Hasil untuk "Diseases of the genitourinary system. Urology"

Muhammad Hammad Maqsood, Mubashir Sajid, Khubaib Ahmed et al.

This research paper outlines the development and implementation of a novel Clinical Decision Support System (CDSS) that integrates AI predictive modeling with medical knowledge bases. It utilizes the quantifiable information elements in lab results for inferring likely diagnoses a patient might have. Subsequently, suggesting investigations to confirm the likely diagnoses -- an assistive tool for physicians. The system fuses knowledge contained in a rule-base expert system with inferences of data driven predictors based on the features in labs. The data for 593,055 patients was collected from 547 primary care centers across the US to model our decision support system and derive Real-Word Evidence (RWE) to make it relevant for a large demographic of patients. Our Rule-Base comprises clinically validated rules, modeling 59 health conditions that can directly confirm one or more of diseases and assign ICD-10 codes to them. The Likely Diagnosis system uses multi-class classification, covering 37 ICD-10 codes, which are grouped together into 11 categories based on the labs that physicians prescribe to confirm the diagnosis. This research offers a novel system that assists a physician by utilizing medical profile of a patient and routine lab investigations to predict a group of likely diseases and then confirm them, coupled with providing explanations for inferences, thereby assisting physicians to reduce misdiagnosis of patients in clinical decision-making.

Volodymyr Sydorskyi, Igor Krashenyi, Oleksii Yakubenko

Melanoma detection is vital for early diagnosis and effective treatment. While deep learning models on dermoscopic images have shown promise, they require specialized equipment, limiting their use in broader clinical settings. This study introduces a multi-modal melanoma detection system using conventional photo images, making it more accessible and versatile. Our system integrates image data with tabular metadata, such as patient demographics and lesion characteristics, to improve detection accuracy. It employs a multi-modal neural network combining image and metadata processing and supports a two-step model for cases with or without metadata. A three-stage pipeline further refines predictions by boosting algorithms and enhancing performance. To address the challenges of a highly imbalanced dataset, specific techniques were implemented to ensure robust training. An ablation study evaluated recent vision architectures, boosting algorithms, and loss functions, achieving a peak Partial ROC AUC of 0.18068 (0.2 maximum) and top-15 retrieval sensitivity of 0.78371. Results demonstrate that integrating photo images with metadata in a structured, multi-stage pipeline yields significant performance improvements. This system advances melanoma detection by providing a scalable, equipment-independent solution suitable for diverse healthcare environments, bridging the gap between specialized and general clinical practices.

Daniel Alazard, Francesco Sanfedino, Ervan Kassarian

This paper presents causal block-diagram models to represent the equations of motion of multi-body systems in a very compact and simple closed form. Both the forward dynamics (from the forces and torques imposed at the various degrees-of-freedom to the motions of these degrees-of-freedom) or the inverse dynamics (from the motions imposed at the degrees-of-freedom to the resulting forces and torques) can be considered and described by a block diagram model. This work extends the Two-Input Two-Output Port (TITOP) theory by including all non-linear terms and uniform or gravitational acceleration fields. Connection among different blocks is possible through the definition of the motion vector. The model of a system composed of a floating base, rigid bodies, revolute and prismatic joints, working under gravity is developed to illustrate the methodology. The proposed model is validated by simulation and cross-checking with a model built using an alternative modeling tool on a scenario where the nonlinear terms are determining.

Yuri Gardinazzi, Roger Gonzaléz March, Suprabhath Kalahasti et al.

Comorbidity networks, which capture disease-disease co-occurrence usually based on electronic health records, reveal structured patterns in how diseases cluster and progress across individuals. However, how these networks evolve across different age groups and how this evolution relates to properties like disease prevalence and mortality remains understudied. To address these issues, we used publicly available comorbidity networks extracted from a comprehensive dataset of 45 million Austrian hospital stays from 1997 to 2014, covering 8.9 million patients. These networks grow and become denser with age. We identified groups of diseases that exhibit similar patterns of structural centrality throughout the lifespan, revealing three dominant age-related components with peaks in early childhood, midlife, and late life. To uncover the drivers of this structural change, we examined the relationship between prevalence and degree. This allowed us to identify conditions that were disproportionately connected to other diseases. Using betweenness centrality in combination with mortality data, we further identified high-mortality bridging diseases. Several diseases show high connectivity relative to their prevalence, such as iron deficiency anemia (D50) in children, nicotine dependence (F17), and lipoprotein metabolism disorders (E78) in adults. We also highlight structurally central diseases with high mortality that emerge at different life stages, including cancers (C group), liver cirrhosis (K74), subarachnoid hemorrhage (I60), and chronic kidney disease (N18). These findings underscore the importance of targeting age-specific, network-central conditions with high mortality for prevention and integrated care.

Siqi Ouyang, Xi Xu, Lei Li

This paper presents CMU's submission to the IWSLT 2025 Simultaneous Speech Translation (SST) task for translating unsegmented English speech into Chinese and German text in a streaming manner. Our end-to-end speech-to-text system integrates a chunkwise causal Wav2Vec 2.0 speech encoder, an adapter, and the Qwen2.5-7B-Instruct as the decoder. We use a two-stage simultaneous training procedure on robust speech segments curated from LibriSpeech, CommonVoice, and VoxPopuli datasets, utilizing standard cross-entropy loss. Our model supports adjustable latency through a configurable latency multiplier. Experimental results demonstrate that our system achieves 44.3 BLEU for English-to-Chinese and 25.1 BLEU for English-to-German translations on the ACL60/60 development set, with computation-aware latencies of 2.7 seconds and 2.3 seconds, and theoretical latencies of 2.2 and 1.7 seconds, respectively.

Yathwin Kanagavel, D. Rajiv Raj, Pavitra Vittalraj et al.

Para-testicular fibrous pseudotumors (PFP) are rare benign reactive lesions comprising of 6 % of para-testicular masses. Often misdiagnosed as malignant due to clinical and radiological overlap, they are frequently treated with aggressive surgery. We report a case of a 70-year-old male with a left inguinal swelling diagnosed post-orchidectomy as PFP. Histopathology revealed collagen-rich fibrotic tissue with lymphoplasmacytic infiltrates and IgG4-positive plasma cells. While PFP treatment requires surgical resection, testicle-sparing procedures with intraoperative frozen section assessment may prevent unnecessary orchidectomy. Further studies are needed to establish diagnostic protocols and explore the association between PFP and IgG4-related diseases.

J. Pépin, S. Bailly, P. Rinder et al.

The nationwide claims data lake for sleep apnoea (ALASKA)—real-life data for understanding and increasing obstructive sleep apnea (OSA) quality of care study—investigated long-term continuous positive airway pressure (CPAP) termination rates, focusing on the contribution of comorbidities. The French national health insurance reimbursement system data for new CPAP users aged ≥18 years were analyzed. Innovative algorithms were used to determine the presence of specific comorbidities (hypertension, diabetes and chronic obstructive pulmonary disease (COPD)). Therapy termination was defined as cessation of CPAP reimbursements. A total of 480,000 patients were included (mean age 59.3 ± 13.6 years, 65.4% male). An amount of 50.7, 24.4 and 4.3% of patients, respectively, had hypertension, diabetes and COPD. Overall CPAP termination rates after 1, 2 and 3 years were 23.1, 37.1 and 47.7%, respectively. On multivariable analysis, age categories, female sex (1.09 (1.08–1.10) and COPD (1.12 (1.10–1.13)) and diabetes (1.18 (1.16–1.19)) were significantly associated with higher CPAP termination risk; patients with hypertension were more likely to continue using CPAP (hazard ratio 0.96 (95% confidence interval 0.95–0.97)). Therapy termination rates were highest in younger or older patients with ≥1 comorbidity. Comorbidities have an important influence on long-term CPAP continuation in patients with OSA.

Manqin Chen, Xinbin Chen, Huaxiang Ling et al.

BackgroundFibrinogen plays a pivotal role in the inflammatory cascade and is intricately linked to the pathogenesis of sepsis. Nevertheless, its significance as a prognostic marker for sepsis-associated acute kidney injury (SA-AKI) remains uncertain. This study aimed to investigate the association between fibrinogen levels and 28-day mortality with sepsis-associated acute kidney injury.MethodThe fibrinogen levels of patients admitted to the intensive care unit of Beth Israel Deaconess Medical Center between 2008 and 2019 were retrospectively assessed, and those diagnosed with SA-AKI were divided into low, middle and high fibrinogen level groups according to tertiles. Multivariate Cox proportional hazards model was used to assess the 28-day mortality risk of the SA-AKI patients.ResultsA total of 3,479 patients with SA-AKI were included in the study. Fibrinogen demonstrated an independent association with 28-day mortality, yielding a hazard ratio (HR) of 0.961 (95% confidence interval [CI]: 0.923-0.999, P = 0.0471). Notably, a non-linear relationship between fibrinogen levels and 28-day mortality was observed, with the threshold observed at approximately 1.6 g/l. The effect sizes and corresponding CIs below and above this threshold were 0.509 (0.367, 0.707) and 1.011 (0.961, 1.064), respectively. Specifically, the risk of mortality among SA-AKI patients decreased by 49.1% for every 1 g/l increment in fibrinogen, provided that fibrinogen levels were less than 1.6 g/l.ConclusionIn patients with SA-AKI, a non-linear relationship was identified between fibrinogen levels and 28-day mortality. Particularly, when their fibrinogen levels were less than 1.6 g/l, a concomitant decrease in 28-day mortality was observed as fibrinogen levels increased.

Scarleth Pinto Plaza, Eliodoro Rodrigo Galarza Fuentes, Laura Silvia Baez Tolay et al.

Jonatan C. S. de Carvalho, P. V. da Silva-Neto, D. M. Toro et al.

COVID-19 is associated with a dysregulated immune response. Currently, several medicines are licensed for the treatment of this disease. Due to their significant role in inhibiting pro-inflammatory cytokines and lipid mediators, glucocorticoids (GCs) have attracted a great deal of attention. Similarly, the endocannabinoid (eCB) system regulates various physiological processes including the immunological response. Additionally, during inflammatory and thrombotic processes, phospholipids from cell membranes are cleaved to produce platelet-activating factor (PAF), another lipid mediator. Nonetheless, the effect of GCs on this lipid pathway during COVID-19 therapy is still unknown. This is a cross-sectional study involving COVID-19 patients (n = 200) and healthy controls (n = 35). Target tandem mass spectrometry of plasma lipid mediators demonstrated that COVID-19 severity affected eCBs and PAF synthesis. This increased synthesis of eCB was adversely linked with systemic inflammatory markers IL-6 and sTREM-1 levels and neutrophil counts. The use of GCs altered these lipid pathways by reducing PAF and increasing 2-AG production. Corroborating this, transcriptome analysis of GC-treated patients blood leukocytes showed differential modulation of monoacylglycerol lipase and phospholipase A2 gene expression. Altogether, these findings offer a breakthrough in our understanding of COVID-19 pathophysiology, indicating that GCs may promote additional protective pharmacological effects by influencing the eCB and PAF pathways involved in the disease course.

A. Nogueira, Joaquim Barreto, F. Moura et al.

Background The escalating prevalence of type 2 diabetes (T2DM) poses an unparalleled economic catastrophe to developing countries. Cardiovascular diseases remain the primary source of costs among individuals with T2DM, incurring expenses for medications, hospitalizations, and surgical interventions. Compelling evidence suggests that the risk of cardiovascular outcomes can be reduced by three classes of glucose-lowering therapies (GLT), including SGLT2i, GLP-1A, and pioglitazone. However, an evidence-based and cost-effective protocol is still unavailable for many countries. The objective of the current study is to compare the effectiveness and cost-effectiveness of GLT in individuals with T2DM in Brazil. Methods We employed Bayesian Networks to calculate the incremental cost-effectiveness ratios (ICER), expressed in international dollars (Int$) per disease-adjusted life years [DALYs] averted. To determine the effectiveness of GLT, we conducted a systematic review with network meta-analysis (NMA) to provide insights for our model. Additionally, we obtained cardiovascular outcome incidence data from two real-world cohorts comprising 851 and 1337 patients in primary and secondary prevention, respectively. Our cost analysis took into account the perspective of the Brazilian public health system, and all values were converted to Int$. Results In the NMA, SGLT2i [HR: 0.81 (95% CI 0.69–0.96)], GLP-1A [HR: 0.79 (95% CI 0.67–0.94)], and pioglitazone [HR: 0.73 (95% CI 0.59–0.91)] demonstrated reduced relative risks of non-fatal cardiovascular events. In the context of primary prevention, pioglitazone yielded 0.2339 DALYs averted, with an ICER of Int$7,082 (95% CI 4,521–10,770) per DALY averted when compared to standard care. SGLT2i and GLP-1A also increased effectiveness, resulting in 0.261 and 0.259 DALYs averted, respectively, but with higher ICERs of Int$12,061 (95% CI: 7,227–18,121) and Int$29,119 (95% CI: 23,811–35,367) per DALY averted. In the secondary prevention scenario, all three classes of treatments were deemed cost-effective at a maximum willingness-to-pay threshold of Int$26,700. Notably, pioglitazone consistently exhibited the highest probability of being cost-effective in both scenarios. Conclusions In Brazil, pioglitazone presented a higher probability of being cost-effective both in primary and secondary prevention, followed by SGLT2i and GLP-1A. Our findings support the use of cost-effectiveness models to build optimized and hierarchical therapeutic strategy in the management of T2DM. Trial registration CRD42020194415.

A. Bahadori, B. Kuhlmann, D. Debray et al.

Background: Congenital portosystemic shunts (CPSS) are rare vascular anomalies resulting in communications between the portal venous system and the systemic venous circulation, affecting an estimated 30,000 to 50,000 live births. CPSS can present at any age as a multi-system disease of variable severity mimicking both common and rare pediatric conditions. Case presentations: Case A: A vascular malformation was identified in the liver of a 10-year-old girl with tall stature, advanced somatic maturation, insulin resistance with hyperinsulinemia, hyperandrogenemia and transient hematuria. Work-up also suggested elevated pulmonary pressures. Case B: A young girl with trisomy 8 mosaicism with a history of neonatal hypoglycemia, transient neonatal cholestasis and tall stature presented newly increased aminotransferase levels at 6 years of age. Case C: A 3-year-old boy with speech delay, tall stature and abdominal pain underwent abdominal ultrasound (US) showing multiple liver nodules, diagnosed as liver hemangiomas by hepatic magnetic resonance imaging (MRI). Management and outcome: After identification of a venous malformation on liver Doppler US, all three patients were referred to a specialized liver center for further work-up within 12 to 18 months from diagnosis. Angio-computed tomography (CT) scan confirmed the presence of either an intrahepatic or extrahepatic CPSS with multiples liver nodules. All three had a hyperintense signal in the globus pallidus on T1 weighted cerebral MRI. Right heart catheterization confirmed pulmonary hypertension in cases A and C. Shunts were closed either using an endovascular or surgical approach. Liver nodules were either surgically removed if there was a risk of malignant degeneration or closely monitored by serial imaging when benign. Conclusion: These cases illustrate most of the common chief complaints and manifestations of CPSS. Liver Doppler US is the key to diagnosis. Considering portosystemic shunts in the diagnostic work-up of a patient with unexplained endocrine, liver, gastro-intestinal, cardiovascular, hematological, renal or neurocognitive disorder is important as prompt referral to a specialized center may significantly impact patient outcome.

T. V. Afanasieva, A. P. Kuzlyakin, A. V. Komolov

Cardiovascular diseases are widespread among patients with chronic noncommunicable diseases and are one of the leading causes of death, including in the working age. The article presents the relevance of the development and application of patient-oriented systems, in which machine learning (ML) is a promising technology that allows predicting cardiovascular diseases. Automated machine learning (AutoML) makes it possible to simplify and speed up the process of developing AI/ML applications, which is key in the development of patient-oriented systems by application users, in particular medical specialists. The authors propose a framework for the application of automatic machine learning and three scenarios that allowed for data combining five data sets of cardiovascular disease indicators from the UCI Machine Learning Repository to investigate the effectiveness in detecting this class of diseases. The study investigated one AutoML model that used and optimized the hyperparameters of thirteen basic ML models (KNeighborsUnif, KNeighborsDist, LightGBMXT, LightGBM, RandomForestGini, RandomForestEntr, CatBoost, ExtraTreesGini, ExtraTreesEntr, NeuralNetFastA, XGBoost, NeuralNetTorch, LightGBMLarge) and included the most accurate models in the weighted ensemble. The results of the study showed that the structure of the AutoML model for detecting cardiovascular diseases depends not only on the efficiency and accuracy of the basic models used, but also on the scenarios for preprocessing the initial data, in particular, on the technique of data normalization. The comparative analysis showed that the accuracy of the AutoML model in detecting cardiovascular disease varied in the range from 87.41% to 92.3%, and the maximum accuracy was obtained when normalizing the source data into binary values, and the minimum was obtained when using the built-in AutoML technique.

Achyut Tiwari, Aryan Chugh, Aman Sharma

Heart disease is the major cause of non-communicable and silent death worldwide. Heart diseases or cardiovascular diseases are classified into four types: coronary heart disease, heart failure, congenital heart disease, and cardiomyopathy. It is vital to diagnose heart disease early and accurately in order to avoid further injury and save patients' lives. As a result, we need a system that can predict cardiovascular disease before it becomes a critical situation. Machine learning has piqued the interest of researchers in the field of medical sciences. For heart disease prediction, researchers implement a variety of machine learning methods and approaches. In this work, to the best of our knowledge, we have used the dataset from IEEE Data Port which is one of the online available largest datasets for cardiovascular diseases individuals. The dataset isa combination of Hungarian, Cleveland, Long Beach VA, Switzerland & Statlog datasets with important features such as Maximum Heart Rate Achieved, Serum Cholesterol, Chest Pain Type, Fasting blood sugar, and so on. To assess the efficacy and strength of the developed model, several performance measures are used, such as ROC, AUC curve, specificity, F1-score, sensitivity, MCC, and accuracy. In this study, we have proposed a framework with a stacked ensemble classifier using several machine learning algorithms including ExtraTrees Classifier, Random Forest, XGBoost, and so on. Our proposed framework attained an accuracy of 92.34% which is higher than the existing literature.

Yagya Raj Pandeya, Samin Karki, Ishan Dangol et al.

We have developed a comprehensive computer system to assist farmers who practice traditional farming methods and have limited access to agricultural experts for addressing crop diseases. Our system utilizes artificial intelligence (AI) to identify and provide remedies for vegetable diseases. To ensure ease of use, we have created a mobile application that offers a user-friendly interface, allowing farmers to inquire about vegetable diseases and receive suitable solutions in their local language. The developed system can be utilized by any farmer with a basic understanding of a smartphone. Specifically, we have designed an AI-enabled mobile application for identifying and suggesting remedies for vegetable diseases, focusing on tomato diseases to benefit the local farming community in Nepal. Our system employs state-of-the-art object detection methodology, namely You Only Look Once (YOLO), to detect tomato diseases. The detected information is then relayed to the mobile application, which provides remedy suggestions guided by domain experts. In order to train our system effectively, we curated a dataset consisting of ten classes of tomato diseases. We utilized various data augmentation methods to address overfitting and trained a YOLOv5 object detector. The proposed method achieved a mean average precision of 0.76 and offers an efficient mobile interface for interacting with the AI system. While our system is currently in the development phase, we are actively working towards enhancing its robustness and real-time usability by accumulating more training samples.

Maria Damps, E. Byrska-Maciejasz, M. Kowalska et al.

Background: Children suffering from COVID-19 constitute about 10% of the entire population infected with the virus. In most of them, we observe asymptomatic or mild courses; however, about 1% of affected children require a stay in a paediatric intensive care unit (PICU) due to the course of the disease becoming severely life-threatening. The risk of respiratory failure, as with adults, is associated with the coexistence of concomitant diseases. The aim of our study was to analyse patients admitted to PICUs due to the severe course of their SARS-CoV-2 infection. We studied epidemiological and laboratory parameters, as well as the endpoint (survival or death). Methods: A retrospective multi-centre study, the analysis covered all children with a confirmed diagnosis of SARS-CoV-2 virus infection who were admitted to PICUs in the period from November 2020 to August 2021. We studied epidemiological and laboratory parameters, as well as the endpoint (survival or death). Results: The study analysed 45 patients (0.075% of all children hospitalised in Poland due to COVID-19 at that time). Mortality calculated in the entire study group was 40% (n = 18). Statistically significant differences between the compared groups (survived and died) concerned the parameters of the respiratory system. Lung Injury Score and the Paediatric Sequential Organ Failure Assessment were used. A significant correlation between disease severity and the patient’s prognosis was shown by the liver function parameter AST (p = 0.028). During the analysis of patients requiring mechanical ventilation and assuming survival as the primary outcome, a significantly higher oxygen index on the first day of hospitalisation, lower pSOFA scores and lower AST levels (p: 0.007; 0.043; 0.020; 0.005; 0.039, respectively) were found. Conclusions: As with adults, children with comorbidities are most frequently at risk of severe SARS-CoV-2 infection. Increasing symptoms of respiratory failure, the need for mechanical ventilation and persistently high values of aspartate aminotransferase are indicators of poor prognosis.

countries, Initiatives, in Europe et al.

Abstract Background Global food insecurity (FI) prevalence in 2020 was 30.4%. In Israel, in 2021, it was 16.2%. FI is associated with a high prevalence of chronic diseases, more hospital admissions and visits, and a shorter lifespan. Screening for FI in the health setting is less common, despite recommendations. Methods Between July 2022 - February 2023, a mixed-methods study distributed an online survey and a request for qualitative interviews among a convenience sample of registered dietitians (RDs). The survey obtained sociodemographic characteristics and information on work experience, knowledge, attitudes, and barriers toward screening for FI. Sixty-one questions were modified from existing questionnaires. An expert committee reviewed the questions. Later, the questionnaire was pilot-tested by ten RDs and amended according to their comments on the clarity. Results Overall, 140 RDs were surveyed, and 7 RDs were interviewed. 96.7% of the participants were female, with a mean of 13.36±9.9 years of experience. 97% of RDs didn't screen for FI. 65.5% didn't know the percentage of households living with FI in Israel, and 72.1% of RDs didn't know where to refer food-insecure patients for additional assistance. Positive attitudes toward screening and treating FI were documented. About 80% of RDs indicated that FI is relevant to their patients and are willing to screen for FI. Religious and traditional RDs had 10.08 times and 4.46 times, respectively, greater odds of having positive attitudes toward screening and treating food-insecure patients. The main barriers identified were a lack of time, knowledge of screening tools, and missing information on appropriate treatment and referral. Conclusions Further education and training in screening FI should be implemented among RDs. System barriers should be addressed to allow RDs routine screening for FI. Additional research is needed to explore healthcare providers’ attitudes and barriers toward screening and treating FI. Key messages • Most registered dietitians had a low level of knowledge and did not screen routinely for food insecurity. • The majority were positive towards screening, highlighting system and training barriers.

Omar Alwattar‐Ceballos, Fernando Moro‐Bolado, Laura Martínez‐Montalvo et al.

Abstract Acquired perforating dermatosis (APD) is a disorder of transepidermal elimination of keratin, collagen or elastic fibers. APD is mainly associated with diabetes mellitus and chronic kidney disease. We present the case of a 66‐year‐old male, poorly controlled diabetic with peripheral arterial disease, obesity, arterial hypertension, Cushing's syndrome and chronic kidney disease requiring hemodialysis who develops an acquired perforating dermatosis. Due to the patient's comorbidities, treatment with corticosteroids, cyclosporine and phototherapy is not possible. He does not respond to gabapentinoids or allopurinol. The outbreaks of skin lesions and pruritus were accentuated in situations of renal function deterioration; however, hemodialysis did not improve her dermatosis, causing a great deterioration of her quality of life. It was decided to start treatment with dupilumab with the dosage used in atopic dermatitis, achieving a complete response of both pruritus and skin lesions. With this case, we want to show how dupilumab can be an effective tool to treat acquired perforating dermatosis.

V. Panebianco, B. Turkbey

M. A. Rodriguez, H. AlMarzouqi, P. Liatsis

Early detection of retinal diseases is one of the most important means of preventing partial or permanent blindness in patients. In this research, a novel multi-label classification system is proposed for the detection of multiple retinal diseases, using fundus images collected from a variety of sources. First, a new multi-label retinal disease dataset, the MuReD dataset, is constructed, using a number of publicly available datasets for fundus disease classification. Next, a sequence of post-processing steps is applied to ensure the quality of the image data and the range of diseases, present in the dataset. For the first time in fundus multi-label disease classification, a transformer-based model optimized through extensive experimentation is used for image analysis and decision making. Numerous experiments are performed to optimize the configuration of the proposed system. It is shown that the approach performs better than state-of-the-art works on the same task by 7.9% and 8.1% in terms of AUC score for disease detection and disease classification, respectively. The obtained results further support the potential applications of transformer-based architectures in the medical imaging field.