not readily apparent on examination. Widefield autofluorescence findings of irregularly distributed speckled hypoautofluorescence are particularly revealing. This series of newly diagnosed adults with CRR may represent the milder end of the phenotypic spectrum of this condition, highlighting imaging findings that may aid in diagnostically challenging cases of CRR.

An advanced deep learning method to detect and classify diabetic retinopathy based on color fundus images.

BACKGROUND: In this article, we present a computerized system for the analysis and assessment of diabetic retinopathy (DR) based on retinal fundus photographs. DR is a chronic ophthalmic disease and a major reason for blindness in people with diabetes. Consistent examination and prompt diagnosis are the vital approaches to control DR. METHODS: With the aim of enhancing the reliability of DR diagnosis, we utilized the deep learning model called You Only Look Once V3 (YOLO V3) to recognize and classify DR from retinal images. The DR was classified into five major stages: normal, mild, moderate, severe, and proliferative. We evaluated the performance of the YOLO V3 algorithm based on color fundus images. RESULTS: We have achieved high precision and sensitivity on the train and test data for the DR classification and mean average precision (mAP) is calculated on DR lesion detection. CONCLUSIONS: The results indicate that the suggested model distinguishes all phases of DR and performs better than existing models in terms of accuracy and implementation time.

Type 1 and type 2 torpedo maculopathy.

PURPOSE: To analyze torpedo maculopathy (TM) and to report the characteristics of the disease. METHODS: Retrospective study. The review of a database for clinical diagnosis identified eight patients with TM lesions in the retina between 2016 and 2022. Multimodal imaging was used to analyze the cases. RESULTS: All cases were unilateral, asymptomatic, and hypopigmented. They were associated by surrounding hyperpigmented retinal pigment epithelium changes to varying degrees. All lesions were located in the temporal retina on the horizontal axis, pointing towards the fovea, except for one patient with a lesion inferior to the fovea. Optical coherence tomography imaging revealed a normal inner retina in all eyes. In the area of the TM lesion, attenuation of the interdigitation zone was seen in mild cases (three cases). All other five patients had thinning of the outer nuclear layer and loss of ellipsoid zone and interdigitation zone of the TM lesion. Four of these cases had a subretinal cavitation/cleft, and two of them additionally an inner choroidal excavation. No patient had any sign of choroidal neovascularization. The average age for patients with type 1 TM was 18 years and for type 2 TM 16.5 years. CONCLUSION: In this large case series, we could not detect an age difference between the different types of the TM. Contrary to previous discussions, type 2 TM can also occur in young patients.

Prognostic and reproductive outcomes in women who had uterine myomas removed during cesarean section and sutured using different techniques.

BACKGROUND: In this study, the prognostic and reproductive outcomes of women who underwent excision of uterine myomas and were sutured using different techniques while undergoing a cesarean section were investigated. METHODS: A total of 299 females who underwent cesarean section between January 2015 and June 2022 due to a scarred uterus were enrolled in this study. These participants were segregated into two categories: the experimental group (comprising 155 cases) in which uterine myoma (single lesion) was excised during the cesarean procedure, and the control group (consisting of 144 cases) in which only the cesarean section was conducted. A comparison between the two groups was carried out based on the following parameters: volume of intraoperative bleeding (mL), additional measures taken for intraoperative hemostasis (n, %), percentage (%) of patients experiencing postoperative fever, duration required for the passage of gas (hours [h]), length of hospital stay (days [d]), weight of newborns (kg) and their Apgar scores, and the reproductive outcomes of the experimental group assessed two years after the surgical procedure. RESULTS: In the experimental group, the amount of bleeding during surgery, occurrence of postoperative fever among women, time taken for patients to resume passing gas, and length of hospital stay were 540.65 ± 269.12 mL, 9.03%, 15.99 ± 4.68 h, and 5.08 ± 1.18 days, respectively. In contrast, the control group had values of 409.03 ± 93.24 mL, 2.77%, 16.24 ± 4.92, and 4.47 ± 0.70 days, respectively (P < 0.05). No notable increase was observed in the need for additional intraoperative hemostasis measures, and there was no significant difference in the time it took for patients to pass gas after the surgery. All newborns had positive health status. In the experimental group, 25 patients underwent subsequent pregnancies, and 15 of them successfully reached full-term deliveries, all of which had positive outcomes. CONCLUSION: Combining myomectomy with various suture methods during cesarean delivery did not cause excessive bleeding and resulted in healthy newborns. This approach offers the advantage of avoiding additional surgeries under anesthesia and can be considered a viable option. Subsequent pregnancies after myomectomy were considered high-risk.

Improving diabetic retinopathy screening at the point of care: integrating telemedicine to overcome current challenges.

OBJECTIVE: To investigate the utility of point of care screening of diabetic retinopathy (DR) and the impact of a telemedicine program to overcome current challenges. METHODS: This was a retrospective study on people with type 2 diabetes mellitus (T2DM) who were screened for DR using the single-field non-mydriatic fundus photography at the point of care during routine follow-up visits at endocrinology clinic. Retinal images were uploaded and sent to a retina specialist for review. Reports indicating retinopathy status and the need for direct retinal examination were transmitted back to the endocrinology clinic. All patients were informed about DR status and, if needed, referred to the retina specialist for direct retinal examination. RESULTS: Of the 1159 individuals screened for DR, 417 persons (35.98%) were screen-positive and referred to the retina specialist for direct retinal examination. A total of 121 individuals (29.01%) underwent direct retinal examination by the specialist. Diabetes macular edema (DME) was detected in 12.1%. In addition, non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR) were detected in 53.4% and 2.6% of the patients, respectively. CONCLUSION: Integrating DR screening program at the point of care at the secondary care services improves the rate of DR screening as well as detection of sight threatening retinopathy and provides the opportunity for timely intervention in order to prevent advanced retinopathy in people with T2DM.

Research on segmentation model of optic disc and optic cup in fundus.

BACKGROUND: Glaucoma is a worldwide eye disease that can cause irreversible vision loss. Early detection of glaucoma is important to reduce vision loss, and retinal fundus image examination is one of the most commonly used solutions for glaucoma diagnosis due to its low cost. Clinically, the cup-disc ratio of fundus images is an important indicator for glaucoma diagnosis. In recent years, there have been an increasing number of algorithms for segmentation and recognition of the optic disc (OD) and optic cup (OC), but these algorithms generally have poor universality, segmentation performance, and segmentation accuracy. METHODS: By improving the YOLOv8 algorithm for segmentation of OD and OC. Firstly, a set of algorithms was designed to adapt the REFUGE dataset's result images to the input format of the YOLOv8 algorithm. Secondly, in order to improve segmentation performance, the network structure of YOLOv8 was improved, including adding a ROI (Region of Interest) module, modifying the bounding box regression loss function from CIOU to Focal-EIoU. Finally, by training and testing the REFUGE dataset, the improved YOLOv8 algorithm was evaluated. RESULTS: The experimental results show that the improved YOLOv8 algorithm achieves good segmentation performance on the REFUGE dataset. In the OD and OC segmentation tests, the F1 score is 0.999. CONCLUSIONS: We improved the YOLOv8 algorithm and applied the improved model to the segmentation task of OD and OC in fundus images. The results show that our improved model is far superior to the mainstream U-Net model in terms of training speed, segmentation performance, and segmentation accuracy.

Implementing and evaluating a fully functional AI-enabled model for chronic eye disease screening in a real clinical environment.

BACKGROUND: Artificial intelligence (AI) has the potential to increase the affordability and accessibility of eye disease screening, especially with the recent approval of AI-based diabetic retinopathy (DR) screening programs in several countries. METHODS: This study investigated the performance, feasibility, and user experience of a seamless hardware and software solution for screening chronic eye diseases in a real-world clinical environment in Germany. The solution integrated AI grading for DR, age-related macular degeneration (AMD), and glaucoma, along with specialist auditing and patient referral decision. The study comprised several components: (1) evaluating the entire system solution from recruitment to eye image capture and AI grading for DR, AMD, and glaucoma; (2) comparing specialist's grading results with AI grading results; (3) gathering user feedback on the solution. RESULTS: A total of 231 patients were recruited, and their consent forms were obtained. The sensitivity, specificity, and area under the curve for DR grading were 100.00%, 80.10%, and 90.00%, respectively. For AMD grading, the values were 90.91%, 78.79%, and 85.00%, and for glaucoma grading, the values were 93.26%, 76.76%, and 85.00%. The analysis of all false positive cases across the three diseases and their comparison with the final referral decisions revealed that only 17 patients were falsely referred among the 231 patients. The efficacy analysis of the system demonstrated the effectiveness of the AI grading process in the study's testing environment. Clinical staff involved in using the system provided positive feedback on the disease screening process, particularly praising the seamless workflow from patient registration to image transmission and obtaining the final result. Results from a questionnaire completed by 12 participants indicated that most found the system easy, quick, and highly satisfactory. The study also revealed room for improvement in the AMD model, suggesting the need to enhance its training data. Furthermore, the performance of the glaucoma model grading could be improved by incorporating additional measures such as intraocular pressure. CONCLUSIONS: The implementation of the AI-based approach for screening three chronic eye diseases proved effective in real-world settings, earning positive feedback on the usability of the integrated platform from both the screening staff and auditors. The auditing function has proven valuable for obtaining efficient second opinions from experts, pointing to its potential for enhancing remote screening capabilities. TRIAL REGISTRATION: Institutional Review Board of the Hamburg Medical Chamber (Ethik-Kommission der Ärztekammer Hamburg): 2021-10574-BO-ff.