Artificial Intelligence: What Is It and How Can It Expand the Ultrasound Potential in the Future?

Student ultrasound education, current view and controversies. Role of Artificial Intelligence, Virtual Reality and telemedicine

The digitization of medicine will play an increasingly significant role in future years. In particular, telemedicine, Virtual Reality (VR) and innovative Artificial Intelligence (AI) systems offer tremendous potential in imaging diagnostics and are expected to shape ultrasound diagnostics and teaching significantly. However, it is crucial to consider the advantages and disadvantages of employing these new technologies and how best to teach and manage their use. This paper provides an overview of telemedicine, VR and AI in student ultrasound education, presenting current perspectives and controversies.

Past and Present of Point-of-Care Ultrasound (PoCUS): A Narrative Review

This article aims to conduct a literature review to gain insight into point-of-care ultrasound (PoCUS). PoCUS is a rapid, accurate, non-invasive, and radiation-free imaging modality that can be used in stable and unstable patients. PoCUS can be performed parallel to physical examination, resuscitation, and stabilization; repeated exams in critical patients are essential for improving sensitivity. The review highlights how PoCUS, which was initially used to detect free intraperitoneal fluid in trauma patients, has developed into a life-saving diagnostic tool that could be utilized by treating physicians during various stages of diagnosis, resuscitation, operation, and postoperative critical care when managing sick patients. The review also notes the barriers to the widespread uptake of PoCUS in general internal medicine and the recent commercial availability of "pocket" or handheld probes that have made PoCUS more readily available. This review concludes that adopting a focused binary decision-making approach can maximize PoCUS's value in many clinical settings, including emergency departments, intensive care units, and operation theatres. Overall, the review emphasizes the importance of awareness of common indications, limitations, and strengths of this evolving and promising technology to determine its future trajectory: Providing comprehensive PoCUS training within internal medicine curriculums and supporting trainers to do so.

Professional Standards in Medical Ultrasound - EFSUMB Position Paper (Short Version) - General Aspects

This first position paper of the European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) on professional standards presents a common position across the different medical professions within EFSUMB regarding optimal standards for the performing and reporting of ultrasound examinations by any professional ultrasound operator. It describes general aspects of professionality that ensure procedure quality, effectiveness, efficiency, and sustainability in virtually all application fields of medical ultrasound. Recommendations are given related to safety and indication of ultrasound examinations, requirements for examination rooms, structured examination, systematic reporting of results, and management, communication and archiving of ultrasound data. The print version of this article is a short version. The long version is published online.

Professional Standards in Medical Ultrasound - EFSUMB Position Paper (Long Version) - General Aspects

This first position paper of the European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) on professional standards presents a common position across the different medical professions within EFSUMB regarding optimal standards for the performing and reporting of ultrasound examinations by any professional ultrasound operator. It describes general aspects of professionality that ensure procedure quality, effectiveness, efficiency, and sustainability in virtually all application fields of medical ultrasound. Recommendations are given related to safety and indication of ultrasound examinations, requirements for examination rooms, structured examination, systematic reporting of results, and management, communication and archiving of ultrasound data. The print version of this article is a short version. The long version is published online.

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

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.

Abstract

Machine learning (ML) is an interdisciplinary sector in the subset of artificial intelligence (AI) that creates systems to set up logical connections using algorithms, and thus offers predictions for complex data analysis. In the present review, an up-to-date summary of the current state of the art regarding ML and AI implementation for thyroid nodule ultrasound characterization and cancer is provided, highlighting controversies over AI application as well as possible benefits of ML, such as, for example, training purposes. There is evidence that AI increases diagnostic accuracy and significantly limits inter-observer variability by using standardized mathematical algorithms. It could also be of aid in practice settings with limited sub-specialty expertise, offering a second opinion by means of radiomics and computer-assisted diagnosis. The introduction of AI represents a revolutionary event in thyroid nodule evaluation, but key issues for further implementation include integration with radiologist expertise, impact on workflow and efficiency, and performance monitoring.

Differential diagnosis of thyroid nodules by the Demetics ultrasound-assisted diagnosis system and contrast-enhanced ultrasound combined with thyroid image reporting and data systems

BACKGROUND

More and more new ultrasound techniques with their own characteristics are applied in the differential diagnosis of thyroid nodules. This study amied to assess and compare the diagnostic value of the Demetics ultrasound-assisted diagnosis system and contrast-enhanced ultrasound (CEUS) combined with the Thyroid Image Reporting and Data Systems (TI-RADS) for thyroid nodules.

DESIGN AND PATIENTS

A total of 600 thyroid nodules with pathological findings were retrospectively analysed. Demetics and CEUS were performed for all nodules. The diagnostic efficacy of Demetics and CEUS for nodules of different sizes was evaluated and compared in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+) and negative likelihood ratio (LR-). The characteristics of nodules diagnosed and misdiagnosed by Demetics were compared to analyse the factors affecting the diagnostic accuracy of Demetics. The necessity of CEUS for nodules that are prone to misdiagnosis in Demetics was assessed.

RESULTS

Both Demetics and CEUS can be used for the differential diagnosis of benign and malignant thyroid nodules of different sizes. The diagnostic agreement between Demetics and CEUS for thyroid nodules of different sizes was moderate, substantial and fair, respectively. The sensitivity and NPV of Demetics were higher than those of CEUS, and the specificity, PPV and LR+ of CEUS were higher than that of Demetics. The LR- of Demetics was lower than those of CEUS. There were significant differences in age, calcification and margin in analysing the factors affecting Demetics. CEUS correctly diagnosed 50 of the 101 nodules misdiagnosed by Demetics.

CONCLUSIONS

Demetics showed high sensitivity in diagnosing thyroid nodules, while CEUS showed high specificity. In clinical practice, CEUS can further improve the diagnostic accuracy for nodules that are easily misdiagnosed by Demetics.

An Automated Algorithm for Optic Nerve Sheath Diameter Assessment from B-mode Ultrasound Images

BACKGROUND AND PURPOSE

The optic nerve sheath diameter (ONSD) is a promising surrogate marker for the detection of raised intracranial pressure (ICP). However, inconsistencies in manual ONSD assessment are thought to affect ONSD and the corresponding ONSD cutoff values for the diagnosis of elevated ICP, hereby hampering the full potential of ONSD. In this study, we developed an image intensity-invariant algorithm to automatically estimate ONSD from B-mode ultrasound images at multiple depths.

METHODS

The outcomes of the algorithm were validated against manual ONSD measurements by two human experts. Each expert analyzed the images twice (M1 and M2) in unknown order.

RESULTS

The algorithm proved capable of segmenting the ONSD in 39 of 42 images, hereby showing mean differences of -.08 ± .45 and -.05 ± .41 mm compared to averaged ONSD values (M1 + M2/2) of Operator 1 and Operator 2, respectively, whereas the mean difference between the two experts was .03 ± .26 mm. Moreover, differences between algorithm-derived and expert-derived ONSD values were found to be much smaller than the 1 mm difference that is expected between patients with normal and elevated ICP, making it likely that our algorithm can distinguish between these patient groups.

CONCLUSIONS

Our algorithm has the potential to improve the accuracy of ONSD as a surrogate marker for elevated ICP because it has no intrinsic variability. However, future research should be performed to validate if the algorithm does indeed result in more accurate noninvasive ICP predictions.