Musculoskeletal System Ultrasound Examination Data Collection Study for the Development of an Artificial Intelligence Software

NCT ID: NCT06025279

Last Updated: 2024-04-18

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

300 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-08-03

Study Completion Date

2023-11-29

Brief Summary

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The primary objective of this observational study is to acquire ultrasound images (raw data) encompassing various planes within the musculoskeletal system. This data will be instrumental in the development of artificial intelligence-guided software. The study aims to enlist 300 volunteers, comprising individuals with both healthy musculoskeletal systems and those presenting pathologies. These participants will undergo ultrasound scans administered by two experienced professionals, employing FDA-cleared ultrasound devices.

The main question it aims to answer is:

-Are the collected ultrasound images of diagnostic quality?

Detailed Description

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Ultrasound's cost-effective and user-friendly attributes have positioned it as a cornerstone in diagnosing musculoskeletal system disorders.

In this single-centered and prospective study, the study aims to enlist 300 volunteers, comprising both individuals with healthy musculoskeletal systems and those with pathologies. The collected ultrasound raw data will be used to train models for the identification and highlighting of key anatomical landmarks on ultrasound images. Participants' gender, age, BMI, and medical history will be considered and reported. All scans will be performed on FDA-cleared general-purpose ultrasound devices. Obtained images will be used to develop artificial intelligence-based medical software by Smart Alfa Teknoloji San. Ve Tic. A.Ş., Ankara, Turkey. Smart Alfa has similarly conducted a study in the field of anesthesia using the same method in Nerveblox artificial intelligence software.

The study methodology encompasses the following components:

* Specific body views, guided by established protocols, will be scanned from different body planes. The focus areas encompass musculoskeletal structures.
* A cohort of 300 volunteers, evenly distributed by gender (150 male, 150 female), will have their demographic data (BMI, gender, age) documented.
* To counteract potential biases, the sequence of volunteer participation will be randomized.
* Each scan is expected to take 45 minutes.

Conditions

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Ultrasound Imaging of Anatomical Structures Musculoskeletal Diseases

Study Design

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Observational Model Type

CASE_ONLY

Study Time Perspective

PROSPECTIVE

Interventions

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Ultrasound Scan

Clinical professionals will conduct non-invasive ultrasound scans from the specified body views and subsequently save the acquired data.

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* Volunteers over the age of 18
* Able to accept and sign the Informed Consent Form before participating in the study

Exclusion Criteria

* Volunteers below the age of 18
* Unwilling to accept or having psychiatric or neurological diseases to sign an Informed Consent Form before participating in the study
* Inability to lie flat
* Anatomical deformity in the area to be scanned
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Ankara University

OTHER

Sponsor Role collaborator

Smart Alfa Teknoloji San. ve Tic. A.S.

INDUSTRY

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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Ankara University School of Medicine

Altındağ, Ankara, Turkey (Türkiye)

Site Status

Countries

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Turkey (Türkiye)

References

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Gungor I, Gunaydin B, Buyukgebiz Yesil BM, Bagcaz S, Ozdemir MG, Inan G, Oktar SO. Evaluation of the effectiveness of artificial intelligence for ultrasound guided peripheral nerve and plane blocks in recognizing anatomical structures. Ann Anat. 2023 Oct;250:152143. doi: 10.1016/j.aanat.2023.152143. Epub 2023 Aug 11.

Reference Type BACKGROUND
PMID: 37572764 (View on PubMed)

Ozcakar L, Tok F, Ricci V, Mezian K, Wu CH, Wu WT, Park GY, Kwon DR, Prieto MG, Dughbaj M, Dogan Y, Aksoz B, Guvener O, Ekiz T, Tiras M, Karacoban L, Menderes Y, Ciftci E, Ilicepinar OF, Kaya U, Kara M, Chang KV. Artificial Intelligence Featuring EURO-MUSCULUS/USPRM Basic Scanning Protocols. Am J Phys Med Rehabil. 2022 Nov 1;101(11):e174-e175. doi: 10.1097/PHM.0000000000002070. Epub 2022 Jul 7. No abstract available.

Reference Type BACKGROUND
PMID: 35802706 (View on PubMed)

Other Identifiers

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SMARTALPHA-CURIOUS-1000

Identifier Type: -

Identifier Source: org_study_id

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