Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data-A Systematic Review and Assessment.

INTRODUCTION: Accurate risk prediction can facilitate screening and early detection of pancreatic cancer (PC). We conducted a systematic review to critically evaluate effectiveness of machine learning (ML) and artificial intelligence (AI) techniques applied to electronic health records (EHR) for PC risk prediction. METHODS: Ovid MEDLINE(R), Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Scopus, and Web of Science were searched for articles that utilized ML/AI techniques to predict PC, published between January 1, 2012, and February 1, 2024. Study selection and data extraction were conducted by 2 independent reviewers. Critical appraisal and data extraction were performed using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Risk of bias and applicability were examined using prediction model risk of bias assessment tool. RESULTS: Thirty studies including 169,149 PC cases were identified. Logistic regression was the most frequent modeling method. Twenty studies utilized a curated set of known PC risk predictors or those identified by clinical experts. ML model discrimination performance (C-index) ranged from 0.57 to 1.0. Missing data were underreported, and most studies did not implement explainable-AI techniques or report exclusion time intervals. DISCUSSION: AI/ML models for PC risk prediction using known risk factors perform reasonably well and may have near-term applications in identifying cohorts for targeted PC screening if validated in real-world data sets. The combined use of structured and unstructured EHR data using emerging AI models while incorporating explainable-AI techniques has the potential to identify novel PC risk factors, and this approach merits further study.

Practicing Digital Gastroenterology through Phonoenterography Leveraging Artificial Intelligence: Future Perspectives Using Microwave Systems.

Production of bowel sounds, established in the 1900s, has limited application in existing patient-care regimes and diagnostic modalities. We review the physiology of bowel sound production, the developments in recording technologies and the clinical application in various scenarios, to understand the potential of a bowel sound recording and analysis device-the phonoenterogram in future gastroenterological practice. Bowel sound production depends on but is not entirely limited to the type of food consumed, amount of air ingested and the type of intestinal contractions. Recording technologies for extraction and analysis of these include the wavelet-based filtering, autoregressive moving average model, multivariate empirical mode decompression, radial basis function network, two-dimensional positional mapping, neural network model and acoustic biosensor technique. Prior studies evaluate the application of bowel sounds in conditions such as intestinal obstruction, acute appendicitis, large bowel disorders such as inflammatory bowel disease and bowel polyps, ascites, post-operative ileus, sepsis, irritable bowel syndrome, diabetes mellitus, neurodegenerative disorders such as Parkinson's disease and neonatal conditions such as hypertrophic pyloric stenosis. Recording and analysis of bowel sounds using artificial intelligence is crucial for creating an accessible, inexpensive and safe device with a broad range of clinical applications. Microwave-based digital phonoenterography has huge potential for impacting GI practice and patient care.

Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence: Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging.

Respiratory disorders, being one of the leading causes of disability worldwide, account for constant evolution in management technologies, resulting in the incorporation of artificial intelligence (AI) in the recording and analysis of lung sounds to aid diagnosis in clinical pulmonology practice. Although lung sound auscultation is a common clinical practice, its use in diagnosis is limited due to its high variability and subjectivity. We review the origin of lung sounds, various auscultation and processing methods over the years and their clinical applications to understand the potential for a lung sound auscultation and analysis device. Respiratory sounds result from the intra-pulmonary collision of molecules contained in the air, leading to turbulent flow and subsequent sound production. These sounds have been recorded via an electronic stethoscope and analyzed using back-propagation neural networks, wavelet transform models, Gaussian mixture models and recently with machine learning and deep learning models with possible use in asthma, COVID-19, asbestosis and interstitial lung disease. The purpose of this review was to summarize lung sound physiology, recording technologies and diagnostics methods using AI for digital pulmonology practice. Future research and development in recording and analyzing respiratory sounds in real time could revolutionize clinical practice for both the patients and the healthcare personnel.

Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives.

The measurement of physiologic pressure helps diagnose and prevent associated health complications. From typical conventional methods to more complicated modalities, such as the estimation of intracranial pressures, numerous invasive and noninvasive tools that provide us with insight into daily physiology and aid in understanding pathology are within our grasp. Currently, our standards for estimating vital pressures, including continuous BP measurements, pulmonary capillary wedge pressures, and hepatic portal gradients, involve the use of invasive modalities. As an emerging field in medical technology, artificial intelligence (AI) has been incorporated into analyzing and predicting patterns of physiologic pressures. AI has been used to construct models that have clinical applicability both in hospital settings and at-home settings for ease of use for patients. Studies applying AI to each of these compartmental pressures were searched and shortlisted for thorough assessment and review. There are several AI-based innovations in noninvasive blood pressure estimation based on imaging, auscultation, oscillometry and wearable technology employing biosignals. The purpose of this review is to provide an in-depth assessment of the involved physiologies, prevailing methodologies and emerging technologies incorporating AI in clinical practice for each type of compartmental pressure measurement. We also bring to the forefront AI-based noninvasive estimation techniques for physiologic pressure based on microwave systems that have promising potential for clinical practice.

Machine Learning-Based Classification of Abnormal Liver Tissues Using Relative Permittivity.

The search for non-invasive, fast, and low-cost diagnostic tools has gained significant traction among many researchers worldwide. Dielectric properties calculated from microwave signals offer unique insights into biological tissue. Material properties, such as relative permittivity (εr) and conductivity (σ), can vary significantly between healthy and unhealthy tissue types at a given frequency. Understanding this difference in properties is key for identifying the disease state. The frequency-dependent nature of the dielectric measurements results in large datasets, which can be postprocessed using artificial intelligence (AI) methods. In this work, the dielectric properties of liver tissues in three mouse models of liver disease are characterized using dielectric spectroscopy. The measurements are grouped into four categories based on the diets or disease state of the mice, i.e., healthy mice, mice with non-alcoholic steatohepatitis (NASH) induced by choline-deficient high-fat diet, mice with NASH induced by western diet, and mice with liver fibrosis. Multi-class classification machine learning (ML) models are then explored to differentiate the liver tissue groups based on dielectric measurements. The results show that the support vector machine (SVM) model was able to differentiate the tissue groups with an accuracy up to 90%. This technology pipeline, thus, shows great potential for developing the next generation non-invasive diagnostic tools.

Cardiac MR elastography using reduced-FOV, single-shot, spin-echo EPI.

PURPOSE: To implement a reduced field of view (rFOV) technique for cardiac MR elastography (MRE) and to demonstrate the improvement in image quality of both magnitude images and post-processed MRE stiffness maps compared to the conventional full field of view (full-FOV) acquisition. METHODS: With Institutional Review Board approval, 17 healthy volunteers underwent both full-FOV and rFOV cardiac MRE scans using 140-Hz vibrations. Two cardiac radiologists blindly compared the magnitude images and stiffness maps and graded the images based on several image quality attributes using a 5-point ordinal scale. Fisher's combined probability test was performed to assess the overall evaluation. The octahedral shear strain-based signal-to-noise ratio (OSS-SNR) and median stiffness over the left ventricular myocardium were also compared. RESULTS: One volunteer was excluded because of an inconsistent imaging resolution during the exam. In the remaining 16 volunteers (9 males, 7 females), the rFOV scans outperformed the full-FOV scans in terms of subjective image quality and ghosting artifacts in the magnitude images and stiffness maps, as well as the overall preference. The quantitative measurements showed that rFOV had significantly higher OSS-SNR (median: 1.4 [95% confidence interval (CI): 1.2-1.5] vs. 2.1 [95% CI: 1.8-2.4]), P < 0.05) compared to full-FOV. Although no significant change was found in the median myocardial stiffness between the 2 scans, we observed a decrease in the stiffness variation within the myocardium from 2.1 kPa (95% CI: [1.9, 2.3]) to 1.9 kPa (95% CI: [1.7, 2.0]) for full-FOV and rFOV, respectively (P < 0.05) in a subgroup of 7 subjects with ghosting present in the myocardium. CONCLUSION: This pilot volunteer study demonstrated that rFOV cardiac MRE has the capability to reduce ghosting and to improve image quality in both MRE magnitude images and stiffness maps. Magn Reson Med 80:231-238, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Regional assessment of in vivo myocardial stiffness using 3D magnetic resonance elastography in a porcine model of myocardial infarction.

PURPOSE: The stiffness of a myocardial infarct affects the left ventricular pump function and remodeling. Magnetic resonance elastography (MRE) is a noninvasive imaging technique for measuring soft-tissue stiffness in vivo. The purpose of this study was to investigate the feasibility of assessing in vivo regional myocardial stiffness with high-frequency 3D cardiac MRE in a porcine model of myocardial infarction, and compare the results with ex vivo uniaxial tensile testing. METHODS: Myocardial infarct was induced in a porcine model by embolizing the left circumflex artery. Fourteen days postinfarction, MRE imaging was performed in diastole using an echocardiogram-gated spin-echo echo-planar-imaging sequence with 140-Hz vibrations and 3D MRE processing. The MRE stiffness and tensile modulus from uniaxial testing were compared between the remote and infarcted myocardium. RESULTS: Myocardial infarcts showed increased in vivo MRE stiffness compared with remote myocardium (4.6 ± 0.7 kPa versus 3.0 ± 0.6 kPa, P = 0.02) within the same pig. Ex vivo uniaxial mechanical testing confirmed the in vivo MRE results, showing that myocardial infarcts were stiffer than remote myocardium (650 ± 80 kPa versus 110 ± 20 kPa, P = 0.01). CONCLUSIONS: These results demonstrate the feasibility of assessing in vivo regional myocardial stiffness with high-frequency 3D cardiac MRE. Magn Reson Med 79:361-369, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Quantitative 3D magnetic resonance elastography: Comparison with dynamic mechanical analysis.

PURPOSE: Magnetic resonance elastography (MRE) is a rapidly growing noninvasive imaging technique for measuring tissue mechanical properties in vivo. Previous studies have compared two-dimensional MRE measurements with material properties from dynamic mechanical analysis (DMA) devices that were limited in frequency range. Advanced DMA technology now allows broad frequency range testing, and three-dimensional (3D) MRE is increasingly common. The purpose of this study was to compare 3D MRE stiffness measurements with those of DMA over a wide range of frequencies and shear stiffnesses. METHODS: 3D MRE and DMA were performed on eight different polyvinyl chloride samples over 20-205 Hz with stiffness between 3 and 23 kPa. Driving frequencies were chosen to create 1.1, 2.2, 3.3, 4.4, 5.5, and 6.6 effective wavelengths across the diameter of the cylindrical phantoms. Wave images were analyzed using direct inversion and local frequency estimation algorithm with the curl operator and compared with DMA measurements at each corresponding frequency. Samples with sufficient spatial resolution and with an octahedral shear strain signal-to-noise ratio > 3 were compared. RESULTS: Consistency between the two techniques was measured with the intraclass correlation coefficient (ICC) and was excellent with an overall ICC of 0.99. CONCLUSIONS: 3D MRE and DMA showed excellent consistency over a wide range of frequencies and stiffnesses. Magn Reson Med 77:1184-1192, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

In vivo, high-frequency three-dimensional cardiac MR elastography: Feasibility in normal volunteers.

PURPOSE: Noninvasive stiffness imaging techniques (elastography) can image myocardial tissue biomechanics in vivo. For cardiac MR elastography (MRE) techniques, the optimal vibration frequency for in vivo experiments is unknown. Furthermore, the accuracy of cardiac MRE has never been evaluated in a geometrically accurate phantom. Therefore, the purpose of this study was to determine the necessary driving frequency to obtain accurate three-dimensional (3D) cardiac MRE stiffness estimates in a geometrically accurate diastolic cardiac phantom and to determine the optimal vibration frequency that can be introduced in healthy volunteers. METHODS: The 3D cardiac MRE was performed on eight healthy volunteers using 80 Hz, 100 Hz, 140 Hz, 180 Hz, and 220 Hz vibration frequencies. These frequencies were tested in a geometrically accurate diastolic heart phantom and compared with dynamic mechanical analysis (DMA). RESULTS: The 3D Cardiac MRE was shown to be feasible in volunteers at frequencies as high as 180 Hz. MRE and DMA agreed within 5% at frequencies greater than 180 Hz in the cardiac phantom. However, octahedral shear strain signal to noise ratios and myocardial coverage was shown to be highest at a frequency of 140 Hz across all subjects. CONCLUSION: This study motivates future evaluation of high-frequency 3D MRE in patient populations. Magn Reson Med 77:351-360, 2017. © 2016 Wiley Periodicals, Inc.

Cardiac MR elastography for quantitative assessment of elevated myocardial stiffness in cardiac amyloidosis.

PURPOSE: To evaluate if cardiac magnetic resonance elastography (MRE) can measure increased stiffness in patients with cardiac amyloidosis. Myocardial tissue stiffness plays an important role in cardiac function. A noninvasive quantitative imaging technique capable of measuring myocardial stiffness could aid in disease diagnosis, therapy monitoring, and disease prognostic strategies. We recently developed a high-frequency cardiac MRE technique capable of making noninvasive stiffness measurements. MATERIALS AND METHODS: In all, 16 volunteers and 22 patients with cardiac amyloidosis were enrolled in this study after Institutional Review Board approval and obtaining formal written consent. All subjects were imaged head-first in the supine position in a 1.5T closed-bore MR imager. 3D MRE was performed using 5 mm isotropic resolution oblique short-axis slices and a vibration frequency of 140 Hz to obtain global quantitative in vivo left ventricular stiffness measurements. The median stiffness was compared between the two cohorts. An octahedral shear strain signal-to-noise ratio (OSS-SNR) threshold of 1.17 was used to exclude exams with insufficient motion amplitude. RESULTS: Five volunteers and six patients had to be excluded from the study because they fell below the 1.17 OSS-SNR threshold. The myocardial stiffness of cardiac amyloid patients (median: 11.4 kPa, min: 9.2, max: 15.7) was significantly higher (P = 0.0008) than normal controls (median: 8.2 kPa, min: 7.2, max: 11.8). CONCLUSION: This study demonstrates the feasibility of 3D high-frequency cardiac MRE as a contrast-agent-free diagnostic imaging technique for cardiac amyloidosis. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1361-1367.

Understanding Women's Awareness and Access to Preconception Health Care in a Rural Population: A Cross Sectional Study.

Despite evidence of the benefits of preconception health care (PCHC), little is known about awareness and access to PCHC for rural, reproductive-aged women. This study aimed to assess the prevalence of PCHC conversations between rural reproductive-age women and health care providers, PCHC interventions received in the past year, and ascertain predictors of PCHC conversations and interventions. Women (n = 868; 18-45 years) completed a questionnaire including reproductive history, health care services utilization, and interest in PCHC. The prevalence of health care providers' PCHC conversations was 53.9 %, and the mean number of interventions reported was 2.6 ± 2.7 (±SD). Significant predictors of PCHC conversation based on adjusted odds ratios from logistic regression were race (Native American 76 % greater than White), health care provider type (non-physician 63 % greater than physician), visits to a health care provider (3+ times 32 % greater than 1-2 times), and pregnancy planning (considering in next 1-5 years 51 % greater than no plans). Significant predictors of PCHC interventions received in the past 12 months based on adjusted risk ratios from negative binomial regression were race (Native American 22 % greater than White), PCHC conversation with a health care provider (yes 52 % lower than no), reporting PCHC as beneficial (yes 32 % greater than don't know), and visits to a health care provider in the past year (3+ times 90 % greater than 1-2 times). Increasing conversations about PCHC between health care providers and their reproductive-aged patients can improve awareness and increase their likelihood of receiving all of the recommended interventions.

Image-Decomposition-Enhanced Deep Learning for Detection of Rotor Cores in Cardiac Fibrillation.

OBJECTIVE: Rotors, regions of spiral wave reentry in cardiac tissues, are considered as the drivers of atrial fibrillation (AF), the most common arrhythmia. Whereas physics-based approaches have been widely deployed to detect the rotors, in-depth knowledge in cardiac physiology and electrogram interpretation skills are typically needed. The recent leap forward in smart sensing, data acquisition, and Artificial Intelligence (AI) has offered an unprecedented opportunity to transform diagnosis and treatment in cardiac ailment, including AF. This study aims to develop an image-decomposition-enhanced deep learning framework for automatic identification of rotor cores on both simulation and optical mapping data. METHODS: We adopt the Ensemble Empirical Mode Decomposition algorithm (EEMD) to decompose the original image, and the most representative component is then fed into a You-Only-Look-Once (YOLO) object-detection architecture for rotor detection. Simulation data from a bi-domain simulation model and optical mapping acquired from isolated rabbit hearts are used for training and validation. RESULTS: This integrated EEMD-YOLO model achieves high accuracy on both simulation and optical mapping data (precision: 97.2%, 96.8%, recall: 93.8%, 92.2%, and F1 score: 95.5%, 94.4%, respectively). CONCLUSION: The proposed EEMD-YOLO yields comparable accuracy in rotor detection with the gold standard in literature.

Sex Differences in Aging-related Myocardial Stiffening Quantitatively Measured with MR Elastography.

Purpose To investigate the feasibility of using quantitative MR elastography (MRE) to characterize the influence of aging and sex on left ventricular (LV) shear stiffness. Materials and Methods In this prospective study, LV myocardial shear stiffness was measured in 109 healthy volunteers (age range: 18-84 years; mean age, 40 years ± 18 [SD]; 57 women, 52 men) enrolled between November 2018 and September 2019, using a 5-minute MRE acquisition added to a clinical MRI protocol. Linear regression models were used to estimate the association of cardiac MRI and MRE characteristics with age and sex; models were also fit to assess potential age-sex interaction. Results Myocardial shear stiffness significantly increased with age in female (age slope = 0.03 kPa/year ± 0.01, P = .009) but not male (age slope = 0.008 kPa/year ± 0.009, P = .38) volunteers. LV ejection fraction (LVEF) increased significantly with age in female volunteers (0.23% ± 0.08 per year, P = .005). LV end-systolic volume (LVESV) decreased with age in female volunteers (-0.20 mL/m2 ± 0.07, P = .003). MRI parameters, including T1, strain, and LV mass, did not demonstrate this interaction (P > .05). Myocardial shear stiffness was not significantly correlated with LVEF, LV stroke volume, body mass index, or any MRI strain metrics (P > .05) but showed significant correlations with LV end-diastolic volume/body surface area (BSA) (slope = -3 kPa/mL/m2 ± 1, P = .004, r2 = 0.08) and LVESV/BSA (-1.6 kPa/mL/m2 ± 0.5, P = .003, r2 = 0.08). Conclusion This study demonstrates that female, but not male, individuals experience disproportionate LV stiffening with natural aging, and these changes can be noninvasively measured with MRE. Keywords: Cardiac, Elastography, Biological Effects, Experimental Investigations, Sexual Dimorphisms, MR Elastography, Myocardial Shear Stiffness, Quantitative Stiffness Imaging, Aging Heart, Myocardial Biomechanics, Cardiac MRE Supplemental material is available for this article. Published under a CC BY 4.0 license.

Prospective of Pancreatic Cancer Diagnosis Using Cardiac Sensing.

Pancreatic carcinoma (Ca Pancreas) is the third leading cause of cancer-related deaths in the world. The malignancies of the pancreas can be diagnosed with the help of various imaging modalities. An endoscopic ultrasound with a tissue biopsy is so far considered to be the gold standard in terms of the detection of Ca Pancreas, especially for lesions <2 mm. However, other methods, like computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI), are also conventionally used. Moreover, newer techniques, like proteomics, radiomics, metabolomics, and artificial intelligence (AI), are slowly being introduced for diagnosing pancreatic cancer. Regardless, it is still a challenge to diagnose pancreatic carcinoma non-invasively at an early stage due to its delayed presentation. Similarly, this also makes it difficult to demonstrate an association between Ca Pancreas and other vital organs of the body, such as the heart. A number of studies have proven a correlation between the heart and pancreatic cancer. The tumor of the pancreas affects the heart at the physiological, as well as the molecular, level. An overexpression of the SMAD4 gene; a disruption in biomolecules, such as IGF, MAPK, and ApoE; and increased CA19-9 markers are a few of the many factors that are noted to affect cardiovascular systems with pancreatic malignancies. A comprehensive review of this correlation will aid researchers in conducting studies to help establish a definite relation between the two organs and discover ways to use it for the early detection of Ca Pancreas.

Targeted Atrial Fibrillation Therapy and Risk Stratification Using Atrial Alternans.

Atrial fibrillation (AF) is the most persistent arrhythmia today, with its prevalence increasing exponentially with the rising age of the population. Particularly at elevated heart rates, a functional abnormality known as cardiac alternans can occur prior to the onset of lethal arrhythmias. Cardiac alternans are a beat-to-beat oscillation of electrical activity and the force of cardiac muscle contraction. Extensive evidence has demonstrated that microvolt T-wave alternans can predict ventricular fibrillation vulnerability and the risk of sudden cardiac death. The majority of our knowledge of the mechanisms of alternans stems from studies of ventricular electrophysiology, although recent studies offer promising evidence of the potential of atrial alternans in predicting the risk of AF. Exciting preclinical and clinical studies have demonstrated a link between atrial alternans and the onset of atrial tachyarrhythmias. Here, we provide a comprehensive review of the clinical utility of atrial alternans in identifying the risk and guiding treatment of AF.

The Impact of Microbiota on the Gut-Brain Axis: Examining the Complex Interplay and Implications.

The association and interaction between the central nervous system (CNS) and enteric nervous system (ENS) is well established. Essentially ENS is the second brain, as we call it. We tried to understand the structure and function, to throw light on the functional aspect of neurons, and address various disease manifestations. We summarized how various neurological disorders influence the gut via the enteric nervous system and/or bring anatomical or physiological changes in the enteric nervous system or the gut and vice versa. It is known that stress has an effect on Gastrointestinal (GI) motility and causes mucosal erosions. In our literature review, we found that stress can also affect sensory perception in the central nervous system. Interestingly, we found that mutations in the neurohormone, serotonin (5-HT), would result in dysfunctional organ development and further affect mood and behavior. We focused on the developmental aspects of neurons and cognition and their relation to nutritional absorption via the gastrointestinal tract, the development of neurodegenerative disorders in relation to the alteration in gut microbiota, and contrariwise associations between CNS disorders and ENS. This paper further summarizes the synergetic relation between gastrointestinal and neuropsychological manifestations and emphasizes the need to include behavioral therapies in management plans.

Mapping of Neuro-Cardiac Electrophysiology: Interlinking Epilepsy and Arrhythmia.

The interplay between neurology and cardiology has gained significant attention in recent years, particularly regarding the shared pathophysiological mechanisms and clinical comorbidities observed in epilepsy and arrhythmias. Neuro-cardiac electrophysiology mapping involves the comprehensive assessment of both neural and cardiac electrical activity, aiming to unravel the intricate connections and potential cross-talk between the brain and the heart. The emergence of artificial intelligence (AI) has revolutionized the field by enabling the analysis of large-scale data sets, complex signal processing, and predictive modeling. AI algorithms have been applied to neuroimaging, electroencephalography (EEG), electrocardiography (ECG), and other diagnostic modalities to identify subtle patterns, classify disease subtypes, predict outcomes, and guide personalized treatment strategies. In this review, we highlight the potential clinical implications of neuro-cardiac mapping and AI in the management of epilepsy and arrhythmias. We address the challenges and limitations associated with these approaches, including data quality, interpretability, and ethical considerations. Further research and collaboration between neurologists, cardiologists, and AI experts are needed to fully unlock the potential of this interdisciplinary field.

Association of multiple sclerosis with chronic fatigue syndrome, restless legs syndrome, and various sleep disorders, along with the recent updates.

Multiple sclerosis (MS) and myalgic encephalomyelitis (ME)/chronic fatigue syndrome (CFS) share the symptom of fatigue, and might even coexist together. Specifically focusing on genetics, pathophysiology, and neuroimaging data, the authors discuss an overview of the parallels, correlation, and differences in fatigue between MS and ME/CFS along with ME/CFS presence in MS. Studies have revealed that the prefrontal cortex and basal ganglia regions, which are involved in fatigue regulation, have similar neuroimaging findings in the brains of people with both MS and ME/CFS. Additionally, in both conditions, genetic factors have been implicated, with particular genes known to enhance susceptibility to MS and CFS. Management approaches for fatigue in MS and ME/CFS differ based on the underlying factors contributing to fatigue. The authors also focus on the recent updates and the relationship between MS and sleep disorders, including restless legs syndrome, focusing on pathophysiology and therapeutic approaches. Latest therapeutic approaches like supervised physical activity and moderate-intensity exercises have shown better outcomes.

Deep Neural Network for Cardiac Magnetic Resonance Image Segmentation.

The analysis and interpretation of cardiac magnetic resonance (CMR) images are often time-consuming. The automated segmentation of cardiac structures can reduce the time required for image analysis. Spatial similarities between different CMR image types were leveraged to jointly segment multiple sequences using a segmentation model termed a multi-image type UNet (MI-UNet). This model was developed from 72 exams (46% female, mean age 63 ± 11 years) performed on patients with hypertrophic cardiomyopathy. The MI-UNet for steady-state free precession (SSFP) images achieved a superior Dice similarity coefficient (DSC) of 0.92 ± 0.06 compared to 0.87 ± 0.08 for a single-image type UNet (p < 0.001). The MI-UNet for late gadolinium enhancement (LGE) images also had a superior DSC of 0.86 ± 0.11 compared to 0.78 ± 0.11 for a single-image type UNet (p = 0.001). The difference across image types was most evident for the left ventricular myocardium in SSFP images and for both the left ventricular cavity and the left ventricular myocardium in LGE images. For the right ventricle, there were no differences in DCS when comparing the MI-UNet with single-image type UNets. The joint segmentation of multiple image types increases segmentation accuracy for CMR images of the left ventricle compared to single-image models. In clinical practice, the MI-UNet model may expedite the analysis and interpretation of CMR images of multiple types.