EBD: an eye biomarker database.

MOTIVATION: Many ophthalmic disease biomarkers have been identified through comprehensive multiomics profiling, and hold significant potential in advancing the diagnosis, prognosis, and management of diseases. Meanwhile, the eye itself serves as a natural biomarker for several systemic diseases including neurological, renal, and cardiovascular systems. We aimed to collect and standardize this eye biomarkers information and construct the eye biomarker database (EBD) to provide ophthalmologists with a platform to search, analyze, and download these eye biomarker data. RESULTS: In this study, we present the EBD , a world-first online compilation comprising 889 biomarkers for 26 ocular diseases and 939 eye biomarkers for 181 systemic diseases. The EBD also includes the information of 78 "nonbiomarkers"-the objects that have been proven cannot be biomarkers. Biological function and network analysis were conducted for these ocular disease biomarkers, and several hub pathways and common network topology characteristics were newly identified, which may promote future ocular disease biomarker discovery and characterizes the landscape of biomarkers for eye diseases at the pathway and network level. The EBD is expected to yield broader utility among developmental biologists and clinical scientists in and outside of the eye field by assisting in the identification of biomarkers linked to eye disorders and related systemic diseases. AVAILABILITY AND IMPLEMENTATION: EBD is available at http://www.eyeseeworld.com/ebd/index.html.

Contrastive pre-training and linear interaction attention-based transformer for universal medical reports generation.

Interpreting medical images such as chest X-ray images and retina images is an essential step for diagnosing and treating relevant diseases. Proposing automatic and reliable medical report generation systems can reduce the time-consuming workload, improve efficiencies of clinical workflows, and decrease practical variations between different clinical professionals. Many recent approaches based on image-encoder and language-decoder structure have been proposed to tackle this task. However, some technical challenges remain to be solved, including the fusion efficacy between the language and visual cues and the difficulty of obtaining an effective pre-trained image feature extractor for medical-specific tasks. In this work, we proposed the weighted query-key interacting attention module, including both the second-order and first-order interactions. Compared with the conventional scaled dot-product attention, this design generates a strong fusion mechanism between language and visual signals. In addition, we also proposed the contrastive pre-training step to reduce the domain gap between the image encoder and the target dataset. To test the generalizability of our learning scheme, we collected and verified our model on the world-first multi-modality retina report generation dataset referred to as Retina ImBank and another large-scale retina Chinese-based report dataset referred to as Retina Chinese. These two datasets will be made publicly available and serve as benchmarks to encourage further research exploration in this field. From our experimental results, we demonstrate that our proposed method has outperformed multiple state-of-the-art image captioning and medical report generation methods on IU X-RAY, MIMIC-CXR, Retina ImBank, and Retina Chinese datasets.

Laparoscopic total extraperitoneal (TEP) inguinal hernia repair with preperitoneal closed-suction drainage reduced postoperative complications.

BACKGROUND: Although laparoscopic total extraperitoneal (TEP) inguinal hernia repair has the advantages of less bleeding, less trauma, less pain, and fast recovery, there are several issues that need to be addressed. This study aims to evaluate the effectiveness of preperitoneal closed­suction drainage on reducing postoperative complications in TEP inguinal hernia repair. METHODS: A retrospective analysis of 122 patients who underwent TEP inguinal hernia repair between June 2018 and June 2021 was performed. The patients were divided into the drainage group and the non-drainage group according to whether the drainage tube was placed or not. Clinical data, surgical procedures and outcome of these patients were collected and analyzed to assess the effectiveness of drainage. RESULTS: A total of 122 patients undergoing TEP surgery were screened, of which 22 were excluded. Most of the patients were male with right indirect inguinal hernia. There was no difference in the mean length of hospital stay between the two groups. Postoperative pain was alleviated by preperitoneal closed­suction drainage 24 h after operation (p = 0.03). The rate of complications such as scrotal edema, seroma and urinary retention in the drainage group was significantly lower than that in the non-drainage group (p < 0.05). Multivariate regression analysis showed that drainage was beneficial to reduce postoperative complications (OR, 0.015; 95% CI, 0.002-0.140; p < 0.01). In addition, it was worth noting that in subgroup analysis, patients with hernia sac volume > 10 cm3 might receive more clinical benefits by placing drainage tube. CONCLUSION: In TEP inguinal hernia repair, placing drainage tube is a simple and feasible traditional surgical treatment, which can promote postoperative recovery without increasing the risk of infection, especially in patients with large hernia sac volume.

Three dimensional evaluation of the skeletal and temporomandibular joint changes following stabilization splint therapy in patients with temporomandibular joint disorders and mandibular deviation: a retrospective study.

BACKGROUND: Three-dimensional (3D) detailed evaluations of the mandibular mediolateral position, mandibular condylar position, and temporomandibular joint (TMJ) spaces following stabilization splints (SS) therapy in patients with temporomandibular joint disorders (TMD) and mandibular deviation (MD) have not been reported in the available literature. Accordingly, this study aimed to three-dimensionally analyze the skeletal and bony temporomandibular joint changes following stabilization splint therapy in adult patients with temporomandibular joint disorders and mandibular deviation. METHODS: This study is a retrospective clinical study that enrolled 26 adult patients with TMD and MD with a mean age of 24.86 years. The Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) was used to diagnose TMD. SS was adjusted weekly until occlusal contact stabilization occurred, and then adjusted monthly, patients were instructed to wear it at night for at least 10 h. The SS was removed after the elimination of TMD symptoms (TMJ/muscle pain on palpation, muscle spasm, and clicking) and having both condyles completely seated in a musculoskeletally stable position. Pre- and post-therapeutic Cone Beam Computed Tomography (CBCT) was analyzed. Mandibular mediolateral position, TMJ spaces, and mandibular condyle position were analyzed three-dimensionally using Mimics 21.0 software. Paired t-test or Wilcoxon rank-sum test was performed, and the significance level was considered at P < 0.05. RESULTS: The treatment period with SS therapy was 10.07 ± 3.1 months. The deviated chin was improved in 69.23% of the sample; the range of improvement was > 0 mm ≤ 3.9 mm. The mandibular rotation was significantly decreased from 3.58 ± 2.02° to 3.17 ± 1.60. The deviated side's superior and posterior joint TMJ spaces were significantly increased from 2.49 ± 0.88 mm and 1.25 ± 0.79 mm to 2.98 ± 1.02 mm and 1.86 ± 0.72 mm, respectively. The value of the difference from the bilateral condyle head position to the X and Z axes significantly decreased from 2.50 ± 1.56 mm and 2.30 ± 1.57 mm to 1.64 ± 1.58 mm and 1.82 ± 1.11 mm, respectively. CONCLUSION: The main positional effect of the stabilization splint treatment in TMD patients with MD includes considerable correction of mandibular deviation, improving facial asymmetry, and moving the condyle into a stable condylar position; these were done by promoting the mandible to rotate around the Z (roll) and Y (yaw) axes and by forward, downward, and outward condylar movement on the deviated side, respectively.

Association of Retinal Age Gap With Arterial Stiffness and Incident Cardiovascular Disease.

BACKGROUND: Retinal parameters could reflect systemic vascular changes. With the advances of deep learning technology, we have recently developed an algorithm to predict retinal age based on fundus images, which could be a novel biomarker for aging and mortality. Therefore, we aim to investigate associations of retinal age gap with arterial stiffness index and incident cardiovascular disease (CVD). METHODS: A deep learning model was trained based on 19 200 fundus images of 11 052 participants without any medical history at baseline to predict the retinal age. Retinal age gap (retinal age predicted minus chronological age) was generated for the remaining 35 917 participants. Regression models were used to assess the association between retinal age gap and arterial stiffness index. Cox proportional hazards regression models and restricted cubic splines were used to explore the association between retinal age gap and incident CVD. RESULTS: We found each 1-year increase in retinal age gap was associated with increased arterial stiffness index (ß=0.002 [95% CI, 0.001-0.003]; P<0.001). After a median follow-up of 5.83 years (interquartile range: 5.73-5.97), 675 (2.00%) developed CVD. In the fully adjusted model, each 1-year increase in retinal age gap was associated with a 3% increase in the risk of incident CVD (hazard ratio=1.03 [95% CI, 1.01-1.06]; P=0.014). In the restricted cubic splines analysis, the risk of incident CVD increased significantly when retinal age gap reached 1.21 (hazard ratio=1.05 [95% CI, 1.00-1.10]; P-overall <0.0001; P-nonlinear=0.0681). CONCLUSIONS: We found that retinal age gap was significantly associated with arterial stiffness index and incident CVD events, supporting the potential of this novel biomarker in identifying individuals at high risk of future CVD events.

Retinal age gap as a predictive biomarker of stroke risk.

BACKGROUND: The aim of this study is to investigate the association of retinal age gap with the risk of incident stroke and its predictive value for incident stroke. METHODS: A total of 80,169 fundus images from 46,969 participants in the UK Biobank cohort met the image quality standard. A deep learning model was constructed based on 19,200 fundus images of 11,052 disease-free participants at baseline for age prediction. Retinal age gap (retinal age predicted based on the fundus image minus chronological age) was generated for the remaining 35,917 participants. Stroke events were determined by data linkage to hospital records on admissions and diagnoses, and national death registers, whichever occurred earliest. Cox proportional hazards regression models were used to estimate the effect of retinal age gap on risk of stroke. Logistic regression models were used to estimate the predictive value of retinal age and well-established risk factors in 10-year stroke risk. RESULTS: A total of 35,304 participants without history of stroke at baseline were included. During a median follow-up of 5.83 years, 282 (0.80%) participants had stroke events. In the fully adjusted model, each one-year increase in the retinal age gap was associated with a 4% increase in the risk of stroke (hazard ratio [HR] = 1.04, 95% confidence interval [CI]: 1.00-1.08, P = 0.029). Compared to participants with retinal age gap in the first quintile, participants with retinal age gap in the fifth quintile had significantly higher risks of stroke events (HR = 2.37, 95% CI: 1.37-4.10, P = 0.002). The predictive capability of retinal age alone was comparable to the well-established risk factor-based model (AUC=0.676 vs AUC=0.661, p=0.511). CONCLUSIONS: We found that retinal age gap was significantly associated with incident stroke, implying the potential of retinal age gap as a predictive biomarker of stroke risk.

Unveiling the clinical incapabilities: a benchmarking study of GPT-4V(ision) for ophthalmic multimodal image analysis.

PURPOSE: To evaluate the capabilities and incapabilities of a GPT-4V(ision)-based chatbot in interpreting ocular multimodal images. METHODS: We developed a digital ophthalmologist app using GPT-4V and evaluated its performance with a dataset (60 images, 60 ophthalmic conditions, 6 modalities) that included slit-lamp, scanning laser ophthalmoscopy, fundus photography of the posterior pole (FPP), optical coherence tomography, fundus fluorescein angiography and ocular ultrasound images. The chatbot was tested with ten open-ended questions per image, covering examination identification, lesion detection, diagnosis and decision support. The responses were manually assessed for accuracy, usability, safety and diagnosis repeatability. Auto-evaluation was performed using sentence similarity and GPT-4-based auto-evaluation. RESULTS: Out of 600 responses, 30.6% were accurate, 21.5% were highly usable and 55.6% were deemed as no harm. GPT-4V performed best with slit-lamp images, with 42.0%, 38.5% and 68.5% of the responses being accurate, highly usable and no harm, respectively. However, its performance was weaker in FPP images, with only 13.7%, 3.7% and 38.5% in the same categories. GPT-4V correctly identified 95.6% of the imaging modalities and showed varying accuracies in lesion identification (25.6%), diagnosis (16.1%) and decision support (24.0%). The overall repeatability of GPT-4V in diagnosing ocular images was 63.3% (38/60). The overall sentence similarity between responses generated by GPT-4V and human answers is 55.5%, with Spearman correlations of 0.569 for accuracy and 0.576 for usability. CONCLUSION: GPT-4V currently is not yet suitable for clinical decision-making in ophthalmology. Our study serves as a benchmark for enhancing ophthalmic multimodal models.

Hot Carrier Trapping and It's Influence to the Carrier Diffusion in CsPbBr3 Perovskite Film Revealed by Transient Absorption Microscopy.

The defects in perovskite film can cause charge carrier trapping which shortens carrier lifetime and diffusion length. So defects passivation has become promising for the perovskite studies. However, how defects disturb the carrier transport and how the passivating affects the carrier transport in CsPbBr3 are still unclear. Here the carrier dynamics and diffusion processes of CsPbBr3 and LiBr passivated CsPbBr3 films are investigated by using transient absorption spectroscopy and transient absorption microscopy. It's found that there is a fast hot carrier trapping process with the above bandgap excitation, and the hot carrier trapping would decrease the population of cold carriers which are diffusible, then lower the carrier diffusion constant. It's proved that LiBr can passivate the defect and lower the trapping probability of hot carriers, thus improve the carrier diffusion rate. The finding demonstrates the influence of hot carrier trapping to the carrier diffusion in CsPbBr3 film.

FFA-GPT: an automated pipeline for fundus fluorescein angiography interpretation and question-answer.

Fundus fluorescein angiography (FFA) is a crucial diagnostic tool for chorioretinal diseases, but its interpretation requires significant expertise and time. Prior studies have used Artificial Intelligence (AI)-based systems to assist FFA interpretation, but these systems lack user interaction and comprehensive evaluation by ophthalmologists. Here, we used large language models (LLMs) to develop an automated interpretation pipeline for both report generation and medical question-answering (QA) for FFA images. The pipeline comprises two parts: an image-text alignment module (Bootstrapping Language-Image Pre-training) for report generation and an LLM (Llama 2) for interactive QA. The model was developed using 654,343 FFA images with 9392 reports. It was evaluated both automatically, using language-based and classification-based metrics, and manually by three experienced ophthalmologists. The automatic evaluation of the generated reports demonstrated that the system can generate coherent and comprehensible free-text reports, achieving a BERTScore of 0.70 and F1 scores ranging from 0.64 to 0.82 for detecting top-5 retinal conditions. The manual evaluation revealed acceptable accuracy (68.3%, Kappa 0.746) and completeness (62.3%, Kappa 0.739) of the generated reports. The generated free-form answers were evaluated manually, with the majority meeting the ophthalmologists' criteria (error-free: 70.7%, complete: 84.0%, harmless: 93.7%, satisfied: 65.3%, Kappa: 0.762-0.834). This study introduces an innovative framework that combines multi-modal transformers and LLMs, enhancing ophthalmic image interpretation, and facilitating interactive communications during medical consultation.

ICGA-GPT: report generation and question answering for indocyanine green angiography images.

BACKGROUND: Indocyanine green angiography (ICGA) is vital for diagnosing chorioretinal diseases, but its interpretation and patient communication require extensive expertise and time-consuming efforts. We aim to develop a bilingual ICGA report generation and question-answering (QA) system. METHODS: Our dataset comprised 213 129 ICGA images from 2919 participants. The system comprised two stages: image-text alignment for report generation by a multimodal transformer architecture, and large language model (LLM)-based QA with ICGA text reports and human-input questions. Performance was assessed using both qualitative metrics (including Bilingual Evaluation Understudy (BLEU), Consensus-based Image Description Evaluation (CIDEr), Recall-Oriented Understudy for Gisting Evaluation-Longest Common Subsequence (ROUGE-L), Semantic Propositional Image Caption Evaluation (SPICE), accuracy, sensitivity, specificity, precision and F1 score) and subjective evaluation by three experienced ophthalmologists using 5-point scales (5 refers to high quality). RESULTS: We produced 8757 ICGA reports covering 39 disease-related conditions after bilingual translation (66.7% English, 33.3% Chinese). The ICGA-GPT model's report generation performance was evaluated with BLEU scores (1-4) of 0.48, 0.44, 0.40 and 0.37; CIDEr of 0.82; ROUGE of 0.41 and SPICE of 0.18. For disease-based metrics, the average specificity, accuracy, precision, sensitivity and F1 score were 0.98, 0.94, 0.70, 0.68 and 0.64, respectively. Assessing the quality of 50 images (100 reports), three ophthalmologists achieved substantial agreement (kappa=0.723 for completeness, kappa=0.738 for accuracy), yielding scores from 3.20 to 3.55. In an interactive QA scenario involving 100 generated answers, the ophthalmologists provided scores of 4.24, 4.22 and 4.10, displaying good consistency (kappa=0.779). CONCLUSION: This pioneering study introduces the ICGA-GPT model for report generation and interactive QA for the first time, underscoring the potential of LLMs in assisting with automated ICGA image interpretation.

Translating color fundus photography to indocyanine green angiography using deep-learning for age-related macular degeneration screening.

Age-related macular degeneration (AMD) is the leading cause of central vision impairment among the elderly. Effective and accurate AMD screening tools are urgently needed. Indocyanine green angiography (ICGA) is a well-established technique for detecting chorioretinal diseases, but its invasive nature and potential risks impede its routine clinical application. Here, we innovatively developed a deep-learning model capable of generating realistic ICGA images from color fundus photography (CF) using generative adversarial networks (GANs) and evaluated its performance in AMD classification. The model was developed with 99,002 CF-ICGA pairs from a tertiary center. The quality of the generated ICGA images underwent objective evaluation using mean absolute error (MAE), peak signal-to-noise ratio (PSNR), structural similarity measures (SSIM), etc., and subjective evaluation by two experienced ophthalmologists. The model generated realistic early, mid and late-phase ICGA images, with SSIM spanned from 0.57 to 0.65. The subjective quality scores ranged from 1.46 to 2.74 on the five-point scale (1 refers to the real ICGA image quality, Kappa 0.79-0.84). Moreover, we assessed the application of translated ICGA images in AMD screening on an external dataset (n = 13887) by calculating area under the ROC curve (AUC) in classifying AMD. Combining generated ICGA with real CF images improved the accuracy of AMD classification with AUC increased from 0.93 to 0.97 (P < 0.001). These results suggested that CF-to-ICGA translation can serve as a cross-modal data augmentation method to address the data hunger often encountered in deep-learning research, and as a promising add-on for population-based AMD screening. Real-world validation is warranted before clinical usage.

Retinal Vascular Measurements and Mortality Risk: Evidence From the UK Biobank Study.

Purpose: This study aimed to investigate the association between quantitative retinal vascular measurements and the risk of all-cause and premature mortality. Methods: In this population-based cohort study using the UK Biobank data, we employed the Retina-based Microvascular Health Assessment System to assess fundus images for image quality and extracted 392 retinal vascular measurements per fundus image. These measurements encompass six categories of vascular features: caliber, density, length, tortuosity, branching angle, and complexity. Univariate Cox regression models were used to identify potential indicators of mortality risk using data on all-cause and premature mortality from death registries. Multivariate Cox regression models were then used to test these associations while controlling for confounding factors. Results: The final analysis included 66,415 participants. After adjusting for demographic, health, and lifestyle factors and genetic risk score, 18 and 10 retinal vascular measurements were significantly associated with all-cause mortality and premature mortality, respectively. In the fully adjusted model, the following measurements of different vascular features were significantly associated with all-cause mortality and premature mortality: arterial bifurcation density (branching angle), number of arterial segments (complexity), interquartile range and median absolute deviation of arterial curve angle (tortuosity), mean and median values of mean pixel widths of all arterial segments in each image (caliber), skeleton density of arteries in macular area (density), and minimum venular arc length (length). Conclusions: The study revealed 18 retinal vascular measurements significantly associated with all-cause mortality and 10 associated with premature mortality. Those identified parameters should be further studied for biological mechanisms connecting them to increased mortality risk. Translational Relevance: This study identifies retinal biomarkers for increased mortality risk and provides novel targets for investigating the underlying biological mechanisms.

Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case.

Artificial intelligence (AI) models have shown great accuracy in health screening. However, for real-world implementation, high accuracy may not guarantee cost-effectiveness. Improving AI's sensitivity finds more high-risk patients but may raise medical costs while increasing specificity reduces unnecessary referrals but may weaken detection capability. To evaluate the trade-off between AI model performance and the long-running cost-effectiveness, we conducted a cost-effectiveness analysis in a nationwide diabetic retinopathy (DR) screening program in China, comprising 251,535 participants with diabetes over 30 years. We tested a validated AI model in 1100 different diagnostic performances (presented as sensitivity/specificity pairs) and modeled annual screening scenarios. The status quo was defined as the scenario with the most accurate AI performance. The incremental cost-effectiveness ratio (ICER) was calculated for other scenarios against the status quo as cost-effectiveness metrics. Compared to the status quo (sensitivity/specificity: 93.3%/87.7%), six scenarios were cost-saving and seven were cost-effective. To achieve cost-saving or cost-effective, the AI model should reach a minimum sensitivity of 88.2% and specificity of 80.4%. The most cost-effective AI model exhibited higher sensitivity (96.3%) and lower specificity (80.4%) than the status quo. In settings with higher DR prevalence and willingness-to-pay levels, the AI needed higher sensitivity for optimal cost-effectiveness. Urban regions and younger patient groups also required higher sensitivity in AI-based screening. In real-world DR screening, the most accurate AI model may not be the most cost-effective. Cost-effectiveness should be independently evaluated, which is most likely to be affected by the AI's sensitivity.

Cross-modality Labeling Enables Noninvasive Capillary Quantification as a Sensitive Biomarker for Assessing Cardiovascular Risk.

Purpose: We aim to use fundus fluorescein angiography (FFA) to label the capillaries on color fundus (CF) photographs and train a deep learning model to quantify retinal capillaries noninvasively from CF and apply it to cardiovascular disease (CVD) risk assessment. Design: Cross-sectional and longitudinal study. Participants: A total of 90732 pairs of CF-FFA images from 3893 participants for segmentation model development, and 49229 participants in the UK Biobank for association analysis. Methods: We matched the vessels extracted from FFA and CF, and used vessels from FFA as labels to train a deep learning model (RMHAS-FA) to segment retinal capillaries using CF. We tested the model's accuracy on a manually labeled internal test set (FundusCapi). For external validation, we tested the segmentation model on 7 vessel segmentation datasets, and investigated the clinical value of the segmented vessels in predicting CVD events in the UK Biobank. Main Outcome Measures: Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity for segmentation. Hazard ratio (HR; 95% confidence interval [CI]) for Cox regression analysis. Results: On the FundusCapi dataset, the segmentation performance was AUC = 0.95, accuracy = 0.94, sensitivity = 0.90, and specificity = 0.93. Smaller vessel skeleton density had a stronger correlation with CVD risk factors and incidence (P < 0.01). Reduced density of small vessel skeletons was strongly associated with an increased risk of CVD incidence and mortality for women (HR [95% CI] = 0.91 [0.84-0.98] and 0.68 [0.54-0.86], respectively). Conclusions: Using paired CF-FFA images, we automated the laborious manual labeling process and enabled noninvasive capillary quantification from CF, supporting its potential as a sensitive screening method for identifying individuals at high risk of future CVD events. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Association of greenspace and natural environment with brain volumes mediated by lifestyle and biomarkers among urban residents.

OBJECTIVES: To examine the associaiton between environmental measures and brain volumes and its potential mediators. STUDY DESIGN: This was a prospective study. METHODS: Our analysis included 34,454 participants (53.4% females) aged 40-73 years at baseline (between 2006 and 2010) from the UK Biobank. Brain volumes were measured using magnetic resonance imaging between 2014 and 2019. RESULTS: Greater proximity to greenspace buffered at 1000 m at baseline was associated with larger volumes of total brain measured 8.8 years after baseline assessment (standardized ß (95% CI) for each 10% increment in coverage: 0.013(0.005,0.020)), grey matter (0.013(0.006,0.020)), and white matter (0.011(0.004,0.017)) after adjustment for covariates and air pollution. The corresponding numbers for natural environment buffered at 1000 m were 0.010 (0.004,0.017), 0.009 (0.004,0.015), and 0.010 (0.004,0.016), respectively. Similar results were observed for greenspace and natural environment buffered at 300 m. The strongest mediator for the association between greenspace buffered at 1000 m and total brain volume was smoking (percentage (95% CI) of total variance explained: 7.9% (5.5-11.4%)) followed by mean sphered cell volume (3.3% (1.8-5.8%)), vitamin D (2.9% (1.6-5.1%)), and creatinine in blood (2.7% (1.6-4.7%)). Significant mediators combined explained 18.5% (13.2-25.3%) of the association with total brain volume and 32.9% (95% CI: 22.3-45.7%) of the association with grey matter volume. The percentage (95% CI) of the association between natural environment and total brain volume explained by significant mediators combined was 20.6% (14.7-28.1%)). CONCLUSIONS: Higher coverage percentage of greenspace and environment may benefit brain health by promoting healthy lifestyle and improving biomarkers including vitamin D and red blood cell indices.

Association of retinal microvascular density and complexity with incident coronary heart disease.

BACKGROUND AND AIMS: The high mortality rate and huge disease burden of coronary heart disease (CHD) highlight the importance of its early detection and timely intervention. Given the non-invasive nature of fundus photography and recent development in the quantification of retinal microvascular parameters with deep learning techniques, our study aims to investigate the association between incident CHD and retinal microvascular parameters. METHODS: UK Biobanks participants with gradable fundus images and without a history of diagnosed CHD at recruitment were included for analysis. A fully automated artificial intelligence system was used to extract quantitative measurements that represent the density and complexity of the retinal microvasculature, including fractal dimension (Df), number of vascular segments (NS), vascular skeleton density (VSD) and vascular area density (VAD). RESULTS: A total of 57,947 participants (mean age 55.6 ± 8.1 years; 56% female) without a history of diagnosed CHD were included. During a median follow-up of 11.0 (interquartile range, 10.88 to 11.19) years, 3211 incident CHD events occurred. In multivariable Cox proportional hazards models, we found decreasing Df (adjusted HR = 0.80, 95% CI, 0.65-0.98, p = 0.033), lower NS of arteries (adjusted HR = 0.69, 95% CI, 0.54-0.88, p = 0.002) and venules (adjusted HR = 0.77, 95% CI, 0.61-0.97, p = 0.024), and reduced arterial VSD (adjusted HR = 0.72, 95% CI, 0.57-0.91, p = 0.007) and venous VSD (adjusted HR = 0.78, 95% CI, 0.62-0.98, p = 0.034) were related to an increased risk of incident CHD. CONCLUSIONS: Our study revealed a significant association between retinal microvascular parameters and incident CHD. As the lower complexity and density of the retinal vascular network may indicate an increased risk of incident CHD, this may empower its prediction with the quantitative measurements of retinal structure.

Comparing Common Retinal Vessel Caliber Measurement Software with an Automatic Deep Learning System.

PURPOSE: To compare the Retina-based Microvascular Health Assessment System (RMHAS) with Integrative Vessel Analysis (IVAN) for retinal vessel caliber measurement. METHODS: Eligible fundus photographs from the Lingtou Eye Cohort Study were obtained alongside their corresponding participant data. Vascular diameter was automatically measured using IVAN and RMHAS software, and intersoftware variations were assessed by intra-class correlation coefficients (ICC), and 95% confidence intervals (CIs). Scatterplots and Bland-Altman plots assessed the agreement between programs, and a Pearson's correlation test assessed the strength of associations between systemic variables and retinal calibers. An algorithm was proposed to convert measurements between software for interchangeability. RESULTS: ICCs between IVAN and RMHAS were moderate for CRAE and AVR (ICC; 95%CI)(0.62; 0.60 to 0.63 and 0.42; 0.40 to 0.44 respectively) and excellent for CRVE (0.76; 0.75 to 0.77). When comparing retinal vascular calibre measurements between tools, mean differences (MD, 95% confidence intervals) in CRAE, CRVE, and AVR were 22.34 (-7.29 to 51.97 µm),-7.01 (-37.68 to 23.67 µm), and 0.12 (-0.02 to 0.26 µm), respectively. The correlation of systemic parameters with CRAE/CRVE was poor and the correlation of CRAE with age, sex, systolic blood pressure, and CRVE with age, sex, and serum glucose were significantly different between IVAN and RMHAS (p < 0.05). CONCLUSIONS: CRAE and AVR correlated moderately between retinal measurement software systems while CRVE correlated well. Further studies confirming this agreeability and interchangeability in large-scale datasets are needed before softwares are deemed comparable in clinical practice.

A deep learning model for generating fundus autofluorescence images from color fundus photography.

Background: Fundus Autofluorescence (FAF) is a valuable imaging technique used to assess metabolic alterations in the retinal pigment epithelium (RPE) associated with various age-related and disease-related changes. The practical uses of FAF are ever-growing. This study aimed to evaluate the effectiveness of a generative deep learning (DL) model in translating color fundus (CF) images into synthetic FAF images and explore its potential for enhancing screening of age-related macular degeneration (AMD). Methods: A generative adversarial network (GAN) model was trained on pairs of CF and FAF images to generate synthetic FAF images. The quality of synthesized FAF images was assessed objectively by common generation metrics. Additionally, the clinical effectiveness of the generated FAF images in AMD classification was evaluated by measuring the area under the curve (AUC), using the LabelMe dataset. Results: A total of 8410 FAF images from 2586 patients were analyzed. The synthesized FAF images exhibited an impressive objectively assessed quality, achieving a multi-scale structural similarity index (MS-SSIM) of 0.67. When evaluated on the LabelMe dataset, the combination of generated FAF images and CF images resulted in a noteworthy improvement in AMD classification accuracy, with the AUC increasing from 0.931 to 0.968. Conclusions: This study presents the first attempt to use a generative deep learning model to create authentic and high-quality FAF images from CF images. The incorporation of the translated FAF images on top of CF images improved the accuracy of AMD classification. Overall, this study presents a promising approach to enhance large-scale AMD screening.

Medical visual question answering: A survey.

Medical Visual Question Answering (VQA) is a combination of medical artificial intelligence and popular VQA challenges. Given a medical image and a clinically relevant question in natural language, the medical VQA system is expected to predict a plausible and convincing answer. Although the general-domain VQA has been extensively studied, the medical VQA still needs specific investigation and exploration due to its task features. In the first part of this survey, we collect and discuss the publicly available medical VQA datasets up-to-date about the data source, data quantity, and task feature. In the second part, we review the approaches used in medical VQA tasks. We summarize and discuss their techniques, innovations, and potential improvements. In the last part, we analyze some medical-specific challenges for the field and discuss future research directions. Our goal is to provide comprehensive and helpful information for researchers interested in the medical visual question answering field and encourage them to conduct further research in this field.

Associations of ophthalmic and systemic conditions with incident dementia in the UK Biobank.

AIMS: To examine independent and interactive associations of ophthalmic and systemic conditions with incident dementia. METHODS: Our analysis included 12 364 adults aged 55-73 years from the UK Biobank cohort. Participants were assessed between 2006 and 2010 at baseline and were followed up until the early of 2021. Incident dementia was ascertained using hospital inpatient, death records and self-reported data. RESULTS: Over 1 263 513 person-years of follow-up, 2304 cases of incident dementia were documented. The multivariable-adjusted HRs (95% CI) for dementia associated with age-related macular degeneration (AMD), cataract, diabetes-related eye disease (DRED) and glaucoma at baseline were 1.26 (1.05 to 1.52), 1.11 (1.00 to 1.24), 1.61 (1.30 to 2.00) and (1.07 (0.92 to 1.25), respectively. Diabetes, heart disease, stroke and depression at baseline were all associated with an increased risk of dementia. Of the combination of AMD and a systemic condition, AMD-diabetes was associated with the highest risk for incident dementia (HR (95% CI): 2.73 (1.79 to 4.17)). Individuals with cataract and a systemic condition were 1.19-2.29 times more likely to develop dementia compared with those without cataract and systemic conditions. The corresponding number for DRED and a systemic condition was 1.50-3.24. Diabetes, hypertension, heart disease, depression and stroke newly identified during follow-up mediated the association between cataract and incident dementia as well as the association between DRED and incident dementia. CONCLUSIONS: AMD, cataract and DRED but not glaucoma are associated with an increased risk of dementia. Individuals with both ophthalmic and systemic conditions are at higher risk of dementia compared with those with an ophthalmic or systemic condition only.