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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
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Pesquisa Biomédica , Biomarcadores , Bases de Dados Factuais , MultiômicaRESUMO
BACKGROUND: For medical artificial intelligence (AI) training and validation, human expert labels are considered the gold standard that represents the correct answers or desired outputs for a given data set. These labels serve as a reference or benchmark against which the model's predictions are compared. OBJECTIVE: This study aimed to assess the accuracy of a custom deep learning (DL) algorithm on classifying diabetic retinopathy (DR) and further demonstrate how label errors may contribute to this assessment in a nationwide DR-screening program. METHODS: Fundus photographs from the Lifeline Express, a nationwide DR-screening program, were analyzed to identify the presence of referable DR using both (1) manual grading by National Health Service England-certificated graders and (2) a DL-based DR-screening algorithm with validated good lab performance. To assess the accuracy of labels, a random sample of images with disagreement between the DL algorithm and the labels was adjudicated by ophthalmologists who were masked to the previous grading results. The error rates of labels in this sample were then used to correct the number of negative and positive cases in the entire data set, serving as postcorrection labels. The DL algorithm's performance was evaluated against both pre- and postcorrection labels. RESULTS: The analysis included 736,083 images from 237,824 participants. The DL algorithm exhibited a gap between the real-world performance and the lab-reported performance in this nationwide data set, with a sensitivity increase of 12.5% (from 79.6% to 92.5%, P<.001) and a specificity increase of 6.9% (from 91.6% to 98.5%, P<.001). In the random sample, 63.6% (560/880) of negative images and 5.2% (140/2710) of positive images were misclassified in the precorrection human labels. High myopia was the primary reason for misclassifying non-DR images as referable DR images, while laser spots were predominantly responsible for misclassified referable cases. The estimated label error rate for the entire data set was 1.2%. The label correction was estimated to bring about a 12.5% enhancement in the estimated sensitivity of the DL algorithm (P<.001). CONCLUSIONS: Label errors based on human image grading, although in a small percentage, can significantly affect the performance evaluation of DL algorithms in real-world DR screening.
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Aprendizado Profundo , Retinopatia Diabética , Retinopatia Diabética/diagnóstico , Humanos , Algoritmos , Programas de Rastreamento/métodos , Programas de Rastreamento/normas , Feminino , Masculino , Pessoa de Meia-IdadeRESUMO
BACKGROUND: Large language models (LLMs) demonstrated advanced performance in processing clinical information. However, commercially available LLMs lack specialized medical knowledge and remain susceptible to generating inaccurate information. Given the need for self-management in diabetes, patients commonly seek information online. We introduce the RISE framework and evaluate its performance in enhancing LLMs to provide accurate responses to diabetes-related inquiries. OBJECTIVE: This study aimed to evaluate the potential of RISE framework, an information retrieval and augmentation tool, to improve the LLM's performance to accurately and safely respond to diabetes-related inquiries. METHODS: The RISE, an innovative retrieval augmentation framework, comprises four steps: Rewriting Query, Information Retrieval, Summarization, and Execution. Using a set of 43 common diabetes-related questions, we evaluated three base LLMs (GPT-4, Anthropic Claude 2, Google Bard) and their RISE-enhanced versions. Assessments were conducted by clinicians for accuracy and comprehensiveness, and by patients for understandability. RESULTS: The integration of RISE significantly improved the accuracy and comprehensiveness of responses from all three based LLMs. On average, the percentage of accurate responses increased by 12% (122 - 107/129) with RISE. Specifically, the rates of accurate responses increased by 7% (42 - 39/43) for GPT-4, 19% (39 - 31/43) for Claude 2, and 9% (41 - 37/43) for Google Bard. The framework also enhanced response comprehensiveness, with mean scores improving by 0.44. Understandability was also enhanced by 0.19 on average. Data collection was conducted from Sept. 30, 2023, to Feb. 05, 2024. CONCLUSIONS: RISE significantly improves LLMs' performance in responding to diabetes-related inquiries, enhancing accuracy, comprehensiveness, and understandability. These improvements have crucial implications for RISE's future role in patient education and chronic illness self-management, which contributes to relieving medical resource pressures and raising public awareness of medical knowledge.
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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.
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Benchmarking , Idioma , Aprendizagem , Prontuários Médicos , RegistrosRESUMO
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.
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Hérnia Inguinal , Laparoscopia , Humanos , Masculino , Feminino , Hérnia Inguinal/complicações , Sucção/efeitos adversos , Estudos Retrospectivos , Herniorrafia/métodos , Complicações Pós-Operatórias/epidemiologia , Complicações Pós-Operatórias/prevenção & controle , Complicações Pós-Operatórias/etiologia , Laparoscopia/métodos , Dor Pós-Operatória/etiologia , Telas Cirúrgicas/efeitos adversos , Resultado do TratamentoRESUMO
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.
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Má Oclusão , Placas Oclusais , Transtornos da Articulação Temporomandibular , Adulto , Humanos , Adulto Jovem , Má Oclusão/diagnóstico por imagem , Má Oclusão/terapia , Côndilo Mandibular/diagnóstico por imagem , Estudos Retrospectivos , Contenções , Articulação Temporomandibular/diagnóstico por imagem , Transtornos da Articulação Temporomandibular/diagnóstico por imagem , Transtornos da Articulação Temporomandibular/terapiaRESUMO
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.
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Doenças Cardiovasculares , Rigidez Vascular , Humanos , Doenças Cardiovasculares/epidemiologia , Modelos de Riscos Proporcionais , Retina , Fatores de Risco , IncidênciaRESUMO
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.
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Acidente Vascular Cerebral , Humanos , Biomarcadores , Acidente Vascular Cerebral/diagnóstico , Acidente Vascular Cerebral/epidemiologia , Modelos Logísticos , Intervalo Livre de Doença , HospitalizaçãoRESUMO
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.
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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.
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Existing automatic analysis of fundus fluorescein angiography (FFA) images faces limitations, including a predetermined set of possible image classifications and being confined to text-based question-answering (QA) approaches. This study aims to address these limitations by developing an end-to-end unified model that utilizes synthetic data to train a visual question-answering model for FFA images. To achieve this, we employed ChatGPT to generate 4,110,581 QA pairs for a large FFA dataset, which encompassed a total of 654,343 FFA images from 9,392 participants. We then fine-tuned the Bootstrapping Language-Image Pre-training (BLIP) framework to enable simultaneous handling of vision and language. The performance of the fine-tuned model (ChatFFA) was thoroughly evaluated through automated and manual assessments, as well as case studies based on an external validation set, demonstrating satisfactory results. In conclusion, our ChatFFA system paves the way for improved efficiency and feasibility in medical imaging analysis by leveraging generative large language models.
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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.
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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.
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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.
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BACKGROUND: There is growing evidence supporting that vascular abnormalities contribute to multiple sclerosis (MS), and retinal microvasculature functions as a visible window to observe vessels. We hypothesized that retinal vascular curve tortuosity is associated with MS, which this study aims to address. METHODS: Participants from the UK Biobank with complete clinical records and gradable fundus photos were included in the study. Arteriolar and venular curve tortuosity and vessel area density are quantified automatically using a deep learning system. Individuals with MS were matched to healthy controls using propensity score matching (PSM). Conditional logistic regression was used to investigate the association between retinal vascular characteristics and MS. We also used a receiver operating characteristic (ROC) curve to assess the diagnostic performance of MS. RESULTS: Venular curve tortuosity (VCT) was found to be significantly associated with MS. And patients with multiple sclerosis were probable to have lower VCT than the non-MS group (OR = 0.22 [95 % CI, 0.05 to 0.92], P < 0.05). CONCLUSIONS: Our study reveals a significant association between vessel curve tortuosity and MS. The lower curve tortuosity of the retinal venular network may indicate a higher risk of incident multiple sclerosis.
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Bancos de Espécimes Biológicos , Esclerose Múltipla , Vasos Retinianos , Humanos , Esclerose Múltipla/fisiopatologia , Esclerose Múltipla/diagnóstico por imagem , Esclerose Múltipla/epidemiologia , Esclerose Múltipla/diagnóstico , Feminino , Masculino , Pessoa de Meia-Idade , Reino Unido , Vasos Retinianos/diagnóstico por imagem , Vasos Retinianos/patologia , Estudos Transversais , Adulto , Microvasos/patologia , Microvasos/diagnóstico por imagem , Microvasos/fisiopatologia , Idoso , Aprendizado Profundo , Biobanco do Reino UnidoRESUMO
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.
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Vasos Retinianos , Biobanco do Reino Unido , Humanos , Vasos Retinianos/diagnóstico por imagem , Estudos de Coortes , Bancos de Espécimes Biológicos , Retina/diagnóstico por imagemRESUMO
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.
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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.
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The comorbidity of Alzheimer's disease (AD) and age-related macular degeneration (AMD) has been established in clinical and genetic studies. There is growing interest in determining the shared environmental factors associated with both conditions. Recent advancements in record linkage techniques enable us to identify the contributing factors to AD and AMD from a wide range of variables. As such, we first constructed a knowledge graph based on the literature, which included all statistically significant risk factors for AD and AMD. An environment-wide association study (EWAS) was conducted to assess the contribution of various environmental factors to the comorbidity of AD and AMD based on the UK biobank. Based on the conditional Q-Q plots and Bayesian algorithm, several shared environmental factors were identified, which could be categorized into the domains of health condition, biological sample parameters, body index, and attendance availability. Finally, we generated a shared etiology landscape for AD and AMD by combining existing knowledge with our novel findings.
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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.