Structurally-constrained optical-flow-guided adversarial generation of synthetic CT for MR-only radiotherapy treatment planning.

The rapid progress in image-to-image translation methods using deep neural networks has led to advancements in the generation of synthetic CT (sCT) in MR-only radiotherapy workflow. Replacement of CT with MR reduces unnecessary radiation exposure, financial cost and enables more accurate delineation of organs at risk. Previous generative adversarial networks (GANs) have been oriented towards MR to sCT generation. In this work, we have implemented multiple augmented cycle consistent GANs. The augmentation involves structural information constraint (StructCGAN), optical flow consistency constraint (FlowCGAN) and the combination of both the conditions (SFCGAN). The networks were trained and tested on a publicly available Gold Atlas project dataset, consisting of T2-weighted MR and CT volumes of 19 subjects from 3 different sites. The network was tested on 8 volumes acquired from the third site with a different scanner to assess the generalizability of the network on multicenter data. The results indicate that all the networks are robust to scanner variations. The best model, SFCGAN achieved an average ME of 0.9   5.9 HU, an average MAE of 40.4   4.7 HU and 57.2   1.4 dB PSNR outperforming previous research works. Moreover, the optical flow constraint between consecutive frames preserves the consistency across all views compared to 2D image-to-image translation methods. SFCGAN exploits the features of both StructCGAN and FlowCGAN by delivering structurally robust and 3D consistent sCT images. The research work serves as a benchmark for further research in MR-only radiotherapy.

Facial analysis technology for the detection of Down syndrome in the Democratic Republic of the Congo.

Down syndrome is one of the most common chromosomal anomalies affecting the world's population, with an estimated frequency of 1 in 700 live births. Despite its relatively high prevalence, diagnostic rates based on clinical features have remained under 70% for most of the developed world and even lower in countries with limited resources. While genetic and cytogenetic confirmation greatly increases the diagnostic rate, such resources are often non-existent in many low- and middle-income countries, particularly in Sub-Saharan Africa. To address the needs of countries with limited resources, the implementation of mobile, user-friendly and affordable technologies that aid in diagnosis would greatly increase the odds of success for a child born with a genetic condition. Given that the Democratic Republic of the Congo is estimated to have one of the highest rates of birth defects in the world, our team sought to determine if smartphone-based facial analysis technology could accurately detect Down syndrome in individuals of Congolese descent. Prior to technology training, we confirmed the presence of trisomy 21 using low-cost genomic applications that do not need advanced expertise to utilize and are available in many low-resourced countries. Our software technology trained on 132 Congolese subjects had a significantly improved performance (91.67% accuracy, 95.45% sensitivity, 87.88% specificity) when compared to previous technology trained on individuals who are not of Congolese origin (p < 5%). In addition, we provide the list of most discriminative facial features of Down syndrome and their ranges in the Congolese population. Collectively, our technology provides low-cost and accurate diagnosis of Down syndrome in the local population.

Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study.

BACKGROUND: Deep learning is a novel machine learning technique that has been shown to be as effective as human graders in detecting diabetic retinopathy from fundus photographs. We used a cost-minimisation analysis to evaluate the potential savings of two deep learning approaches as compared with the current human assessment: a semi-automated deep learning model as a triage filter before secondary human assessment; and a fully automated deep learning model without human assessment. METHODS: In this economic analysis modelling study, using 39 006 consecutive patients with diabetes in a national diabetic retinopathy screening programme in Singapore in 2015, we used a decision tree model and TreeAge Pro to compare the actual cost of screening this cohort with human graders against the simulated cost for semi-automated and fully automated screening models. Model parameters included diabetic retinopathy prevalence rates, diabetic retinopathy screening costs under each screening model, cost of medical consultation, and diagnostic performance (ie, sensitivity and specificity). The primary outcome was total cost for each screening model. Deterministic sensitivity analyses were done to gauge the sensitivity of the results to key model assumptions. FINDINGS: From the health system perspective, the semi-automated screening model was the least expensive of the three models, at US$62 per patient per year. The fully automated model was $66 per patient per year, and the human assessment model was $77 per patient per year. The savings to the Singapore health system associated with switching to the semi-automated model are estimated to be $489 000, which is roughly 20% of the current annual screening cost. By 2050, Singapore is projected to have 1 million people with diabetes; at this time, the estimated annual savings would be $15 million. INTERPRETATION: This study provides a strong economic rationale for using deep learning systems as an assistive tool to screen for diabetic retinopathy. FUNDING: Ministry of Health, Singapore.

A Body Tracking-Based Low-Cost Solution for Monitoring Workers' Hygiene Best Practices during Pandemics.

Since its beginning at the end of 2019, the pandemic spread of the severe acute respiratory syndrome coronavirus 2 (Sars-CoV-2) caused more than one million deaths in only nine months. The threat of emerging and re-emerging infectious diseases exists as an imminent threat to human health. It is essential to implement adequate hygiene best practices to break the contagion chain and enhance society preparedness for such critical scenarios and understand the relevance of each disease transmission route. As the unconscious hand-face contact gesture constitutes a potential pathway of contagion, in this paper, the authors present a prototype system based on low-cost depth sensors able to monitor in real-time the attitude towards such a habit. The system records people's behavior to enhance their awareness by providing real-time warnings, providing for statistical reports for designing proper hygiene solutions, and better understanding the role of such route of contagion. A preliminary validation study measured an overall accuracy of 91%. A Cohen's Kappa equal to 0.876 supports rejecting the hypothesis that such accuracy is accidental. Low-cost body tracking technologies can effectively support monitoring compliance with hygiene best practices and training people in real-time. By collecting data and analyzing them with respect to people categories and contagion statistics, it could be possible to understand the importance of this contagion pathway and identify for which people category such a behavioral attitude constitutes a significant risk.

Novel Cost Function Definition for Minimum-Cost Tractography in MR Diffusion Tensor Imaging.

Magnetic resonance imaging (MRI) is an established clinical technique that measures diffusion-weighted signals, applied primarily in brain studies. Diffusion tensor imaging (DTI) is a technique that uses the diffusion-weighted signals to obtain information about tissue connectivity, which recently started to become established in clinical use. The extraction of tracts (tractography) is an issue under active research. In this work we present an algorithm for recovering tracts, based on Dijkstra's minimum-cost path. A novel cost definition algorithm is presented that allows tract reconstruction, considering the tract's curvature, as well as its alignment with the diffusion vector field. The proposed cost function is able to adapt to linear, planar, and spherical diffusion. Thus, it can handle issues of fiber crossing, which pose considerable problems to tractography algorithms. A simple method for generating synthetic diffusion - weighted MR signals from known fibers - is also presented and utilized in this work. Results are shown for two (2D)- and three-dimensional (3D) synthetic data, as well as for a clinical MRI-DTI brain study.

Digital Manipulation of Images of Models' Appearance in Advertising: Strategies for Action Through Law and Corporate Social Responsibility Incentives to Protect Public Health.

CONTEXT: Widespread digital retouching of advertising imagery in the fashion, beauty, and other consumer industries promotes unrealistic beauty standards that have harmful effects on public health. In particular, exposure to misleading beauty imagery is linked with greater body dissatisfaction, worse mood, poorer self-esteem, and increased risk for disordered eating behaviors. Moreover, given the social, psychological, medical, and economic burden of eating disorders, there is an urgent need to address environmental risk factors and to scale up prevention efforts by increasing the regulation of digitally altered advertising imagery. METHODS: This manuscript summarizes the health research literature linking digital retouching of advertising to increased risk of eating disorders, disordered weight and appearance control behaviors, and body dissatisfaction in consumers, followed by a review of global policy initiatives designed to regulate digital retouching to reduce health harms to consumers. Next, we turn to the US legal context, reporting on findings generated through legal research via Westlaw and LexisNexis, congressional records, federal agency websites, law review articles, and Supreme Court opinions, in addition to consulting legal experts on both tax law and the First Amendment, to evaluate the viability of various policy initiatives proposed to strengthen regulation on digital retouching in the United States. FINDINGS: Influencing advertising practices via tax incentives combined with corporate social responsibility initiatives may be the most constitutionally feasible options for the US legal context to reduce the use of digitally alternated images of models' bodies in advertising. CONCLUSIONS: Policy and corporate initiatives to curtail use of digitally altered images found to be harmful to mental and behavioral health of consumers could reduce the burden of eating disorders, disordered weight and appearance control behaviors, and body dissatisfaction and thereby improve population health in the United States.

Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization.

Automated diagnosis of tuberculosis (TB) from chest X-Rays (CXR) has been tackled with either hand-crafted algorithms or machine learning approaches such as support vector machines (SVMs) and convolutional neural networks (CNNs). Most deep neural network applied to the task of tuberculosis diagnosis have been adapted from natural image classification. These models have a large number of parameters as well as high hardware requirements, which makes them prone to overfitting and harder to deploy in mobile settings. We propose a simple convolutional neural network optimized for the problem which is faster and more efficient than previous models but preserves their accuracy. Moreover, the visualization capabilities of CNNs have not been fully investigated. We test saliency maps and grad-CAMs as tuberculosis visualization methods, and discuss them from a radiological perspective.

Fast, versatile and quantitative annotation of complex images.

We report a generic smartphone app for quantitative annotation of complex images. The app is simple enough to be used by children, and annotation tasks are distributed across app users, contributing to efficient annotation. We demonstrate its flexibility and speed by annotating >30,000 images, including features of rice root growth and structure, stem cell aggregate morphology, and complex worm (Caenorhabditis elegans) postures, for which we show that the speed of annotation is >130-fold faster than state-of-the-art techniques with similar accuracy.

Evaluation of the scars' vascularization using computer processing of the digital images.

BACKGROUND: The application of new techniques of the scars' correction requires the objective evaluation of their vascularization. OBJECTIVE: To evaluate the effectiveness of digital program ImageJ in assessing neovascularization of pathologic scars. MATERIAL AND METHODS: In this cross-sectional study, a total of 25 patients with pathologic scars were enrolled. Vessel selection in the first set of digital images of their scars was performed by computer processing started from thresholding with subsequent manual correction. In the second set of the same pictures, Vessel Analysis plugin was used. Comparison of both approaches was performed by three independent investigators. The time required for images processing was measured. RESULTS: The average time that image processing and calculation have taken in the first group (753.3 ± 88.02 seconds) was statistically longer (P < 0.0001) than in the second one (358.1 ± 105.91 seconds). Independent investigators scored the precision of vessel selection in the first group as 80.4 ± 9.82, in the second group as 72.6 ± 10.53 (P < 0.0001). Kolmogorov-Smirnov test demonstrated higher precision of vessel selection by method that involves manual correction (P < 0.001). The results of Vascular Density measurements were obviously overestimated in the second group. More expedient looks calculation of the Vascular Length Density: ratio of skeletonized vasculature area to total area. Skeletonization avoids overestimation of Vascular Density, but the density of the vessel mesh can be determined. CONCLUSIONS: Computer processing of the scars' digital photographs using ImageJ software gives cheap, technically easy and not cumbersome way of superficial scars' vascularization objectifying. Vessel selection with subsequent manual correction has advantage of higher precision.

Recent advancement in the early detection of melanoma using computerized tools: An image analysis perspective.

BACKGROUND: The paper reviews the advancement of tools and current technologies for the detection of melanoma. We discussed several computational strategies from pre- to postprocessing image operations, descriptors, and popular classifiers to diagnose a suspected skin lesion based on its virtual similarity to the malignant lesion with known histopathology. We reviewed the current state of smart phone-based apps as diagnostic tools for screening. METHODS: A literature survey was conducted using a combination of keywords in the bibliographic databases: PubMed, AJCC, PH2, EDRA, and ISIC melanoma project. A number of melanoma detection apps were downloaded for two major mobile operating systems, iOS and Android; their important uses, key challenges, and various expert opinions were evaluated and also discussed. RESULTS: We have provided an overview of research on the computer-aided diagnosis methods to estimate melanoma risk and early screening. Dermoscopic images are the most viable option for the advent of new image processing technologies based on which many of the skin cancer detection apps are being developed recently. We have categorized and explored their potential uses, evaluation criteria, limitations, and other details. CONCLUSION: Such advancements are helpful in the sense they are raising awareness. Diagnostic accuracy is the major issue of smart phone-based apps and it cannot replace an adequate clinical experience and biopsy procedures.

Global optimization using Gaussian processes to estimate biological parameters from image data.

Parameter estimation is a major challenge in computational modeling of biological processes. This is especially the case in image-based modeling where the inherently quantitative output of the model is measured against image data, which is typically noisy and non-quantitative. In addition, these models can have a high computational cost, limiting the number of feasible simulations, and therefore rendering most traditional parameter estimation methods unsuitable. In this paper, we present a pipeline that uses Gaussian process learning to estimate biological parameters from noisy, non-quantitative image data when the model has a high computational cost. This approach is first successfully tested on a parametric function with the goal of retrieving the original parameters. We then apply it to estimating parameters in a biological setting by fitting artificial in-situ hybridization (ISH) data of the developing murine limb bud. We expect that this method will be of use in a variety of modeling scenarios where quantitative data is missing and the use of standard parameter estimation approaches in biological modeling is prohibited by the computational cost of the model.

QuickCount®: a novel automated software for rapid cell detection and quantification.

We describe a novel automated cell detection and counting software, QuickCount® (QC), designed for rapid quantification of cells. The Bland-Altman plot and intraclass correlation coefficient (ICC) analyses demonstrated strong agreement between cell counts from QC to manual counts (mean and SD: -3.3 ± 4.5; ICC = 0.95). QC has higher recall in comparison to ImageJauto, CellProfiler and CellC and the precision of QC, ImageJauto, CellProfiler and CellC are high and comparable. QC can precisely delineate and count single cells from images of different cell densities with precision and recall above 0.9. QC is unique as it is equipped with real-time preview while optimizing the parameters for accurate cell count and needs minimum hands-on time where hundreds of images can be analyzed automatically in a matter of milliseconds. In conclusion, QC offers a rapid, accurate and versatile solution for large-scale cell quantification and addresses the challenges often faced in cell biology research.

Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.

BACKGROUND: Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. However, these tests are typically expensive, time consuming, and tissue-destructive. METHODS: In this paper, we evaluate the ability of computer-extracted nuclear morphology features from routine hematoxylin and eosin (H&E) stained images of 178 early stage ER+ breast cancer patients to predict corresponding risk categories derived using the Oncotype DX test. A total of 216 features corresponding to the nuclear shape and architecture categories from each of the pathologic images were extracted and four feature selection schemes: Ranksum, Principal Component Analysis with Variable Importance on Projection (PCA-VIP), Maximum-Relevance, Minimum Redundancy Mutual Information Difference (MRMR MID), and Maximum-Relevance, Minimum Redundancy - Mutual Information Quotient (MRMR MIQ), were employed to identify the most discriminating features. These features were employed to train 4 machine learning classifiers: Random Forest, Neural Network, Support Vector Machine, and Linear Discriminant Analysis, via 3-fold cross validation. RESULTS: The four sets of risk categories, and the top Area Under the receiver operating characteristic Curve (AUC) machine classifier performances were: 1) Low ODx and Low mBR grade vs. High ODx and High mBR grade (Low-Low vs. High-High) (AUC = 0.83), 2) Low ODx vs. High ODx (AUC = 0.72), 3) Low ODx vs. Intermediate and High ODx (AUC = 0.58), and 4) Low and Intermediate ODx vs. High ODx (AUC = 0.65). Trained models were tested independent validation set of 53 cases which comprised of Low and High ODx risk, and demonstrated per-patient accuracies ranging from 75 to 86%. CONCLUSION: Our results suggest that computerized image analysis of digitized H&E pathology images of early stage ER+ breast cancer might be able predict the corresponding Oncotype DX risk categories.

A low-cost multimodal head-mounted display system for neuroendoscopic surgery.

Background: With rapid advances in technology, wearable devices as head-mounted display (HMD) have been adopted for various uses in medical science, ranging from simply aiding in fitness to assisting surgery. We aimed to investigate the feasibility and practicability of a low-cost multimodal HMD system in neuroendoscopic surgery. Methods: A multimodal HMD system, mainly consisted of a HMD with two built-in displays, an action camera, and a laptop computer displaying reconstructed medical images, was developed to assist neuroendoscopic surgery. With this intensively integrated system, the neurosurgeon could freely switch between endoscopic image, three-dimensional (3D) reconstructed virtual endoscopy images, and surrounding environment images. Using a leap motion controller, the neurosurgeon could adjust or rotate the 3D virtual endoscopic images at a distance to better understand the positional relation between lesions and normal tissues at will. Results: A total of 21 consecutive patients with ventricular system diseases underwent neuroendoscopic surgery with the aid of this system. All operations were accomplished successfully, and no system-related complications occurred. The HMD was comfortable to wear and easy to operate. Screen resolution of the HMD was high enough for the neurosurgeon to operate carefully. With the system, the neurosurgeon might get a better comprehension on lesions by freely switching among images of different modalities. The system had a steep learning curve, which meant a quick increment of skill with it. Compared with commercially available surgical assistant instruments, this system was relatively low-cost. Conclusions: The multimodal HMD system is feasible, practical, helpful, and relatively cost efficient in neuroendoscopic surgery.

Computational sensing of herpes simplex virus using a cost-effective on-chip microscope.

Caused by the herpes simplex virus (HSV), herpes is a viral infection that is one of the most widespread diseases worldwide. Here we present a computational sensing technique for specific detection of HSV using both viral immuno-specificity and the physical size range of the viruses. This label-free approach involves a compact and cost-effective holographic on-chip microscope and a surface-functionalized glass substrate prepared to specifically capture the target viruses. To enhance the optical signatures of individual viruses and increase their signal-to-noise ratio, self-assembled polyethylene glycol based nanolenses are rapidly formed around each virus particle captured on the substrate using a portable interface. Holographic shadows of specifically captured viruses that are surrounded by these self-assembled nanolenses are then reconstructed, and the phase image is used for automated quantification of the size of each particle within our large field-of-view, ~30 mm2. The combination of viral immuno-specificity due to surface functionalization and the physical size measurements enabled by holographic imaging is used to sensitively detect and enumerate HSV particles using our compact and cost-effective platform. This computational sensing technique can find numerous uses in global health related applications in resource-limited environments.

Portable electronic vision enhancement systems in comparison with optical magnifiers for near vision activities: an economic evaluation alongside a randomized crossover trial.

PURPOSE: To determine the incremental cost-effectiveness of portable electronic vision enhancement system (p-EVES) devices compared with optical low vision aids (LVAs), for improving near vision visual function, quality of life and well-being of people with a visual impairment. METHODS: An AB/BA randomized crossover trial design was used. Eighty-two participants completed the study. Participants were current users of optical LVAs who had not tried a p-EVES device before and had a stable visual impairment. The trial intervention was the addition of a p-EVES device to the participant's existing optical LVA(s) for 2 months, and the control intervention was optical LVA use only, for 2 months. Cost-effectiveness and cost-utility analyses were conducted from a societal perspective. RESULTS: The mean cost of the p-EVES intervention was £448. Carer costs were £30 (4.46 hr) less for the p-EVES intervention compared with the LVA only control. The mean difference in total costs was £417. Bootstrapping gave an incremental cost-effectiveness ratio (ICER) of £736 (95% CI £481 to £1525) for a 7% improvement in near vision visual function. Cost per quality-adjusted life year (QALY) ranged from £56 991 (lower 95% CI = £19 801) to £66 490 (lower 95% CI = £23 055). Sensitivity analysis varying the commercial price of the p-EVES device reduced ICERs by up to 75%, with cost per QALYs falling below £30 000. CONCLUSION: Portable electronic vision enhancement system (p-EVES) devices are likely to be a cost-effective use of healthcare resources for improving near vision visual function, but this does not translate into cost-effective improvements in quality of life, capability or well-being.