Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT04654546 |
| Other study ID # |
0177-20-EMC |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
N/A
|
| First received |
|
| Last updated |
|
| Start date |
August 24, 2021 |
| Est. completion date |
June 2025 |
Study information
| Verified date |
September 2023 |
| Source |
HaEmek Medical Center, Israel |
| Contact |
Israel Aharony?, M.D. Ph.D |
| Phone |
97246495635 |
| Email |
elik.aharony[@]gmail.com |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial Summary
Background: Ultrasound imaging is an imaging method that uses sound waves to characterize the
structure and function of various organs in health and disease conditions. This technique is
widely used in clinical day-to-day life and has many advantages, such as real-time imaging,
availability for imaging at the patient's bedside, and lack of ionizing radiation. Aside from
the mentioned advantages, the ultrasound test also has notable drawbacks. These include the
absence of sound wave penetration through a medium containing air such as intestinal loops,
dependence on operator skill, and the need for the subject's cooperation during the test.
Compared to the ultrasound examination, the CT scan allows for a broader anatomical view and
is not limited by physiological factors such as bones and air. on the other hand, the test
requires ionizing radiation that inevitably carries a direct and indirect danger to the
patient's health, and requires more financial resources.
Objectives of the study: Using artificial intelligence to bridge the gap between ultrasound
and CT scans, and to create a uniform system that takes advantage of them. This is to allow
for better spatial orientation as well as a better characterization of the anatomical
structures being scanned.
Participants: Women and/or men over the age of 18, who performed an abdominal CT scan during
the previous month for the ultrasound examination in the experiment.
Methods: The study is a prospective open-label research, in which both the physician and the
patient are aware of the manner and purposes of the scan. Participants who meet the threshold
conditions will be summoned for examination in the rooms of the Imaging Institute at Haemek
medical center, during which the participants will undergo a complete ultrasound scan of the
abdominal organs using a clinical ultrasound device. The ultrasound images will be visually
coupled to previous CT images of the same patient at the time of the examination, using a
Fusion system located in the ultrasound device mentioned above. The conjugated CT and
ultrasound images will be encoded and will be sent without identifying details to the SAMPL
laboratory, to be used as a learning platform for the artificial intelligence system. The
images will be transferred after the subject's personal details have been encoded in an EXCEL
file and saved by the principal investigator.
Description:
Background and rationale of the medical experiment:
Ultrasound imaging is an imaging method that uses sound waves to characterize the structure
and function of various organs in health and disease conditions. This technique is widely
used in clinical daily life and has many benefits. The exam does not involve the use of
ionizing radiation, so its use is safer than other techniques such as X-rays or CT scans
(Computed Tomography). The images acquired and viewed on the ultrasound device are real-time,
so changes can be identified, which in many cases affects the final diagnosis. The test is
non-invasive, nor does the test require the use of a contrast agent that contains substances
that may cause an allergic reaction or impair kidney function. In addition, the equipment is
widely available, and can also be used bedside. Aside from the mentioned advantages, the
ultrasound test also has notable drawbacks. The ultrasound waves do not penetrate well
through bones or air, thus impairing test quality. In addition, the method is highly
dependent on the skill of the operator, so substantial experience is required in order to
produce sufficient quality information and make an accurate diagnosis. The quality of the
test also depends on the cooperation of the subject during the test, such as changes in
posture and deep breathing.
Compared to the ultrasound examination, the CT scan allows for a broader anatomical view, and
is not limited by physiological factors such as bones and air. The most notable shortcomings
of the CT scan include the fact that the test requires ionizing radiation that inevitably
carries a direct and indirect danger to the patient's health. In addition, the test requires
more resources financially and in terms of manpower, which is a limitation in its use outside
the hospital or in countries with poor socio-economic status.
In this study, the investigators aim to bridge the gap between these two types of techniques
and create a uniform system that takes advantage of both. This is done by creating CT images
from ultrasound images. This process will involve the use of artificial intelligence methods,
namely machine learning algorithms.
Machine learning in general, and deep learning in particular, have gained momentum in recent
years in the field of computer vision and more recently also in the field of medical imaging.
In addition, one can already see significant successes in the classification of retinal
diseases, in which the sensor is a fundus camera or Optical Coherence Tomography (OCT), in
the classification of classifications In MRI imaging of the breast 3, and more recently, in
the classification of the severity of the disease caused by the Corona virus, using chest
X-rays and ultrasound scans.
Machine learning includes two phases. In the first phase, the algorithm trains on tagged
data, using architectures and target functions, which allow high accuracy to be achieved. In
the second step, the investigators will test the algorithm for data that has not yet been
observed. It should be noted that a large amount of tagged data can be very helpful in
developing the algorithm, but at the same time, it may be difficult to produce tagged data
and it can be a long and expensive process. Therefore, the investigators will utilize
unsupervised or semi-supervised approaches.
Objectives of medical research: Providing sonographic and radiographic information to an
artificial intelligence system for the purpose of creating CT images from ultrasound images,
in order to allow better spatial orientation as well as better characterization of the
scanned anatomical structures.
Type of study: A prospective, open label study in which both the physician and the patient
are aware of the manner and purposes of the scan.
Experimental procedure: Participants who meet the study criteria will undergo a complete
ultrasound scan of the abdominal organs using a clinical ultrasound device in the Imaging
Institute at Haemek medical center. The estimated duration of the exam is 20 minutes. The
ultrasound images will be visually coupled to previous CT images of the same patient at the
time of the examination, using a Fusion system located in the ultrasound device mentioned
above.
Note that if the participant came for a clinical ultrasound examination which was supposed to
take place in any case, he will pass the clinical examination as usual, and then will
separately go through the examination described above as part of the study.
The coupled CT and ultrasound images will be encoded and be sent to the SAMPL laboratory in
Weizmann institute, there the images will serve as a learning platform for the artificial
intelligence system. The images will be transferred only after the subject personal details
have been encoded in an EXCEL file and saved by the principal investigator. Only the lead
researcher and secondary researchers will be exposed to pre-encoding information, which will
be stored on a dedicated computer at the lead researcher, password protected.
The coded information collected will be passed continuously during the research to the
Weizmann Institute, in order to ensure good acquisition of information and the possibility of
real-time feedback to the lead researcher in favor of acquiring better quality information
that enables data analysis.
In any future application of the SAMPL laboratory for details about the participants, it will
have to contact with the serial number when the coding key will be exclusively at the
clinical site (Haemek Hospital). The SAMPL laboratory at the Weizmann Institute will not be
exposed in any way and at any stage in the experiment to identifying information about the
participants.
Monitoring of participants and reporting of medical findings
If unexpected medical findings that are important to the patient's health are discovered
during the study, it is the responsibility of the principal investigator to transfer them to
the attending physician without delay for further follow-up and treatment as necessary by
standard medical care. Participants will not be followed up after the experimental scan as
part of the clinical trial.
