Economic Evaluation of Telerobotic Ultrasound Technology to Remotely Provide Ultrasound Services in Rural and Remote Communities.

INTRODUCTION: Telerobotic ultrasound technology allows radiologists and sonographers to remotely provide ultrasound services in underserved areas. This study aimed to compare costs associated with using telerobotic ultrasound to provide ultrasound services in rural and remote communities to costs associated with alternate models. METHODS: A cost-minimization approach was used to compare four ultrasound service delivery models: telerobotic ultrasound (Model 1), telerobotic ultrasound and an itinerant sonographer (Model 2), itinerant sonographer without telerobotic ultrasound (Model 3), and travel to another community for all exams (Model 4). In Models 1-3, travel was assumed when exams could not be successfully performed telerobotically or by an itinerant sonographer. A publicly funded healthcare payer perspective was used for the reference case and a societal perspective was used for a secondary non-reference case. Costs were based on the literature and experience using telerobotic ultrasound in Saskatchewan, Canada. Costs were expressed in 2020 Canadian dollars. RESULTS: Average cost per ultrasound exam was $342, $323, $368, and $478 for Models 1, 2, 3, and 4, respectively, from a publicly funded healthcare payer perspective, and $461, $355, $447, and $849, respectively, from a societal perspective. In one-way sensitivity analyses, Model 2 was the lowest cost from a payer perspective for communities with population >2075 people, distance >350 km from the nearest ultrasound facility, or >47% of the population eligible for publicly funded medical transportation. CONCLUSION: Health systems may wish to consider solutions such as telerobotic ultrasound and itinerant sonographers to reduce healthcare costs and improve access to ultrasound in rural and remote communities.

A pictorial review of the radiographic skeletal findings in Morquio syndrome (mucopolysaccharidosis type IV).

Morquio syndrome, also known as Morquio-Brailsford syndrome or mucopolysaccharidosis type IV (MPS IV), is a subgroup of mucopolysaccharidosis. It is an autosomal recessive lysosomal storage disorder. Two subtypes of Morquio syndrome have been identified. In MPS IVA, a deficiency in N-acetylgalactosamine-6-sulfate sulfatase interrupts the normal metabolic pathway of degrading glycosaminoglycans. Accumulated undigested glycosaminoglycans in the tissue and bones result in complications leading to severe skeletal deformity. In MPS IVB, a deficiency in beta-galactosidase results in a milder phenotype than in MPS IVA. Morquio syndrome presents a variety of clinical manifestations in a spectrum of mild to severe. It classically has been considered a skeletal dysplasia with significant skeletal involvement. However, the extraskeletal features can also provide valuable information to guide further work-up to assess the possibility of the disorder. Although the disease involves almost all parts of the body, it most commonly affects the axial skeleton, specifically the vertebrae. The characteristic radiologic findings in MPS IV, such as paddle-shaped ribs, odontoid hypoplasia, vertebral deformity, metaphyseal and epiphyseal bone dysplasia, and steep acetabula, are encompassed in the term "dysostosis multiplex," which is a common feature among other types of MPS and storage disorders. Myelopathy due to spinal cord compression and respiratory airway obstruction are the most critical complications related to mortality and morbidity. The variety of clinical features, as well as overlapping of radiological findings with other disorders, make diagnosis challenging, and delays in diagnosis and treatment may lead to critical complications. Timely imaging and radiologic expertise are important components for diagnosis. Gene therapies may provide robust treatment, particularly if genetic variations can be screened in utero.

Neonatal Catheter and Tube Placement and Radiographic Assessment Statistics in Relation to Important Anatomic Landmarks.

OBJECTIVE: Neonatal catheters and tubes are commonly used for monitoring and support for intensive care and must be correctly positioned to avoid complications. Position assessment is routinely done by radiography. The objective of this study is to characterize neonatal catheter and tube placement in terms of the proportion of those devices that are malpositioned. STUDY DESIGN: Using an institutional dataset of 723 chest/abdominal radiographs of neonatal intensive care unit (ICU) patients (all within 60 days of birth), we assessed the proportion of catheters that are malpositioned. Many radiographs contained multiple catheter types. Umbilical venous catheters (UVCs; 448 radiographs), umbilical arterial catheters (UACs; 259 radiographs), endotracheal tubes (ETTs; 451 radiographs), and nasogastric tubes (NGTs; 603 radiographs) were included in our analysis. RESULTS: UVCs were malpositioned in 90% of radiographs, while UACs were malpositioned in 36%, ETTs in 30%, and NGTs in just 5%. The most common locations in which UVCs were malpositioned were in the right atrium (31%) and umbilical vein (21%), and for UACs the most common malpositioned tip location was the aortic arch (8%). For the remaining tubes, 5% of ETTs were found to be in the right main bronchus and 4% of NGTs were found in the esophagus. CONCLUSION: A substantial proportion of catheters and tubes are malpositioned, suggesting that optimizing methods of catheter placement and assessment ought to be areas of focus for future work. KEY POINTS: · Neonatal catheters are frequently malpositioned.. · Most umbilical venous catheters need readjustment.. · X-ray and ultrasound are important for assessment.. · Catheter tips should be assessed in all X-rays..

Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review.

Most artificial intelligence (AI) studies have focused primarily on adult imaging, with less attention to the unique aspects of pediatric imaging. The objectives of this study were to (1) identify all publicly available pediatric datasets and determine their potential utility and limitations for pediatric AI studies and (2) systematically review the literature to assess the current state of AI in pediatric chest radiograph interpretation. We searched PubMed, Web of Science and Embase to retrieve all studies from 1990 to 2021 that assessed AI for pediatric chest radiograph interpretation and abstracted the datasets used to train and test AI algorithms, approaches and performance metrics. Of 29 publicly available chest radiograph datasets, 2 datasets included solely pediatric chest radiographs, and 7 datasets included pediatric and adult patients. We identified 55 articles that implemented an AI model to interpret pediatric chest radiographs or pediatric and adult chest radiographs. Classification of chest radiographs as pneumonia was the most common application of AI, evaluated in 65% of the studies. Although many studies report high diagnostic accuracy, most algorithms were not validated on external datasets. Most AI studies for pediatric chest radiograph interpretation have focused on a limited number of diseases, and progress is hindered by a lack of large-scale pediatric chest radiograph datasets.

Sociodemographic and Geographic Factors Associated With Non-Obstetrical Ultrasound Imaging Utilization: A Population-Based Study.

OBJECTIVE: Ultrasound is one of the most commonly used imaging modalities, though some populations face barriers in accessing ultrasound services, potentially resulting in disparities in utilization. The objective of this study was to assess the association between sociodemographic and geographic factors and non-obstetrical ultrasound utilization in the province of Saskatchewan, Canada. METHODS: All non-obstetrical ultrasound exams performed from 2014 to 2018 in Saskatchewan, Canada were retrospectively identified from province-wide databases. Univariate and multivariate Poisson regression analyses were performed to assess the association between ultrasound utilization and sex, age, First Nations status, Charlson Comorbidity Index, urban vs. rural residence, geographic remoteness, and neighborhood income. RESULTS: A total of 1,324,846 individuals (5,857,044 person-years) were included in the analysis. Female sex (adjusted incidence rate ratio [aIRR], 2.20; 95% confidence interval [CI], 2.19-2.22), age (aIRR, 4.97; 95% CI, 4.90-5.05 for ≥57 years vs. <11 years), comorbidities (aIRR, 4.36 for Charlson Comorbidity Index >10 vs. 0; 95% CI, 3.78-5.03), and higher neighborhood income (aIRR, 1.04; 95% CI, 1.02-1.05 for highest vs. lowest quintile) were associated with higher rates of ultrasound utilization. Individuals who were status First Nations (aIRR, 0.91; 95% CI, 0.90-0.92) or resided in geographically remote areas (aIRR, 0.87 for most vs. least remote; 95% CI, 0.83-0.91) had lower rates of ultrasound utilization. Individuals who lived in a rural area also had lower rates of ultrasound utilization (aIRR, 0.93; 95% CI, 0.92-0.94). CONCLUSION: Substantial disparities exist in non-obstetrical ultrasound utilization among individuals in low-income neighborhoods, status First Nations individuals, and individuals in rural and remote communities.

Telerobotic Sonography for Remote Diagnostic Imaging: Narrative Review of Current Developments and Clinical Applications.

Access to sonographers and sonologists is limited in many communities around the world. Telerobotic sonography (robotic ultrasound) is a new technology to increase access to sonography, providing sonographers and sonologists the ability to manipulate an ultrasound probe from a distant location and remotely perform ultrasound examinations. This narrative review discusses the development of telerobotic ultrasound systems, clinical studies evaluating the feasibility and diagnostic accuracy of telerobotic sonography, and emerging use of telerobotic sonography in clinical settings. Telerobotic sonography provides an opportunity to provide real-time ultrasound examinations to underserviced rural and remote communities to increase equity in the delivery of diagnostic imaging.

Automatic Detection and Classification of Multiple Catheters in Neonatal Radiographs with Deep Learning.

We develop and evaluate a deep learning algorithm to classify multiple catheters on neonatal chest and abdominal radiographs. A convolutional neural network (CNN) was trained using a dataset of 777 neonatal chest and abdominal radiographs, with a split of 81%-9%-10% for training-validation-testing, respectively. We employed ResNet-50 (a CNN), pre-trained on ImageNet. Ground truth labelling was limited to tagging each image to indicate the presence or absence of endotracheal tubes (ETTs), nasogastric tubes (NGTs), and umbilical arterial and venous catheters (UACs, UVCs). The dataset included 561 images containing two or more catheters, 167 images with only one, and 49 with none. Performance was measured with average precision (AP), calculated from the area under the precision-recall curve. On our test data, the algorithm achieved an overall AP (95% confidence interval) of 0.977 (0.679-0.999) for NGTs, 0.989 (0.751-1.000) for ETTs, 0.979 (0.873-0.997) for UACs, and 0.937 (0.785-0.984) for UVCs. Performance was similar for the set of 58 test images consisting of two or more catheters, with an AP of 0.975 (0.255-1.000) for NGTs, 0.997 (0.009-1.000) for ETTs, 0.981 (0.797-0.998) for UACs, and 0.937 (0.689-0.990) for UVCs. Our network thus achieves strong performance in the simultaneous detection of these four catheter types. Radiologists may use such an algorithm as a time-saving mechanism to automate reporting of catheters on radiographs.

Artificial Intelligence Solutions for Analysis of X-ray Images.

Artificial intelligence (AI) presents a key opportunity for radiologists to improve quality of care and enhance the value of radiology in patient care and population health. The potential opportunity of AI to aid in triage and interpretation of conventional radiographs (X-ray images) is particularly significant, as radiographs are the most common imaging examinations performed in most radiology departments. Substantial progress has been made in the past few years in the development of AI algorithms for analysis of chest and musculoskeletal (MSK) radiographs, with deep learning now the dominant approach for image analysis. Large public and proprietary image data sets have been compiled and have aided the development of AI algorithms for analysis of radiographs, many of which demonstrate accuracy equivalent to radiologists for specific, focused tasks. This article describes (1) the basis for the development of AI solutions for radiograph analysis, (2) current AI solutions to aid in the triage and interpretation of chest radiographs and MSK radiographs, (3) opportunities for AI to aid in noninterpretive tasks related to radiographs, and (4) considerations for radiology practices selecting AI solutions for radiograph analysis and integrating them into existing IT systems. Although comprehensive AI solutions across modalities have yet to be developed, institutions can begin to select and integrate focused solutions which increase efficiency, increase quality and patient safety, and add value for their patients.

Artificial intelligence-based imaging analytics and lung cancer diagnostics: Considerations for health system leaders.

Lung cancer is a leading cause of cancer death in Canada, and accurate, early diagnosis are critical to improving clinical outcomes. Artificial Intelligence (AI)-based imaging analytics are a promising healthcare innovation that aim to improve the accuracy and efficiency of lung cancer diagnosis. Maximizing their clinical potential while mitigating their risks and limitations will require focused leadership informed by interdisciplinary expertise and system-wide insight. We convened a knowledge exchange workshop with diverse Saskatchewan health system leaders and stakeholders to explore issues surrounding the use of AI in diagnostic imaging for lung cancer, including implementation opportunities, challenges, and priorities. This technology is anticipated to improve patient outcomes, reduce unnecessary healthcare spending, and increase knowledge. However, health system leaders must also address the needs for robust data, financial investment, effective communication and collaboration between healthcare sectors, privacy and data protections, and continued interdisciplinary research to achieve this technology's potential benefits.

Use of ultrasound for assessment of musculoskeletal disease in persons with haemophilia: Results of an International Prophylaxis Study Group global survey.

AIM: The objective of this survey was to understand the global trends of imaging assessments in persons with haemophilia, focusing on point-of-care ultrasound (POCUS). Insights into the barriers impeding its widespread proliferation as a frontline imaging modality were obtained. METHODS: The survey opened in September of 2017 and closed in May of 2018. Haemophilia Treatment Centres (HTCs) treating both paediatric/adult patients were the population of interest. A REDCap survey of 25 questions was disseminated to 232 clinical staff in 26 countries. RESULTS: The majority of respondents (88.3%, 91/103) reported that POCUS is most useful to confirm or rule out a presumed acute joint bleed. European HTCs reported the highest routine use of POCUS at 59.5% (22/37) followed by HTCs in the "Other" countries of the world at 46.7% (7/15) and North American HTCs at 43.9% (25/57). At the time of the survey, physiotherapists were identified as the clinical staff who perform POCUS 52.8% (28/53) of the time, in contrast with nurses/nurse practitioners who represent only 5.7% (3/53) of users. The greatest perceived barriers to the implementation of POCUS are the lack of trained healthcare professionals who can perform POCUS at 69.2% (74/107) and the overall time commitment required at 68.2% (73/107). CONCLUSION: Despite POCUS being used in 49.5% (54/109) of sampled HTCs, it is still utilized almost 30% less globally than full diagnostic ultrasound. A list of barriers has been identified to inform HTCs which challenges they will likely need to overcome should they choose to incorporate this imaging modality into their practice.

Comparison of the gamma-Pareto convolution with conventional methods of characterising metformin pharmacokinetics in dogs.

A model was developed for long term metformin tissue retention based upon temporally inclusive models of serum/plasma concentration ([Formula: see text]) having power function tails called the gamma-Pareto type I convolution (GPC) model and was contrasted with biexponential (E2) and noncompartmental (NC) metformin models. GPC models of [Formula: see text] have a peripheral venous first arrival of drug-times parameter, early [Formula: see text] peaks and very slow washouts of [Formula: see text]. The GPC, E2 and NC models were applied to a total of 148 serum samples drawn from 20 min to 72 h following bolus intravenous metformin in seven healthy mongrel dogs. The GPC model was used to calculate area under the curve (AUC), clearance ([Formula: see text]), and functions of time, f(t), for drug mass remaining (M), apparent volume of distribution ([Formula: see text]), as well as [Formula: see text] for [Formula: see text], [Formula: see text] and [Formula: see text]. The GPC models of [Formula: see text] yielded metformin [Formula: see text]-values that were 84.8% of total renal plasma flow (RPF) as estimated from meta-analysis. The GPC [Formula: see text]-values were significantly less than the corresponding NC and E2 [Formula: see text]-values of 104.7% and 123.7% of RPF, respectively. The GPC plasma/serum only model predicted 78.9% drug [Formula: see text] average urinary recovery at 72 h; similar to prior human urine drug [Formula: see text] collection results. The GPC model [Formula: see text] of [Formula: see text], [Formula: see text] and [Formula: see text], were asymptotically proportional to elapsed time, with a constant limiting [Formula: see text] ratio of M/C averaging 7.0 times, a result in keeping with prior simultaneous [Formula: see text] and urine [Formula: see text] collection studies and exhibiting a rate of apparent volume growth of [Formula: see text] that achieved limiting constant values. A simulated constant average drug mass multidosing protocol exhibited increased [Formula: see text] and [Formula: see text] with elapsing time, effects that have been observed experimentally during same-dose multidosing. The GPC heavy-tailed models explained multiple documented phenomena that were unexplained with lighter-tailed models.

Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer.

Low-dose CT denoising is a challenging task that has been studied by many researchers. Some studies have used deep neural networks to improve the quality of low-dose CT images and achieved fruitful results. In this paper, we propose a deep neural network that uses dilated convolutions with different dilation rates instead of standard convolution helping to capture more contextual information in fewer layers. Also, we have employed residual learning by creating shortcut connections to transmit image information from the early layers to later ones. To further improve the performance of the network, we have introduced a non-trainable edge detection layer that extracts edges in horizontal, vertical, and diagonal directions. Finally, we demonstrate that optimizing the network by a combination of mean-square error loss and perceptual loss preserves many structural details in the CT image. This objective function does not suffer from over smoothing and blurring effects causing by per-pixel loss and grid-like artifacts resulting from perceptual loss. The experiments show that each modification to the network improves the outcome while changing the complexity of the network, minimally.

Automatic Catheter and Tube Detection in Pediatric X-ray Images Using a Scale-Recurrent Network and Synthetic Data.

Catheters are commonly inserted life supporting devices. Because serious complications can arise from malpositioned catheters, X-ray images are used to assess the position of a catheter immediately after placement. Previous computer vision approaches to detect catheters on X-ray images were either rule-based or only capable of processing a limited number or type of catheters projecting over the chest. With the resurgence of deep learning, supervised training approaches are beginning to show promising results. However, dense annotation maps are required, and the work of a human annotator is difficult to scale. In this work, we propose an automatic approach for detection of catheters and tubes on pediatric X-ray images. We propose a simple way of synthesizing catheters on X-ray images to generate a training dataset by exploiting the fact that catheters are essentially tubular structures with various cross sectional profiles. Further, we develop a UNet-style segmentation network with a recurrent module that can process inputs at multiple scales and iteratively refine the detection result. By training on adult chest X-rays, the proposed network exhibits promising detection results on pediatric chest/abdomen X-rays in terms of both precision and recall, with Fß = 0.8. The approach described in this work may contribute to the development of clinical systems to detect and assess the placement of catheters on X-ray images. This may provide a solution to triage and prioritize X-ray images with potentially malpositioned catheters for a radiologist's urgent review and help automate radiology reporting.

Computer-Aided Diagnosis (CAD) of Pulmonary Nodule of Thoracic CT Image Using Transfer Learning.

Computer-aided diagnosis (CAD) has already been widely used in medical image processing. We recently make another trial to implement convolutional neural network (CNN) on the classification of pulmonary nodules of thoracic CT images. The biggest challenge in medical image classification with the help of CNN is the difficulty of acquiring enough samples, and overfitting is a common problem when there are not enough images for training. Transfer learning has been verified as reasonable in dealing with such problems with an acceptable loss value. We use the classic LeNet-5 model to classify pulmonary nodules of thoracic CT images, including benign and malignant pulmonary nodules, and different malignancies of the malignant nodules. The CT images are obtained from Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) where both pulmonary nodule scanning and nodule annotations are available. These images are labeled and stored in a medical images knowledge base (KB), which is designed and implemented in our previous work. We implement the 10-folder cross validation (CV) to testify the robustness of the classification model we trained. The result demonstrates that the transfer learning of the LeNet-5 is good for classifying pulmonary nodules of thoracic CT images, and the average values of Top-1 accuracy are 97.041% and 96.685% respectively. We believe that our work is beneficial and has potential for practical diagnosis of lung nodules.

Randomized Clinical Trial Evaluating Complications and Complication-Related Removal of Arm-Situated Power-Injectable and Non-Power-Injectable Totally Implanted Venous Access Devices among Cancer Patients.

PURPOSE: To evaluate the hypothesis that power-injectable (PI) totally implanted venous access devices (TIVADs) situated in the arm are associated with more frequent complications and complication-related removal than non-power-injectable (NPI) arm TIVADs among adult cancer patients. MATERIALS AND METHODS: In this single-center trial, 211 adult chemotherapy patients were randomized to receive either a PI or a NPI arm TIVAD. Follow-up involved a standardized telephone interview 1 week after insertion, followed by a chest X-ray, arm X-ray, and Doppler ultrasound at 3 months and 12 months. Online complication reporting was also provided by patients and care providers for a minimum of 1 year. The primary end point was removal for port-related complications; the secondary end point was the occurrence of any port-related complication. RESULTS: Forty-two complications occurred (19.9% of patients), precipitating the removal of 6 PI ports and 7 standard ports. Time-to-removal did not differ between TIVAD types (hazard ratio 0.75, 95% confidence interval [CI] 0.25-2.24; P = .61). Complications were related to thrombosis, infection, or mechanical issues, with no statistical difference between groups for overall occurrence (23.1% vs 17.0%, odds ratio 1.47, 95% CI 0.74-2.92; P = .27); however, by type of complication, thrombosis occurred more frequently among PI TIVAD patients (15.2% vs 6.1%, odds ratio 2.79, 95% CI 1.04-7.44; P = .03). CONCLUSIONS: There was no difference in port-related complication occurrence or complication-related removal when using the arm PI port compared with the NPI port among cancer patients.

Motion-compensated data decomposition algorithm to accelerate dynamic cardiac MRI.

OBJECTIVES: In dynamic cardiac magnetic resonance imaging (MRI), the spatiotemporal resolution is often limited by low imaging speed. Compressed sensing (CS) theory can be applied to improve imaging speed and spatiotemporal resolution. The combination of compressed sensing and low-rank matrix completion represents an attractive means to further increase imaging speed. By extending prior work, a Motion-Compensated Data Decomposition (MCDD) algorithm is proposed to improve the performance of CS for accelerated dynamic cardiac MRI. MATERIALS AND METHODS: The process of MCDD can be described as follows: first, we decompose the dynamic images into a low-rank (L) and a sparse component (S). The L component includes periodic motion in the background, since it is highly correlated among frames, and the S component corresponds to respiratory motion. A motion-estimation/motion-compensation (ME-MC) algorithm is then applied to the low-rank component to reconstruct a cardiac motion compensated dynamic cardiac MRI. RESULTS: With validations on the numerical phantom and in vivo cardiac MRI data, we demonstrate the utility of the proposed scheme in significantly improving compressed sensing reconstructions by minimizing motion artifacts. The proposed method achieves higher PSNR and lower MSE and HFEN for medium to high acceleration factors. CONCLUSION: The proposed method is observed to yield reconstructions with minimal spatiotemporal blurring and motion artifacts in comparison to the existing state-of-the-art methods.

A Crossover Comparison of Standard and Telerobotic Approaches to Prenatal Sonography.

OBJECTIVES: To determine the feasibility of a telerobotic approach to remotely perform prenatal sonographic examinations. METHODS: Thirty participants were prospectively recruited. Participants underwent a limited examination (assessing biometry, placental location, and amniotic fluid; n = 20) or a detailed examination (biometry, placental location, amniotic fluid, and fetal anatomic survey; n = 10) performed with a conventional ultrasound system. This examination was followed by an equivalent examination performed with a telerobotic ultrasound system, which enabled sonographers to remotely control all ultrasound settings and fine movements of the ultrasound transducer from a distance. Telerobotic images were read independently from conventional images. RESULTS: The mean gestational age ± SD of the 30 participants was 22.9 ± 5.3 weeks. Paired-sample t tests showed no statistically significant difference between conventional and telerobotic measurements of fetal head circumference, biparietal diameter, or single deepest vertical pocket of amniotic fluid; however, a small but statistically significant difference was observed in measurements of abdominal circumference and femur length (P < .05). Intraclass correlations showed excellent agreement (>0.90) between telerobotic and conventional measurements of all 4 biometric parameters. Of 21 fetal structures included in the anatomic survey, 80% of the structures attempted across all patients were sufficiently visualized by the telerobotic system (range, 57%-100% per patient). Ninety-seven percent of patients strongly or somewhat agreed that they would be willing to have another telerobotic examination in the future. CONCLUSIONS: A telerobotic approach is feasible for remotely performing prenatal sonographic examinations. Telerobotic sonography (robotic telesonography) may allow for the development of satellite ultrasound clinics in rural, remote, or low-volume communities, thereby increasing access to prenatal imaging in underserved communities.

Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network.

Low-dose computed tomography (LDCT) has offered tremendous benefits in radiation-restricted applications, but the quantum noise as resulted by the insufficient number of photons could potentially harm the diagnostic performance. Current image-based denoising methods tend to produce a blur effect on the final reconstructed results especially in high noise levels. In this paper, a deep learning-based approach was proposed to mitigate this problem. An adversarially trained network and a sharpness detection network were trained to guide the training process. Experiments on both simulated and real dataset show that the results of the proposed method have very small resolution loss and achieves better performance relative to state-of-the-art methods both quantitatively and visually.