Ignite Creation Date:
2024-07-17 @ 12:01 PM
Last Modification Date:
2024-10-26 @ 3:32 PM
Study NCT ID:
NCT06467006
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-06-20
First Post:
2024-06-14
Brief Title:
AI PREDICTION FOR PROXIMAL HUMERAL FRACTURES
Sponsor:
Consorci Sanitari de lAnoia
Organization:
Consorci Sanitari de lAnoia
Study Overview
Official Title:
ARTIFICIAL INTELLIGENCE-BASED PREDICTION OF CLINICAL OUTCOMES IN PATIENTS SUSTAINING PROXIMAL HUMERAL FRACTURES
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
Orthopredict
Brief Summary:
Our smartphones can recognize the pictures of our family loved ones and friends Face recognition software leverages artificial intelligence AI image recognition and other advanced technology to map analyze and confirm the identity of a face
We humans do a poor job when classifying the injury related to a patient sustaining a proximal humeral fracture In consequence there is great heterogeneity in the treatment of proximal humerus fractures Moreover offering relevant information to patients regarding the risk of complications or fracture sequelae is challenging given that the current series are based on obsolete classifications and the published series bring together just over hundreds of patients analyzed With these limitations patients have few opportunities to participate in decision-making about their injury
The present project aim is to integrate new technologies for the prediction of relevant clinical results for the patients presenting a proximal humeral fracture In brief AI can help identify similar fracture patterns without human inference while humans can feed the algorithm with variables of interest such as the functional outcomes and complications related to this particular type of fracture
We humans do a poor job when classifying the injury related to a patient sustaining a proximal humeral fracture In consequence there is great heterogeneity in the treatment of proximal humerus fractures Moreover offering relevant information to patients regarding the risk of complications or fracture sequelae is challenging given that the current series are based on obsolete classifications and the published series bring together just over hundreds of patients analyzed With these limitations patients have few opportunities to participate in decision-making about their injury
The present project aim is to integrate new technologies for the prediction of relevant clinical results for the patients presenting a proximal humeral fracture In brief AI can help identify similar fracture patterns without human inference while humans can feed the algorithm with variables of interest such as the functional outcomes and complications related to this particular type of fracture
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None