Acetabular FracturesNCT07673991
Diagnostic Reliability of ChatGPT in Letournel-Judet Acetabular Fracture Classification
An observational study of Acetabular Fractures and Pelvic Injury, run by Ankara City Hospital Bilkent at 1 site in Turkey (Türkiye).
Trial summary
This study aims to evaluate the diagnostic reliability of the multimodal artificial intelligence model ChatGPT-4o in classifying acetabular fractures using the Letournel-Judet classification system. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Acetabular Fractures, Pelvic Injury
- What is being tested?
- Diagnostic Assessment by ChatGPT-4o
- How many people
- 184 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 Feb 2026to 12 May 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study population consists of 184 patients who presented to Ankara Bilkent City Hospital with pelvic injuries and underwent surgical treatment. The cohort includes individuals with various acetabular fracture patterns requiring the Letournel-Judet classification system for preoperative planning. Excluded populations were patients with non-acetabular pelvic fractures, incomplete radiographic series, poor image quality, or a history of previous pelvic surgery or trauma.
Who may be able to join
- Patients older than 18 years old and presenting with pelvic injuries and undergoing surgical treatment.
Who cannot join
- Patients with pelvic injuries but with non-acetabular fractures, those with incomplete imaging, those with poor radiographic quality or those who had previously undergone pelvic surgery or trauma were excluded.
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site
Ankara Bilkent Şehir Hastanesi
Ankara, Çankaya, 06210
About this study
Official title: Comparison of the Diagnostic Reliability of ChatGPT in Letournel-Judet Acetabulum Fracture Classification Based on Judet Radiographs According to Orthopaedic Residents
This study aims to evaluate the diagnostic reliability of the multimodal artificial intelligence model ChatGPT-4o in classifying acetabular fractures using the Letournel-Judet classification system. The study retrospectively analyzed standardized pelvic radiographs (anteroposterior, iliac oblique, and obturator oblique) from 184 patients presenting with pelvic injuries. The diagnostic performance of ChatGPT-4o was compared against the independent assessments of two fourth-year orthopaedic residents and a reference standard established by an experienced trauma surgeon using multiplanar computed tomography (CT) and intraoperative findings. By utilizing a systematic radiographic checklist, the study assesses the AI (artificial intelligence) model's ability to identify key anatomical landmarks and integrate them into a final fracture pattern. This research aims to provide critical data on the current feasibility of using large language models as decision-support tools in complex orthopaedic trauma.
Read the detailed description
This retrospective observational study was conducted at Ankara Bilkent City Hospital to investigate the diagnostic accuracy of ChatGPT-4o in complex acetabular fracture classification. Imaging data included anonymized anteroposterior, iliac oblique, and obturator oblique radiographs from 184 patients.After approval from the Institutional Review Board, the investigators retrospectively analyzed patients presenting with pelvic injuries and undergoing surgical treatment at our center. Patients with pelvic injuries but with non-acetabular fractures, those with incomplete imaging, those with poor radiographic quality or those who had previously undergone pelvic surgery or trauma were excluded.
Assessment Protocol
Imaging from cases presenting with pelvic injuries and undergoing surgical treatment at our center was selected from the institution's radiology archive. Standard anteroposterior pelvic, iliac oblique and obturator oblique radiographs were collected for each case.
The article "Acetabular Fractures: Easier Classification with a Systematic Approach" by Brandser and Marsh was uploaded to Chat GPT. The prompt used when evaluating the cases was as follows: "In these AP pelvic, iliac oblique, and obturator oblique radiographs, identify the acetabulum fracture as defined by Judet, using the systematic approach in the provided article and answering each question on the radiological checklist individually." Each case was evaluated using the same prompt previously defined. Chat GPT was asked to answer each question individually based on the systematic approach defined in the article and ultimately indicate the type of fracture.These questions were designed to evaluate key radiographic landmarks and fracture components including:
Is there a fracture of the obturator ring? Is the ilioischial line disrupted? Is the iliopectineal line disrupted? Is there a fracture of the iliac wing? Is there a fracture of posterior wall? Does the fracture divide the acetabulum into top and bottom halves or front and back halves? Is there a spur sign?
Based on the responses to these questions, the final fracture type was determined according to the Letournel-Judet acetabular fracture classification system.
Cases included in the study were independently classified by two fourth-year orthopaedic residents. To ensure objectivity, assessments were based solely on anteroposterior (AP), obturator oblique, and iliac oblique radiographs, without access to CT scans or intraoperative findings. Also a trauma surgeon with over a decade of experience in pelvic trauma surgery independently classified cases. To establish the true fracture pattern, the experienced trauma surgeon serving as the reference standard had full access to multiplanar computed tomography (CT) scans and intraoperative records. In contrast, the AI model and the orthopaedic residents were completely blinded to any CT data, basing their evaluations solely on the plain Judet radiographs.
Statistical Analysis
Descriptive statistics were used to summarize patient demographics and fracture characteristics. Continuous variables (e.g., patient age) were expressed as mean ± standard deviation (SD) and range, whereas categorical variables (e.g., sex and fracture types) were presented as frequencies and percentages.
The primary outcome measure was the diagnostic accuracy of ChatGPT-4o, orthopaedic residents, and the trauma surgeon in classifying acetabular fractures according to the Letournel-Judet classification system. The reference standard was established using three-dimensional pelvic CT findings together with the final intraoperative assessment of the experienced trauma surgeon. Accuracy was calculated as the proportion of correctly classified fractures relative to the reference standard.
Interobserver agreement between evaluators and the reference standard was assessed using Cohen's kappa (κ) coefficient for pairwise comparisons. Kappa values were interpreted according to the criteria described by Landis and Koch: <0, poor agreement; 0.01-0.20, slight agreement; 0.21-0.40, fair agreement; 0.41-0.60, moderate agreement; 0.61-0.80, substantial agreement; and 0.81-1.00, almost perfect agreement.
Comparisons of diagnostic accuracy rates between ChatGPT-4o and human evaluators were performed using McNemar's test for paired categorical data. A p value of <0.05 was considered statistically significant. All statistical analyses were conducted using IBM SPSS Statistics version 29.0 (IBM Corp., Armonk, NY, USA).
- Conditions
- Acetabular Fractures, Pelvic Injury
- Being tested
- Diagnostic Assessment by ChatGPT-4o
- Keywords
- Artificial Intelligence, ChatGPT, Letournel-Judet Classification, Acetabular Fracture
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Diagnostic Assessment by ChatGPT-4oOther
No active intervention was performed; this study retrospectively analyzed radiographic images using a multimodal artificial intelligence model to assess its diagnostic accuracy compared to human clinicians.
| Study group | Role | What they receive |
|---|---|---|
| Acetabular Fracture Cases | Retrospective cohort of 184 patients presenting with pelvic injuries who underwent surgical treatment and had standardized Judet radiographs available for analysis Receives: Other: Diagnostic Assessment by ChatGPT-4o |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Accuracy of ChatGPT-4o in Acetabular Fracture Classification.
The diagnostic accuracy is defined as the proportion of correctly classified acetabular fractures by ChatGPT-4o compared to the reference standard (determined by 3D CT and intraoperative findings).
Measured: Through study completion
Secondary outcomes (2)
Interobserver Agreement (Cohen's Kappa)
Evaluation of the level of agreement between ChatGPT-4o and the orthopaedic residents compared to the reference standard.
Measured: Through study completion
Systematic Radiographic Checklist Accuracy
Percentage of correct responses provided by ChatGPT-4o for the seven individual radiographic landmarks (e.g., iliac wing, spur sign, posterior wall).
Measured: Through study completion
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Jun 2026
- Study start1 Feb 2026
- Primary completion1 Apr 2026
- Study completion12 May 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (15)Papers the sponsor has linked to this study.
- Other trials for Acetabular FracturesStudies of the same condition, open and under way.
- More trials run by Ankara City Hospital BilkentThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Retrospective
- Enrolment
- 184 participants (actual)
Sponsor and investigators
- Lead sponsor
- Ankara City Hospital Bilkent
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, muhammed kılıç, Ankara City Hospital Bilkent
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07673991
- Sponsor's protocol ID
- TABED 2-26-1888
- Status last verified
- Jun 2026
- First submitted
- 22 Jun 2026
- Last updated
- 29 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Anonymized individual participant data (IPD) regarding acetabular fracture classification results and radiographic checklist scores will be shared. No patient-identifying information will be included.
Publications
Lindsey R, Daluiski A, Chopra S, Lachapelle A, Mozer M, Sicular S, Hanel D, Gardner M, Gupta A, Hotchkiss R, Potter H. Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci U S A. 2018 Nov 6;115(45):11591-11596. doi: 10.1073/pnas.1806905115. Epub 2018 Oct 22.
PubMed 30348771Brandser E, Marsh JL. Acetabular fractures: easier classification with a systematic approach. AJR Am J Roentgenol. 1998 Nov;171(5):1217-28. doi: 10.2214/ajr.171.5.9798851. No abstract available.
PubMed 9798851Walker AN, Smith JB, Simister SK, Patel O, Choudhary S, Seidu M, Dallas-Orr D, Tse S, Shahzad H, Wise P, Scott M, Saiz AM, Lum ZC. Assessing Inter-rater Reliability of ChatGPT-4 and Orthopaedic Clinicians in Radiographic Fracture Classification. J Orthop Trauma. 2025 Sep 19. doi: 10.1097/BOT.0000000000003079. Online ahead of print.
PubMed 40970709Olczak J, Fahlberg N, Maki A, Razavian AS, Jilert A, Stark A, Skoldenberg O, Gordon M. Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop. 2017 Dec;88(6):581-586. doi: 10.1080/17453674.2017.1344459. Epub 2017 Jul 6.
PubMed 28681679Cha Y, Kim JT, Park CH, Kim JW, Lee SY, Yoo JI. Artificial intelligence and machine learning on diagnosis and classification of hip fracture: systematic review. J Orthop Surg Res. 2022 Dec 1;17(1):520. doi: 10.1186/s13018-022-03408-7.
PubMed 36456982Kuo RYL, Harrison C, Curran TA, Jones B, Freethy A, Cussons D, Stewart M, Collins GS, Furniss D. Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis. Radiology. 2022 Jul;304(1):50-62. doi: 10.1148/radiol.211785. Epub 2022 Mar 29.
PubMed 35348381Yucens M, Aydemir AN, Demirkan AF. ASSESSMENT OF INTEROBSERVER RELIABILITY FOR THE LETOURNEL AND JUDET CLASSIFICATION. Acta Ortop Bras. 2024 Mar 22;32(1):e267640. doi: 10.1590/1413-785220243201e267640. eCollection 2024.
PubMed 38532863Mack LA, Harley JD, Winquist RA. CT of acetabular fractures: analysis of fracture patterns. AJR Am J Roentgenol. 1982 Mar;138(3):407-12. doi: 10.2214/ajr.138.3.407.
PubMed 6977989Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine Learning for Medical Imaging. Radiographics. 2017 Mar-Apr;37(2):505-515. doi: 10.1148/rg.2017160130. Epub 2017 Feb 17.
PubMed 28212054O'Toole RV, Cox G, Shanmuganathan K, Castillo RC, Turen CH, Sciadini MF, Nascone JW. Evaluation of computed tomography for determining the diagnosis of acetabular fractures. J Orthop Trauma. 2010 May;24(5):284-90. doi: 10.1097/BOT.0b013e3181c83bc0.
PubMed 20418733Petrisor BA, Bhandari M, Orr RD, Mandel S, Kwok DC, Schemitsch EH. Improving reliability in the classification of fractures of the acetabulum. Arch Orthop Trauma Surg. 2003 Jun;123(5):228-33. doi: 10.1007/s00402-003-0507-y. Epub 2003 Apr 26.
PubMed 12720013Beaule PE, Dorey FJ, Matta JM. Letournel classification for acetabular fractures. Assessment of interobserver and intraobserver reliability. J Bone Joint Surg Am. 2003 Sep;85(9):1704-9.
PubMed 12954828Letournel E. Acetabulum fractures: classification and management. Clin Orthop Relat Res. 1980 Sep;(151):81-106.
PubMed 7418327JUDET R, JUDET J, LETOURNEL E. FRACTURES OF THE ACETABULUM: CLASSIFICATION AND SURGICAL APPROACHES FOR OPEN REDUCTION. PRELIMINARY REPORT. J Bone Joint Surg Am. 1964 Dec;46:1615-46. No abstract available.
PubMed 14239854Ziran N, Soles GLS, Matta JM. Outcomes after surgical treatment of acetabular fractures: a review. Patient Saf Surg. 2019 Mar 16;13:16. doi: 10.1186/s13037-019-0196-2. eCollection 2019.
PubMed 30923570
Study at a glance
From the registry record, last updated by the sponsor on 29 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Comparison of the Diagnostic Reliability of ChatGPT in Letournel-Judet Acetabulum Fracture Classification Based on Judet Radiographs According to Orthopaedic Residents
- ClinicalTrials.gov ID
- NCT07673991
- Acronym / sponsor ID
- TABED 2-26-1888
- Conditions
- Acetabular Fractures, Pelvic Injury
- Keywords
- Artificial Intelligence, ChatGPT, Letournel-Judet Classification, Acetabular Fracture
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 184 participants (actual)
- Lead sponsor
- Ankara City Hospital BilkentUniversity, hospital or other
- Study start
- 1 Feb 2026 (actual)
- Primary completion
- 1 Apr 2026 (actual)
- Study completion
- 12 May 2026 (actual)
- First posted
- 29 Jun 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This study aims to evaluate the diagnostic reliability of the multimodal artificial intelligence model ChatGPT-4o in classifying acetabular fractures using the Letournel-Judet classification system. The study retrospectively analyzed standardized pelvic radiographs (anteroposterior, iliac oblique, and obturator oblique) from 184 patients presenting with pelvic injuries. The diagnostic performance of ChatGPT-4o was compared against the independent assessments of two fourth-year orthopaedic residents and a reference standard established by an experienced trauma surgeon using multiplanar computed tomography (CT) and intraoperative findings. By utilizing a systematic radiographic checklist, the study assesses the AI (artificial intelligence) model's ability to identify key anatomical landmarks and integrate them into a final fracture pattern. This research aims to provide critical data on the current feasibility of using large language models as decision-support tools in complex orthopaedic trauma.
Detailed description
This retrospective observational study was conducted at Ankara Bilkent City Hospital to investigate the diagnostic accuracy of ChatGPT-4o in complex acetabular fracture classification. Imaging data included anonymized anteroposterior, iliac oblique, and obturator oblique radiographs from 184 patients.After approval from the Institutional Review Board, the investigators retrospectively analyzed patients presenting with pelvic injuries and undergoing surgical treatment at our center. Patients with pelvic injuries but with non-acetabular fractures, those with incomplete imaging, those with poor radiographic quality or those who had previously undergone pelvic surgery or trauma were excluded.
Assessment Protocol
Imaging from cases presenting with pelvic injuries and undergoing surgical treatment at our center was selected from the institution's radiology archive. Standard anteroposterior pelvic, iliac oblique and obturator oblique radiographs were collected for each case.
The article "Acetabular Fractures: Easier Classification with a Systematic Approach" by Brandser and Marsh was uploaded to Chat GPT. The prompt used when evaluating the cases was as follows: "In these AP pelvic, iliac oblique, and obturator oblique radiographs, identify the acetabulum fracture as defined by Judet, using the systematic approach in the provided article and answering each question on the radiological checklist individually." Each case was evaluated using the same prompt previously defined. Chat GPT was asked to answer each question individually based on the systematic approach defined in the article and ultimately indicate the type of fracture.These questions were designed to evaluate key radiographic landmarks and fracture components including:
Is there a fracture of the obturator ring? Is the ilioischial line disrupted? Is the iliopectineal line disrupted? Is there a fracture of the iliac wing? Is there a fracture of posterior wall? Does the fracture divide the acetabulum into top and bottom halves or front and back halves? Is there a spur sign?
Based on the responses to these questions, the final fracture type was determined according to the Letournel-Judet acetabular fracture classification system.
Cases included in the study were independently classified by two fourth-year orthopaedic residents. To ensure objectivity, assessments were based solely on anteroposterior (AP), obturator oblique, and iliac oblique radiographs, without access to CT scans or intraoperative findings. Also a trauma surgeon with over a decade of experience in pelvic trauma surgery independently classified cases. To establish the true fracture pattern, the experienced trauma surgeon serving as the reference standard had full access to multiplanar computed tomography (CT) scans and intraoperative records. In contrast, the AI model and the orthopaedic residents were completely blinded to any CT data, basing their evaluations solely on the plain Judet radiographs.
Statistical Analysis
Descriptive statistics were used to summarize patient demographics and fracture characteristics. Continuous variables (e.g., patient age) were expressed as mean ± standard deviation (SD) and range, whereas categorical variables (e.g., sex and fracture types) were presented as frequencies and percentages.
The primary outcome measure was the diagnostic accuracy of ChatGPT-4o, orthopaedic residents, and the trauma surgeon in classifying acetabular fractures according to the Letournel-Judet classification system. The reference standard was established using three-dimensional pelvic CT findings together with the final intraoperative assessment of the experienced trauma surgeon. Accuracy was calculated as the proportion of correctly classified fractures relative to the reference standard.
Interobserver agreement between evaluators and the reference standard was assessed using Cohen's kappa (κ) coefficient for pairwise comparisons. Kappa values were interpreted according to the criteria described by Landis and Koch: <0, poor agreement; 0.01-0.20, slight agreement; 0.21-0.40, fair agreement; 0.41-0.60, moderate agreement; 0.61-0.80, substantial agreement; and 0.81-1.00, almost perfect agreement.
Comparisons of diagnostic accuracy rates between ChatGPT-4o and human evaluators were performed using McNemar's test for paired categorical data. A p value of <0.05 was considered statistically significant. All statistical analyses were conducted using IBM SPSS Statistics version 29.0 (IBM Corp., Armonk, NY, USA).
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Acetabular Fracture Cases | – | Other: Diagnostic Assessment by ChatGPT-4oRetrospective cohort of 184 patients presenting with pelvic injuries who underwent surgical treatment and had standardized Judet radiographs available for analysis |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of 184 patients who presented to Ankara Bilkent City Hospital with pelvic injuries and underwent surgical treatment. The cohort includes individuals with various acetabular fracture patterns requiring the Letournel-Judet classification system for preoperative planning. Excluded populations were patients with non-acetabular pelvic fractures, incomplete radiographic series, poor image quality, or a history of previous pelvic surgery or trauma.
Key inclusion criteria (1 listed)
- Patients older than 18 years old and presenting with pelvic injuries and undergoing surgical treatment.
Key exclusion criteria (1 listed)
- Patients with pelvic injuries but with non-acetabular fractures, those with incomplete imaging, those with poor radiographic quality or those who had previously undergone pelvic surgery or trauma were excluded.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Diagnostic Assessment by ChatGPT-4oNo active intervention was performed; this study retrospectively analyzed radiographic images using a multimodal artificial intelligence model to assess its diagnostic accuracy compared to human clinicians. | Other | Acetabular Fracture Cases |
Primary outcome
Diagnostic Accuracy of ChatGPT-4o in Acetabular Fracture Classification.
Time frame: Through study completion
The diagnostic accuracy is defined as the proportion of correctly classified acetabular fractures by ChatGPT-4o compared to the reference standard (determined by 3D CT and intraoperative findings).
2 secondary outcomes
Interobserver Agreement (Cohen's Kappa)
Time frame: Through study completion
Systematic Radiographic Checklist Accuracy
Time frame: Through study completion
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Jun 2026.
- Background: PubMed 30348771Lindsey R, Daluiski A, Chopra S, Lachapelle A, Mozer M, Sicular S, Hanel D, Gardner M, Gupta A, Hotchkiss R, Potter H. Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci U S A. 2018 Nov 6;115(45):11591-11596. doi: 10.1073/pnas.1806905115. Epub 2018 Oct 22.
- Background: PubMed 9798851Brandser E, Marsh JL. Acetabular fractures: easier classification with a systematic approach. AJR Am J Roentgenol. 1998 Nov;171(5):1217-28. doi: 10.2214/ajr.171.5.9798851. No abstract available.
- Background: PubMed 40970709Walker AN, Smith JB, Simister SK, Patel O, Choudhary S, Seidu M, Dallas-Orr D, Tse S, Shahzad H, Wise P, Scott M, Saiz AM, Lum ZC. Assessing Inter-rater Reliability of ChatGPT-4 and Orthopaedic Clinicians in Radiographic Fracture Classification. J Orthop Trauma. 2025 Sep 19. doi: 10.1097/BOT.0000000000003079. Online ahead of print.
- Background: PubMed 28681679Olczak J, Fahlberg N, Maki A, Razavian AS, Jilert A, Stark A, Skoldenberg O, Gordon M. Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop. 2017 Dec;88(6):581-586. doi: 10.1080/17453674.2017.1344459. Epub 2017 Jul 6.
- Background: PubMed 36456982Cha Y, Kim JT, Park CH, Kim JW, Lee SY, Yoo JI. Artificial intelligence and machine learning on diagnosis and classification of hip fracture: systematic review. J Orthop Surg Res. 2022 Dec 1;17(1):520. doi: 10.1186/s13018-022-03408-7.
- Background: PubMed 35348381Kuo RYL, Harrison C, Curran TA, Jones B, Freethy A, Cussons D, Stewart M, Collins GS, Furniss D. Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis. Radiology. 2022 Jul;304(1):50-62. doi: 10.1148/radiol.211785. Epub 2022 Mar 29.
- Background: PubMed 38532863Yucens M, Aydemir AN, Demirkan AF. ASSESSMENT OF INTEROBSERVER RELIABILITY FOR THE LETOURNEL AND JUDET CLASSIFICATION. Acta Ortop Bras. 2024 Mar 22;32(1):e267640. doi: 10.1590/1413-785220243201e267640. eCollection 2024.
- Background: PubMed 6977989Mack LA, Harley JD, Winquist RA. CT of acetabular fractures: analysis of fracture patterns. AJR Am J Roentgenol. 1982 Mar;138(3):407-12. doi: 10.2214/ajr.138.3.407.
- Background: PubMed 28212054Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine Learning for Medical Imaging. Radiographics. 2017 Mar-Apr;37(2):505-515. doi: 10.1148/rg.2017160130. Epub 2017 Feb 17.
- Background: PubMed 20418733O'Toole RV, Cox G, Shanmuganathan K, Castillo RC, Turen CH, Sciadini MF, Nascone JW. Evaluation of computed tomography for determining the diagnosis of acetabular fractures. J Orthop Trauma. 2010 May;24(5):284-90. doi: 10.1097/BOT.0b013e3181c83bc0.
- Background: PubMed 12720013Petrisor BA, Bhandari M, Orr RD, Mandel S, Kwok DC, Schemitsch EH. Improving reliability in the classification of fractures of the acetabulum. Arch Orthop Trauma Surg. 2003 Jun;123(5):228-33. doi: 10.1007/s00402-003-0507-y. Epub 2003 Apr 26.
- Background: PubMed 12954828Beaule PE, Dorey FJ, Matta JM. Letournel classification for acetabular fractures. Assessment of interobserver and intraobserver reliability. J Bone Joint Surg Am. 2003 Sep;85(9):1704-9.
- Individual participant dataSharing planned: Yes. Anonymized individual participant data (IPD) regarding acetabular fracture classification results and radiographic checklist scores will be shared. No patient-identifying information will be included.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Acetabular Fractures and Pelvic Injury, run by Ankara City Hospital Bilkent at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 29 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Accuracy of ChatGPT-4o in Acetabular Fracture Classification. (time frame: Through study completion). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07673991, which also lists 15 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 Jun 2026.