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| Name | Class |
|---|---|
| Ankara University | OTHER |
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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?
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.Åž., 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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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Ultrasound Scan | Other | Clinical professionals will conduct non-invasive ultrasound scans from the specified body views and subsequently save the acquired data. |
| Measure | Description | Time Frame |
|---|---|---|
| Collecting ultrasound data for artificial intelligence software that highlighted structures | 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. | 4 months |
| Measure | Description | Time Frame |
|---|---|---|
| Assessment of image quality | The usability of the collected data in the artificial intelligence software will be verified. | 4 months |
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Inclusion Criteria:
Exclusion Criteria:
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A total of 300 participants, divided between genders (150 females and 150 males), will take part in the study. These participants will include both healthy individuals and pathological conditions related to specific views of the musculoskeletal system. Their involvement will be based on informed consent. Pertinent data, such as medical history, body mass index (BMI), age, and gender, will be reported.
The logistical expenses of the volunteers during the study will be covered by the sponsor, Smart Alfa Teknoloji San. ve Tic. A.Åž.
The collection of raw ultrasound data will be undertaken by two experienced clinicians in physical medicine and rehabilitation, and ultrasound scanning. Their expertise ensures accurate data collection.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Ankara University School of Medicine | Altındağ | Ankara | 06230 | Turkey (Türkiye) |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 37572764 | Background | 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. | |
| 35802706 |
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| 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. |
| ID | Term |
|---|---|
| D009140 | Musculoskeletal Diseases |
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| ID | Term |
|---|---|
| D019220 | High-Energy Shock Waves |
| ID | Term |
|---|---|
| D000069453 | Ultrasonic Waves |
| D013016 | Sound |
| D011840 | Radiation, Nonionizing |
| D011827 | Radiation |
| D055585 | Physical Phenomena |
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