Ignite Creation Date:
2024-06-16 @ 11:52 AM
Last Modification Date:
2024-10-26 @ 3:32 PM
Study NCT ID:
NCT06456853
Status:
RECRUITING
Last Update Posted:
2024-06-13
First Post:
2024-06-08
Brief Title:
Comparison of AI-Generated Pain Scoring Visuals With Visual Analog Scale VAS for Pain Assessment
Sponsor:
Kanuni Sultan Suleyman Training and Research Hospital
Organization:
Kanuni Sultan Suleyman Training and Research Hospital
Study Overview
Official Title:
Comparison of AI-Generated Pain Scoring Visuals With Visual Analog Scale VAS for Pain Assessment
Status:
RECRUITING
Status Verified Date:
2024-10
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:
None
Brief Summary:
This prospective study will be conducted in surgical wards assessing postoperative patients Initially patients will be evaluated using the VAS method Subsequently they will be shown five AI-generated images depicting different pain levels and will select the image that best represents their pain A follow-up survey will assess the effectiveness of each method
Using ChatGPT-4DALL-E images corresponding to VAS scores of 1-2 3-4 5-6 7-8 and 9-10 will be created Patients will choose the image that best describes their pain aiming to determine if AI-supported visuals offer a more accurate alternative to VAS for pain assessment
Using ChatGPT-4DALL-E images corresponding to VAS scores of 1-2 3-4 5-6 7-8 and 9-10 will be created Patients will choose the image that best describes their pain aiming to determine if AI-supported visuals offer a more accurate alternative to VAS for pain assessment
Detailed Description:
Study Objective The primary objective of this study is to compare and evaluate the effectiveness of AI-generated pain visuals in assisting patients to express their pain levels with the Visual Analog Scale VAS By allowing patients to more accurately depict their pain through AI-supported visuals the study aims to enhance pain management practices in clinical settings
Study Significance Pain management is a critical component of healthcare directly impacting patient well-being and treatment success The VAS is a widely used tool for subjective pain assessment but can be challenging for some patients due to its abstract nature AI-generated visuals offer a potentially more precise and understandable way for patients to communicate their pain potentially leading to more accurate and personalized pain assessments and management
This study aims to measure the contribution of AI-generated pain visuals to more accurate pain assessment and to explore the potential applications of this technology Additionally the study seeks to understand the advantages and limitations of this approach compared to traditional methods like VAS thereby enhancing the role of AI in pain management practices
Expected Benefits and Risks
Expected Benefits
Improved Pain Expression AI-generated visuals may help patients articulate their pain more clearly leading to better pain management in clinical settings
Personalized Treatment Approaches Enhanced pain expression can provide healthcare providers with opportunities to create more personalized treatment plans especially beneficial for chronic pain patients
Enhanced Clinical Decision-Making The use of AI visuals may facilitate more objective and reproducible pain assessments improving overall pain management strategies
Potential Risks
Misinterpretation Risk AI-generated visuals might misinterpret patient pain in certain cases especially if the visuals are misleading or complex
Dependence on Technology Over-reliance on AI tools may overlook the importance of human judgment and the subjective nature of pain assessment
Study Design This prospective study will be conducted in surgical wards assessing postoperative patients Initially patients will be evaluated using the conventional VAS method which involves marking their pain on a 0-10 scale Subsequently patients will be shown five AI-generated images depicting different pain levels and asked to select the image that best represents their pain A follow-up survey will assess which method the patients found more effective for expressing their pain
VAS Scoring
Patients will mark their pain level on a line ranging from 0 no pain to 10 worst pain
AI-Generated Visuals
Using ChatGPT-4DALL-E images corresponding to VAS scores of 1-2 3-4 5-6 7-8 and 9-10 will be created These images will specifically depict facial expressions reflecting the respective pain levels Patients will choose the image that best describes their pain
This study aims to identify whether AI-supported visuals provide a more accurate and user-friendly alternative to traditional VAS scoring for pain assessment
VAS Score Descriptions VAS Score 1-2 A middle-aged man showing signs of mild discomfort
VAS Score 3-4 A young female athlete on a soccer field expressing moderate pain
VAS Score 5-6 A man in a kitchen environment displaying severe pain from a cut
VAS Score 7-8 A young male feeling severe shoulder pain
VAS Score 9-10 A young woman experiencing intense pain
Study Significance Pain management is a critical component of healthcare directly impacting patient well-being and treatment success The VAS is a widely used tool for subjective pain assessment but can be challenging for some patients due to its abstract nature AI-generated visuals offer a potentially more precise and understandable way for patients to communicate their pain potentially leading to more accurate and personalized pain assessments and management
This study aims to measure the contribution of AI-generated pain visuals to more accurate pain assessment and to explore the potential applications of this technology Additionally the study seeks to understand the advantages and limitations of this approach compared to traditional methods like VAS thereby enhancing the role of AI in pain management practices
Expected Benefits and Risks
Expected Benefits
Improved Pain Expression AI-generated visuals may help patients articulate their pain more clearly leading to better pain management in clinical settings
Personalized Treatment Approaches Enhanced pain expression can provide healthcare providers with opportunities to create more personalized treatment plans especially beneficial for chronic pain patients
Enhanced Clinical Decision-Making The use of AI visuals may facilitate more objective and reproducible pain assessments improving overall pain management strategies
Potential Risks
Misinterpretation Risk AI-generated visuals might misinterpret patient pain in certain cases especially if the visuals are misleading or complex
Dependence on Technology Over-reliance on AI tools may overlook the importance of human judgment and the subjective nature of pain assessment
Study Design This prospective study will be conducted in surgical wards assessing postoperative patients Initially patients will be evaluated using the conventional VAS method which involves marking their pain on a 0-10 scale Subsequently patients will be shown five AI-generated images depicting different pain levels and asked to select the image that best represents their pain A follow-up survey will assess which method the patients found more effective for expressing their pain
VAS Scoring
Patients will mark their pain level on a line ranging from 0 no pain to 10 worst pain
AI-Generated Visuals
Using ChatGPT-4DALL-E images corresponding to VAS scores of 1-2 3-4 5-6 7-8 and 9-10 will be created These images will specifically depict facial expressions reflecting the respective pain levels Patients will choose the image that best describes their pain
This study aims to identify whether AI-supported visuals provide a more accurate and user-friendly alternative to traditional VAS scoring for pain assessment
VAS Score Descriptions VAS Score 1-2 A middle-aged man showing signs of mild discomfort
VAS Score 3-4 A young female athlete on a soccer field expressing moderate pain
VAS Score 5-6 A man in a kitchen environment displaying severe pain from a cut
VAS Score 7-8 A young male feeling severe shoulder pain
VAS Score 9-10 A young woman experiencing intense pain
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