Full-colour 3D holographic augmented-reality displays with metasurface waveguides.

Emerging spatial computing systems seamlessly superimpose digital information on the physical environment observed by a user, enabling transformative experiences across various domains, such as entertainment, education, communication and training1-3. However, the widespread adoption of augmented-reality (AR) displays has been limited due to the bulky projection optics of their light engines and their inability to accurately portray three-dimensional (3D) depth cues for virtual content, among other factors4,5. Here we introduce a holographic AR system that overcomes these challenges using a unique combination of inverse-designed full-colour metasurface gratings, a compact dispersion-compensating waveguide geometry and artificial-intelligence-driven holography algorithms. These elements are co-designed to eliminate the need for bulky collimation optics between the spatial light modulator and the waveguide and to present vibrant, full-colour, 3D AR content in a compact device form factor. To deliver unprecedented visual quality with our prototype, we develop an innovative image formation model that combines a physically accurate waveguide model with learned components that are automatically calibrated using camera feedback. Our unique co-design of a nanophotonic metasurface waveguide and artificial-intelligence-driven holographic algorithms represents a significant advancement in creating visually compelling 3D AR experiences in a compact wearable device.

Evaluating GPT-V4 (GPT-4 with Vision) on Detection of Radiologic Findings on Chest Radiographs.

Background Generating radiologic findings from chest radiographs is pivotal in medical image analysis. The emergence of OpenAI's generative pretrained transformer, GPT-4 with vision (GPT-4V), has opened new perspectives on the potential for automated image-text pair generation. However, the application of GPT-4V to real-world chest radiography is yet to be thoroughly examined. Purpose To investigate the capability of GPT-4V to generate radiologic findings from real-world chest radiographs. Materials and Methods In this retrospective study, 100 chest radiographs with free-text radiology reports were annotated by a cohort of radiologists, two attending physicians and three residents, to establish a reference standard. Of 100 chest radiographs, 50 were randomly selected from the National Institutes of Health (NIH) chest radiographic data set, and 50 were randomly selected from the Medical Imaging and Data Resource Center (MIDRC). The performance of GPT-4V at detecting imaging findings from each chest radiograph was assessed in the zero-shot setting (where it operates without prior examples) and few-shot setting (where it operates with two examples). Its outcomes were compared with the reference standard with regards to clinical conditions and their corresponding codes in the International Statistical Classification of Diseases, Tenth Revision (ICD-10), including the anatomic location (hereafter, laterality). Results In the zero-shot setting, in the task of detecting ICD-10 codes alone, GPT-4V attained an average positive predictive value (PPV) of 12.3%, average true-positive rate (TPR) of 5.8%, and average F1 score of 7.3% on the NIH data set, and an average PPV of 25.0%, average TPR of 16.8%, and average F1 score of 18.2% on the MIDRC data set. When both the ICD-10 codes and their corresponding laterality were considered, GPT-4V produced an average PPV of 7.8%, average TPR of 3.5%, and average F1 score of 4.5% on the NIH data set, and an average PPV of 10.9%, average TPR of 4.9%, and average F1 score of 6.4% on the MIDRC data set. With few-shot learning, GPT-4V showed improved performance on both data sets. When contrasting zero-shot and few-shot learning, there were improved average TPRs and F1 scores in the few-shot setting, but there was not a substantial increase in the average PPV. Conclusion Although GPT-4V has shown promise in understanding natural images, it had limited effectiveness in interpreting real-world chest radiographs. © RSNA, 2024 Supplemental material is available for this article.

Error-compensation network for ringing artifact reduction in holographic displays.

Recent advances in learning-based computer-generated holography (CGH) have unlocked novel possibilities for crafting phase-only holograms. However, existing approaches primarily focus on the learning ability of network modules, often neglecting the impact of diffraction propagation models. The resulting ringing artifacts, emanating from the Gibbs phenomenon in the propagation model, can degrade the quality of reconstructed holographic images. To this end, we explore a diffraction propagation error-compensation network that can be easily integrated into existing CGH methods. This network is designed to correct propagation errors by predicting residual values, thereby aligning the diffraction process closely with an ideal state and easing the learning burden of the network. Simulations and optical experiments demonstrate that our method, when applied to state-of-the-art HoloNet and CCNN, achieves PSNRs of up to 32.47 dB and 29.53 dB, respectively, surpassing baseline methods by 3.89 dB and 0.62 dB. Additionally, real-world experiments have confirmed a significant reduction in ringing artifacts. We envision this approach being applied to a variety of CGH algorithms, paving the way for improved holographic displays.

A survey of recent methods for addressing AI fairness and bias in biomedicine.

OBJECTIVES: Artificial intelligence (AI) systems have the potential to revolutionize clinical practices, including improving diagnostic accuracy and surgical decision-making, while also reducing costs and manpower. However, it is important to recognize that these systems may perpetuate social inequities or demonstrate biases, such as those based on race or gender. Such biases can occur before, during, or after the development of AI models, making it critical to understand and address potential biases to enable the accurate and reliable application of AI models in clinical settings. To mitigate bias concerns during model development, we surveyed recent publications on different debiasing methods in the fields of biomedical natural language processing (NLP) or computer vision (CV). Then we discussed the methods, such as data perturbation and adversarial learning, that have been applied in the biomedical domain to address bias. METHODS: We performed our literature search on PubMed, ACM digital library, and IEEE Xplore of relevant articles published between January 2018 and December 2023 using multiple combinations of keywords. We then filtered the result of 10,041 articles automatically with loose constraints, and manually inspected the abstracts of the remaining 890 articles to identify the 55 articles included in this review. Additional articles in the references are also included in this review. We discuss each method and compare its strengths and weaknesses. Finally, we review other potential methods from the general domain that could be applied to biomedicine to address bias and improve fairness. RESULTS: The bias of AIs in biomedicine can originate from multiple sources such as insufficient data, sampling bias and the use of health-irrelevant features or race-adjusted algorithms. Existing debiasing methods that focus on algorithms can be categorized into distributional or algorithmic. Distributional methods include data augmentation, data perturbation, data reweighting methods, and federated learning. Algorithmic approaches include unsupervised representation learning, adversarial learning, disentangled representation learning, loss-based methods and causality-based methods.

Leveraging generative AI for clinical evidence synthesis needs to ensure trustworthiness.

Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task. However, developing accountable, fair, and inclusive models remains a complicated undertaking. In this perspective, we discuss the trustworthiness of generative AI in the context of automated summarization of medical evidence.

Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias.

OBJECTIVE: To develop a natural language processing (NLP) package to extract social determinants of health (SDoH) from clinical narratives, examine the bias among race and gender groups, test the generalizability of extracting SDoH for different disease groups, and examine population-level extraction ratio. METHODS: We developed SDoH corpora using clinical notes identified at the University of Florida (UF) Health. We systematically compared 7 transformer-based large language models (LLMs) and developed an open-source package - SODA (i.e., SOcial DeterminAnts) to facilitate SDoH extraction from clinical narratives. We examined the performance and potential bias of SODA for different race and gender groups, tested the generalizability of SODA using two disease domains including cancer and opioid use, and explored strategies for improvement. We applied SODA to extract 19 categories of SDoH from the breast (n = 7,971), lung (n = 11,804), and colorectal cancer (n = 6,240) cohorts to assess patient-level extraction ratio and examine the differences among race and gender groups. RESULTS: We developed an SDoH corpus using 629 clinical notes of cancer patients with annotations of 13,193 SDoH concepts/attributes from 19 categories of SDoH, and another cross-disease validation corpus using 200 notes from opioid use patients with 4,342 SDoH concepts/attributes. We compared 7 transformer models and the GatorTron model achieved the best mean average strict/lenient F1 scores of 0.9122 and 0.9367 for SDoH concept extraction and 0.9584 and 0.9593 for linking attributes to SDoH concepts. There is a small performance gap (∼4%) between Males and Females, but a large performance gap (>16 %) among race groups. The performance dropped when we applied the cancer SDoH model to the opioid cohort; fine-tuning using a smaller opioid SDoH corpus improved the performance. The extraction ratio varied in the three cancer cohorts, in which 10 SDoH could be extracted from over 70 % of cancer patients, but 9 SDoH could be extracted from less than 70 % of cancer patients. Individuals from the White and Black groups have a higher extraction ratio than other minority race groups. CONCLUSIONS: Our SODA package achieved good performance in extracting 19 categories of SDoH from clinical narratives. The SODA package with pre-trained transformer models is available at https://github.com/uf-hobi-informatics-lab/SODA_Docker.

Exploring angular-steering illumination-based eyebox expansion for holographic displays.

Holography represents an enabling technology for next-generation virtual and augmented reality systems. However, it remains challenging to achieve both wide field of view and large eyebox at the same time for holographic near-eye displays, mainly due to the essential étendue limitation of existing hardware. In this work, we present an approach to expanding the eyebox for holographic displays without compromising their underlying field of view. This is achieved by utilizing a compact 2D steering mirror to deliver angular-steering illumination beams onto the spatial light modulator in alignment with the viewer's eye movements. To facilitate the same image for the virtual objects perceived by the viewer when the eye moves, we explore an off-axis computational hologram generation scheme. Two bench-top holographic near-eye display prototypes with the proposed angular-steering scheme are developed, and they successfully showcase an expanded eyebox up to 8 mm × 8 mm for both VR- and AR-modes, as well as the capability of representing multi-depth holographic images.

Investigating learning-empowered hologram generation for holographic displays with ill-tuned hardware.

Existing computational holographic displays often suffer from limited reconstruction image quality mainly due to ill-conditioned optics hardware and hologram generation software. In this Letter, we develop an end-to-end hardware-in-the-loop approach toward high-quality hologram generation for holographic displays. Unlike other hologram generation methods using ideal wave propagation, ours can reduce artifacts introduced by both the light propagation model and the hardware setup, in particular non-uniform illumination. Experimental results reveal that, compared with classical computer-generated hologram algorithm counterparts, better quality of holographic images can be delivered without a strict requirement on both the fine assembly of optical components and the good uniformity of laser sources.

Comparison of State-of-the-Art Neural Network Survival Models with the Pooled Cohort Equations for Cardiovascular Disease Risk Prediction.

BACKGROUND: The Pooled Cohort Equations (PCEs) are race- and sex-specific Cox proportional hazards (PH)-based models used for 10-year atherosclerotic cardiovascular disease (ASCVD) risk prediction with acceptable discrimination. In recent years, neural network models have gained increasing popularity with their success in image recognition and text classification. Various survival neural network models have been proposed by combining survival analysis and neural network architecture to take advantage of the strengths from both. However, the performance of these survival neural network models compared to each other and to PCEs in ASCVD prediction is unknown. METHODS: In this study, we used 6 cohorts from the Lifetime Risk Pooling Project (with 5 cohorts as training/internal validation and one cohort as external validation) and compared the performance of the PCEs in 10-year ASCVD risk prediction with an all two-way interactions Cox PH model (Cox PH-TWI) and three state-of-the-art neural network survival models including Nnet-survival, Deepsurv, and Cox-nnet. For all the models, we used the same 7 covariates as used in the PCEs. We fitted each of the aforementioned models in white females, white males, black females, and black males, respectively. We evaluated models' internal and external discrimination power and calibration. RESULTS: The training/internal validation sample comprised 23216 individuals. The average age at baseline was 57.8 years old (SD = 9.6); 16% developed ASCVD during average follow-up of 10.50 (SD = 3.02) years. Based on 10 × 10 cross-validation, the method that had the highest C-statistics was Deepsurv (0.7371) for white males, Deepsurv and Cox PH-TWI (0.7972) for white females, PCE (0.6981) for black males, and Deepsurv (0.7886) for black females. In the external validation dataset, Deepsurv (0.7032), Cox-nnet (0.7282), PCE (0.6811), and Deepsurv (0.7316) had the highest C-statistics for white male, white female, black male, and black female population, respectively. Calibration plots showed that in 10 × 10 validation, all models had good calibration in all race and sex groups. In external validation, all models overestimated the risk for 10-year ASCVD. CONCLUSIONS: We demonstrated the use of the state-of-the-art neural network survival models in ASCVD risk prediction. Neural network survival models had similar if not superior discrimination and calibration compared to PCEs.

A scoping review on multimodal deep learning in biomedical images and texts.

OBJECTIVE: Computer-assisted diagnostic and prognostic systems of the future should be capable of simultaneously processing multimodal data. Multimodal deep learning (MDL), which involves the integration of multiple sources of data, such as images and text, has the potential to revolutionize the analysis and interpretation of biomedical data. However, it only caught researchers' attention recently. To this end, there is a critical need to conduct a systematic review on this topic, identify the limitations of current work, and explore future directions. METHODS: In this scoping review, we aim to provide a comprehensive overview of the current state of the field and identify key concepts, types of studies, and research gaps with a focus on biomedical images and texts joint learning, mainly because these two were the most commonly available data types in MDL research. RESULT: This study reviewed the current uses of multimodal deep learning on five tasks: (1) Report generation, (2) Visual question answering, (3) Cross-modal retrieval, (4) Computer-aided diagnosis, and (5) Semantic segmentation. CONCLUSION: Our results highlight the diverse applications and potential of MDL and suggest directions for future research in the field. We hope our review will facilitate the collaboration of natural language processing (NLP) and medical imaging communities and support the next generation of decision-making and computer-assisted diagnostic system development.

Representing and utilizing clinical textual data for real world studies: An OHDSI approach.

Clinical documentation in electronic health records contains crucial narratives and details about patients and their care. Natural language processing (NLP) can unlock the information conveyed in clinical notes and reports, and thus plays a critical role in real-world studies. The NLP Working Group at the Observational Health Data Sciences and Informatics (OHDSI) consortium was established to develop methods and tools to promote the use of textual data and NLP in real-world observational studies. In this paper, we describe a framework for representing and utilizing textual data in real-world evidence generation, including representations of information from clinical text in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), the workflow and tools that were developed to extract, transform and load (ETL) data from clinical notes into tables in OMOP CDM, as well as current applications and specific use cases of the proposed OHDSI NLP solution at large consortia and individual institutions with English textual data. Challenges faced and lessons learned during the process are also discussed to provide valuable insights for researchers who are planning to implement NLP solutions in real-world studies.

Investigating deep optics model representation in affecting resolved all-in-focus image quality and depth estimation fidelity.

The end-to-end (E2E) optimization of optics and image processing, dubbed deep optics, has renewed the state-of-the-art in various computer vision tasks. However, specifying the proper model representation or parameterization of the optical elements remains elusive. This article comprehensibly investigates three modeling hypotheses of the phase coded-aperture imaging under a representative context of deep optics, joint all-in-focus (AiF) imaging and monocular depth estimation (MDE). Specifically, we analyze the respective trade-off of these models and provide insights into relevant domain-specific requirements, explore the connection between the spatial feature of the point spread function (PSF) and the performance trade-off between the AiF and MDE tasks, and discuss the model sensitivity to possible fabrication errors. This study provides new prospects for future deep optics designs, particularly those aiming for AiF and/or MDE.

Trustworthy assertion classification through prompting.

Accurate identification of the presence, absence or possibility of relevant entities in clinical notes is important for healthcare professionals to quickly understand crucial clinical information. This introduces the task of assertion classification - to correctly identify the assertion status of an entity in the unstructured clinical notes. Recent rule-based and machine-learning approaches suffer from labor-intensive pattern engineering and severe class bias toward majority classes. To solve this problem, in this study, we propose a prompt-based learning approach, which treats the assertion classification task as a masked language auto-completion problem. We evaluated the model on six datasets. Our prompt-based method achieved a micro-averaged F-1 of 0.954 on the i2b2 2010 assertion dataset, with ∼1.8% improvements over previous works. In particular, our model showed excellence in detecting classes with few instances (few-shot). Evaluations on five external datasets showcase the outstanding generalizability of the prompt-based method to unseen data. To examine the rationality of our model, we further introduced two rationale faithfulness metrics: comprehensiveness and sufficiency. The results reveal that compared to the "pre-train, fine-tune" procedure, our prompt-based model has a stronger capability of identifying the comprehensive (∼63.93%) and sufficient (∼11.75%) linguistic features from free text. We further evaluated the model-agnostic explanations using LIME. The results imply a better rationale agreement between our model and human beings (∼71.93% in average F-1), which demonstrates the superior trustworthiness of our model.

All-day thin-lens computational imaging with scene-specific learning recovery.

Modern imaging optics ensures high-quality photography at the cost of a complex optical form factor that deviates from the portability. The drastic development of image processing algorithms, especially advanced neural networks, shows great promise to use thin optics but still faces the challenges of residual artifacts and chromatic aberration. In this work, we investigate photorealistic thin-lens imaging that paves the way to actual applications by exploring several fine-tunes. Notably, to meet all-day photography demands, we develop a scene-specific generative-adversarial-network-based learning strategy and develop an integral automatic acquisition and processing pipeline. Color fringe artifacts are reduced by implementing a chromatic aberration pre-correction trick. Our method outperforms existing thin-lens imaging work with better visual perception and excels in both normal-light and low-light scenarios.

Deep learning multi-shot 3D localization microscopy using hybrid optical-electronic computing.

Current 3D localization microscopy approaches are fundamentally limited in their ability to image thick, densely labeled specimens. Here, we introduce a hybrid optical-electronic computing approach that jointly optimizes an optical encoder (a set of multiple, simultaneously imaged 3D point spread functions) and an electronic decoder (a neural-network-based localization algorithm) to optimize 3D localization performance under these conditions. With extensive simulations and biological experiments, we demonstrate that our deep-learning-based microscope achieves significantly higher 3D localization accuracy than existing approaches, especially in challenging scenarios with high molecular density over large depth ranges.

Unfiltered holography: optimizing high diffraction orders without optical filtering for compact holographic displays.

Computer-generated holography suffers from high diffraction orders (HDOs) created from pixelated spatial light modulators, which must be optically filtered using bulky optics. Here, we develop an algorithmic framework for optimizing HDOs without optical filtering to enable compact holographic displays. We devise a wave propagation model of HDOs and use it to optimize phase patterns, which allows HDOs to contribute to forming the image instead of creating artifacts. The proposed method significantly outperforms previous algorithms in an unfiltered holographic display prototype.

Foveated light-field display and real-time rendering for virtual reality.

Glasses-free light field displays have significantly progressed due to advances in high-resolution microdisplays and high-end graphics processing units (GPUs). However, for near-eye light-field displays requiring portability, the fundamental trade-off regarding achieved spatial resolution remains: retinal blur quality must be degraded; otherwise, computational consumption increases. This has prevented synthesizing the high-quality light field from being fast. By integrating off-the-shelf gaze tracking modules into near-eye light-field displays, we present wearable virtual reality prototypes supporting human visual system-oriented focus cues. An optimized, foveated light field is delivered to each eye subject to the gaze point, providing more natural visual experiences than state-of-the-art solutions. Importantly, the factorization runtime can be immensely reduced, since the image resolution is only high within the gaze cone. In addition, we demonstrate significant improvements in computation and retinal blur quality over counterpart near-eye displays.

Suppression of Rice Planthopper Populations by the Entomopathogenic Fungus Metarhizium anisopliae without Affecting the Rice Microbiota.

Entomopathogenic fungi can regulate insect populations and function as crucial biological control agents against insect pests, but their impacts on nontarget microorganisms are poorly understood. In this study, we investigated the potential of the fungal strain Metarhizium anisopliae CQMa421 to control rice planthoppers under field conditions and its effects on rice microbiota. This fungus suppressed rice planthoppers during this period, and its control efficiency was more than 60% 7 days after application and did not significantly differ from that of the chemical treatment except in 2019. Both treatments showed a smaller population of rice planthoppers than the controls. After application, M. anisopliae was maintained on rice plants for approximately 14 days, showing a decreasing trend over time. Furthermore, the results showed that the bacterial and fungal richness (operational taxonomic units) and diversity (Shannon index) did not significantly differ between the fungal treatment and the controls after application. The major bacterial taxa of Proteobacteria (including Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, and Deltaproteobacteria), Actinobacteria, Bacteroidetes, and Cyanobacteria accounted for more than 80% of the bacterial community after fungal application, and the major fungal taxa Ascomycota (including Eurotiomycetes, Dothideomycetes, and Sordariomycetes) and Basidiomycota (including Ustilaginomycetes) represented more than 90% of the fungal community. However, the microbial communities of the rice phyllosphere did not significantly change after entomopathogenic-agent application, indicating that the indigenous microbial communities may adapt to fungal insecticide application. Taken together, the results suggest that this fungal agent has good potential for rice planthopper control with no substantial effects on rice microbial communities.IMPORTANCE Entomopathogenic fungi may be used as crucial biocontrol agents for the control of insect pests, but few effective fungal strains have been reported for the control of the rice planthopper, a major pest of rice. More importantly, the impacts of fungal insecticide application on nontarget microorganisms have not been well evaluated, especially under field conditions. Therefore, in this study, we investigated the effects of the fungal strain M. anisopliae CQMa421 on rice planthopper populations from 2017 to 2019 and evaluated its potential impacts on the microbiota of rice plants after application. The results suggested that this fungal agent has good potential for use in the control of rice planthoppers with no significant effects on rice microbial communities, representing an alternative strategy for the control of rice pests.

Deep Learning Automated Detection of Reticular Pseudodrusen from Fundus Autofluorescence Images or Color Fundus Photographs in AREDS2.

PURPOSE: To develop deep learning models for detecting reticular pseudodrusen (RPD) using fundus autofluorescence (FAF) images or, alternatively, color fundus photographs (CFP) in the context of age-related macular degeneration (AMD). DESIGN: Application of deep learning models to the Age-Related Eye Disease Study 2 (AREDS2) dataset. PARTICIPANTS: FAF and CFP images (n = 11 535) from 2450 AREDS2 participants. Gold standard labels from reading center grading of the FAF images were transferred to the corresponding CFP images. METHODS: A deep learning model was trained to detect RPD in eyes with intermediate to late AMD using FAF images (FAF model). Using label transfer from FAF to CFP images, a deep learning model was trained to detect RPD from CFP (CFP model). Performance was compared with 4 ophthalmologists using a random subset from the full test set. MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve (AUC), κ value, accuracy, and F1 score. RESULTS: The FAF model had an AUC of 0.939 (95% confidence interval [CI], 0.927-0.950), a κ value of 0.718 (95% CI, 0.685-0.751), and accuracy of 0.899 (95% CI, 0.887-0.911). The CFP model showed equivalent values of 0.832 (95% CI, 0.812-0.851), 0.470 (95% CI, 0.426-0.511), and 0.809 (95% CI, 0.793-0.825), respectively. The FAF model demonstrated superior performance to 4 ophthalmologists, showing a higher κ value of 0.789 (95% CI, 0.675-0.875) versus a range of 0.367 to 0.756 and higher accuracy of 0.937 (95% CI, 0.907-0.963) versus a range of 0.696 to 0.933. The CFP model demonstrated substantially superior performance to 4 ophthalmologists, showing a higher κ value of 0.471 (95% CI, 0.330-0.606) versus a range of 0.105 to 0.180 and higher accuracy of 0.844 (95% CI, 0.798-0.886) versus a range of 0.717 to 0.814. CONCLUSIONS: Deep learning-enabled automated detection of RPD presence from FAF images achieved a high level of accuracy, equal or superior to that of ophthalmologists. Automated RPD detection using CFP achieved a lower accuracy that still surpassed that of ophthalmologists. Deep learning models can assist, and even augment, the detection of this clinically important AMD-associated lesion.

LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC.

The identification and interpretation of genomic variants play a key role in the diagnosis of genetic diseases and related research. These tasks increasingly rely on accessing relevant manually curated information from domain databases (e.g. SwissProt or ClinVar). However, due to the sheer volume of medical literature and high cost of expert curation, curated variant information in existing databases are often incomplete and out-of-date. In addition, the same genetic variant can be mentioned in publications with various names (e.g. 'A146T' versus 'c.436G>A' versus 'rs121913527'). A search in PubMed using only one name usually cannot retrieve all relevant articles for the variant of interest. Hence, to help scientists, healthcare professionals, and database curators find the most up-to-date published variant research, we have developed LitVar for the search and retrieval of standardized variant information. In addition, LitVar uses advanced text mining techniques to compute and extract relationships between variants and other associated entities such as diseases and chemicals/drugs. LitVar is publicly available at https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/LitVar.