La diagnostica per immagini sta vivendo un periodo di cambiamenti importanti e l’ecografia, in particolare, ne è fortemente coinvolta.
Le nuove tecnologie iniziano a consentirci, ormai anche nella pratica di tutti i giorni, sfide diagnostiche sempre più avanzate e l’ecografia diventa più che mai fondamentale per un ottimale inquadramento diagnostico iniziale ma frequentemente anche definitivo.
L’Intelligenza Artificiale (AI, Artificial Intelligence), all’interno della quale è programmato un mondo tecnologico estremamente affascinante, si propone con sempre più evidenza di affiancare il medico nelle decisioni cliniche ed il medico dovrà imparare a confrontarsi con l’Intelligenza Artificiale.
Per tali motivi, abbiamo deciso di dare spazio nel sito all’AI, con materiale bibliografico propedeutico e di ricerca, per iniziare a far conoscere meglio quel futuro che è già attualità.
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.
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
Estratto: Lo scopo del presente regolamento è migliorare il funzionamento del mercato interno istituendo un quadro giuridico uniforme in particolare per quanto riguarda lo sviluppo, l’immissione sul mercato, la messa in servizio e l’uso di sistemi di intelligenza artificiale (sistemi di IA) nell’Unione, in conformità dei valori dell’Unione, promuovere la diffusione di un’intelligenza artificiale (IA) antropocentrica e affidabile, garantendo nel contempo un livello elevato di protezione della salute, della sicurezza e dei diritti fondamentali sanciti dalla Carta dei diritti fondamentali dell’Unione europea («Carta»), compresi la democrazia, lo Stato di diritto e la protezione dell’ambiente, proteggere contro gli effetti nocivi dei sistemi di IA nell’Unione, nonché promuovere l’innovazione. Il presente regolamento garantisce la libera circolazione transfrontaliera di beni e servizi basati sull’IA, impedendo così agli Stati membri di imporre restrizioni allo sviluppo, alla commercializzazione e all’uso di sistemi di IA, salvo espressa autorizzazione del presente regolamento.
Fonte:
Gazzetta ufficiale dell’Unione Europea
Pubblicato il 12.07.2024
Authors: Tong Xu, Xian-Ya Zhang, Na Yang, Fan Jiang, Gong-Quan Chen, Xiao-Fang Pan, Yue-Xiang Peng and Xin-Wu Cui
Abstract: Kidney disease is a serious public health problem and various kidney diseases could progress to end-stage renal disease. The many complications of end-stage renal disease. have a significant impact on the physical and mental health of patients. Ultrasound can be the test of choice for evaluating the kidney and perirenal tissue as it is real-time, available and non-radioactive. To overcome substantial interobserver variability in renal ultrasound interpretation, artificial intelligence (AI) has the potential to be a new method to help radiologists make clinical decisions. This review introduces the applications of AI in renal ultrasound, including automatic segmentation of the kidney, measurement of the renal volume, prediction of the kidney function, diagnosis of the kidney diseases. The advantages and disadvantages of the applications will also be presented clinicians to conduct research. Additionally, the challenges and future perspectives of AI are discussed.
Fonte:
Frontiers in Oncology 2023; 13: 1252630.
Published online 2024 Mar 1
DOI: 10.3389/fonc.2023.1252630
© 2024 Xu, Zhang, Yang, Jiang, Chen, Pan, Peng and Cui
Authors: Gennaro Tagliamonte, Fabrizio Santagata, Mirella Fraquelli
Abstract: Intestinal ultrasound is a non-invasive, safe, and cost-effective technique to study the small and large intestines. In addition to conventional B-mode and color doppler imaging, new US tools have been developed in more recent years that provide auxiliary data on many GI conditions, improving the diagnosis and assessment of relevant outcomes. We have reviewed the more recent literature (from 2010 onwards) on auxiliary tools in bowel ultrasound such as elastography techniques, CEUS, SICUS, and the potential contribution by artificial intelligence (AI) to overcome current intestinal ultrasound limitations. For this scoping review, we performed an extensive literature search on PubMed and EMBASE to identify studies published until December 2023 and investigating the application of elastography techniques, CEUS, SICUS, and AI in the ultrasonographic assessment of the small and large intestines. Multiparametric intestinal ultrasound shows promising capabilities in Crohn’s disease, while less is known about the role in ulcerative colitis. Despite some evidence, the CEUS role as a point-of-care examination tool for rare conditions such as intestinal GvHD and ischemic small bowel disease seems promising, possibly avoiding the need to perform further cross-sectional imaging. The use of AI in intestinal ultrasound is still anecdotical and limited to acute appendicitis.
Fonte:
Diagnostics (Basel). 2024 Apr 3;14(7):759.
DOI: 10.3390/diagnostics14070759
© 2024 by the authors. Licensee MDPI, Basel, Switzerland.
Authors: Roger T. Tomihama, Saharsh Dass, Sally Chen, Sharon C. Kiang
Abstract: Deep learning, a subset of machine learning within artificial intelligence, has been successful in medical image analysis in vascular surgery. Unlike traditional computer-based segmentation methods that manually extract features from input images, deep learning methods learn image features and classify data without making prior assumptions. Convolutional neural networks, the main type of deep learning for computer vision processing, are neural networks with multilevel architecture and weighted connections between nodes that can “auto-learn” through repeated exposure to training data without manual input or supervision. These networks have numerous applications in vascular surgery imaging analysis, particularly in disease classification, object identification, semantic segmentation, and instance segmentation. The purpose of this review article was to review the relevant concepts of machine learning image analysis and its application to the field of vascular surgery.
Fonte:
Seminars in Vascular Surgery, Volume 36, Issue 3, 2023. Pages 413-418.
DOI: https://doi.org/10.1053/j.semvascsurg.2023.07.001
© 2023 Elsevier Inc. All rights reserved.
Authors: Thita Chiasakul, Barbara D. Lam, Megan McNichol, William Robertson, Rachel P. Rosovsky, Leslie Lake, Ioannis S. Vlachos, Alys Adamski, Nimia Reyes, Karon Abe, Jeffrey I. Zwicker, Rushad Patell
Abstract: Accurate diagnostic and prognostic predictions of venous thromboembolism (VTE) are crucial for VTE management. Artificial intelligence (AI) enables autonomous identification of the most predictive patterns from large complex data. Although evidence regarding its performance in VTE prediction is emerging, a comprehensive analysis of performance is lacking. To systematically review the performance of AI in the diagnosis and prediction of VTE and compare it to clinical risk assessment models (RAMs) or logistic regression models…
Fonte:
Eur J Haematol. 2023 Dec;111(6):951-962.
Epub 2023 Oct 4.
DOI: 10.1111/ejh.14110.
© 2023 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Authors: Robert M.Wachter; Erik Brynjolfsson
Abstract: Since the introduction of ChatGPT in late 2022, generative artificial intelligence (genAI) has elicited enormous enthusiasm and serious concerns.
History has shown that general purpose technologies often fail to deliver their promised benefits for many years (“the productivity paradox of information technology”). Health care has several attributes that make the successful deployment of new technologies even more difficult than in other industries; these have challenged prior efforts to implement AI and electronic health records…
Fonte:
JAMA.
Published online November 30, 2023.
DOI: 10.1001/jama.2023.25054
© 2023 American Medical Association. All rights reserved.
Authors: Fabien Lareyre, Arindam Chaudhuri, Christian-Alexander Behrendt, Alexandre Pouhin, Martin Teraa, Jonathan R. Boyle, Riikka Tulamo, Juliette Raffort
Abstract: Cardiovascular disease represents a source of major health problems worldwide, and although medical and technical advances have been achieved, they are still associated with high morbidity and mortality rates. Personalized medicine would benefit from novel tools to better predict individual prognosis and outcomes after intervention. Artificial intelligence (AI) has brought new insights to cardiovascular medicine, especially with the use of machine learning techniques that allow the identification of hidden patterns and complex associations in health data without any a priori assumptions. This review provides an overview on the use of artificial intelligence–based prediction models in vascular diseases, specifically focusing on aortic aneurysm, lower extremity arterial disease, and carotid stenosis. Potential benefits include the development of precision medicine in patients with vascular diseases. In addition, the main challenges that remain to be overcome to integrate artificial intelligence–based predictive models in clinical practice are discussed.
Fonte:
Seminars in Vascular Surgery
Volume 36, Issue 3, September 2023, Pages 440-447
DOI: https://doi.org/10.1053/j.semvascsurg.2023.05.002
© 2023 Elsevier Inc. All rights reserved.
Authors: Ahmed Al Kuwaiti, Khalid Nazer, Abdullah Al-Reedy, Shaher Al-Shehri, Afnan Al-Muhanna, Arun Vijay Subbarayalu, Dhoha Al Muhanna, Fahad A Al-Muhanna
Abstract: Artificial intelligence (AI) applications have transformed healthcare. This study is based on a general literature review uncovering the role of AI in healthcare and focuses on the following key aspects: (i) medical imaging and diagnostics, (ii) virtual patient care, (iii) medical research and drug discovery, (iv) patient engagement and compliance, (v) rehabilitation, and (vi) other administrative applications. The impact of AI is observed in detecting clinical conditions in medical imaging and diagnostic services, controlling the outbreak of coronavirus disease 2019 (COVID-19) with early diagnosis, providing virtual patient care using AI-powered tools, managing electronic health records, augmenting patient engagement and compliance with the treatment plan, reducing the administrative workload of healthcare professionals (HCPs), discovering new drugs and vaccines, spotting medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. Nevertheless, this science pitch meets several technical, ethical, and social challenges, including privacy, safety, the right to decide and try, costs, information and consent, access, and efficacy, while integrating AI into healthcare. The governance of AI applications is crucial for patient safety and accountability and for raising HCPs’ belief in enhancing acceptance and boosting significant health consequences. Effective governance is a prerequisite to precisely address regulatory, ethical, and trust issues while advancing the acceptance and implementation of AI. Since COVID-19 hit the global health system, the concept of AI has created a revolution in healthcare, and such an uprising could be another step forward to meet future healthcare needs.
Fonte:
J. Pers. Med. 2023, 13, 951.
DOI: https://doi.org/10.3390/jpm13060951
© 2023 by the authors
A collection of articles from the New England Journal of Medicine, NEJM Catalyst Innovations in Care Delivery, and NEJM Evidence
Fonte:
ai.nejm.org
The New England Journal of Medicine, NEJM Catalyst, and NEJM Evidence, are publications of NEJM Group, a division of the Massachusetts Medical Society.
©2023 Massachusetts Medical Society, All rights reserved.
Authors: Timothy Barry, Juan Maria Farina, Chieh-Ju Chao, Chadi Ayoub, Jiwoong Jeong, Bhavik N Patel, Imon Banerjee, Reza Arsanjani
Abstract: Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care.
Fonte:
J. Imaging 2023, 9, 50.
DOI: 10.3390/jimaging9020050
© 2023 by the authors.
Authors: Gültekin Karakuş, Aleks Değirmencioğlu, Navin C. Nanda
Abstract: In this review we describe the use of artificial intelligence in the field of echocardiography. Various aspects and terminologies used in artificial intelligence are explained in an easy-to-understand manner and supplemented with illustrations related to echocardiography. Limitations of artificial intelligence, including epistemologic concerns from a philosophical standpoint, are also discussed…
Fonte:
Echocardiography. 2022;39:1044–1053
DOI: 10.1111/echo.15417
©2022 Wiley Periodicals LLC
Authors: Salvatore Sorrenti, Vincenzo Dolcetti, Maija Radzina, Maria Irene Bellini, Fabrizio Frezza, Khushboo Munir, Giorgio Grani, Cosimo Durante, Vito D’Andrea, Emanuele David, Pietro Giorgio Calò, Eleonora Lori, Vito Cantisani
Simple Summary: In the present review, an up-to-date summary of the state of the art of artificial intelligence (AI) implementation for thyroid nodule characterization and cancer is provided. The opinion on the real effectiveness of AI systems remains controversial. Taking into consideration the largest and most scientifically valid studies, it is possible to state that AI provides results that are comparable or inferior to expert ultrasound specialists and radiologists. Promising data approve AI as a support tool and simultaneously highlight the need for a radiologist supervisory framework for AI provided results. Therefore, current solutions might be more suitable for educational purposes.
Fonte:
Cancers (Basel). 2022 Jul 10;14(14):3357
DOI: 10.3390/cancers14143357
© 2022 by the authors. All rights reserved
Author: Alder Casadei
Informazioni:
Tipologia: Presentazione
Evento: Corso Teorico Specialistico Eco-Color Doppler Vascolare Periferico | SIUMB Società Italiana di Ultrasonologia in Medicina e Biologia
Sede e Data: Padova Congress, 14-15 Novembre 2022
Fonte:
© Alder Casadei. All rights reserved.
Authors: Li-Qiang Zhou, Jia-Yu Wang, Song-Yuan Yu, Ge-Ge Wu, Qi Wei, You-Bin Deng, Xing-Long Wu, Xin-Wu Cui, Christoph F. Dietrich
Abstract: Artificial intelligence (AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians’ workload. This article illustrates basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially convolutional neural networks, and their clinical application in the medical imaging of liver diseases, such as detecting and evaluating focal liver lesions, facilitating treatment, and predicting liver treatment response. We conclude that machine-assisted medical services will be a promising solution for future liver medical care. Lastly, we discuss the challenges and future directions of clinical application of deep learning techniques.
Fonte:
World J Gastroenterol. 2019 February 14; 25(6): 672–682
DOI: 10.3748/wjg.v25.i6.672
© The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved
Authors: Fabiano Bini, Andrada Pica, Laura Azzimonti, Alessandro Giusti, Lorenzo Ruinelli, Franco Marinozzi, Pierpaolo Trimboli
Simple Summary: The incidence of thyroid pathologies has been increasing worldwide. Historically, the detection of thyroid neoplasms relies on medical imaging analysis, depending mainly on the experience of clinicians. The advent of artificial intelligence (AI) techniques led to a remarkable progress in image-recognition tasks. AI represents a powerful tool that may facilitate understanding of thyroid pathologies, but actually, the diagnostic accuracy is uncertain. This article aims to provide an overview of the basic aspects, limitations and open issues of the AI methods applied to thyroid images. Medical experts should be familiar with the workflow of AI techniques in order to avoid misleading outcomes.
Fonte:
Cancers (Basel). 2021; 13(19):4740
DOI: 10.3390/cancers13194740
© 2021 by the authors
Authors: Mustafa Nasir-Moin, Arief A. Suriawinata, Bing Ren, Xiaoying Liu, Douglas J. Robertson, Srishti Bagchi, Naofumi Tomita, Jason W. Wei, Todd A. MacKenzie, Judy R. Rees, Saeed Hassanpour
Abstract: Colorectal polyps are common, and their histopathologic classification is used in the planning of follow-up surveillance. Substantial variation has been observed in pathologists’ classification of colorectal polyps, and improved assessment by pathologists may be associated with reduced subsequent underuse and overuse of colonoscopy…
Fonte:
JAMA Network Open. 2021;4(11):e2135271
DOI: 10.1001/jamanetworkopen.2021.35271
© 2021 Nasir-Moin M. et al.
Authors: Elena Adriana Dumitrescu, Bogdan Silviu Ungureanu, Irina M. Cazacu, Lucian Mihai Florescu, Liliana Streba, Vlad M. Croitoru, Daniel Sur, Adina Croitoru, Adina Turcu-Stiolica, Cristian Virgil Lungulescu
Abstract: We performed a meta-analysis of published data to investigate the diagnostic value of artificial intelligence for pancreatic cancer. Systematic research was conducted in the following databases: PubMed, Embase, and Web of Science to identify relevant studies up to October 2021. We extracted or calculated the number of true positives, false positives true negatives, and false negatives from the selected publications. In total, 10 studies, featuring 1871 patients, met our inclusion criteria. The risk of bias in the included studies was assessed using the QUADAS-2 tool. R and RevMan 5.4.1 software were used for calculations and statistical analysis. The studies included in the meta-analysis did not show an overall heterogeneity (I2 = 0%), and no significant differences were found from the subgroup analysis. The pooled diagnostic sensitivity and specificity were 0.92 (95% CI, 0.89-0.95) and 0.9 (95% CI, 0.83-0.94), respectively. The area under the summary receiver operating characteristics curve was 0.95, and the diagnostic odds ratio was 128.9 (95% CI, 71.2-233.8), indicating very good diagnostic accuracy for the detection of pancreatic cancer. Based on these promising preliminary results and further testing on a larger dataset, artificial intelligence-assisted endoscopic ultrasound could become an important tool for the computer-aided diagnosis of pancreatic cancer.
Fonte:
Diagnostics (Basel). 2022;12(2):309
DOI: 10.3390/diagnostics12020309
© 2022 by the authors
Authors: Pankaj Mathur, Shweta Srivastava, Xiaowei Xu, Jawahar L. Mehta
Abstract: Artificial intelligence (AI)-based applications have found widespread applications in many fields of science, technology, and medicine. The use of enhanced computing power of machines in clinical medicine and diagnostics has been under exploration since the 1960s. More recently, with the advent of advances in computing, algorithms enabling machine learning, especially deep learning networks that mimic the human brain in function, there has been renewed interest to use them in clinical medicine. In cardiovascular medicine, AI-based systems have found new applications in cardiovascular imaging, cardiovascular risk prediction, and newer drug targets. This article aims to describe different AI applications including machine learning and deep learning and their applications in cardiovascular medicine. AI-based applications have enhanced our understanding of different phenotypes of heart failure and congenital heart disease. These applications have led to newer treatment strategies for different types of cardiovascular diseases, newer approach to cardiovascular drug therapy and postmarketing survey of prescription drugs. However, there are several challenges in the clinical use of AI-based applications and interpretation of the results including data privacy, poorly selected/outdated data, selection bias, and unintentional continuance of historical biases/stereotypes in the data which can lead to erroneous conclusions. Still, AI is a transformative technology and has immense potential in health care.
Fonte:
Clinical Medicine Insights: Cardiology. 2020; 14: 1–9
DOI: 10.1177/1179546820927404
© The Author(s) 2020