Intelligenza Artificiale

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.

Materiale Bibliografico

AI & Machine Learning in Medicine

A collection of articles from the New England Journal of Medicine, NEJM Catalyst Innovations in Care Delivery, and NEJM Evidence

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.


The Role of Artificial Intelligence in Echocardiography

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.

J. Imaging 2023, 9, 50.
DOI: 10.3390/jimaging9020050
© 2023 by the authors.


Artificial intelligence in echocardiography: Review and limitations including epistemological concerns

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…

Echocardiography. 2022;39:1044–1053
DOI: 10.1111/echo.15417
©2022 Wiley Periodicals LLC


Artificial Intelligence for Thyroid Nodule Characterization: Where Are We Standing?

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.

Cancers (Basel). 2022 Jul 10;14(14):3357
DOI: 10.3390/cancers14143357
© 2022 by the authors. All rights reserved


Artificial intelligence in medical imaging of the liver

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.

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


Artificial Intelligence in Thyroid Field — A Comprehensive Review

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.

Cancers (Basel). 2021; 13(19):4740
DOI: 10.3390/cancers13194740
© 2021 by the authors


Evaluation of an Artificial Intelligence-Augmented Digital System for Histologic Classification of Colorectal Polyps

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…

JAMA Network Open. 2021;4(11):e2135271
DOI: 10.1001/jamanetworkopen.2021.35271
© 2021 Nasir-Moin M. et al.


Diagnostic Value of Artificial Intelligence-Assisted Endoscopic Ultrasound for Pancreatic Cancer: A Systematic Review and Meta-Analysis

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.

Diagnostics (Basel). 2022;12(2):309
DOI: 10.3390/diagnostics12020309
© 2022 by the authors


Artificial Intelligence, Machine Learning, and Cardiovascular Disease

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.

Clinical Medicine Insights: Cardiology. 2020; 14: 1–9
DOI: 10.1177/1179546820927404
© The Author(s) 2020