Authors: Bernardo Stefanini, Alice Giamperoli, Eleonora Terzi, Fabio Piscaglia
Abstract: No abstract available
Fonte:
Ultraschall in Med 2024; 45: 444–448. Epub 2024 Sep 6.
DOI: 10.1055/a-2368-9201
© 2024. Thieme. All rights reserved.
The “2024 Guideline for the Primary Prevention of Stroke” replaces the 2014 “Guidelines for the Primary Prevention of Stroke.” This updated guideline is intended to be a resource for clinicians to use to guide various prevention strategies for individuals with no history of stroke…
Thyroid nodules are common incidental findings. Most of them are benign, but many unnecessary fine-needle aspiration procedures, core biopsies, and even thyroidectomies or non-invasive treatments have been performed. To improve thyroid nodule characterization, the use of multiparametric ultrasound evaluation has been encouraged by most experts and several societies…
Steatotic liver disease is the overarching term for conditions characterised by abnormal lipid accumulation in the liver (liver or hepatic steatosis). Steatotic liver disease encompasses what was previously termed non-alcoholic fatty liver disease (NAFLD), which is now called metabolic dysfunction-associated steatotic liver disease (MASLD)…
Authors: Mark N. Rubin, Ruchir Shah, Thomas Devlin, Teddy S. Youn, Michael F. Waters, John J. Volpi, Aaron Stayman, Colleen M. Douville, Ted Lowenkopf, Georgios Tsivgoulis, Andrei V. Alexandrov
Abstract: Background: Right to left shunt (RLS), including patent foramen ovale, is a recognized risk factor for stroke. RLS/patent foramen ovale diagnosis is made by transthoracic echocardiography (TTE), which is insensitive, transesophageal echocardiography, which is invasive, and transcranial Doppler (TCD), which is noninvasive and accurate but scarce…
Fonte:
Stroke. 2023;54:2842–2850.
DOI: 10.1161/STROKEAHA.123.043380
© 2023 The Authors.
Authors: Na Han, Wanjun Hu, Yurong Ma, Yu Zheng, Songhong Yue, Laiyang Ma, Jie Li and Jing Zhang
Abstract: Objectives: To accurately predict the risk of ischemic stroke, we established a radiomics model of carotid atherosclerotic plaque-based high-resolution vessel wall magnetic resonance imaging (HR-VWMRI) and combined it with clinical indicators.
plaques…
Fonte:
Front. Neurol. 15:1343423.
DOI: 10.3389/fneur.2024.1343423
Copyright © 2024 Han, Hu, Ma, Zheng, Yue, Ma, Li and Zhang.
Authors: Chao Hou, Shuo Li, Shuai Zheng, Lu-Ping Liu, Fang Nie, Wei Zhang, Wen He
Abstract: Background: Although imaging techniques provide information about the morphology and stability of carotid plaque, they are operator dependent and may miss certain subtleties. A variety of radiomics models for carotid plaque have recently been proposed for identifying vulnerable plaques and predicting cardiovascular and cerebrovascular diseases. The purpose of this review was to assess the risk of bias, reporting, and methodological quality of radiomics models for carotid atherosclerosis plaques…
Fonte:
Quant Imaging Med Surg 2024;14(1):1141-1154
DOI: 10.21037/qims-23-712
© Quantitative Imaging in Medicine and Surgery. All rights reserved.
Authors: Haochen Cui, Yuchong Zhao, Si Xiong, Yunlu Feng, Peng Li, Ying Lv, Qian Chen, Ronghua Wang, Pengtao Xie, Zhenlong Luo, Sideng Cheng, Wujun Wang, Xing Li, Dingkun Xiong, Xinyuan Cao, Shuya Bai, Aiming Yang, Bin Cheng
Abstract: Importance: Diagnosing solid lesions in the pancreas via endoscopic ultrasonographic (EUS) images is challenging. Artificial intelligence (AI) has the potential to help with such diagnosis, but existing AI models focus solely on a single modality. Objective: To advance the clinical diagnosis of solid lesions in the pancreas through developing a multimodal AI model integrating both clinical information and EUS images…
Fonte:
JAMA Network Open. 2024;7(7):e2422454
DOI: 10.1001/jamanetworkopen.2024.22454
© 2024 Cui H et al. JAMA Network Open.
Authors: Kun-Hsing Yu, Elizabeth Healey, Tze-Yun Leong, Isaac S. Kohane, and Arjun K. Manrai
Abstract: In this article in the series on artificial intelligence in medicine, the authors explore how human values influence the outputs of large language models and other artificial intelligence models.
Fonte:
N Engl J Med 2024;390:1895-1904
DOI: 10.1056/NEJMra2214183
Copyright © 2024 Massachusetts Medical Society. All rights reserved.
Authors: Julia Arribas Anta, Juan Moreno-Vedia, Javier García López, Miguel Angel Rios-Vives, Josep Munuera, Júlia Rodríguez-Comas
Abstract: Focal liver lesions (FLL) are common incidental findings in abdominal imaging. While the majority of FLLs are benign and asymptomatic, some can be malignant or pre-malignant, and need accurate detection and classification. Current imaging techniques, such as computed tomography (CT) and magnetic resonance imaging (MRI), play a crucial role in assessing these lesions. Artificial intelligence (AI), particularly deep learning (DL), offers potential solutions by analyzing large data to identify patterns and extract clinical features that aid in the early detection and classification of FLLs. This manuscript reviews the diagnostic capacity of AI-based algorithms in processing CT and MRIs to detect benign and malignant FLLs, with an emphasis in the characterization and classification of these lesions and focusing on differentiating benign from pre-malignant and potentially malignant lesions. A comprehensive literature search from January 2010 to April 2024 identified 45 relevant studies. The majority of AI systems employed convolutional neural networks (CNNs), with expert radiologists providing reference standards through manual lesion delineation, and histology as the gold standard. The studies reviewed indicate that AI-based algorithms demonstrate high accuracy, sensitivity, specificity, and AUCs in detecting and characterizing FLLs. These algorithms excel in differentiating between benign and malignant lesions, optimizing diagnostic protocols, and reducing the needs of invasive procedures. Future research should concentrate on the expansion of data sets, the improvement of model explainability, and the validation of AI tools across a range of clinical setting to ensure the applicability and reliability of such tools.
Fonte:
Abdom Radiol (NY). 2024 Oct 5.
DOI: 10.1007/s00261-024-04597-x
© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Author: Rahul C. Deo
Extract: Seven years ago, I wrote a review in this journal on the state of machine learning in medicine. My tenet then was that although numerous medical applications could benefit from machine learning, and the requisites for such models—data and algorithms—were widely present, few examples had made their way into practice. I struggled to find illustrative examples from cardiovascular research, let alone commercial products. Since that time, much has changed. Artificial intelligence (AI), a broader term that includes machine learning as a subdiscipline, dominates research publications and the news. And with innovation comes uncertainty, such as concerns over which professions (physicians included) will be made redundant by these innovations. It is thus timely to revisit this topic, offering some perspective on the dizzying pace of innovation.
Fonte:
Circulation. 2024;149:1235–1237
DOI: 10.1161/CIRCULATIONAHA.123.065469
© 2024 American Heart Association, Inc.
Authors: Thomas E. Brothers, Prabhakar Baliga
Abstract: Artificial intelligence (AI) tools created to enhance decision-making may have a significant impact on treatment algorithms for peripheral arterial disease (PAD). A Markov-based AI model was developed to predict optimal therapy based on maximization of calculated quality of life (cQoL), a patient-centered system of assessment designed to report outcomes directly linked to health-related quality of life…
Fonte:
J Am Coll Surg. 2024 Apr 1;238(4):481-488.
DOI: 10.1097/XCS.0000000000000958
© 2024 by the American College of Surgeons. Published by Wolters Kluwer Health, Inc. All rights reserved.
Authors: Robert D. McBane II, Dennis H. Murphree, David Liedl, Francisco Lopez-Jimenez, Adelaide Arruda-Olson, Christopher G. Scott, Naresh Prodduturi, Steve E. Nowakowski, Thom W. Rooke, Ana I. Casanegra, Waldemar E. Wysokinski, Damon E. Houghton, Kalpana Muthusamy, Paul W. Wennberg
Abstract: Patients with diabetes mellitus (DM) are at increased risk for peripheral artery disease (PAD) and its complications. Arterial calcification and non-compressibility may limit test interpretation in this population…
Fonte:
J Vasc Surg. 2024 Jul;80(1):251-259.e3.
DOI: 10.1016/j.jvs.2024.02.024
© 2024 Published by Elsevier Inc. on behalf of the Society for Vascular Surgery.
Authors: Ben Li, Naomi Eisenberg, Derek Beaton, Douglas S. Lee, Badr Aljabri, Raj Verma, Duminda N. Wijeysundera, Ori D. Rotstein, Charles de Mestral, Muhammad Mamdani, Graham Roche-Nagle, Mohammed Al-Omran
Abstract: Objective: To develop machine learning (ML) algorithms that predict outcomes after infrainguinal bypass. Infrainguinal bypass for peripheral artery disease carries significant surgical risks; however, outcome prediction tools remain limited…
Fonte:
Ann Surg. 2024 Apr 1;279(4):705-713.
DOI: 10.1097/SLA.0000000000006181.
© 2023 Wolters Kluwer Health, Inc. All rights reserved.
Authors: Ben Li, Blair E. Warren, Naomi Eisenberg, Derek Beaton, Douglas S. Lee, Badr Aljabri, Raj Verma, Duminda N. Wijeysundera, Ori D. Rotstein, Charles de Mestral, Muhammad Mamdani, Graham Roche-Nagle, Mohammed Al-Omran
Abstract: Endovascular intervention for peripheral artery disease (PAD) carries nonnegligible perioperative risks; however, outcome prediction tools are limited. The objective is to develop machine learning (ML) algorithms that can predict outcomes following endovascular intervention for PAD…
Fonte:
JAMA Netw Open. 2024;7(3):e242350.
DOI: 10.1001/jamanetworkopen.2024.2350.
© The Author(s) 2023.
Authors: Becky Long, Danielle L Cremat, Eduardo Serpa, Sinong Qian, John Blebea
Abstract: Complications after Endovascular Aneurysm Repair (EVAR) can be fatal. Patient follow-up for surveillance imaging is becoming more challenging as fewer patients are seen, particularly after the first year. The aim of this study was to develop an artificial intelligence model to predict the complication probability of individual patients to better identify those needing more intensive post-operative surveillance…
Fonte:
Vasc Endovascular Surg. 2024 Jan;58(1):65-75.
DOI: 10.1177/15385744231189024.
© The Author(s) 2023
Acute vertigo is defined as the perception of movement of oneself or the surroundings in the absence of actual motion and it is a frequent cause for emergency department admissions. The utilization of medical resources and the duration of hospital stay for this kind of symptom is high…
This special issue on “emerging topics in neuroimaging”, including 6 papers (4 original articles and 2 systematic reviews), is presented by international widely recognized leaders on the use of molecular neuroimaging in neurological diseases. The general aim of the special issue is to provide an overview of the emerging topics, imaging techniques…
TAO, also known as Buerger disease, is a rare condition that is seen in those who smoke. The disease causes a lack of blood flow to small and medium-sized blood vessels through inflammatory thrombus formation. TAO typically occurs in younger people with heavy tobacco exposure and can cause severe pain in the fingers and toes, sometimes leading to ulceration…
Importance: Renal cell carcinoma (RCC) is a common malignancy, with an estimated 434 840 incident cases worldwide in 2022. In the US, it is the sixth most common cancer among males and ninth among females.
Observations: Clear cell RCC is the most common histologic subtype (75%-80% of cases) and is characterized by inactivation of the von Hippel Lindau (VHL) tumor suppressor gene…