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Abstract The increasing prevalence of myopia worldwide presents a significant public health challenge. A key strategy to combat myopia is with early detection and prediction in children as such examination allows for effective intervention using readily accessible imaging technique. To this end, we introduced DeepMyopia, an artificial intelligence (AI)-enabled decision support system to detect and predict myopia onset and facilitate targeted interventions for children at risk using routine retinal fundus images. Based on deep learning architecture, DeepMyopia had been trained and internally validated on a large cohort of retinal fundus images (n = 1,638,315) and then externally tested on datasets from seven sites in China (n = 22,060). Our results demonstrated robustness of DeepMyopia, with AUCs of 0.908, 0.813, and 0.810 for 1-, 2-, and 3-year myopia onset prediction with the internal test set, and AUCs of 0.796, 0.808, and 0.767 with the external test set. DeepMyopia also effectively stratified children into low- and high-risk groups (p

Abstract Radiogenomics relationships (RRs) aims to identify statistically significant correlations between medical image features and molecular characteristics from analysing tissue samples. Previous radiogenomics studies mainly relied on a single category of image feature extraction techniques (ETs); these are (i) handcrafted ETs that encompass visual imaging characteristics, curated from knowledge of human experts and, (ii) deep ETs that quantify abstract-level imaging characteristics from large data. Prior studies therefore failed to leverage the complementary information that are accessible from fusing the ETs. In this study, we propose a fused feature signature (FFSig): a selection of image features from handcrafted and deep ETs (e.g., transfer learning and fine-tuning of deep learning models). We evaluated the FFSig’s ability to better represent RRs compared to individual ET approaches with two public datasets: the first dataset was used to build the FFSig using 89 patients with non-small cell lung cancer (NSCLC) comprising of gene expression data and CT images of the thorax and the upper abdomen for each patient; the second NSCLC dataset comprising of 117 patients with CT images and RNA-Seq data and was used as the validation set. Our results show that our FFSig encoded complementary imaging characteristics of tumours and identified more RRs with a broader range of genes that are related to important biological functions such as tumourigenesis. We suggest that the FFSig has the potential to identify important RRs that may assist cancer diagnosis and treatment in the future.

ObjectivesThe accurate assessment of lymph node metastases (LNMs) and the preoperative nodal (N) stage are critical for the precise treatment of patients with gastric cancer (GC). The diagnostic performance, however, of current imaging procedures used for this assessment is sub-optimal. Our aim was to investigate the value of preoperative 18F-FDG PET/CT radiomic features to predict LNMs and the N stage.MethodsWe retrospectively collected clinical and 18F-FDG PET/CT imaging data of 185 patients with GC who underwent total or partial radical gastrectomy. Patients were allocated to training and validation sets using the stratified method at a fixed ratio (8:2). There were 2,100 radiomic features extracted from the 18F-FDG PET/CT scans. After selecting radiomic features by the random forest, relevancy-based, and sequential forward selection methods, the BalancedBagging ensemble classifier was established for the preoperative prediction of LNMs, and the OneVsRest classifier for the N stage. The performance of the models was primarily evaluated by the AUC and accuracy, and validated by the independent validation methods. Analysis of the feature importance and the correlation were also conducted. We also compared the predictive performance of our radiomic models to that with the contrast-enhanced CT (CECT) and 18F-FDG PET/CT.ResultsThere were 185 patients—127 men, 58 women, with the median age of 62, and an age range of 22–86 years. One CT feature and one PET feature were selected to predict LNMs and achieved the best performance (AUC: 82.2%, accuracy: 85.2%). This radiomic model also detected some LNMs that were missed in CECT (19.6%) and 18F-FDG PET/CT (35.7%). For predicting the N stage, four CT features and one PET feature were selected (AUC: 73.7%, accuracy: 62.3%). Of note, a proportion of patients in the validation set whose LNMs were incorrectly staged by CECT (57.4%) and 18F-FDG PET/CT (55%) were diagnosed correctly by our radiomic model.ConclusionWe developed and validated two machine learning models based on the preoperative 18F-FDG PET/CT images that have a predictive value for LNMs and the N stage in GC. These predictive models show a promise to offer a potentially useful adjunct to current staging approaches for patients with GC.

Background: Metabolic syndrome (MetS) is highly related to the excessive accumulation of visceral adipose tissue (VAT). Quantitative measurements of VAT are commonly applied in clinical practice for measurement of metabolic risks; however, it remains largely unknown whether the texture of VAT can evaluate visceral adiposity, stratify MetS and predict surgery-induced weight loss effects. Methods: 675 Chinese adult volunteers and 63 obese patients (with bariatric surgery) were enrolled. Texture features were extracted from VATs of the computed tomography (CT) scans and machine learning was applied to identify significant imaging biomarkers associated with metabolic-related traits. Findings: Combined with sex, ten VAT texture features achieved areas under the curve (AUCs) of 0.872, 0.888, 0.961, and 0.947 for predicting the prevalence of insulin resistance, MetS, central obesity, and visceral obesity, respectively. A novel imaging biomarker, RunEntropy, was identified to be significantly associated with major metabolic outcomes and a 3.5-year follow-up in 338 volunteers demonstrated its long-term effectiveness. More importantly, the preoperative imaging biomarkers yielded high AUCs and accuracies for estimation of surgery responses, including the percentage of excess weight loss (%EWL) (0.867 and 74.6%), postoperative BMI group (0.930 and 76.1%), postoperative insulin resistance (0.947 and 88.9%), and excess visceral fat loss (the proportion of visceral fat reduced over 50%; 0.928 and 84.1%). Interpretation: This study shows that the texture features of VAT have significant clinical implications in evaluating metabolic disorders and predicting surgery-induced weight loss effects. Funding: The complete list of funders can be found in the Acknowledgement section.

Video summarization (VS) is to identify important content from a given video, which can help users quickly comprehend video content. Recently, sparse dictionary selection (SDS) has demonstrated to be an effective solution for VS problems, which generally assumes a linear relationship between keyframes and non-keyframes. However, this assumption is not always true for video frames which possess intrinsic nonlinear structures and properties. In this paper, by exploiting the nonlinearity between video frames, a nonlinear SDS model is formulated for VS, in which the nonlinearity is transformed to linearity by projecting a video to a high-dimensional feature space induced by a kernel function. We also propose two greedy optimization algorithms to solve the resulting model, namely the standard kernel SDS (KSDS) greedy algorithm and the robust KSDS greedy algorithm with a backtracking strategy. In order to achieve an intuitive and flexible configuration of the VS process, an adaptive criterion, namely energy ratio, is devised to produce video summaries with different lengths for different video contents. Experimental results on two different benchmark video datasets demonstrate that the proposed algorithm outperforms several state-of-the-art VS algorithms.

Kernel-based discriminant analysis is an effective nonlinear mechanism for pattern analysis. Conventional kernel-based discriminant analysis mainly based on a single kernel function may be insufficient when dealing with datasets with complicated geometric structures. A combination of multiple kernels is able to represent the complementary information of the original data from multiple views and thereby improves recognition performance. However, the discriminant analysis methods based on the combination of multiple kernels face the challenges of optimizing the weights of the “base kernels” and the heavy computational burden. To address these challenges, this paper proposes a novel multi-kernel discriminant analysis method based on support vectors (MKDASV) to represent the data structure more effectively by incorporating the between-class and within-class information. First, the multi-kernel SVM algorithm is utilized to obtain the weight of each “base kernel” and the support vectors; and then the criteria of discriminant analysis method are constructed by taking into account both the margin maximizing classification theory of SVM and the expression of the within-class scatter in LDA algorithm; and finally, to effectively reduce the amount of computation, only the support vectors are used as the training samples to participate in the dimensionality reduction operation. The experimental results on six standard databases validated that our proposed method outperformed the other five methods in terms of classification accuracy and the computational efficiency as well.

Compared to conventional cancer treatment, combination therapy based on well-designed nanoscale platforms may offer an opportunity to eliminate tumors and reduce recurrence and metastasis. In this study, we prepared multifunctional microspheres loading 131I-labeled hollow copper sulfide nanoparticles and paclitaxel (131I-HCuSNPs-MS-PTX) for imaging and therapeutics of W256/B breast tumors in rats. 18F-fluordeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging detected that the expansion of the tumor volume was delayed (P

Due to being derived from linear assumption, most elastic body based non-rigid image registration algorithms are facing challenges for soft tissues with complex nonlinear behavior and with large deformations. To take into account the geometric nonlinearity of soft tissues, we propose a registration algorithm on the basis of Newtonian differential equation. The material behavior of soft tissues is modeled as St. Venant-Kirchhoff elasticity, and the nonlinearity of the continuum represents the quadratic term of the deformation gradient under the Green- St.Venant strain. In our algorithm, the elastic force is formulated as the derivative of the deformation energy with respect to the nodal displacement vectors of the finite element; the external force is determined by the registration similarity gradient flow which drives the floating image deforming to the equilibrium condition. We compared our approach to three other models: 1) the conventional linear elastic finite element model (FEM); 2) the dynamic elastic FEM; 3) the robust block matching (RBM) method. The registration accuracy was measured using three similarities: MSD (Mean Square Difference), NC (Normalized Correlation) and NMI (Normalized Mutual Information), and was also measured using the mean and max distance between the ground seeds and corresponding ones after registration. We validated our method on 60 image pairs including 30 medical image pairs with artificial deformation and 30 clinical image pairs for both the chest chemotherapy treatment in different periods and brain MRI normalization. Our method achieved a distance error of 0.320±0.138 mm in x direction and 0.326±0.111 mm in y direction, MSD of 41.96±13.74, NC of 0.9958±0.0019, NMI of 1.2962±0.0114 for images with large artificial deformations; and average NC of 0.9622±0.008 and NMI of 1.2764±0.0089 for the real clinical cases. Student's t-test demonstrated that our model statistically outperformed the other methods in comparison (p-values

Virtual Reality (VR) has emerged as a new safe and efficient tool for the rehabilitation of many childhood and adulthood illnesses. VR-based therapies have the potential to improve both motor and functional skills in a wide range of age groups through cortical reorganization and the activation of various neuronal connections. Recently, the potential for using serious VR-based games that combine perceptual learning and dichoptic stimulation has been explored for the rehabilitation of ophthalmological and neurological disorders. In ophthalmology, several clinical studies have demonstrated the ability to use VR training to enhance stereopsis, contrast sensitivity, and visual acuity. The use of VR technology provides a significant advantage in training each eye individually without requiring occlusion or penalty. In neurological disorders, the majority of patients undergo recurrent episodes (relapses) of neurological impairment, however, in a few cases (60–80%), the illness progresses over time and becomes chronic, consequential in cumulated motor disability and cognitive deficits. Current research on memory restoration has been spurred by theories about brain plasticity and findings concerning the nervous system's capacity to reconstruct cellular synapses as a result of interaction with enriched environments. Therefore, the use of VR training can play an important role in the improvement of cognitive function and motor disability. Although there are several reviews in the community employing relevant Artificial Intelligence in healthcare, VR has not yet been thoroughly examined in this regard. In this systematic review, we examine the key ideas of VR-based training for prevention and control measurements in ocular diseases such as Myopia, Amblyopia, Presbyopia, and Age-related Macular Degeneration (AMD), and neurological disorders such as Alzheimer, Multiple Sclerosis (MS) Epilepsy and Autism spectrum disorder. This review highlights the fundamentals of VR technologies regarding their clinical research in healthcare. Moreover, these findings will raise community awareness of using VR training and help researchers to learn new techniques to prevent and cure different diseases. We further discuss the current challenges of using VR devices, as well as the future prospects of human training.

Deformable image registration is fundamental for many medical image analyses. A key obstacle for accurate image registration lies in image appearance variations such as the variations in texture, intensities, and noise. These variations are readily apparent in medical images, especially in brain images where registration is frequently used. Recently, deep learning-based registration methods (DLRs), using deep neural networks, have shown computational efficiency that is several orders of magnitude faster than traditional optimization-based registration methods (ORs). DLRs rely on a globally optimized network that is trained with a set of training samples to achieve faster registration. DLRs tend, however, to disregard the target-pair-specific optimization inherent in ORs and thus have degraded adaptability to variations in testing samples. This limitation is severe for registering medical images with large appearance variations, especially since few existing DLRs explicitly take into account appearance variations. In this study, we propose an Appearance Adjustment Network (AAN) to enhance the adaptability of DLRs to appearance variations. Our AAN, when integrated into a DLR, provides appearance transformations to reduce the appearance variations during registration. In addition, we propose an anatomy-constrained loss function through which our AAN generates anatomy-preserving transformations. Our AAN has been purposely designed to be readily inserted into a wide range of DLRs and can be trained cooperatively in an unsupervised and end-to-end manner. We evaluated our AAN with three state-of-the-art DLRs - Voxelmorph (VM), Diffeomorphic Voxelmorph (DifVM), and Laplacian Pyramid Image Registration Network (LapIRN) – on three well-established public datasets of 3D brain magnetic resonance imaging (MRI) - IBSR18, Mindboggle101, and LPBA40. The results show that our AAN consistently improved existing DLRs and outperformed state-of-the-art ORs on registration accuracy, while adding a fractional computational load to existing DLRs.