Clinical trial NCT06456853: Comparison of AI-Generated Pain Scoring Visuals With Visual Analog Scale (VAS) for Pain Assessment
Official title: Comparison of AI-Generated Pain Scoring Visuals With Visual Analog Scale (VAS) for Pain Assessment
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Pain, Postoperative
Keywords: Postoperative Pain
Artificial Intelligence
Pain assesment
Lead sponsor: Kanuni Sultan Suleyman Training and Research Hospital
Sponsor class: OTHER
First posted: 2024-06-13
Enrollment: 400
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_ONLY
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: VAS scoring and pain assessment using visuals will be conducted on at least 398 patients aged 18 and above who have undergone any surgery, at any time within the first 24 hours postoperatively.

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-4/DALL-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-4/DALL-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.

Eligibility criteria: Inclusion Criteria:

* 18 years old and above
* Underwent surgery for any reason
* Consented to participate in the study and signed the informed consent form

Exclusion Criteria:

* Patients under 18 years old
* Patients who did not sign the informed consent form
* Patients with visual impairments
* Patients whose level of consciousness is not sufficient to complete the survey
* Patients with a history of psychiatric disorders

Primary outcomes:
measure: Pain assesment
timeFrame: 10 minutes
Secondary outcomes:
Other outcomes:
Locations:
city: Istanbul
country: Turkey
facility: Health Science University İstanbul Kanuni Sultan Süleyman Education and Training Hospital
Study officials:
References: