Artificial Intelligence-assisted Ultrasound Diagnosis of Musculoskeletal Injuries. (ECO-IA)
Artificial Intelligence-assisted Ultrasound Diagnosis of Musculoskeletal Injuries for Physiotherapy, Traumatology and Orthopaedic Practice (ECO-IA).
The proposal combines clinical research in physiotherapy, traumatology and orthopaedics with artificial intelligence, computer vision and health-data science.
研究概览
详细说明
ECO-IA is submitted as an individual research project under the "Non-oriented research" modality. The project addresses a relevant gap in clinical musculoskeletal care: ultrasound is widely available, safe, radiation-free and useful for the assessment of tendons, muscles, ligaments and joints, but its diagnostic value depends strongly on operator training, image-acquisition protocol and interpretative experience.
The proposal combines clinical research in physiotherapy, traumatology and orthopaedics with artificial intelligence, computer vision and health-data science. It aims to develop, validate and prepare for clinical translation an AI-based decision-support system capable of assisting healthcare professionals in the identification and characterisation of common musculoskeletal injuries from ultrasound images. This is consistent with the purpose of the call: generating new knowledge while creating opportunities for predoctoral research training.
研究类型
注册 (估计的)
联系人和位置
学习联系方式
- 姓名：JORGE VELÁZQUEZ SAORNIL, PhD.
- 电话号码：923277100
- 邮箱：jvelazquezsa@upsa.es
研究联系人备份
- 姓名：Jorge Velázquez, PhD.
- 邮箱：jvelazquezsa@upsa.es
学习地点
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Salamanca
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Salamanca、Salamanca、西班牙、37002
- Universidad Pontificia de Salamanca
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接触：
- Jorge Velázquez Saornil, PhD.
- 电话号码：923277100
-
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参与标准
资格标准
适合学习的年龄
接受健康志愿者
有资格学习的性别
取样方法
研究人群
Patients with musculoskeletal pain affecting tendons, muscles, ligaments or bursae
描述
Inclusion Criteria:
- Patients with musculoskeletal pain affecting tendons, muscles, ligaments or bursae
Exclusion Criteria:
- Patients with severe, acute pain consistent with an open fracture; surgical procedures in the area under examination and diagnosis. Uncooperative patients who do not sign the informed consent form.
学习计划
研究是如何设计的？
设计细节
- 观测模型：COHORT
- 时间观点：CROSS_SECTIONAL
队列和干预
团体/队列 |
|---|
|
IA Group
Diagnosis by IA |
|
Medical Expert Group
Diagnosis by a medical expert |
研究衡量的是什么？
主要结果指标
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
|
Diagnostic imaging
大体时间：1 day
|
Ability to carry out a medical diagnosis based on imaging findings |
1 day
|
合作者和调查者
调查人员
- 首席研究员：Jorge Velázquez Saornil, Principal investigator、Universidad Pontíficia de Salamanca
出版物和有用的链接
一般刊物
- Humphries MP, Kaye D, Stankeviciute G, Halliwell J, Wright AI, Bansal D, Brettle D, Treanor D. Development of a multi-scanner facility for data acquisition for digital pathology artificial intelligence. J Pathol. 2024 Sep;264(1):80-89. doi: 10.1002/path.6326. Epub 2024 Jul 10.
- Berezowska S, Cathomas G, Grobholz R, Henkel M, Jochum W, Koelzer VH, Kreutzfeldt M, Mertz KD, Rossle M, Soldini D, Zlobec I, Janowczyk A. Digital image analysis and artificial intelligence in pathology diagnostics-the Swiss view. Pathologie (Heidelb). 2023 Dec;44(Suppl 3):222-224. doi: 10.1007/s00292-023-01262-w. Epub 2023 Nov 21.
- Shafi S, Parwani AV. Artificial intelligence in diagnostic pathology. Diagn Pathol. 2023 Oct 3;18(1):109. doi: 10.1186/s13000-023-01375-z.
研究记录日期
研究主要日期
学习开始 (估计的)
初级完成 (估计的)
研究完成 (估计的)
研究注册日期
首次提交
首先提交符合 QC 标准的
首次发布 (实际的)
研究记录更新
最后更新发布 (实际的)
上次提交的符合 QC 标准的更新
最后验证
更多信息
与本研究相关的术语
其他研究编号
- UPSA05112026
计划个人参与者数据 (IPD)
计划共享个人参与者数据 (IPD)？
药物和器械信息、研究文件
研究美国 FDA 监管的药品
研究美国 FDA 监管的设备产品
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