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Abstract Diabetic retinopathy (DR) is the most prevalent cause of preventable vision loss worldwide, imposing a significant economic and medical burden on society today, of which early identification is the cornerstones of the management. The diagnosis and severity grading of DR rely on scales based on clinical visualized features, but lack detailed quantitative parameters. Retinal non-perfusion area (NPA) is a pathogenic characteristic of DR that symbolizes retinal hypoxia conditions, and was found to be intimately associated with disease progression, prognosis, and management. However, the practical value of NPA is constrained since it appears on fundus fluorescein angiography (FFA) as distributed, irregularly shaped, darker plaques that are challenging to measure manually. In this study, we propose a deep learning-based method, NPA-Net, for accurate and automatic segmentation of NPAs from FFA images acquired in clinical practice. NPA-Net uses the U-net structure as the basic backbone, which has an encoder-decoder model structure. To enhance the recognition performance of the model for NPA, we adaptively incorporate multi-scale features and contextual information in feature learning and design three modules: Adaptive Encoder Feature Fusion (AEFF) module, Multilayer Deep Supervised Loss, and Atrous Spatial Pyramid Pooling (ASPP) module, which enhance the recognition ability of the model for NPAs of different sizes from different perspectives. We conducted extensive experiments on a clinical dataset with 163 eyes with NPAs manually annotated by ophthalmologists, and NPA-Net achieved better segmentation performance compared to other existing methods with an area under the receiver operating characteristic curve (AUC) of 0.9752, accuracy of 0.9431, sensitivity of 0.8794, specificity of 0.9459, IOU of 0.3876 and Dice of 0.5686. This new automatic segmentation model is useful for identifying NPA in clinical practice, generating quantitative parameters that can be useful for further research as well as guiding DR detection, grading severity, treatment planning, and prognosis.

Abstract The question of whether artificial intelligence (AI) can surpass human capabilities is crucial in the application of AI in clinical medicine. To explore this, an interpretable deep learning (DL) model was developed to assess myopia status using retinal refraction maps obtained with a novel peripheral refractor. The DL model demonstrated promising performance, achieving an AUC of 0.9074 (95% CI 0.83–0.97), an accuracy of 0.8140 (95% CI 0.70–0.93), a sensitivity of 0.7500 (95% CI 0.51–0.90), and a specificity of 0.8519 (95% CI 0.68–0.94). Grad-CAM analysis provided interpretable visualization of the attention of DL model and revealed that the DL model utilized information from the central retina, similar to human readers. Additionally, the model considered information from vertical regions across the central retina, which human readers had overlooked. This finding suggests that AI can indeed surpass human capabilities, bolstering our confidence in the use of AI in clinical practice, especially in new scenarios where prior human knowledge is limited.

Abstract Background It is uncertain whether the weekend warrior pattern is associated with all-cause mortality among adults living with type 2 diabetes. This study explored how the ‘weekend warrior’ physical activity (PA) pattern was associated with all-cause mortality among adults living with type 2 diabetes. Methods This prospective cohort study investigated US adults living with type 2 diabetes in the National Health and Nutrition Examination Survey (NHANES). Mortality data was linked to the National Death Index. Based on self-reported leisure-time and occupational moderate-to-vigorous PA (MVPA), participants were categorized into 3 groups: physically inactive (

Abstract Background Sexually transmitted infections (STIs) pose a significant global public health challenge. Early diagnosis and treatment reduce STI transmission, but rely on recognising symptoms and care-seeking behaviour of the individual. Digital health software that distinguishes STI skin conditions could improve health-seeking behaviour. We developed and evaluated a deep learning model to differentiate STIs from non-STIs based on clinical images and symptoms. Methods We used 4913 clinical images of genital lesions and metadata from the Melbourne Sexual Health Centre collected during 2010–2023. We developed two binary classification models to distinguish STIs from non-STIs: (1) a convolutional neural network (CNN) using images only and (2) an integrated model combining both CNN and fully connected neural network (FCN) using images and metadata. We evaluated the model performance by the area under the ROC curve (AUC) and assessed metadata contributions to the Image-only model. Results Our study included 1583 STI and 3330 non-STI images. Common STI diagnoses were syphilis (34.6%), genital warts (24.5%) and herpes (19.4%), while most non-STIs (80.3%) were conditions such as dermatitis, lichen sclerosis and balanitis. In both STI and non-STI groups, the most frequently observed groups were 25–34 years (48.6% and 38.2%, respectively) and heterosexual males (60.3% and 45.9%, respectively). The Image-only model showed a reasonable performance with an AUC of 0.859 (SD 0.013). The Image + Metadata model achieved a significantly higher AUC of 0.893 (SD 0.018) compared to the Image-only model (p

Summary: Intraocular pressure (IOP) is closely correlated with blood pressure (BP), and while BP has been linked to brain volumes, the effect of IOP on brain volumes remains unclear. This study analyzed participants from the UK Biobank with MRI-measured brain volumes. Observational analyses included 8,634 participants for IOP and 36,069 for BP, followed by Mendelian randomization (MR) analyses of 37,410 participants. Observational analyses revealed that each 10-mmHg increase in diastolic BP was linked to a 0.13 mL larger white matter hyperintensity (WMH) after adjusting for covariates. Associations between IOP and brain volumes were more pronounced in younger individuals or those without hypertension. MR analyses confirmed significant relationships between diastolic BP and WMH, and each 5-mmHg increase in IOP reduced gray matter volumes by 3.24 mL. The study suggests that targeting IOP and BP could help prevent brain volume reduction.

BackgroundThe 2022 global outbreak of mpox has significantly impacted health facilities, and necessitated additional infection prevention and control measures and alterations to clinic processes. Early identification of suspected mpox cases will assist in mitigating these impacts. ObjectiveWe aimed to develop and evaluate an artificial intelligence (AI)–based tool to differentiate mpox lesion images from other skin lesions seen in a sexual health clinic. MethodsWe used a data set with 2200 images, that included mpox and non-mpox lesions images, collected from Melbourne Sexual Health Centre and web resources. We adopted deep learning approaches which involved 6 different deep learning architectures to train our AI models. We subsequently evaluated the performance of each model using a hold-out data set and an external validation data set to determine the optimal model for differentiating between mpox and non-mpox lesions. ResultsThe DenseNet-121 model outperformed other models with an overall area under the receiver operating characteristic curve (AUC) of 0.928, an accuracy of 0.848, a precision of 0.942, a recall of 0.742, and an F1-score of 0.834. Implementation of a region of interest approach significantly improved the performance of all models, with the AUC for the DenseNet-121 model increasing to 0.982. This approach resulted in an increase in the correct classification of mpox images from 79% (55/70) to 94% (66/70). The effectiveness of this approach was further validated by a visual analysis with gradient-weighted class activation mapping, demonstrating a reduction in false detection within the background of lesion images. On the external validation data set, ResNet-18 and DenseNet-121 achieved the highest performance. ResNet-18 achieved an AUC of 0.990 and an accuracy of 0.947, and DenseNet-121 achieved an AUC of 0.982 and an accuracy of 0.926. ConclusionsOur study demonstrated it was possible to use an AI-based image recognition algorithm to accurately differentiate between mpox and common skin lesions. Our findings provide a foundation for future investigations aimed at refining the algorithm and establishing the place of such technology in a sexual health clinic.

Abstract Background Little is known regarding the leading risk factors for dementia/Alzheimer’s disease (AD) in individuals with and without APOE4. The identification of key risk factors for dementia/Alzheimer’s disease (AD) in individuals with and without the APOE4 gene is of significant importance in global health. Methods Our analysis included 110,354 APOE4 carriers and 220,708 age- and sex-matched controls aged 40–73 years at baseline (between 2006–2010) from UK Biobank. Incident dementia was ascertained using hospital inpatient, or death records until January 2021. Individuals of non-European ancestry were excluded. Furthermore, individuals without medical record linkage were excluded from the analysis. Moderation analysis was tested for 134 individual factors. Results During a median follow-up of 11.9 years, 4,764 cases of incident all-cause dementia and 2065 incident AD cases were documented. Hazard ratios (95% CIs) for all-cause dementia and AD associated with APOE4 were 2.70(2.55–2.85) and 3.72(3.40–4.07), respectively. In APOE4 carriers, the leading risk factors for all-cause dementia included low self-rated overall health, low household income, high multimorbidity risk score, long-term illness, high neutrophil percentage, and high nitrogen dioxide air pollution. In non-APOE4 carriers, the leading risk factors included high multimorbidity risk score, low overall self-rated health, low household income, long-term illness, high microalbumin in urine, high neutrophil count, and low greenspace percentage. Population attributable risk for these individual risk factors combined was 65.1%, and 85.8% in APOE4 and non-APOE4 carriers, respectively. For 20 risk factors including multimorbidity risk score, unhealthy lifestyle habits, and particulate matter air pollutants, their associations with incident dementia were stronger in non-APOE4 carriers. For only 2 risk factors (mother’s history of dementia, low C-reactive protein), their associations with incident all-cause dementia were stronger in APOE4 carriers. Conclusions Our findings provide evidence for personalized preventative approaches to dementia/AD in APOE4 and non-APOE4 carriers. A mother’s history of dementia and low levels of C-reactive protein were more important risk factors of dementia in APOE4 carriers whereas leading risk factors including unhealthy lifestyle habits, multimorbidity risk score, inflammation and immune-related markers were more predictive of dementia in non-APOE4 carriers.

Visual recognition with deep learning has recently been shown to be effective in robotic vision. However, these algorithms tend to be build under fixed and structured environment, which is rarely the case in real life. When facing unknown objects, avoidance or human interactions are required, which may miss critical objects or be prohibitively costly to obtain on robots in the real world. We consider a practical problem setting that aims to allow robots to automatically discover novel classes with only labelled known class samples in hand, defined as open-set clustering (OSC). To address the OSC problem, we propose a framework combining three approaches: 1) using selfsupervised vision transformers to mitigate the discard of information needed for clustering unknown classes; 2) adaptive weighting for image patches to prioritize patches with richer textures; and 3) incorporating a temperature scaling strategy to generate more separable feature embeddings for clustering. We demonstrate the efficacy of our approach in six fine-grained image datasets.

Abstract It is unclear regarding associations of dietary patterns with a wide range of chronic diseases and which dietary score is more predictive of major chronic diseases. Using the UK Biobank, we examine associations of four individual healthy dietary scores with the risk of 48 individual chronic diseases. Higher Alternate Mediterranean Diet score is associated with a lower risk of 32 (all 8 cardiometabolic disorders, 3 out of 10 types of cancers, 7 out of 10 psychological/neurological disorders, 5 out of 6 digestive disorders, and 9 out of 14 other chronic diseases). Alternate Healthy Eating Index-2010 and Healthful Plant-based Diet Index are inversely associated with the risk of 29 and 23 individual chronic diseases, respectively. A higher Anti-Empirical Dietary Inflammatory Index is associated with a lower risk of 14 individual chronic diseases and a higher incidence of two diseases. Our findings support dietary guidelines for the prevention of most chronic diseases.

Abstract Background Screening for hyperthyroidism using gold-standard diagnostic criteria in the general population is not cost-effective, leading to a relatively high rate of undiagnosed and untreated patients. This study aimed to establish a deep learning-based system to detect hyperthyroidism based on retinal photographs. Methods The multicenter, observational study included retinal photographs taken from participants in two hospitals and 24 health care centers throughout China. We first trained two models to identify hyperthyroidism: in model #1, the non-hyperthyroidism individuals were randomly selected, while in model #2, the non-hyperthyroidism group was matched for age and gender with the hyperthyroidism group. After internal validation, we selected the better model for further evaluation using external validation datasets. Results The study included 22,940 retinal photographs of 11,409 participants for the model development, and 3862 retinal photographs (1870 participants) which were obtained from two hospitals and four medical centers as the external validation datasets. Model #1 achieved a higher area under the receiver operator curve (AUC) than model #2 (0.907, 95% CI: 0.894–0.918 versus 0.850, 95% CI: 0.832–0.866) in the internal validation so that model #1 was used for further evaluation. In external datasets, model #1 reached AUCs ranging from 0.816 (95% CI 0.789–0.846) to 0.849 (95% CI 0.824–0.874) and achieved accuracies between 0.735 (95% CI 0.700–0.773) and 0.796 (95% CI 0.765–0.824). Heatmaps showed a focus of the DL-algorism on large fundus vessels and the optic nerve head. Conclusions Retinal fundus photographs may serve for DL systems for a cost-effective and non-invasive method to detect hyperthyroidism.

BackgroundData regarding the association between antioxidant supplementation and incident dementia are limited.MethodsWe included 494,632 adults (54.5% females) aged 40–71 years at baseline from the United Kingdom Biobank in the final analysis. Incident dementia was ascertained using hospital inpatient and death records up to January 2021.ResultsOver a median follow-up of 11.9 years, 7,128 new cases of all-cause dementia, 2,772 cases of Alzheimer’s disease, and 1,397 cases of vascular dementia were recorded. The hazard ratio (95% CI) for incident dementia associated with zinc supplementation was 0.84 (0.74–0.96), and the association remained significant after adjusting for all confounders (0.84 (0.74–0.96)). In the full model, zinc supplementation was associated with a reduced risk of Alzheimer’s disease [HR (95% CI): 0.71 (0.57–0.88)]. There was no significant association between zinc supplementation and the risk of vascular dementia. No significant associations with incident dementia were observed for other antioxidant supplementation. The association between zinc supplementation and incident dementia was significant among individuals with [HR (95% CI): 0.34 (0.15–0.77)] and without cataract [0.87 (0.77–0.99)] but it was stronger among those with cataract (p value for interaction = 0.0271).ConclusionOur findings suggest that zinc supplementation may help reduce the risk of all-cause dementia and Alzheimer’s disease in middle-aged or older adults, especially among those with cataracts.

Summary: Retinopathy of prematurity (ROP) is currently one of the leading causes of infant blindness worldwide. Recently significant progress has been made in deep learning-based computer-aided diagnostic methods. However, deep learning often requires a large amount of annotated data for model optimization, but this requires long hours of effort by experienced doctors in clinical scenarios. In contrast, a large number of unlabeled images are relatively easy to obtain. In this paper, we propose a new semi-supervised learning framework to reduce annotation costs for automatic ROP staging. We design two consistency regularization strategies, prediction consistency loss and semantic structure consistency loss, which can help the model mine useful discriminative information from unlabeled data, thus improving the generalization performance of the classification model. Extensive experiments on a real clinical dataset show that the proposed method promises to greatly reduce the labeling requirements in clinical scenarios while achieving good classification performance.

Abstract Optical coherence tomography angiography (OCTA) has been a frequently used diagnostic method in neovascular age-related macular degeneration (nAMD) because it is non-invasive and provides a comprehensive view of the characteristic lesion, choroidal neovascularization (CNV). In order to study its characteristics, an automated method is needed to identify and quantify CNV. Here, we have developed a deep learning model that can automatically segment CNV regions from OCTA images. Specifically, we use the ResNeSt block as our basic backbone, which learns better feature representations through group convolution and split-attention mechanisms. In addition, considering the varying sizes of CNVs, we developed a spatial pyramid pooling module, which uses different receptive fields to enable the model to extract contextual information at different scales to better segment CNVs of different sizes, thus further improving the segmentation performance of the model. Experimental results on a clinical OCTA dataset containing 116 OCTA images show that the CNV segmentation model has an AUC of 0.9476 (95% CI 0.9473–0.9479), with specificity and sensitivity of 0.9950 (95% CI 0.9945–0.9955) and 0.7271 (95% CI 0.7265–0.7277), respectively. In summary, the model has satisfactory performance in extracting CNV regions from the background of OCTA images of nAMD patients.

Abstract Electroencephalogram (EEG) datasets from epilepsy patients have been used to develop seizure detection and prediction algorithms using machine learning (ML) techniques with the aim of implementing the learned model in a device. However, the format and structure of publicly available datasets are different from each other, and there is a lack of guidelines on the use of these datasets. This impacts the generatability, generalizability, and reproducibility of the results and findings produced by the studies. In this narrative review, we compiled and compared the different characteristics of the publicly available EEG datasets that are commonly used to develop seizure detection and prediction algorithms. We investigated the advantages and limitations of the characteristics of the EEG datasets. Based on our study, we identified 17 characteristics that make the EEG datasets unique from each other. We also briefly looked into how certain characteristics of the publicly available datasets affect the performance and outcome of a study, as well as the influences it has on the choice of ML techniques and preprocessing steps required to develop seizure detection and prediction algorithms. In conclusion, this study provides a guideline on the choice of publicly available EEG datasets to both clinicians and scientists working to develop a reproducible, generalizable, and effective seizure detection and prediction algorithm.

Introduction Despite significant advances in managing acute stroke and reducing stroke mortality, preventing complications like post-stroke epilepsy (PSE) has seen limited progress. PSE research has been scattered worldwide with varying methodologies and data reporting. To address this, we established the International Post-stroke Epilepsy Research Consortium (IPSERC) to integrate global PSE research efforts. This protocol outlines an individual patient data meta-analysis (IPD-MA) to determine outcomes in patients with post-stroke seizures (PSS) and develop/validate PSE prediction models, comparing them with existing models. This protocol informs about creating the International Post-stroke Epilepsy Research Repository (IPSERR) to support future collaborative research.Methods and analysis We utilised a comprehensive search strategy and searched MEDLINE, Embase, PsycInfo, Cochrane, and Web of Science databases until 30 January 2023. We extracted observational studies of stroke patients aged ≥18 years, presenting early or late PSS with data on patient outcome measures, and conducted the risk of bias assessment. We did not apply any restriction based on the date or language of publication. We will invite these study authors and the IPSERC collaborators to contribute IPD to IPSERR. We will review the IPD lodged within IPSERR to identify patients who developed epileptic seizures and those who did not. We will merge the IPD files of individual data and standardise the variables where possible for consistency. We will conduct an IPD-MA to estimate the prognostic value of clinical characteristics in predicting PSE.Ethics and dissemination Ethics approval is not required for this study. The results will be published in peer-reviewed journals. This study will contribute to IPSERR, which will be available to researchers for future PSE research projects. It will also serve as a platform to anchor future clinical trials.Trial registration number NCT06108102