Volume Of Image Data Research Articles

Brain tumor classification utilizing pixel distribution and spatial dependencies higher-order statistical measurements through explainable ML models

Brain tumors are among the most fatal and devastating diseases, and they often result in a significant reduction in life expectancy. The devising of treatment plans that can extend the lives of affected individuals hinges on an accurate diagnosis of these tumors. Identifying and analyzing large volumes of magnetic resonance imaging (MRI) data manually proves to be both challenging and time-consuming. As a result, there exists a pressing need for a reliable machine-learning approach to accurately diagnose brain tumors, and numerous methods have already been proposed over the last decade. In this paper, a novel, comprehensive approach is proposed for identifying and classifying a given MR brain image as abnormal. Three common brain diseases, namely glioma, meningioma, and pituitary tumor, are chosen as abnormal brains, and the Figshare MRI brain image dataset was collected from the Kaggle and IEEE websites. The proposed method is initiated by employing 1st-order statistics, 2nd-order statistics, and higher-order transformed (DWT) feature extraction to extract features from images. Then missing data is addressed and handled using KNNImputer, followed by the application of the ExtratreesClassifier and PCA feature selection methods to identify the most relevant features and reduce the dimensions of these features. Subsequently, the reduced features are submitted to seven machine learning models, namely RF, GB, CB, SVM, LGBM, DT, and LR. The strategy of k-fold cross-validation is utilized to enhance the performance of those models. Finally, the models are evaluated using XAI approaches, which ensure transparent decision-making processes and provide insights into the model’s predictions. Remarkably, our approach achieves the highest accuracy, precision, recall, F1 score, MCC, Kappa, AUC-ROC, and R2, as well as the lowest loss, among the seven models evaluated, proving its effectiveness and applicability in multiple analytic applications relying on publicly available datasets.

The Visual Monitoring Camera (VMC) on Mars Express: A new science instrument made from an old webcam orbiting Mars

The Visual Monitoring Camera (VMC) is a small imaging instrument onboard Mars Express with a field of view of ∼40°x30°. The camera was initially intended to provide visual confirmation of the separation of the Beagle 2 lander and has similar technical specifications to a typical webcam of the 2000s. In 2007, a few years after the end of its original mission, VMC was turned on again to obtain full-disk images of Mars to be used for outreach purposes. As VMC obtained more images, the scientific potential of the camera became evident, and in 2018 the camera was given an upgraded status of a new scientific instrument, with science goals in the field of Martian atmosphere meteorology. The wide Field of View of the camera combined with the orbit of Mars Express enable the acquisition of full-disk images of the planet showing different local times, which for a long time has been rare among orbital missions around Mars. The small data volume of images also allows videos that show the atmospheric dynamics of dust and cloud systems to be obtained. This paper is intended to be the new reference paper for VMC as a scientific instrument, and thus provides an overview of the updated procedures to plan, command and execute science observations of the Martian atmosphere. These observations produce valuable science data that is calibrated and distributed to the community for scientific use.

A Multi-Institutional Study of Magnetic Resonance/Ultrasound Fusion-Guided Nanoparticle-Directed Focal Therapy for Prostate Ablation.

Focal therapy aims to provide a durable oncologic treatment option for men with prostate cancer (PCa), while preserving their quality of life. Most focal therapy modalities rely on the direct tissue effect, resulting in a possible nontargeted approach to ablation. Here, we report the results of the first human feasibility trial utilizing nanoparticle-directed focal photothermal ablation for PCa. A prospective, open-label, single-arm, multicenter study of men with localized PCa in Gleason Grade Group 1 to 3 was conducted. Men received a single infusion of gold nanoparticles (AuroShells), followed by magnetic resonance (MR)/ultrasound (US) fusion-guided laser excitation of the target tissue to induce photothermal ablation. MRI was used to assess the effectiveness of prostate tissue ablation at 48 to 96 hours, 3 months, and 12 months post treatment. At 3 months, a targeted fusion biopsy of the lesion(s) was conducted. At 12 months, a targeted fusion biopsy and standard templated biopsy were performed. Treatment success was determined based on a negative MR/US fusion biopsy outcome within the treated area. Forty-six men were enrolled in the study, and 44 men with 45 lesions completed nanoparticle infusion and laser treatment. The mean PSA level at baseline was 9.5 ng/mL, which decreased to 5.9 ng/mL at 3 months and to 4.7 ng/mL at 12 months (P < .0001). The oncologic success rates at 3 and 12 months resulted in 29 (66%) and 32 (73%) of 44 patients, respectively, being successfully treated, confirmed with negative MR/US fusion biopsies within the ablation zone. Among Gleason Grade Group, maximum lesion diameter on MRI, prostate volume, and Prostate Imaging Reporting and Data System scoring, the maximum lesion diameter was significantly associated with the odds of treatment failure at 12 months (P = .046). Nanoparticle-directed focal laser ablation of neoplastic prostate tissue resulted in 73% of patients with successful treatment at 12 months post treatment, confirmed by negative MR/US fusion biopsy of the treated lesion and a systematic biopsy. 02680535.

Physics and chemistry from parsimonious representations: image analysis via invariant variational autoencoders

Electron, optical, and scanning probe microscopy methods are generating ever increasing volume of image data containing information on atomic and mesoscale structures and functionalities. This necessitates the development of the machine learning methods for discovery of physical and chemical phenomena from the data, such as manifestations of symmetry breaking phenomena in electron and scanning tunneling microscopy images, or variability of the nanoparticles. Variational autoencoders (VAEs) are emerging as a powerful paradigm for the unsupervised data analysis, allowing to disentangle the factors of variability and discover optimal parsimonious representation. Here, we summarize recent developments in VAEs, covering the basic principles and intuition behind the VAEs. The invariant VAEs are introduced as an approach to accommodate scale and translation invariances present in imaging data and separate known factors of variations from the ones to be discovered. We further describe the opportunities enabled by the control over VAE architecture, including conditional, semi-supervised, and joint VAEs. Several case studies of VAE applications for toy models and experimental datasets in Scanning Transmission Electron Microscopy are discussed, emphasizing the deep connection between VAE and basic physical principles. Python codes and datasets discussed in this article are available at https://github.com/saimani5/VAE-tutorials and can be used by researchers as an application guide when applying these to their own datasets.

Pavement Crack Detection Using Fractal Dimension and Semi-Supervised Learning

Pavement cracks are crucial indicators for assessing the structural health of asphalt roads. Existing automated crack detection models depend on large quantities of precisely annotated crack sample data. The irregular morphology of cracks makes manual annotation time-consuming and costly, thereby posing challenges to the practical application of these models. This study proposes a pavement crack image detection method integrating fractal dimension analysis and semi-supervised learning. It identifies the self-similarity characteristics within the crack regions by analyzing pavement crack images and using fractal dimensions to preliminarily determine the candidate crack regions. The Crack Similarity Learning Network (CrackSL-Net) is then employed to learn the semantic similarity of crack image regions. Semi-supervised learning facilitates automatic crack detection by combining a small amount of labeled data with a large volume of unlabeled image data. Comparative experiments are conducted on two public pavement crack datasets against the HED, U-Net, and RCF models to comprehensively evaluate the performance of the proposed method. The results indicate that, with a 50% annotation ratio, the proposed method achieves high-precision crack detection, with an intersection over union (IoU) exceeding 0.84, which is close to that of U-Net. Visual analysis of the detection results confirms the method’s effectiveness in identifying cracks in complex environments.

DCNNLFS: A Dilated Convolutional Neural Network With Late Fusion Strategy for Intelligent Classification of Gastric Histopathology Images.

Gastric cancer has a high incidence rate, significantly threatening patients' health. Gastric histopathology images can reliably diagnose related diseases. Still, the data volume of histopathology images is too large, making misdiagnosis or missed diagnosis easy. The classification model based on deep learning has made some progress on gastric histopathology images. However, traditional convolutional neural networks (CNNs) generally use pooling operations, which will reduce the spatial resolution of the image, resulting in poor prediction results. The image feature in previous CNN has a poor perception of details. Therefore, we design a dilated CNN with a late fusion strategy (DCNNLFS) for gastric histopathology image classification. The DCNNLFS model utilizes dilated convolutions, enabling it to expand the receptive field. The dilated convolutions can learn the different contextual information by adjusting the dilation rate. The DCNNLFS model uses a late fusion strategy to enhance the classification ability of DCNNLFS. We run related experiments on a gastric histopathology image dataset to verify the excellence of the DCNNLFS model, where the three metrics Precision, Accuracy, and F1-Score are 0.938, 0.935, and 0.959.

Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.

Unleashing the potential of cell painting assays for compound activities and hazards prediction.

The cell painting (CP) assay has emerged as a potent imaging-based high-throughput phenotypic profiling (HTPP) tool that provides comprehensive input data for in silico prediction of compound activities and potential hazards in drug discovery and toxicology. CP enables the rapid, multiplexed investigation of various molecular mechanisms for thousands of compounds at the single-cell level. The resulting large volumes of image data provide great opportunities but also pose challenges to image and data analysis routines as well as property prediction models. This review addresses the integration of CP-based phenotypic data together with or in substitute of structural information from compounds into machine (ML) and deep learning (DL) models to predict compound activities for various human-relevant disease endpoints and to identify the underlying modes-of-action (MoA) while avoiding unnecessary animal testing. The successful application of CP in combination with powerful ML/DL models promises further advances in understanding compound responses of cells guiding therapeutic development and risk assessment. Therefore, this review highlights the importance of unlocking the potential of CP assays when combined with molecular fingerprints for compound evaluation and discusses the current challenges that are associated with this approach.

Multi-Scale and Multi-Factor ViT Attention Model for Classification and Detection of Pest and Disease in Agriculture

Agriculture has a crucial impact on the economic, ecological, and social development of the world. More rapid and precise prevention and control work, especially for accurate classification and detection, is required due to the increasing severity of agricultural pests and diseases. However, the results of the image classification and detection are unsatisfactory because of the limitation of image data volume acquisition and the wide range of influencing factors of pests and diseases. In order to solve these problems, the vision transformer (ViT) model is improved, and a multi-scale and multi-factor ViT attention model (SFA-ViT) is proposed in this paper. Data augmentation considering multiple influencing factors is implemented in SFA-ViT to mitigate the impact of insufficient experimental data. Meanwhile, SFA-ViT optimizes the ViT model from a multi-scale perspective, and encourages the model to understand more features, from fine-grained to coarse-grained, during the classification task. Further, the detection model based on the self-attention mechanism of the multi-scale ViT is constructed to achieve the accurate localization of the pest and disease. Finally, experimental validation of the model, based on the IP102 and Plant Village dataset, is carried out. The results indicate that the various components of SFA-ViT effectively enhance the final classification and detection outcomes, and our model outperforms the current models significantly.

Role of artificial intelligence in medical radiology and imaging

Abstract Artificial intelligence (AI) is revolutionizing radiology, oncology, and other medicine and veterinary care areas. Adopting deep learning algorithms has significantly advanced image analysis and disease detection. This study explores how AI is reshaping the roles of radiologists and radiographers. It highlights its vital function in infection detection and control, as evidenced by its impact during the coronavirus disease 2019 (COVID-19) pandemic. In veterinary radiation oncology, AI supports complex contouring and treatment planning. However, while AI offers numerous advantages, its implementation must be cautiously approached. Radiologists face challenges, particularly the overwhelming volume of imaging data, which AI helps manage through artificial neural networks and machine learning (ML) algorithms—two significant innovations in this field. In veterinary radiation oncology, AI facilitates collaboration, standardization of data, and the creation of standard operating procedures. Early disease detection, enabled by AI, is essential for initiating treatments that can improve patient outcomes and prognosis. AI is crucial in analyzing large medical datasets, including imaging and clinical data, through advanced algorithms and ML techniques. In veterinary medicine, AI is key to addressing complex challenges in host–pathogen interactions, precision medicine, and predictive epidemiology. AI-powered solutions for continuous monitoring ensure that at-risk patients receive ongoing observation, enabling the rapid detection of changes in health markers. This approach is especially advantageous in managing chronic conditions, enabling proactive healthcare, and facilitating early intervention.

Enhanced Compression Medical Image Speed for Computed Radiography

In recent years, there has been a significant surge in the volume of medical imaging data. This surge poses challenges for the functioning of PACS communication systems and image archiving. The most effective solution to address this issue involves compressing images through digital encryption, which optimally utilizes storage space. This process involves reformatting the imaging data by reducing redundancy, leading to image compression. While this reduction in redundancy is readily apparent in individual images, there is a vulnerability in these methods that tends to overlook a source of repetition present in similar stored images. To emphasize this common occurrence, we introduce the term "redundancy group." Similar images are frequently encountered within medical image databases, resulting in a considerable redundancy reduction. In this paper, our focus is on enhancing the control of redundancy extraction in the data used, specifically medical images. To enhance the compression efficiency of standard image compression, we employ improved methods, namely MinMax Predictive (MMP) and Min-Max Differential (MMD). Our experiments demonstrate that these methods lead to a substantial enhancement in brain CT compression, with potential improvements of up to 130% when using Huffman coding. Similar improvements are observed in arithmetic coding, with a 94% improvement compared to the number-arithmetic code and a 37% improvement compared to the Lempel-Ziv compression. These improvements occur when combining the MMP technology with the MMD technology, utilizing inverse operations that result in lossless compression.

The Microstructural Reconstruction of Variously Sintered Ni-SDC Cermets Using Focused Ion Beam Scanning Electron Microscopy Nanotomography.

This work focuses in-depth on the quantitative relationships between primary first-order microstructural parameters (i.e., volume fractions of various phases and particle size distribution) with the more complex second-order topological features (i.e., connectivity of phases, three-phase boundary length (TPBL), interfacial areas, or tortuosity). As a suitable model material, a cermet nickel/samaria-doped ceria (Ni-SDC) is used as an anode in a solid oxide fuel cell (SOFC). A microstructure description of nano-sized Ni-SDC cermets, fabricated at various sintering conditions from 1100 °C to 1400 °C, was performed using FIB-SEM nanotomography. The samples were serially sectioned employing a fully automated slicing procedure with active drift correction algorithms and an auto-focusing routine to obtain a series of low-loss BSE images. Advanced image processing algorithms were developed and applied directly to image data volume. The microstructural-topological relationships are crucial for the microstructure optimisation and, thus, the improvement of the corresponding electrode performance. Since all grains of individual phases (Ni, SDC, or pores) did not percolate, special attention was given to the visualisation of the so-called active TPBL. Based on the determined microstructure characteristics of the prepared Ni-SDC cermets, including simulations of gas flow and pressure drop, thermal treatment at 1200 °C was recognised as the most appropriate sintering temperature.

Representation Learning Based on Vision Transformer

In recent years, with the rapid development of information technology, the volume of image data has grown exponentially. However, these datasets typically contain a large amount of redundant information. To extract effective features and reduce redundancy from images, a representation learning method based on the Vision Transformer (ViT) has been proposed, and to our best knowledge, Transformer was first applied to zero-shot learning (ZSL). The method adopts a symmetric encoder–decoder structure, where the encoder incorporates Multi-Head Self-Attention (MSA) mechanism of ViT to reduce the dimensionality of image features, eliminate redundant information, and decrease computational burden. Consequently, it effectively extracts features, and the decoder is utilized for reconstructing image data. We evaluated the representation learning capability of the proposed method in various tasks, including data visualization, image reconstruction, face recognition, and ZSL. By comparing with state-of-the-art representation learning methods, the outstanding results obtained validate the effectiveness of this method in the field of representation learning.

Prostate health index density aids the diagnosis of prostate cancer detected using magnetic resonance imaging targeted prostate biopsy in Taiwanese multicenter study.

Multiparametric magnetic resonance imaging (mpMRI) followed by MRI-targeted prostate biopsy is the current standard for diagnosing prostate cancer (PCa). However, studies evaluating the value of biomarkers, including prostate health index (PHI) and its derivatives using this method are limited. We aimed to investigate the efficacy of PHI density (PHID) in guiding MRI-targeted prostate biopsies to identify clinically significant PCas (csPCa). The multicenter prospectively registered prostate biopsy database from three medical centers in Taiwan included patients with PHI and MRI-targeted and/or systematic prostate biopsies. We assessed the required values of prostate-specific antigen (PSA), prostate volume, PHI, PHID, and Prostate Imaging Reporting & Data System (PI-RADS) score using multivariable analyses, receiver operating characteristic curve analysis, and decision curve analyses (DCA). csPCa was defined as the International Society of Urological Pathology Gleason group ≥2 PCa, with an emphasis on reducing unwarranted biopsies. The study cohort comprised 420 individuals. Diagnoses of PCa and csPCa were confirmed in 62.4% and 47.9% of the participants, respectively. The csPCa diagnosis rates were increased with increasing PI-RADS scores (20.5%, 44.2%, and 73.1% for scores 3, 4, and 5, respectively). Independent predictors for csPCa detection included PHI, prostate volume, and PI-RADS scores of 4 and 5 in multivariable analyses. The area under the curve (AUC) for csPCa of PHID (0.815) or PHI (0.788) was superior to that of PSA density (0.746) and PSA (0.635) in the entire cohort, and the superiority of PHID (0.758) was observed in PI-RADS 3 lesions. DCA revealed that PHID achieved the best net clinical benefit in PI-RADS 3-5 and 4/5 cases. Among PI-RADS 3 lesions, cutoff values of PHID 0.70 and 0.43 could eliminate 51.8% and 30.4% of omitted biopsies, respectively. PHI-derived biomarkers, including PHID, performed better than other PSA-derived biomarkers in diagnosing PCa in MRI-detected lesions.

Design of a novel deep network model for spinal cord injury prediction

Degenerative cervical myelopathy must be diagnosed with magnetic resonance imaging (MRI) which predicts spinal cord injury (SCI). The growing volume of medical imaging data can be managed by deep learning models, which provide a preliminary interpretation of images taken in basic care settings. Our main goal was to create a deep-learning approach that could identify SCI using MRI data. This work concentrates on modeling a novel 2D-convolutional neural networks (2D-CNN) approach for predicting SCI. For holdouts, training, and validation, various datasets of patients were created. Two experts assigned labels to the images. The holdout dataset was used to evaluate the performance of our deep convolutional neural network (DCNN) over the image data from the available dataset. The dataset is acquired from the online resource for training and validation purpose. With the available dataset, the anticipated model attains 94% AUC, 0.1 p-value, and 92.2% accuracy. The anticipated model might make cervical spine MRI scan interpretation more accurate and reliable.

Commonality and variance of resting-state networks in common marmoset brains

Animal models of brain function are critical for the study of human diseases and development of effective interventions. Resting-state network (RSN) analysis is a powerful tool for evaluating brain function and performing comparisons across animal species. Several studies have reported RSNs in the common marmoset (Callithrixjacchus; marmoset), a non-human primate. However, it is necessary to identify RSNs and evaluate commonality and inter-individual variance through analyses using a larger amount of data. In this study, we present marmoset RSNs detected using > 100,000 time-course image volumes of resting-state functional magnetic resonance imaging data with careful preprocessing. In addition, we extracted brain regions involved in the composition of these RSNs to understand the differences between humans and marmosets. We detected 16 RSNs in major marmosets, three of which were novel networks that have not been previously reported in marmosets. Since these RSNs possess the potential for use in the functional evaluation of neurodegenerative diseases, the data in this study will significantly contribute to the understanding of the functional effects of neurodegenerative diseases.

Large-volume LEO satellite imaging data networked transmission scheduling problem: Model and algorithm

With the current miniaturization and cost reduction trend, many small imaging satellites are deployed in low Earth orbit (LEO). Efficient scheduling of transmission links is crucial for handling the large volume of imaging data in the time-varying inter-satellite broadband communication networks. This ensures optimal utilization of inter-satellite link resources and gives rise to a novel problem known as the Large-volume LEO satellite Imaging Data Networked Transmission Scheduling Problem (LLSIDNTSP). This problem requires the integrity of the imaging data received on the ground station. A Specific Time-Evolving Graph (STEG) model is introduced to formulate this problem, while a highly effective Sequential Two-Phased Heuristic Algorithm (STPHA) is proposed to solve it. STPHA determines an optimized Contact Plan (CP) with a Contact-Fast Construction Algorithm (CFCA) in the first phase. With this CP as input, it produces an optimal transmission schedule with Linear Programming in the second phase. The CFCA incorporates a novel heuristic called Mission Residual Volume Factor(MRVF), which guides the selection of a proper inter-satellite and satellite-to-ground link at each construction step. Extensive simulation results demonstrate the effectiveness of the proposed STPHA. In particular, it can achieve the optimal solution for small-sized scenarios with one-hundredth of the computing time used by the exact solver CPLEX. Also, it is consistently better than other heuristic algorithms adapted from the literature for large-sized scenarios. Additional experimental analysis is conducted to showcase the effectiveness of the innovative components of STPHA.

Synthetic Aperture Radar Image Compression Based on Low-Frequency Rejection and Quality Map Guidance

Synthetic Aperture Radar (SAR) images are widely utilized in the field of remote sensing. However, there is a limited body of literature specifically addressing the compression of SAR learning images. To address the escalating volume of SAR image data for storage and transmission, which necessitates more effective compression algorithms, this paper proposes a novel framework for compressing SAR images. Experimental validation is performed using a representative low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. Initially, we introduce a novel two-stage transformation-based approach aimed at suppressing the low-frequency components of the input data, thereby achieving a high information entropy and minimizing quantization losses. Subsequently, a quality map guidance image compression algorithm is introduced, involving the fusion of the input SAR images with a target-aware map. This fusion involves convolutional transformations to generate a compact latent representation, effectively exploring redundancies between focused and non-focused areas. To assess the algorithm’s performance, experiments are carried out on both the low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. The results indicate that the low-frequency suppression algorithm significantly outperforms traditional processing algorithms by 3–8 dB when quantifying the input data, effectively preserving image features and improving image performance metrics. Furthermore, the quality map guidance image compression algorithm demonstrates a superior performance compared to the baseline model.

A Machine learning approach for Post-Disaster data curation

Image data collected after natural disasters play an important role in the forensics of structure failures. However, curating and managing large amounts of post-disaster imagery data is challenging. In most cases, data users still have to spend much effort to find and sort images from the massive amounts of images archived for past decades in order to study specific types of disasters. This paper proposes a new machine learning based approach for automating the labeling and classification of large volumes of post-natural disaster image data to address this issue. More specifically, the proposed method couples pre-trained computer vision models and a natural language processing model with an ontology tailed to natural disasters to facilitate the search and query of specific types of image data. The resulting process returns each image with five primary labels and similarity scores, representing its content based on the developed word-embedding model. Validation and accuracy assessment of the proposed methodology was conducted with ground-level residential building panoramic images from Hurricane Harvey. The computed primary labels showed a minimum average difference of 13.32% when compared to manually assigned labels. This versatile and adaptable solution offers a practical and valuable solution for automating image labeling and classification tasks, with the potential to be applied to various image classifications and used in different fields and industries. The flexibility of the method means that it can be updated and improved to meet the evolving needs of various domains, making it a valuable asset for future research and development.

Artificial intelligence in liver imaging: methods and applications.

Liver disease is regarded as one of the major health threats to humans. Radiographic assessments hold promise in terms of addressing the current demands for precisely diagnosing and treating liver diseases, and artificial intelligence (AI), which excels at automatically making quantitative assessments of complex medical image characteristics, has made great strides regarding the qualitative interpretation of medical imaging by clinicians. Here, we review the current state of medical-imaging-based AI methodologies and their applications concerning the management of liver diseases. We summarize the representative AI methodologies in liver imaging with focusing on deep learning, and illustrate their promising clinical applications across the spectrum of precise liver disease detection, diagnosis and treatment. We also address the current challenges and future perspectives of AI in liver imaging, with an emphasis on feature interpretability, multimodal data integration and multicenter study. Taken together, it is revealed that AI methodologies, together with the large volume of available medical image data, might impact the future of liver disease care.