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In recent years, remarkable progress in zero-shot learning (ZSL) has been achieved by generative adversarial networks (GAN). To compensate for the lack of training samples in ZSL, a surge of GAN architectures have been developed by human experts through trial-and-error testing. Despite their efficacy, however, there is still no guarantee that these hand-crafted models can consistently achieve good performance across diversified datasets or scenarios. Accordingly, in this paper, we turn to neural architecture search (NAS) and make the first attempt to bring NAS techniques into the ZSL realm. Specifically, we propose a differentiable GAN architecture search method over a specifically designed search space for zero-shot learning, referred to as ZeroNAS. Considering the relevance and balance of the generator and discriminator, ZeroNAS jointly searches their architectures in a min-max player game via adversarial training. Extensive experiments conducted on four widely used benchmark datasets demonstrate that ZeroNAS is capable of discovering desirable architectures that perform favorably against state-of-the-art ZSL and generalized zero-shot learning (GZSL) approaches. Source code is at https://github.com/caixiay/ZeroNAS.

ImportanceSelection of antiseizure medications (ASMs) for epilepsy remains largely a trial-and-error approach. Under this approach, many patients have to endure sequential trials of ineffective treatments until the “right drugs” are prescribed.ObjectiveTo develop and validate a deep learning model using readily available clinical information to predict treatment success with the first ASM for individual patients.Design, Setting, and ParticipantsThis cohort study developed and validated a prognostic model. Patients were treated between 1982 and 2020. All patients were followed up for a minimum of 1 year or until failure of the first ASM. A total of 2404 adults with epilepsy newly treated at specialist clinics in Scotland, Malaysia, Australia, and China between 1982 and 2020 were considered for inclusion, of whom 606 (25.2%) were excluded from the final cohort because of missing information in 1 or more variables.ExposuresOne of 7 antiseizure medications.Main Outcomes and MeasuresWith the use of the transformer model architecture on 16 clinical factors and ASM information, this cohort study first pooled all cohorts for model training and testing. The model was trained again using the largest cohort and externally validated on the other 4 cohorts. The area under the receiver operating characteristic curve (AUROC), weighted balanced accuracy, sensitivity, and specificity of the model were all assessed for predicting treatment success based on the optimal probability cutoff. Treatment success was defined as complete seizure freedom for the first year of treatment while taking the first ASM. Performance of the transformer model was compared with other machine learning models.ResultsThe final pooled cohort included 1798 adults (54.5% female; median age, 34 years [IQR, 24-50 years]). The transformer model that was trained using the pooled cohort had an AUROC of 0.65 (95% CI, 0.63-0.67) and a weighted balanced accuracy of 0.62 (95% CI, 0.60-0.64) on the test set. The model that was trained using the largest cohort only had AUROCs ranging from 0.52 to 0.60 and a weighted balanced accuracy ranging from 0.51 to 0.62 in the external validation cohorts. Number of pretreatment seizures, presence of psychiatric disorders, electroencephalography, and brain imaging findings were the most important clinical variables for predicted outcomes in both models. The transformer model that was developed using the pooled cohort outperformed 2 of the 5 other models tested in terms of AUROC.Conclusions and RelevanceIn this cohort study, a deep learning model showed the feasibility of personalized prediction of response to ASMs based on clinical information. With improvement of performance, such as by incorporating genetic and imaging data, this model may potentially assist clinicians in selecting the right drug at the first trial.

In many real world medical image classification settings, access to samples of all disease classes is not feasible, affecting the robustness of a system expected to have high performance in analyzing novel test data. This is a case of generalized zero shot learning (GZSL) aiming to recognize seen and unseen classes. We propose a GZSL method that uses self supervised learning (SSL) for: 1) selecting representative vectors of disease classes; and 2) synthesizing features of unseen classes. We also propose a novel approach to generate GradCAM saliency maps that highlight diseased regions with greater accuracy. We exploit information from the novel saliency maps to improve the clustering process by: 1) Enforcing the saliency maps of different classes to be different; and 2) Ensuring that clusters in the space of image and saliency features should yield class centroids having similar semantic information. This ensures the anchor vectors are representative of each class. Different from previous approaches, our proposed approach does not require class attribute vectors which are essential part of GZSL methods for natural images but are not available for medical images. Using a simple architecture the proposed method outperforms state of the art SSL based GZSL performance for natural images as well as multiple types of medical images. We also conduct many ablation studies to investigate the influence of different loss terms in our method.

Background It is unclear whether brain volumes are causally affected by Intraocular pressure (IOP) is highly correlated with blood pressure (BP).Methods The study included 8634 participants for IOP and 36069 participants for BP in observational analyses and 37410 participants for both IOP and BP in Mendelian Randomisation (MR) analyses from UK Biobank. IOP and BP were measured between 2006–2010. Brain volumes were measured using MRI between 2014–2019.Results Higher IOP was associated with smaller volumes of total brain (β (95% CI) for each 5-mmHg increment: -3.24 (-5.05, -1.44) ml) and grey matter (-1.10 (-2.17, -0.03) ml) independent of BP. Diastolic BP (β (95% CI) for each 10-mmHg increment: 0.13 (0.05, 0.21)) was associated with higher white matter hyperintensity (WMH) independent of antihypertensive medications. Associations between IOP and total brain and WMH volumes were stronger in younger individuals or those without hypertension. Associations between DBP/SBP and brain volumes were stronger in younger individuals, women, and lowly educated individuals. All MR analytic methods demonstrated a significant relationship between DBP and WMH (β (95% CI) for each 10-mmHg increment of DBP for inverse-variance weighting method: 0.019 (0.013, 0.026)). The β (95% CI) for grey matter volume (ml) associated with each 5-mmHg increment of IOP for inverse-variance weighting method was − 3.42 (-5.39, -1.45).Conclusions Higher IOP is casually linked to larger grey matter volume reduction while increased DBP casually linked to higher WMH load. Younger or lowly educated individuals deserve more scrutiny for the prevention of brain volume reduction potentially via IOP/DBP lowering.

Given the incomplete knowledge of classes that exist in the world, Open-set Recognition (OSR) enables networks to identify and reject the unseen classes after training. This problem of breaking the common closed-set assumption is far from being solved. Recent studies focus on designing new losses, neural network encoding structures, and calibration methods to optimize a feature space for OSR relevant tasks. In this work, we make the first attempt to tackle OSR by searching the architecture of a Neural Network (NN) under the open-set assumption. In contrast to the prior arts, we develop a mechanism to both search the architecture of the network and train a network suitable for tackling OSR. Inspired by the compact abating probability (CAP) model, which is theoretically proven to reduce the open space risk, we regularize the searching space by VAE contrastive learning. To discover a more robust structure for OSR, we propose Pseudo Auxiliary Searching (PAS), in which we split a pretended set of know-unknown classes from the original training set in the searching phase, hence enabling the super-net to explore an effective architecture that can handle unseen classes in advance. We demonstrate the benefits of this learning pipeline on 5 OSR datasets, including MNIST, SVHN, CIFAR10, CIFARAdd10, and CIFARAdd50, where our approach outperforms prior state-of-the-art networks designed by humans. To spark research in this field, our code is available at https://github.com/zxl101/NAS OSR.

Deep neural networks are known to be data-driven and label noise can have a marked impact on model performance. Recent studies have shown great robustness to classic image recognition even under a high noisy rate. In medical applications, learning from datasets with label noise is more challenging since medical imaging datasets tend to have instance-dependent noise (IDN) and suffer from high observer variability. In this paper, we systematically discuss the two common types of label noise in medical images - disagreement label noise from inconsistency expert opinions and single-target label noise from biased aggregation of individual annotations. We then propose an uncertainty estimation-based framework to handle these two label noise amid the medical image classification task. We design a dual-uncertainty estimation approach to measure the disagreement label noise and single-target label noise via improved Direct Uncertainty Prediction and Monte-Carlo-Dropout. A boosting-based curriculum training procedure is later introduced for robust learning. We demonstrate the effectiveness of our method by conducting extensive experiments on three different diseases with synthesized and real-world label noise: skin lesions, prostate cancer, and retinal diseases. We also release a large re-engineered database that consists of annotations from more than ten ophthalmologists with an unbiased golden standard dataset for evaluation and benchmarking. The dataset is available at https://mmai.group/peoples/julie/.

Dermatologists often diagnose or rule out early melanoma by evaluating the follow-up dermoscopic images of skin lesions. However, existing algorithms for early melanoma diagnosis are developed using single time-point images of lesions. Ignoring the temporal, morphological changes of lesions can lead to misdiagnosis in borderline cases. In this study, we propose a framework for automated early melanoma diagnosis using sequential dermoscopic images. To this end, we construct our method in three steps. First, we align sequential dermoscopic images of skin lesions using estimated Euclidean transformations, extract the lesion growth region by computing image differences among the consecutive images, and then propose a spatio-temporal network to capture the dermoscopic changes from aligned lesion images and the corresponding difference images. Finally, we develop an early diagnosis module to compute probability scores of malignancy for lesion images over time. We collected 179 serial dermoscopic imaging data from 122 patients to verify our method. Extensive experiments show that the proposed model outperforms other commonly used sequence models. We also compared the diagnostic results of our model with those of seven experienced dermatologists and five registrars. Our model achieved higher diagnostic accuracy than clinicians (63.69% vs. 54.33%, respectively) and provided an earlier diagnosis of melanoma (60.7% vs. 32.7% of melanoma correctly diagnosed on the first follow-up images). These results demonstrate that our model can be used to identify melanocytic lesions that are at high-risk of malignant transformation earlier in the disease process and thereby redefine what is possible in the early detection of melanoma.

Prevailing deep methods for image recognition require massive labeled samples in each visual category for training. However, large amounts of data annotations are time-consuming, and some uncommon categories only have rare samples available. For this issue, we focus on more challenging few-shot learning (FSL) task, where just few labeled images are used in the training stage. Existing FSL models are constructed with various convolutional neural networks (CNNs), which are trained on an auxiliary base dataset and evaluated for new few-shot predictions on a novel dataset. The performance of these models is difficult to break through because of the domain shift between base and novel datasets and the monotonous network architectures. Considering that, we propose a novel automatic attribute consistent network called Auto-ACNet to overcome the above problems. On one hand, Auto-ACNet utilizes the attribute information about base and novel categories to guide the procedure of representation learning. It introduces the consistent and non-consistent subnets to capture the common and different attributes of image pair, which helps to mitigate the domain shift problem. On the other hand, the architecture of Auto-ACNet is searched with the popular neural architecture search (NAS) technique DARTS, for obtaining a superior FSL network automatically. And the DARTS’s search space is improved by adding the position-aware module to extract the attribute characteristics better. Extensive experimental results on two datasets indicate that the proposed Auto-ACNet achieves significant improvement over the state-of-the-art competitors in this literature.

Background: Little is known about the association between macronutrient intake and incident dementia. Objective: To identify an optimal range of macronutrient intake associated with reduced risk of dementia. Methods: Our analysis included 93,389 adults aged 60–75 years from the UK Biobank. Diet was assessed using a web-based 24-h recall questionnaire between 2009–2012. Dementia was ascertained using hospital inpatient, death records, and self-reported data up to January 2021. We calculated a macronutrient score based on associations between an individual’s macronutrient intake and incident dementia. Results: During a median follow-up of 8.7 years, 1,171 incident dementia cases were documented. We found U-shape relationships for carbohydrate, fat, and protein intake with incident dementia. Compared to individuals with optimal carbohydrate intake, those with high intake (HR (95%CI): 1.48(1.15–1.91)) but not low intake (1.19(0.89–1.57)) had a higher risk of dementia. In the multivariable analysis, a low-fat intake (HR (95%CI): 1.42(1.11–1.82)) was associated with a higher risk of all-cause dementia. After adjustment for covariates, a high (HR (95%CI): 1.41(1.09–1.83)) but not low protein intake (1.22(0.94–1.57)) was associated with an increased risk of dementia. Individuals in quintiles 3–5 of optimal macronutrient score had a lower risk of dementia compared with those in quintile 1 (HR (95%CI): 0.76(0.64–0.91) for quintile 3, 0.71(0.60–0.85) for quintile 4, 0.74(0.61–0.91) for quintile 5). The association between macronutrient score and incident dementia was significant across subgroups of age, gender, education, and smoking. Conclusion: Moderate intakes of carbohydrate, fat, and protein were associated with the lowest risk of incident dementia.

The vulnerability of re-identification (re-ID) models under adversarial attacks is of significant concern as criminals may use adversarial perturbations to evade surveillance systems. Unlike a closed-world re-ID setting (i.e., a fixed number of training categories), a reliable re-ID system in the open world raises the concern of training a robust yet discriminative classifier, which still shows robustness in the context of unknown examples of an identity. In this work, we improve the robustness of open-world re-ID models by proposing a generative metric learning approach to generate adversarial examples that are regularized to produce robust distance metric. The proposed approach leverages the expressive capability of generative adversarial networks to defend the re-ID models against feature disturbance attacks. By generating the target people variants and sampling the triplet units for metric learning, our learned distance metrics are regulated to produce accurate predictions in the feature metric space. Experimental results on the three re-ID datasets, i.e., Market-1501, DukeMTMC-reID, and MSMT17 demonstrate the robustness of our method.

Background HIV and sexually transmitted infections (STIs) are major global public health concerns. Over 1 million curable STIs occur every day among people aged 15 years to 49 years worldwide. Insufficient testing or screening substantially impedes the elimination of HIV and STI transmission. Objective The aim of our study was to develop an HIV and STI risk prediction tool using machine learning algorithms. Methods We used clinic consultations that tested for HIV and STIs at the Melbourne Sexual Health Centre between March 2, 2015, and December 31, 2018, as the development data set (training and testing data set). We also used 2 external validation data sets, including data from 2019 as external “validation data 1” and data from January 2020 and January 2021 as external “validation data 2.” We developed 34 machine learning models to assess the risk of acquiring HIV, syphilis, gonorrhea, and chlamydia. We created an online tool to generate an individual’s risk of HIV or an STI. Results The important predictors for HIV and STI risk were gender, age, men who reported having sex with men, number of casual sexual partners, and condom use. Our machine learning–based risk prediction tool, named MySTIRisk, performed at an acceptable or excellent level on testing data sets (area under the curve [AUC] for HIV=0.78; AUC for syphilis=0.84; AUC for gonorrhea=0.78; AUC for chlamydia=0.70) and had stable performance on both external validation data from 2019 (AUC for HIV=0.79; AUC for syphilis=0.85; AUC for gonorrhea=0.81; AUC for chlamydia=0.69) and data from 2020-2021 (AUC for HIV=0.71; AUC for syphilis=0.84; AUC for gonorrhea=0.79; AUC for chlamydia=0.69). Conclusions Our web-based risk prediction tool could accurately predict the risk of HIV and STIs for clinic attendees using simple self-reported questions. MySTIRisk could serve as an HIV and STI screening tool on clinic websites or digital health platforms to encourage individuals at risk of HIV or an STI to be tested or start HIV pre-exposure prophylaxis. The public can use this tool to assess their risk and then decide if they would attend a clinic for testing. Clinicians or public health workers can use this tool to identify high-risk individuals for further interventions.

The need for comprehensive and automated screening methods for retinal image classification has long been recognized. Well-qualified doctors annotated images are very expensive and only a limited amount of data is available for various retinal diseases such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). Some studies show that some retinal diseases such as DR and AMD share some common features like haemorrhages and exudation but most classification algorithms only train those disease models independently when the only single label for one image is available. Inspired by multi-task learning where additional monitoring signals from various sources is beneficial to train a robust model. We propose a method called synergic adversarial label learning (SALL) which leverages relevant retinal disease labels in both semantic and feature space as additional signals and train the model in a collaborative manner using knowledge distillation. Our experiments on DR and AMD fundus image classification task demonstrate that the proposed method can significantly improve the accuracy of the model for grading diseases by 5.91% and 3.69% respectively. In addition, we conduct additional experiments to show the effectiveness of SALL from the aspects of reliability and interpretability in the context of medical imaging application.

Background the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) dementia risk score is a recognised tool for dementia risk stratification. However, its application is limited due to the requirements for multidimensional information and fasting blood draw. Consequently, an effective and non-invasive tool for screening individuals with high dementia risk in large population-based settings is urgently needed. Methods a deep learning algorithm based on fundus photographs for estimating the CAIDE dementia risk score was developed and internally validated by a medical check-up dataset included 271,864 participants in 19 province-level administrative regions of China, and externally validated based on an independent dataset included 20,690 check-up participants in Beijing. The performance for identifying individuals with high dementia risk (CAIDE dementia risk score ≥ 10 points) was evaluated by area under the receiver operating curve (AUC) with 95% confidence interval (CI). Results the algorithm achieved an AUC of 0.944 (95% CI: 0.939–0.950) in the internal validation group and 0.926 (95% CI: 0.913–0.939) in the external group, respectively. Besides, the estimated CAIDE dementia risk score derived from the algorithm was significantly associated with both comprehensive cognitive function and specific cognitive domains. Conclusions this algorithm trained via fundus photographs could well identify individuals with high dementia risk in a population setting. Therefore, it has the potential to be utilised as a non-invasive and more expedient method for dementia risk stratification. It might also be adopted in dementia clinical trials, incorporated as inclusion criteria to efficiently select eligible participants.

Retinopathy of prematurity (ROP) is currently one of the leading causes ofinfant blindness worldwide. In recent years, significant progress has been made incomputer-aided diagnostic methods based on deep learning. However, deeplearning is often data hungry, with its need for large amounts of annotated datafor model optimization, and in clinical scenarios, annotated data requires longhours of effort by experienced physicians. In contrast, a large number ofunlabelled images are relatively easy to obtain. In this paper, we propose a newsemi-supervised learning framework to reduce annotation costs by using partiallylabelled images and a large number of unlabelled images for automatic ROPstaging. Our semi-supervised deep learning framework consists of a studentmodel and a teacher model. We input images that have undergone differentperturbations into the student and teacher models. By encouraging consistencyof predicted output and inter-sample semantic structure between the student andteacher models, useful discriminative information is mined from the unlabelleddata to improve model classification performance. The teacher model parametersare updated by an exponential moving average algorithm. We have conductedextensive experiments on a real clinical dataset. Using only 30% of the labelledimages, our method achieves area under the receiver operating curve (AUC) of0.8630, Accuracy of 0.7895, Sensitivity of 0.7868, and Specificity of 0.8010. Theclassification performance of our method is close to that of using all 100%labelled images when the proportion of labelled images is 50%.

Usually, lesions are not isolated but are associated with the surrounding tissues. For example, the growth of a tumour can depend on or infiltrate into the surrounding tissues. Due to the pathological nature of the lesions, it is challenging to distinguish their boundaries in medical imaging. However, these uncertain regions may contain diagnostic information. Therefore, the simple binarization of lesions by traditional binary segmentation can result in the loss of diagnostic information. In this work, we introduce the image matting into the 3D scenes and use the alpha matte, i.e., a soft mask, to describe lesions in a 3D medical image. The traditional soft mask acted as a training trick to compensate for the easily mislabelled or under-labelled ambiguous regions. In contrast, 3D matting uses soft segmentation to characterize the uncertain regions more finely, which means that it retains more structural information for subsequent diagnosis and treatment. The current study of image matting methods in 3D is limited. To address this issue, we conduct a comprehensive study of 3D matting, including both traditional and deep-learning-based methods. We adapt four state-of-the-art 2D image matting algorithms to 3D scenes and further customize the methods for CT images to calibrate the alpha matte with the radiodensity. Moreover, we propose the first end-to-end deep 3D matting network and implement a solid 3D medical image matting benchmark. Its efficient counterparts are also proposed to achieve a good performance-computation balance. Furthermore, there is no high-quality annotated dataset related to 3D matting, slowing down the development of data-driven deep-learning-based methods. To address this issue, we construct the first 3D medical matting dataset. The validity of the dataset was verified through clinicians' assessments and downstream experiments., Accepted by Computers in Biology and Medicine. arXiv admin note: substantial text overlap with arXiv:2209.07843

AimsTo predict the vault and the EVO-implantable collamer lens (ICL) size by artificial intelligence (AI) and big data analytics.MethodsSix thousand two hundred and ninety-seven eyes implanted with an ICL from 3536 patients were included. The vault values were measured by the anterior segment analyzer (Pentacam HR). Permutation importance and Impurity-based feature importance are used to investigate the importance between the vault and input parameters. Regression models and classification models are applied to predict the vault. The ICL size is set as the target of the prediction, and the vault and the other input features are set as the new inputs for the ICL size prediction. Data were collected from 2015 to 2020. Random Forest, Gradient Boosting and XGBoost were demonstrated satisfying accuracy and mean area under the curve (AUC) scores in vault predicting and ICL sizing.ResultsIn the prediction of the vault, the Random Forest has the best results in the regression model (R2=0.315), then follows the Gradient Boosting (R2=0.291) and XGBoost (R2=0.285). The maximum classification accuracy is 0.828 in Random Forest, and the mean AUC is 0.765. The Random Forest predicts the ICL size with an accuracy of 82.2% and the Gradient Boosting and XGBoost, which are also compatible with 81.5% and 81.8% accuracy, respectively.ConclusionsRandom Forest, Gradient Boosting and XGBoost models are applicable for vault predicting and ICL sizing. AI may assist ophthalmologists in improving ICL surgery safety, designing surgical strategies, and predicting clinical outcomes.

Deep-learning-based segmentation methods have shown great success across many medical image applications. However, the custom training paradigms suffer from a well-known constraint of the requirement of pixel-wise annotations, which is labor-intensive, especially when they are required to learn new classes incrementally. Contemporary incremental learning focuses on dealing with catastrophic forgetting in image classification and object detection. However, this work aims to promote the performance of the current model to learn new classes with the help of the previous model in the context of incremental learning of instance segmentation. It enormously benefits the current model when the labeled data is limited because of the high labor intensity of manual labeling. In this paper, on the Diabetic Retinopathy (DR) lesion segmentation problem, a novel incremental segmentation paradigm is proposed to distill the knowledge of the previous model to improve the current model. Remarkably, we propose various approaches working on the class-based alignment of the probability maps of the current and the previous model, accounting for the difference between the background classes of the two models. The experimental evaluation of DR lesion segmentation shows the effectiveness of the proposed approaches.

Background Little is known regarding whether sex assigned at birth modifies the association between several predictive factors for dementia and the risk of dementia itself. Methods Our retrospective cohort study included 214,670 men and 214,670 women matched by age at baseline from the UK Biobank. Baseline data were collected between 2006 and 2010, and incident dementia was ascertained using hospital inpatient or death records until January 2021. Mediation analysis was tested for 133 individual factors. Results Over 5,117,381 person-years of follow-up, 5928 cases of incident all-cause dementia (452 cases of young-onset dementia, 5476 cases of late-onset dementia) were documented. Hazard ratios (95% CI) for all-cause, young-onset, and late-onset dementias associated with the male sex (female as reference) were 1.23 (1.17–1.29), 1.42 (1.18–1.71), and 1.21 (1.15–1.28), respectively. Out of 133 individual factors, the strongest mediators for the association between sex and incident dementia were multimorbidity risk score (percentage explained (95% CI): 62.1% (45.2–76.6%)), apolipoprotein A in the blood (25.5% (15.2–39.4%)), creatinine in urine (24.9% (16.1–36.5%)), low-density lipoprotein cholesterol in the blood (23.2% (16.2–32.1%)), and blood lymphocyte percentage (21.1% (14.5–29.5%)). Health-related conditions (percentage (95% CI) explained: 74.4% (51.3–88.9%)) and biomarkers (83.0% (37.5–97.5%)), but not lifestyle factors combined (30.1% (20.7–41.6%)), fully mediated sex differences in incident dementia. Health-related conditions combined were a stronger mediator for late-onset (75.4% (48.6–90.8%)) than for young-onset dementia (52.3% (25.8–77.6%)), whilst lifestyle factors combined were a stronger mediator for young-onset (42.3% (19.4–69.0%)) than for late-onset dementia (26.7% (17.1–39.2%)). Conclusions Our analysis matched by age has demonstrated that men had a higher risk of all-cause, young-onset, and late-onset dementias than women. This association was fully mediated by health-related conditions or blood/urinary biomarkers and largely mediated by lifestyle factors. Our findings are important for understanding potential mechanisms of sex in dementia risk.

To investigate the association of type 1 diabetes (T1D) and age at diagnosis of type 2 diabetes (T2D) with brain structure and incident dementia.Our analysis was based on the UK Biobank. We included 1376 participants with diabetes and 2752 randomly selected controls for brain volume analysis, and 25,141 participants with diabetes and 50,282 randomly selected controls for dementia analysis. Brain volume was measured using magnetic resonance imaging. Dementia was identified using hospital inpatient records and mortality register data until January 2021.T2D diagnosed at a younger age was associated with larger reductions in brain volume. After adjustment for glycated haemoglobin (HbA1c) and other covariates, only T2D diagnosed50 years was associated with smaller total brain volume (β (95% CI): -14.56 (-24.67, -4.44) ml), and grey (-6.47[-12.75, -0.20] ml) and white matter volumes (-8.08[-14.66, -1.51] ml). Corresponding numbers for total brain, grey matter and white matter volumes associated with T1D were -62.86 (-93.71,-32.01), -34.27 (-53.72, -14.83), and -28.59 (-47.65, -9.52) ml, respectively. During a median follow-up of 11.9 years, 2035 new dementia cases were identified. Younger age at diagnosis of T2D was associated with larger excessive risk of dementia, whereas T2D diagnosed50 years was associated with the largest hazard ratio (HR) (95% CI: 2.03[1.53-2.69]) in the multivariable analysis. The HR (95% CI) for dementia associated with T1D was 2.08 (1.40-3.09).Individuals with T1D or T2D diagnosed at younger age are at larger excessive risk of brain volume reduction and dementia.

Objectives We aimed to develop machine learning models and evaluate their performance in predicting HIV and sexually transmitted infections (STIs) diagnosis based on a cohort of Australian men who have sex with men (MSM). Methods We collected clinical records of 21,273 Australian MSM during 2011–2017. We compared accuracies for predicting HIV and STIs (syphilis, gonorrhoea, chlamydia) diagnosis using four machine learning approaches against a multivariable logistic regression (MLR) model. Results Machine learning approaches consistently outperformed MLR. Gradient boosting machine (GBM) achieved the highest area under the receiver operator characteristic curve for HIV (76.3%) and STIs (syphilis, 85.8%; gonorrhoea, 75.5%; chlamydia, 68.0%), followed by extreme gradient boosting (71.1%, 82.2%, 70.3%, 66.4%), random forest (72.0%, 81.9%, 67.2%, 64.3%), deep learning (75.8%, 81.0%, 67.5%, 65.4%) and MLR (69.8%, 80.1%, 67.2%, 63.2%). GBM models demonstrated the ten greatest predictors collectively explained 62.7-73.6% of variations in predicting HIV/STIs. STIs symptoms, past syphilis infection, age, time living in Australia, frequency of condom use with casual male sexual partners during receptive anal sex and the number of casual male sexual partners in the past 12 months were most commonly identified predictors. Conclusions Machine learning approaches are advantageous over multivariable logistic regression models in predicting HIV/STIs diagnosis.

RNA binding proteins (RBPs) are critical for the post-transcriptional control of RNAs and play vital roles in a myriad of biological processes, such as RNA localization and gene regulation. Therefore, computational methods that are capable of accurately identifying RBPs are highly desirable and have important implications for biomedical and biotechnological applications. Here, we propose a two-stage deep transfer learning-based framework, termed RBP-TSTL, for accurate prediction of RBPs. In the first stage, the knowledge from the self-supervised pre-trained model was extracted as feature embeddings and used to represent the protein sequences, while in the second stage, a customized deep learning model was initialized based on an annotated pre-training RBPs dataset before being fine-tuned on each corresponding target species dataset. This two-stage transfer learning framework can enable the RBP-TSTL model to be effectively trained to learn and improve the prediction performance. Extensive performance benchmarking of the RBP-TSTL models trained using the features generated by the self-supervised pre-trained model and other models trained using hand-crafting encoding features demonstrated the effectiveness of the proposed two-stage knowledge transfer strategy based on the self-supervised pre-trained models. Using the best-performing RBP-TSTL models, we further conducted genome-scale RBP predictions for Homo sapiens, Arabidopsis thaliana, Escherichia coli, and Salmonella and established a computational compendium containing all the predicted putative RBPs candidates. We anticipate that the proposed RBP-TSTL approach will be explored as a useful tool for the characterization of RNA-binding proteins and exploration of their sequence–structure–function relationships.

Background Little is known regarding life-course trajectories of important diseases. We aimed to identify diseases that were strongly associated with mortality and test temporal trajectories of these diseases before mortality. Methods Our analysis was based on UK Biobank. Diseases were identified using questionnaires, nurses’ interviews, or inpatient data. Mortality register data were used to identify mortality up to January 2021. The association between 60 individual diseases at baseline and in the life course and incident mortality was examined using Cox proportional regression models. Those diseases with great contribution to mortality were identified and disease trajectories in life course were then derived. Results During a median follow-up of 11.8 years, 31,373 individuals (median age at death (interquartile range): 70.7 (65.3–74.8) years, 59.4% male) died of all-cause mortality (with complete data on diagnosis date of disease), with 16,237 dying with cancer and 6702 with cardiovascular disease (CVD). We identified 37 diseases including cancers and heart diseases that were associated with an increased risk of mortality independent of other diseases (hazard ratio ranged from 1.09 to 7.77). Among those who died during follow-up, 2.2% did not have a diagnosis of any disease of interest and 90.1% were diagnosed with two or more diseases in their life course. Individuals who were diagnosed with more diseases in their life course were more likely to have longer longevity. Cancer was more likely to be diagnosed following hypertension, hypercholesterolemia, CVD, or digestive disorders and more likely to be diagnosed ahead of CVD, chronic kidney disease (CKD), or digestive disorders. CVD was more likely to be diagnosed following hypertension, hypercholesterolemia, or digestive disorders and more likely to be diagnosed ahead of cancer or CKD. Hypertension was more likely to precede other diseases, and CKD was more likely to be diagnosed as the last disease before more mortality. Conclusions There are significant interplays between cancer and CVD for mortality. Cancer and CVD were frequently clustered with hypertension, CKD, and digestive disorders with CKD highly being diagnosed as the last disease in the life course. Our findings underline the importance of health checks among middle-aged adults for the prevention of premature mortality.

The lack of experienced ophthalmologists limits the early diagnosis of retinal diseases. Artificial intelligence can be an efficient real-time way for screening retinal diseases.To develop and prospectively validate a deep learning (DL) algorithm that, based on ocular fundus images, recognizes numerous retinal diseases simultaneously in clinical practice.This multicenter, diagnostic study at 65 public medical screening centers and hospitals in 19 Chinese provinces included individuals attending annual routine medical examinations and participants of population-based and community-based studies.Based on 120 002 ocular fundus photographs, the Retinal Artificial Intelligence Diagnosis System (RAIDS) was developed to identify 10 retinal diseases. RAIDS was validated in a prospective collected data set, and the performance between RAIDS and ophthalmologists was compared in the data sets of the population-based Beijing Eye Study and the community-based Kailuan Eye Study.The performance of each classifier included sensitivity, specificity, accuracy, F1 score, and Cohen κ score.In the prospective validation data set of 208 758 images collected from 110 784 individuals (median [range] age, 42 [8-87] years; 115 443 [55.3%] female), RAIDS achieved a sensitivity of 89.8% (95% CI, 89.5%-90.1%) to detect any of 10 retinal diseases. RAIDS differentiated 10 retinal diseases with accuracies ranging from 95.3% to 99.9%, without marked differences between medical screening centers and geographical regions in China. Compared with retinal specialists, RAIDS achieved a higher sensitivity for detection of any retinal abnormality (RAIDS, 91.7% [95% CI, 90.6%-92.8%]; certified ophthalmologists, 83.7% [95% CI, 82.1%-85.1%]; junior retinal specialists, 86.4% [95% CI, 84.9%-87.7%]; and senior retinal specialists, 88.5% [95% CI, 87.1%-89.8%]). RAIDS reached a superior or similar diagnostic sensitivity compared with senior retinal specialists in the detection of 7 of 10 retinal diseases (ie, referral diabetic retinopathy, referral possible glaucoma, macular hole, epiretinal macular membrane, hypertensive retinopathy, myelinated fibers, and retinitis pigmentosa). It achieved a performance comparable with the performance by certified ophthalmologists in 2 diseases (ie, age-related macular degeneration and retinal vein occlusion). Compared with ophthalmologists, RAIDS needed 96% to 97% less time for the image assessment.In this diagnostic study, the DL system was associated with accurately distinguishing 10 retinal diseases in real time. This technology may help overcome the lack of experienced ophthalmologists in underdeveloped areas.

BackgroundThyroid-associated ophthalmopathy (TAO) is one of the most common orbital diseases that seriously threatens visual function and significantly affects patients’ appearances, rendering them unable to work. This study established an intelligent diagnostic system for TAO based on facial images.MethodsPatient images and data were obtained from medical records of patients with TAO who visited Shanghai Changzheng Hospital from 2013 to 2018. Eyelid retraction, ocular dyskinesia, conjunctival congestion, and other signs were noted on the images. Patients were classified according to the types, stages, and grades of TAO based on the diagnostic criteria. The diagnostic system consisted of multiple task-specific models.ResultsThe intelligent diagnostic system accurately diagnosed TAO in three stages. The built-in models pre-processed the facial images and diagnosed multiple TAO signs, with average areas under the receiver operating characteristic curves exceeding 0.85 (F1 score >0.80).ConclusionThe intelligent diagnostic system introduced in this study accurately identified several common signs of TAO.

Artificial intelligence (AI) algorithms have been shown to diagnose skin lesions with impressive accuracy in experimental settings. The majority of the literature to date has compared AI and dermatologists as opponents in skin cancer diagnosis. However, in the real-world clinical setting, the clinician will work in collaboration with AI. Existing evidence regarding the integration of such AI diagnostic tools into clinical practice is limited. Human factors, such as cognitive style, personality, experience, preferences, and attitudes may influence clinicians' use of AI. In this review, we consider these human factors and the potential cognitive errors, biases, and unintended consequences that could arise when using an AI skin cancer diagnostic tool in the real world. Integrating this knowledge in the design and implementation of AI technology will assist in ensuring that the end product can be used effectively. Dermatologist leadership in the development of these tools will further improve their clinical relevance and safety.

The annotation of disease severity for medical image datasets often relies on collaborative decisions from multiple human graders. The intra-observer variability derived from individual differences always persists in this process, yet the influence is often underestimated. In this paper, we cast the intra-observer variability as an uncertainty problem and incorporate the label uncertainty information as guidance into the disease screening model to improve the final decision. The main idea is dividing the images into simple and hard cases by uncertainty information, and then developing a multi-stream network to deal with different cases separately. Particularly, for hard cases, we strengthen the network's capacity in capturing the correct disease features and resisting the interference of uncertainty. Experiments on a fundus image-based glaucoma screening case study show that the proposed model outperforms several baselines, especially in screening hard cases., To appear in ISBI 2022

The key towards learning informative node representations in graphs lies in how to gain contextual information from the neighbourhood. In this work, we present a simple-yet-effective self-supervised node representation learning strategy via directly maximizing the mutual information between the hidden representations of nodes and their neighbourhood, which can be theoretically justified by its link to graph smoothing. Following InfoNCE, our framework is optimized via a surrogate contrastive loss, where the positive selection underpins the quality and efficiency of representation learning. To this end, we propose a topology-aware positive sampling strategy, which samples positives from the neighbourhood by considering the structural dependencies between nodes and thus enables positive selection upfront. In the extreme case when only one positive is sampled, we fully avoid expensive neighbourhood aggregation. Our methods achieve promising performance on various node classification datasets. It is also worth mentioning by applying our loss function to MLP based node encoders, our methods can be orders of faster than existing solutions. Our codes and supplementary materials are available at https://github.com/dongwei156/n2n., Paper is accepted to CVPR 2022. Codes and supplementary materials are available at https://github.com/dongwei156/n2n

Our aim is to establish an AI model for distinguishing color fundus photographs (CFP) of RVO patients from normal individuals.The training dataset included 2013 CFP from fellow eyes of RVO patients and 8536 age- and gender-matched normal CFP. Model performance was assessed in two independent testing datasets. We evaluated the performance of the AI model using the area under the receiver operating characteristic curve (AUC), accuracy, precision, specificity, sensitivity, and confusion matrices. We further explained the probable clinical relevance of the AI by extracting and comparing features of the retinal images.Our model achieved an average AUC was 0.9866 (95% CI: 0.9805-0.9918), accuracy was 0.9534 (95% CI: 0.9421-0.9639), precision was 0.9123 (95% CI: 0.8784-9453), specificity was 0.9810 (95% CI: 0.9729-0.9884), and sensitivity was 0.8367 (95% CI: 0.7953-0.8756) for identifying fundus images of RVO patients in training dataset. In independent external datasets 1, the AUC of the RVO group was 0.8102 (95% CI: 0.7979-0.8226), the accuracy of 0.7752 (95% CI: 0.7633-0.7875), the precision of 0.7041 (95% CI: 0.6873-0.7211), specificity of 0.6499 (95% CI: 0.6305-0.6679) and sensitivity of 0.9124 (95% CI: 0.9004-0.9241) for RVO group. There were significant differences in retinal arteriovenous ratio, optic cup to optic disc ratio, and optic disc tilt angle (p = 0.001, p = 0.0001, and p = 0.0001, respectively) between the two groups in training dataset.We trained an AI model to classify color fundus photographs of RVO patients with stable performance both in internal and external datasets. This may be of great importance for risk prediction in patients with retinal venous occlusion.

Protein secretion has a pivotal role in many biological processes and is particularly important for intercellular communication, from the cytoplasm to the host or external environment. Gram-positive bacteria can secrete proteins through multiple secretion pathways. The non-classical secretion pathway has recently received increasing attention among these secretion pathways, but its exact mechanism remains unclear. Non-classical secreted proteins (NCSPs) are a class of secreted proteins lacking signal peptides and motifs. Several NCSP predictors have been proposed to identify NCSPs and most of them employed the whole amino acid sequence of NCSPs to construct the model. However, the sequence length of different proteins varies greatly. In addition, not all regions of the protein are equally important and some local regions are not relevant to the secretion. The functional regions of the protein, particularly in the N- and C-terminal regions, contain important determinants for secretion. In this study, we propose a new hybrid deep learning-based framework, referred to as ASPIRER, which improves the prediction of NCSPs from amino acid sequences. More specifically, it combines a whole sequence-based XGBoost model and an N-terminal sequence-based convolutional neural network model; 5-fold cross-validation and independent tests demonstrate that ASPIRER achieves superior performance than existing state-of-the-art approaches. The source code and curated datasets of ASPIRER are publicly available at https://github.com/yanwu20/ASPIRER/. ASPIRER is anticipated to be a useful tool for improved prediction of novel putative NCSPs from sequences information and prioritization of candidate proteins for follow-up experimental validation.

BACKGROUND A number of studies have shown promising performance of artificial intelligence (AI) algorithms for diagnosis of lesions in skin cancer. To date, none of these have assessed algorithm performance in the real-world setting. OBJECTIVE The aim of this project is to evaluate practical issues of implementing a convolutional neural network developed by MoleMap Ltd and Monash University eResearch in the clinical setting. METHODS Participants were recruited from the Alfred Hospital and Skin Health Institute, Melbourne, Australia, from November 1, 2019, to May 30, 2021. Any skin lesions of concern and at least two additional lesions were imaged using a proprietary dermoscopic camera. Images were uploaded directly to the study database by the research nurse via a custom interface installed on a clinic laptop. Doctors recorded their diagnosis and management plan for each lesion in real time. A pre-post study design was used. In the preintervention period, participating doctors were blinded to AI lesion assessment. An interim safety analysis for AI accuracy was then performed. In the postintervention period, the AI algorithm classified lesions as benign, malignant, or uncertain after the doctors’ initial assessment had been made. Doctors then had the opportunity to record an updated diagnosis and management plan. After discussing the AI diagnosis with the patient, a final management plan was agreed upon. RESULTS Participants at both sites were high risk (for example, having a history of melanoma or being transplant recipients). 743 lesions were imaged in 214 participants. In total, 28 dermatology trainees and 17 consultant dermatologists provided diagnoses and management decisions, and 3 experienced teledermatologists provided remote assessments. A dedicated research nurse was essential to oversee study processes, maintain study documents, and assist with clinical workflow. In cases where AI algorithm and consultant dermatologist diagnoses were discordant, participant anxiety was an important factor in the final agreed management plan to biopsy or not. CONCLUSIONS Although AI algorithms are likely to be of most use in the primary care setting, higher event rates in specialist settings are important for the initial assessment of algorithm safety and accuracy. This study highlighted the importance of considering workflow issues and doctor-patient-AI interactions prior to larger-scale trials in community-based practices. CLINICALTRIAL ClinicalTrials.gov NCT04040114; https://clinicaltrials.gov/ct2/show/NCT04040114

Little is known regarding associations of conventional and emerging diseases and their multimorbidity with brain volumes.This cross-sectional study included 36,647 European ancestry individuals aged 44-81 years with brain magnetic resonance imaging data from UK Biobank. Brain volumes were measured between 02 May 2014 and 31 October 2019. General linear regression models were used to associate 57 individual major diseases with brain volumes. Latent class analysis was used to identify multimorbidity patterns. A multimorbidity score for brain volumes was computed based on the estimates for individual groups of diseases.Out of 57 major diseases, 16 were associated with smaller volumes of total brain, 14 with smaller volumes of grey matter, and six with smaller hippocampus volumes, and four major diseases were associated with higher white matter hyperintensity (WMH) load after adjustment for all other diseases. The leading contributors to the variance of total brain volume were hypertension (RBesides conventional diseases, we found an association between numerous emerging diseases and smaller brain volumes. CMD-related multimorbidity patterns are associated with smaller brain volumes. Men or younger adults with multimorbidity are more in need of care for promoting brain health. These findings are from an association study and will need confirmation.The Fundamental Research Funds of the State Key Laboratory of Ophthalmology, Project of Investigation on Health Status of Employees in Financial Industry in Guangzhou, China (Z012014075), Science and Technology Program of Guangzhou, China (202,002,020,049).

Person re-identification (re-ID) is of great importance to video surveillance systems by estimating the similarity between a pair of cross-camera person shorts. Current methods for estimating such similarity require a large number of labeled samples for supervised training. In this paper, we present a pseudo-pair based self-similarity learning approach for unsupervised person re-ID without human annotations. Unlike conventional unsupervised re-ID methods that use pseudo labels based on global clustering, we construct patch surrogate classes as initial supervision, and propose to assign pseudo labels to images through the pairwise gradient-guided similarity separation. This can cluster images in pseudo pairs, and the pseudos can be updated during training. Based on pseudo pairs, we propose to improve the generalization of similarity function via a novel self-similarity learning:it learns local discriminative features from individual images via intra-similarity, and discovers the patch correspondence across images via inter-similarity. The intra-similarity learning is based on channel attention to detect diverse local features from an image. The inter-similarity learning employs a deformable convolution with a non-local block to align patches for cross-image similarity. Experimental results on several re-ID benchmark datasets demonstrate the superiority of the proposed method over the state-of-the-arts., Under review

Background: More than one million people acquire sexually transmitted infections (STIs) every day globally. It is possible that predicting an individual’s future risk of HIV/STIs could contribute to behaviour change or improve testing. We developed a series of machine learning models and a subsequent risk-prediction tool for predicting the risk of HIV/STIs over the next 12 months. Methods: Our data included individuals who were re-tested at the clinic for HIV (65,043 consultations), syphilis (56,889 consultations), gonorrhoea (60,598 consultations), and chlamydia (63,529 consultations) after initial consultations at the largest public sexual health centre in Melbourne from 2 March 2015 to 31 December 2019. We used the receiver operating characteristic (AUC) curve to evaluate the model’s performance. The HIV/STI risk-prediction tool was delivered via a web application. Results: Our risk-prediction tool had an acceptable performance on the testing datasets for predicting HIV (AUC = 0.72), syphilis (AUC = 0.75), gonorrhoea (AUC = 0.73), and chlamydia (AUC = 0.67) acquisition. Conclusions: Using machine learning techniques, our risk-prediction tool has acceptable reliability in predicting HIV/STI acquisition over the next 12 months. This tool may be used on clinic websites or digital health platforms to form part of an intervention tool to increase testing or reduce future HIV/STI risk.