Ignite Creation Date:
2026-03-26 @ 3:17 PM
Ignite Modification Date:
2026-03-29 @ 11:56 PM
Study NCT ID:
NCT07394335
Status:
NOT_YET_RECRUITING
Last Update Posted:
2026-02-06
First Post:
2026-01-30
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
LLM in Urodynamic Education
Sponsor:
Huseyin Kocakgol
Organization:
Study Overview
Official Title:
Large Language Model (LLM) as a Tutor in Urodynamic Education: A Prospective Learning Curve Study Among Urology Residents
Status:
NOT_YET_RECRUITING
Status Verified Date:
2026-01
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:
Urodynamic investigations, including cystometry, pressure-flow studies, and electromyography, are considered the gold standard for the objective diagnosis of lower urinary tract dysfunction according to current international guidelines. However, accurate interpretation requires simultaneous analysis of multiple pressure signals, identification of artifacts, and application of complex nomograms, making urodynamics one of the most challenging diagnostic skills to master during urology residency training. Traditional training largely depends on apprenticeship-based exposure, which is highly variable across training centers. The primary aim of this prospective educational study is to evaluate the effectiveness of a large language model (LLM), as an interactive tutor in improving urology residents' urodynamic interpretation skills and learning curve. By providing structured theoretical instruction, case-based guidance, and real-time feedback through a standardized case pool, this study investigates whether AI-assisted mentorship can accelerate skill acquisition, enhance diagnostic accuracy, and offer a standardized, accessible educational model for urodynamic training.
Detailed Description:
Urodynamic testing, including cystometry, pressure-flow studies, and electromyography, represents the gold standard for the objective evaluation of lower urinary tract dysfunction. Despite its clinical importance, urodynamic interpretation requires advanced analytical skills, including simultaneous assessment of vesical, abdominal, and detrusor pressures, recognition of technical artifacts, and application of established nomograms. Consequently, mastery of urodynamic interpretation during urology residency training remains challenging and highly dependent on variable case exposure and faculty availability.
This prospective, single-center educational study is designed to assess the effectiveness of a large language model (LLM) configured as an interactive educational tutor in improving urology residents' urodynamic interpretation skills and learning curve. The study aims to determine whether structured, AI-assisted mentorship can provide a standardized and scalable alternative to traditional apprenticeship-based training.
Eligible participants include urology residents without prior formal urodynamic course certification. The educational intervention utilizes a curated library of 45 fully anonymized urodynamic tracings performed in accordance with International Continence Society standards. These cases represent a balanced spectrum of normal findings and common urodynamic diagnoses, including bladder outlet obstruction, detrusor overactivity, and reduced bladder compliance. All cases are validated by experienced urologists prior to inclusion.
The training protocol consists of sequential phases: a baseline assessment (pre-test), structured theoretical instruction delivered via an LLM-based tutoring interface, supervised case analysis with artifact recognition, interactive mentored interpretation, an intermediate assessment (mid-test), reinforcement through independent interpretation followed by AI-guided debriefing, and a final post-test evaluation. Case difficulty across assessment phases is balanced using a stratified randomization approach to ensure equivalent technical complexity.
Participant performance is evaluated using a predefined 16-item objective scoring system assessing technical validity, numerical parameter interpretation, and diagnostic synthesis. All assessments are independently reviewed by two blinded urologists, with adjudication by a third expert in cases of disagreement. Changes in interpretation accuracy over time are used to quantify the learning curve associated with LLM-assisted education.
All urodynamic data are fully anonymized prior to use, and no patient-identifiable information is shared. Participation is voluntary, and written informed consent is obtained from all residents. The study is conducted following institutional ethical standards and aims to provide evidence for the role of large language models as interactive tutors in advanced medical education.
This prospective, single-center educational study is designed to assess the effectiveness of a large language model (LLM) configured as an interactive educational tutor in improving urology residents' urodynamic interpretation skills and learning curve. The study aims to determine whether structured, AI-assisted mentorship can provide a standardized and scalable alternative to traditional apprenticeship-based training.
Eligible participants include urology residents without prior formal urodynamic course certification. The educational intervention utilizes a curated library of 45 fully anonymized urodynamic tracings performed in accordance with International Continence Society standards. These cases represent a balanced spectrum of normal findings and common urodynamic diagnoses, including bladder outlet obstruction, detrusor overactivity, and reduced bladder compliance. All cases are validated by experienced urologists prior to inclusion.
The training protocol consists of sequential phases: a baseline assessment (pre-test), structured theoretical instruction delivered via an LLM-based tutoring interface, supervised case analysis with artifact recognition, interactive mentored interpretation, an intermediate assessment (mid-test), reinforcement through independent interpretation followed by AI-guided debriefing, and a final post-test evaluation. Case difficulty across assessment phases is balanced using a stratified randomization approach to ensure equivalent technical complexity.
Participant performance is evaluated using a predefined 16-item objective scoring system assessing technical validity, numerical parameter interpretation, and diagnostic synthesis. All assessments are independently reviewed by two blinded urologists, with adjudication by a third expert in cases of disagreement. Changes in interpretation accuracy over time are used to quantify the learning curve associated with LLM-assisted education.
All urodynamic data are fully anonymized prior to use, and no patient-identifiable information is shared. Participation is voluntary, and written informed consent is obtained from all residents. The study is conducted following institutional ethical standards and aims to provide evidence for the role of large language models as interactive tutors in advanced medical education.
Study Oversight
Has Oversight DMC:
False
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?: