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Conversion of Ultrasound Images to CT Format Imaging Using Artificial Intelligence-based Learning

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ClinicalTrials.gov Identifier: NCT04654546
Recruitment Status : Not yet recruiting
First Posted : December 4, 2020
Last Update Posted : September 14, 2021
Sponsor:
Collaborator:
Weizmann Institute of Science
Information provided by (Responsible Party):
Israel Aharoni, HaEmek Medical Center, Israel

Brief 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.


Condition or disease Intervention/treatment Phase
Anatomy Other: Ultrasound abdominal scan Not Applicable

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 50 participants
Allocation: N/A
Intervention Model: Single Group Assignment
Intervention Model Description: Scan of abdominal organs of participants as detailed in the study protocol
Masking: None (Open Label)
Primary Purpose: Diagnostic
Official Title: A Prospective Study for the Conversion of Ultrasound Images to CT Format Imaging Using Artificial Intelligence-based Learning
Estimated Study Start Date : October 1, 2021
Estimated Primary Completion Date : January 2023
Estimated Study Completion Date : June 2023

Resource links provided by the National Library of Medicine


Arm Intervention/treatment
Experimental: Abdominal scans
Participants who performed an abdominal CT exam up to one month prior to the experimental ultrasound exam.
Other: Ultrasound abdominal scan

A complete ultrasound scan of the abdominal organs using a clinical ultrasound device in the Imaging Institute at Haemek medical center. 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 coupled CT and ultrasound images will be encoded and be sent to the SAMPL laboratory in Weizmann institute, there it will serve as a learning platform for the artificial intelligence system.





Primary Outcome Measures :
  1. Normalized cross-correlation between input CT images and the ultrasound-based algorithm-generated CT images [ Time Frame: 2 year ]
    The cross-correlation between the input CT images, serving as Ground Truth, and the algorithm-generated CT images will serve as a measure of similarity (Similarity score), normalized to a range [-1,1].

  2. Accuracy rate [ Time Frame: 2 year ]
    System accuracy rate will be evaluated by comparing the aforementioned similarity score to a success rate threshold (T).



Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Criteria

Inclusion Criteria:

  • Participants who performed an abdominal CT exam up to one month prior to the experimental ultrasound exam.

Exclusion Criteria:

  • Participants who had a change in their medical condition that may have substantial effects on the imaging features of the abdominal organs.
  • Pregnant women

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04654546


Contacts
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Contact: Israel Aharony‬, M.D. Ph.D 97246495635 elik.aharony@gmail.com

Locations
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Israel
Emek medical center
Afula, Israel, 1834111
Sponsors and Collaborators
HaEmek Medical Center, Israel
Weizmann Institute of Science
Investigators
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Principal Investigator: Israel Aharony, M.D. Ph.D Imaging institute, Haemek Medical Center, Afula, Israel.
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Responsible Party: Israel Aharoni, Radiology resident, Haemek medical center Radiology institute, Principal investigator., HaEmek Medical Center, Israel
ClinicalTrials.gov Identifier: NCT04654546    
Other Study ID Numbers: 0177-20-EMC
First Posted: December 4, 2020    Key Record Dates
Last Update Posted: September 14, 2021
Last Verified: September 2021
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No