A Data Collection Study for Artificial Intelligence-guided Musculoskeletal System Examination
Study Details
Study Description
Brief Summary
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?
| Condition or Disease | Intervention/Treatment | Phase |
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Detailed Description
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:
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Specific body views, guided by established protocols, will be scanned from both short and long axes along different body planes. The focus areas encompass tendons, ligaments, bones, and nerves within the shoulder, elbow, hand, hip, knee, and foot regions.
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A cohort of 300 volunteers, evenly distributed by gender (150 male, 150 female), will have their demographic data (BMI, gender, age) documented.
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To counteract potential biases, the sequence of volunteer participation will be randomized.
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Each scan is expected to take 45 minutes.
Study Design
Outcome Measures
Primary Outcome Measures
- Collecting ultrasound data for artificial intelligence software that highlighted structures [4 months]
The gathered images will serve the purpose of annotating anatomical landmarks, enabling the acquisition of diagnostically reliable images through artificial intelligence software. These annotated ultrasound images, validated by experts, will form the basis of a training dataset for the development of a machine learning algorithm.
Secondary Outcome Measures
- Assessment of image quality [4 months]
The usability of the collected data in the artificial intelligence software will be verified.
Eligibility Criteria
Criteria
Inclusion Criteria:
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Volunteers over the age of 18
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Able to accept and sign the Informed Consent Form before participating in the study
Exclusion Criteria:
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Volunteers below the age of 18
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Unwilling to accept or having psychiatric or neurological diseases to sign an Informed Consent Form before participating in the study
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Inability to lie flat
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Anatomical deformity in the area to be scanned
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Ankara University School of Medicine | Altındağ | Ankara | Turkey | 06230 |
Sponsors and Collaborators
- Smart Alfa Teknoloji San. ve Tic. A.S.
- Ankara University
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
- 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 Aug 11;250:152143. doi: 10.1016/j.aanat.2023.152143. Online ahead of print.
- 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.
- SMARTALPHA-CURIOUS-1000