Keywords
AI - radiomics - ultrasound - ethics
During the last 5 years, artificial intelligence (AI) emerged as a revolutionary tool
with significant implications across the healthcare system, heavily influencing scientific
research and different medical fields including the disciplines of pathology, oncology
and radiology at most.
Ultrasound, a cornerstone of medical diagnostics, is also witnessing a transformative
process. Recent advancements in AI tools are in fact starting to radically change
this field.
Interestingly, in a context where the reliance on the expertise of sonographers has
been often considered as a major limitation, ultrasound represents an ideal candidate
to receive significant benefits from the integration of AI, with promises of enhanced
diagnostic accuracy, improved workflow efficiency, and expanded access to high-quality
care.
Additionally, it should be considered that ultrasound is known to be a useful first
diagnostic approach or a screening tool for many medical conditions (e. g. screening
for breast lesions, screening for abdominal aortic aneurysm, screening for thyroid
nodules, surveillance of patients at risk of hepatocellular carcinoma, screening for
carotid or lower limb arteries, atherosclerotic disease, etc.). However, its use cannot
be planned widely enough to satisfy such needs that involve very large populations,
mainly due to the limited availability and costs of expert medical manpower. Automated
use of ultrasound, guided by AI recognition, may have the potential to speed up at
least some of such processes reducing the needed manpower.
Altogether, the impact of AI in ultrasound is multifaceted, affecting different phases
of the examination, ranging from image acquisition, recognition of abnormalities and
interpretation to decision-making and patient outcomes; however, some peculiarities
of AI tools may slow down its application in clinical practice and must be known to
avoid troublesome situations.
Hereinafter, we will discuss the major benefits correlated to the introduction of
different AI tools in ultrasonology and some of the obstacles that need to be overcome.
Enhancing Diagnostic Accuracy
Enhancing Diagnostic Accuracy
One of the most significant and easier to perceive contributions of AI to ultrasound
imaging lies in its ability to enhance diagnostic accuracy. AI algorithms, particularly
those based on deep learning and machine learning, have demonstrated remarkable proficiency
in image analysis.
In a large retrospective analysis on more than 100 000 people, Gao et al. demonstrated
that AI-assisted ultrasound imaging using deep learning techniques enabled to discriminate
between malignant and benign ovarian masses with excellent accuracy performances confirmed
by an AUC of 0.87 on an external validation cohort [1].
A recent systematic review on the topic confirmed optimal accuracy across different
studies with AUC ranging from 0.73 to 0.99; however, it is important to highlight
that only 2 out of 37 studies that evaluated the accuracy of the model used an external
validation set [2], which is a necessary step before any tool can be brought to clinical practice.
Another fascinating application of AI in ultrasound was explored by Fu et al., the
authors conducted a meta-analysis involving 11 005 people which proved that the diagnostic
accuracy of ultrasound-based radiomics had sensitivity and specificity of 0.76 and
0.78 for predicting HER2, 0.80, and 0.76 for Ki67, 2 key biomarkers in the clinical
management of breast cancer [3].
Again in the field of breast cancer, the addition of an AI analysis improved the diagnostic
accuracy of ultrasound, reducing the rate of false-positive cases for cancer [4].
In the oncology field the number of potential applications of AI is impressive [5]. A recent preliminary work claimed ability to improve the capacity of ultrasound
to classify metastatic cervical lymphadenopathies into primary cancer sites [6].
As illustrated through examples, AI in general could provide suggestions for disease
characterization, especially combining various multiparametric ultrasound approaches.
Noteworthy, this might be particularly useful in the instance of patients hosting
unknown rare conditions, which might have never been met before by the operator and
might not even come to his mind, but they are likely to become recognized once suggested
by an AI tool.
Improving Workflow Efficiency
Improving Workflow Efficiency
Beyond diagnostic accuracy, AI is also revolutionizing the operational aspects of
ultrasound imaging. Traditional ultrasound examinations can be time-consuming and
require significant expertise. AI streamlines these processes through automation and
intelligent assistance, thereby improving workflow efficiency.
For instance, a regression convolutional neural network designed by Fiorentino et
al. [7] to measure the fetal head circumference, a crucial parameter to evaluate fetal growth
and development, proved to be able to reduce examination time, inter-clinician variability,
and increase diagnostic accuracy [7].
Another key area where AI has made substantial inroads is in the acquisition of ultrasound
images. Automated image acquisition systems, guided by AI, can assist clinicians and
sonographers in capturing high-quality images consistently. These systems use real-time
feedback to ensure optimal probe positioning and image quality, reducing the dependency
on the operatorʼs skill level.
This particular aspect was proved in a clever prospective: In a multicenter diagnostic
study 8 nurses, with no previous experience of ultrasound imaging, performed an echocardiography
on 240 patients using a deep-learning AI–based software based on 5 million examples
of the outcome of ultrasonographic probe movement on image quality.
The images were later assessed by 5 expert echocardiographers who judged them of diagnostic
quality in 98.8 % of patients for left ventricular size and function, and 92.5 % for
right ventricular size [8].
Expanding Access to High-Quality Care
Expanding Access to High-Quality Care
AI’s transformative impact on ultrasound imaging extends to addressing disparities
in healthcare access. In many parts of the world, especially in low-resource settings,
access to quality ultrasound services is limited due to a shortage of trained personnel
and equipment. AI has the potential to bridge this gap by democratizing access to
high-quality imaging.
Notably, in a retrospective analysis published in this issue of Ultraschall in der
Medizin/European Journal of Ultrasound, Wei et al. used an automatic recognition system
using neural network to diagnose the presence of carotid plaques in real-time and
synchronously to the acquisition of the image, achieving a remarkable accuracy of
98.5 % [9]. Additionally, using live transmission of ultrasound images, they tested the use
of the algorithm to diagnose carotid plaques in ultrasound examinations during their
performance as far as more than 1000 km away, showing how distances can be shortened
thanks to these tools [9]. Such technologies would allow the potential of delivery of care in the absence
of local expertise and also in case of shortage of qualified manpower to assist the
examination live from remote.
In this context portable ultrasound devices are increasingly being deployed in remote
and underserved areas [10]. These devices are compact, affordable, and easy to use, making them ideal for field
conditions. AI assistance in real-time image interpretation could provide instant
diagnostic support to healthcare workers who may lack specialized training, enabling
timely diagnosis and treatment. AI assistance also allows the recognition of anatomic
structures during point-of-care interventional procedures.
This not only enhances diagnostic accuracy, but also fosters local expertise by providing
educational feedback to healthcare providers in remote areas.
Challenges and Ethical Considerations
Challenges and Ethical Considerations
While the progress of AI in ultrasound imaging is undeniably impressive and fast rising,
it is not free of challenges and ethical considerations worth to be taken into account.
First of all, ensuring the reliability and generalizability of AI algorithms is of
paramount importance. Unfortunately, many AI models are trained on specific datasets,
and their performance can vary when applied to different populations or clinical settings.
Therefore, external validation and updating of these models are essential to maintain
their accuracy and relevance.
Secondly, another key issue is represented by the so-called black box problem. This
reflects the inability to understand how deep learning models reach their conclusion,
causing potential catastrophic consequences. In order to safely introduce AI tools
in clinical practice it is mandatory to generate explanation of black box mechanisms
of action, which some authors recently compared to creating explanations in natural
sciences or physics [11].
Strictly connected to the lack of understanding of the AI tool there is the accountability
issue. Accountability is often defined somehow imprecisely, but it can be simplified
as an obligation to inform about, and justify one’s conduct to an authority [12]. This implies both a legal liability, which has to be strictly regulated before
the introduction in clinical practice, and moral responsibility to the patients who
are going to be influenced by decisions taken thanks to the help of AI tools.
To this end, in order to be certified by regulatory agencies and marketed, specific
versions of AI tools should to be finally fixed; otherwise, if a continuous evolution
and improvement of a software takes place (theoretically to be regarded as a great
opportunity), with no obvious overt communication of the pass from a version to a
new one, it could happen that a diagnosis established today could turn into a slightly
different diagnosis tomorrow, despite the same images are analyzed, if an automatic
update version of the system has occurred, puzzling both operators and patients. This
could even raise legal questions for the responsibility in the diagnostic process.
Responsibility of the final diagnosis and possible or not access of patients to information
of the AI software utilized to support the final diagnosis are still matter of debate
and concern.
Lastly, data privacy remains probably the most significant ethical concern. Data leakage
and successful prompt injection attacks to steal data have been reported [13]
[14], showing how AI tools are still lag behind in this aspect which is crucial to allow
them in clinical practice. Moreover, who “owns” the images that have been elaborated
to produce new software which could be needed for new updates is also another serious
concern.
Conclusion
The integration of AI into ultrasound imaging represents a paradigm shift in medical
diagnostics and patient care. By enhancing diagnostic accuracy, improving workflow
efficiency, expanding access to high-quality care, AI is set to redefine the standards
of ultrasound imaging. However, it is imperative to address the challenges and ethical
considerations associated with AI to ensure its responsible and effective implementation.
As we continue to explore the potential of AI in ultrasound imaging, the collaboration
between technologists, clinicians, and policymakers will be crucial. Together, we
can harness the power of AI to transform healthcare delivery, ultimately improving
patient outcomes and advancing the frontiers of medical science.
