Machine learning predicts pulmonary Long Covid sequelae using clinical data.

Long COVID is a multi-systemic disease characterized by the persistence or occurrence of many symptoms that in many cases affect the pulmonary system. These, in turn, may deteriorate the patient's quality of life making it easier to develop severe complications. Being able to predict this syndrome is therefore important as this enables early treatment.

Modeling cardiac microcirculation for the simulation of coronary flow and 3D myocardial perfusion.

Accurate modeling of blood dynamics in the coronary microcirculation is a crucial step toward the clinical application of in silico methods for the diagnosis of coronary artery disease. In this work, we present a new mathematical model of microcirculatory hemodynamics accounting for microvasculature compliance and cardiac contraction; we also present its application to a full simulation of hyperemic coronary blood flow and 3D myocardial perfusion in real clinical cases. Microvasculature hemodynamics is modeled with a compliant multi-compartment Darcy formulation, with the new compliance terms depending on the local intramyocardial pressure generated by cardiac contraction.

Radiologists' perceptions on AI integration: An in-depth survey study.

Purpose: To assess the perceptions and attitudes of radiologists toward the adoption of artificial intelligence (AI) in clinical practice.

Methods: A survey was conducted among members of the SIRM Lombardy. Radiologists' attitudes were assessed comprehensively, covering satisfaction with AI-based tools, propensity for innovation, and optimism for the future.

18 F-FET PET/CT in Calcifying Pseudoneoplasm of the Neuraxis : First Case.

A 66-year-old man has been treated in a psychiatric department for 4-5 years for a depressive syndrome, which is associated with poor motor initiative, confusional state, and dysosmia. Dynamic 18 F-FET PET/CT showed only faint uptake of radiotracer just above the background on the left frontal calcific lesion. The time-activity curve of the neoplasms showed a descending pattern.

Radiomics and Artificial Intelligence in Renal Lesion Assessment.

Radiomics, the extraction and analysis of quantitative features from medical images, has emerged as a promising field in radiology with the potential to revolutionize the diagnosis and management of renal lesions. This comprehensive review explores the radiomics workflow, including image acquisition, feature extraction, selection, and classification, and highlights its application in differentiating between benign and malignant renal lesions. The integration of radiomics with artificial intelligence (AI) techniques, such as machine learning and deep learning, can help patients' management and allow the planning of the appropriate treatments.

Adoption of AI in Oncological Imaging: Ethical, Regulatory, and Medical-Legal Challenges.

Artificial Intelligence (AI) algorithms have shown great promise in oncological imaging, outperforming or matching radiologists in retrospective studies, signifying their potential for advanced screening capabilities. These AI tools offer valuable support to radiologists, assisting them in critical tasks such as prioritizing reporting, early cancer detection, and precise measurements, thereby bolstering clinical decision-making. With the healthcare landscape witnessing a surge in imaging requests and a decline in available radiologists, the integration of AI has become increasingly appealing.

Artificial Intelligence in Lung Cancer Imaging: From Data to Therapy.

Lung cancer remains a global health challenge, leading to substantial morbidity and mortality. While prevention and early detection strategies have improved, the need for precise diagnosis, prognosis, and treatment remains crucial. In this comprehensive review article, we explore the role of artificial intelligence (AI) in reshaping the management of lung cancer.

Personalized Pressure Conditions and Calibration for a Predictive Computational Model of Coronary and Myocardial Blood Flow.

Predictive modeling of hyperemic coronary and myocardial blood flow (MBF) greatly supports diagnosis and prognostic stratification of patients suffering from coronary artery disease (CAD). In this work, we propose a novel strategy, using only readily available clinical data, to build personalized inlet conditions for coronary and MBF models and to achieve an effective calibration for their predictive application to real clinical cases. Experimental data are used to build personalized pressure waveforms at the aortic root, representative of the hyperemic state and adapted to surrogate the systolic contraction, to be used in computational fluid-dynamics analyses.

Amplifying the Effects of Contrast Agents on Magnetic Resonance Images Using a Deep Learning Method Trained on Synthetic Data.

Objectives: Artificial intelligence (AI) methods can be applied to enhance contrast in diagnostic images beyond that attainable with the standard doses of contrast agents (CAs) normally used in the clinic, thus potentially increasing diagnostic power and sensitivity. Deep learning-based AI relies on training data sets, which should be sufficiently large and diverse to effectively adjust network parameters, avoid biases, and enable generalization of the outcome. However, large sets of diagnostic images acquired at doses of CA outside the standard-of-care are not commonly available.

How do patients perceive the AI-radiologists interaction? Results of a survey on 2119 responders.

Purpose: In this study we investigate how patients perceive the interaction between artificial intelligence (AI) and radiologists by designing a survey.

Method: We created a survey focused on the application of Artificial Intelligence in radiology which consisted of 20 questions distributed in three sections:Only completed questionnaires were considered for analysis.

Results: 2119 subjects completed the survey.

Development of Predictive Models for the Response of Vestibular Schwannoma Treated with Cyberknife: A Feasibility Study Based on Radiomics and Machine Learning.

Purpose: to predict vestibular schwannoma (VS) response to radiosurgery by applying machine learning (ML) algorithms on radiomic features extracted from pre-treatment magnetic resonance (MR) images.

Methods: patients with VS treated with radiosurgery in two Centers from 2004 to 2016 were retrospectively evaluated. Brain T1-weighted contrast-enhanced MR images were acquired before and at 24 and 36 months after treatment.

Ultrasound elastography in chronic kidney disease: a systematic review and meta-analysis.

Ultrasound elastography (USE) is a noninvasive technique for assessing tissue elasticity, and its application in nephrology has aroused growing interest in recent years. The purpose of this article is to systematically review the clinical application of USE in patients with chronic kidney disease (CKD), including native and transplanted kidneys, and quantitatively investigate differences in elasticity values between healthy individuals and CKD patients. Furthermore, we provide a qualitative analysis of the studies included, discussing the potential interplay between renal stiffness, estimated glomerular filtration rate, and fibrosis.

Lesion or Pseudolesion? A Comprehensive Description of Perfusion-Based Liver Alterations on Contrast-Enhanced Computed Tomography and Literature Review.

Pseudolesions on contrast-enhanced computed tomography represent a diagnostic challenge for radiologists because they could be difficult to distinguish from true space-occupying lesions. This article aims to provide a detailed overview of these entities based on radiological criteria (hyperattenuation or hypoattenuation, localization, morphology), as well as a brief review of the hepatic vascular anatomy and pathophysiological process. Relevant examples from hospital case series are reported as helpful hints to assist radiologists in recognizing and correctly diagnosing these abnormalities.

OCT Analysis of Retinal Pigment Epithelium in Myopic Choroidal Neovascularization: Correlation Analysis with Different Treatments.

Objective: The objective of this study was to analyze the status of the retinal pigment epithelium (RPE) by means of the spectral domain optical coherence tomography (SD-OCT) overlying the myopic neovascular lesions in the involutive phase, looking for any correlations between the status of the RPE and the size of the lesions and the type and duration of the treatment. Methods: SD-OCT examinations of 83 consecutive patients with myopic choroidal neovascularization (CNV) were reviewed and divided into two groups: group A, patients with CNV characterized by uniformity of the overlying RPE, and group B, patients with CNV characterized by non-uniformity of the overlying RPE. Results: The median lesion area, major diameter, and minimum diameter were, respectively, 0.

The Network That Unites a Qualitative Study on Clinical Psychological Intervention for Women with a History of Breast Cancer and Chronic Pain.

The aim of this qualitative research is to deepen the knowledge in the field of psycho-oncology and the consequences of chronic and persistent pain by listening to patients' experiences, their emotions and difficulties in facing this hard condition, and assessing their perception of the role of the psychologist in pain management. In this qualitative study, a semistructured interview was used, designed from three research questions: chronic pain and quality of life; chronic pain and psychological well-being; and the role and perception of the psychologist in pain management. The sample consists of 29 women who suffered or have recovered from breast carcinoma, and who currently report having chronic pain due either to the presence of the cancer or as a result of surgery or treatment.

Quantification of epicardial adipose tissue in obese patients using an open-bore MR scanner.

Background: Our aim was to evaluate the reproducibility of epicardial adipose tissue (EAT) volume, measured on scans performed using an open-bore magnetic resonance scanner.

Methods: Consecutive patients referred for bariatric surgery, aged between 18 and 65 years who agreed to undergo cardiac imaging (MRI), were prospectively enrolled. All those with cardiac pathology or contraindications to MRI were excluded.

Quantitative Assessment of Late Gadolinium Enhancement and Edema at Cardiac Magnetic Resonance in Low-Risk Myocarditis Patients.

In this study, we aimed to quantify LGE and edema at short-tau inversion recovery sequences on cardiac magnetic resonance (CMR) in patients with myocarditis. We retrospectively evaluated CMR examinations performed during the acute phase and at follow-up. Forty-seven patients were eligible for retrospective LGE assessment, and, among them, twenty-five patients were eligible for edema evaluation.

Development and Validation of an AI-driven Mammographic Breast Density Classification Tool Based on Radiologist Consensus.

Mammographic breast density (BD) is commonly visually assessed using the Breast Imaging Reporting and Data System (BI-RADS) four-category scale. To overcome inter- and intraobserver variability of visual assessment, the authors retrospectively developed and externally validated a software for BD classification based on convolutional neural networks from mammograms obtained between 2017 and 2020. The tool was trained using the majority BD category determined by seven board-certified radiologists who independently visually assessed 760 mediolateral oblique (MLO) images in 380 women (mean age, 57 years ± 6 [SD]) from center 1; this process mimicked training from a consensus of several human readers.

AIforCOVID: Predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study.

Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem.

Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features.

Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann-Whitney test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset split.

Pulmonary thromboembolism in hospitalised COVID-19 patients at moderate to high risk by Wells score: a report from Lombardy, Italy.

Objectives: To present a single-centre experience on CT pulmonary angiography (CTPA) for the assessment of hospitalised COVID-19 patients with moderate-to-high risk of pulmonary thromboembolism (PTE).

Methods: We analysed consecutive COVID-19 patients (RT-PCR confirmed) undergoing CTPA in March 2020 for PTE clinical suspicion. Clinical data were retrieved.

Diagnostic Value of Global Cardiac Strain in Patients With Myocarditis.

Background: Cardiac strain represents an imaging biomarker of contractile dysfunction.

Purpose: The purpose of this study was to investigate the diagnostic value of cardiac strain obtained by feature-tracking cardiac magnetic resonance (MR) in acute myocarditis.

Materials And Methods: Cardiac MR examinations of 46 patients with myocarditis and preserved ejection fraction at acute phase and follow-up were analyzed along with cardiac MR of 46 healthy age- and sex-matched controls.

Open 1.0-T versus closed 1.5-T cardiac MR: Image quality assessment.

Purpose: The aim of this paper was to compare the open 1-T (O-1T) versus the closed 1.5-T (C-1.5T) cardiac magnetic resonance (MR).