Preoperative Risk AssessmentAI-PREOPNCT07364942
Comparison of Artificial Intelligence and Anesthesiologist in Preoperative Risk Assessment
An observational study of Preoperative Risk Assessment, run by Gülgün Elif Aksoy at 1 site in Turkey (Türkiye).
Trial summary
Preoperative evaluation is essential for identifying patient-related risks before elective surgery and for planning safe anesthesia management. Traditionally, this evaluation is performed by anesthesiologists based on clinical history, physical examination, comorbidities, and laboratory findings. Read more about this study
- Who is it for?
- Ages 18 to 99 years
- Conditions the study is for
- Preoperative Risk Assessment
- How many people
- 500 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 Mar 2025to 30 Oct 2025
- 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 to 99 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study population consists of adult patients aged 18 years and older who were scheduled for elective surgical procedures and underwent routine preoperative evaluation at a tertiary care university hospital. Preoperative risk assessments performed by anesthesiologists were compared with artificial intelligence-based risk assessments using the same clinical data.
Who may be able to join
- - Adult patients aged 18 years and older
- Patients scheduled for elective surgery under anesthesia
- Patients who underwent routine preoperative evaluation
- Availability of complete preoperative clinical data required for both anesthesiologist and artificial intelligence-based assessment
Who cannot join
- - Patients younger than 18 years
- Emergency surgery cases
- Patients with incomplete or missing preoperative clinical data
- Patients who declined participation or whose data could not be evaluated
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site
Trabzon Faculty of Medicine, Kanuni Training and Research Hospital,
Trabzon, Trabzon
About this study
Official title: Clinical Performance of a Machine Learning-Based Artificial Intelligence System Compared With Anesthesiologist Assessment in Preoperative Patient Evaluation
Preoperative evaluation is essential for identifying patient-related risks before elective surgery and for planning safe anesthesia management. Traditionally, this evaluation is performed by anesthesiologists based on clinical history, physical examination, comorbidities, and laboratory findings.
This observational study aims to compare the clinical performance of a machine learning-based artificial intelligence system with anesthesiologist assessment during preoperative patient evaluation. The artificial intelligence system independently analyzes patient data and generates risk assessments, which are then compared with evaluations performed by anesthesiologists.
Read more
The primary objective of the study is to assess the level of agreement between the artificial intelligence system and anesthesiologists in preoperative risk assessment. Secondary objectives include evaluating the accuracy and consistency of the artificial intelligence system and exploring its potential role as a decision-support tool in preoperative clinical practice.
The findings of this study may contribute to understanding the potential benefits and limitations of artificial intelligence-assisted decision making in preoperative evaluation
Read the detailed description
Preoperative evaluation is a critical component of perioperative care, aimed at identifying patient-specific risks, optimizing patient safety, and guiding anesthetic planning prior to elective surgical procedures. This process traditionally relies on the clinical judgment of anesthesiologists, who integrate medical history, physical examination findings, comorbid conditions, and relevant laboratory data to assess perioperative risk.
Recent advances in artificial intelligence and machine learning have enabled the development of clinical decision-support systems capable of analyzing complex clinical data and generating predictive risk assessments. Despite increasing interest in these technologies, their clinical performance and reliability in real-world preoperative settings remain insufficiently evaluated.
This observational study is designed to compare preoperative risk assessments generated by a machine learning-based artificial intelligence system with routine anesthesiologist-led evaluations. Adult patients scheduled for elective surgery will undergo standard preoperative assessment performed by anesthesiologists as part of usual clinical care. Independently, anonymized patient data will be processed by the artificial intelligence system to produce preoperative risk assessments. The artificial intelligence output will not be available to clinicians and will not influence patient management.
The primary outcome of the study is the level of agreement between the artificial intelligence system and anesthesiologists in preoperative risk stratification. Secondary outcomes include the consistency, concordance, and overall performance of artificial intelligence-generated assessments compared with clinician evaluations.
This study involves no interventions and does not alter standard patient care. All anesthetic and perioperative management decisions will remain entirely under the responsibility of the treating anesthesiologist. By systematically comparing artificial intelligence-based assessments with clinician evaluations, this study aims to clarify the potential role, strengths, and limitations of artificial intelligence as a supportive tool in routine preoperative evaluation
- Conditions
- Preoperative Risk Assessment
- Keywords
- Preoperative assessment,Artificial intelligence
What is being measured
The primary outcome is the main question the study is designed to answer.
Rate of Agreement Between Artificial Intelligence-Based and Anesthesiologist Preoperative Risk Assessments
This outcome measures the level of agreement between an artificial intelligence-based preoperative evaluation system and anesthesiologist assessment, including American Society of Anesthesiologists (ASA) physical status classification and overall perioperative risk stratification. Agreement will be evaluated using appropriate statistical measures.
Measured: At the time of preoperative evaluation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Jan 2026
- Study start1 Mar 2025
- Primary completion1 May 2025
- Study completion30 Oct 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Preoperative Risk AssessmentStudies of the same condition, open and under way.
- 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 500 participants (actual)
Sponsor and investigators
- Lead sponsor
- Gülgün Elif Aksoy
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Gülgün Elif Aksoy, Trabzon Kanuni Education and Research Hospital
- Investigators
- Gülgün E Aksoy, MD, Principal investigator, Trabzon Faculty of Medicine, Kanuni Training and Research Hospital, Turkey
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07364942
- Sponsor's protocol ID
- PREOP-AI-2025
- Acronym
- AI-PREOP
- Status last verified
- Jan 2026
- First submitted
- 10 Jan 2026
- Last updated
- 23 Jan 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared. The study has been completed, and no prospectively defined plan for data sharing was included in the study protocol or the ethics committee approval.
Study at a glance
From the registry record, last updated by the sponsor on 23 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinical Performance of a Machine Learning-Based Artificial Intelligence System Compared With Anesthesiologist Assessment in Preoperative Patient Evaluation
- ClinicalTrials.gov ID
- NCT07364942
- Acronym / sponsor ID
- AI-PREOP / PREOP-AI-2025
- Conditions
- Preoperative Risk Assessment
- Keywords
- Preoperative assessment,Artificial intelligence
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 500 participants (actual)
- Lead sponsor
- Gülgün Elif AksoyUniversity, hospital or other
- Study start
- 1 Mar 2025 (actual)
- Primary completion
- 1 May 2025 (actual)
- Study completion
- 30 Oct 2025 (actual)
- First posted
- 23 Jan 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.
Preoperative evaluation is essential for identifying patient-related risks before elective surgery and for planning safe anesthesia management. Traditionally, this evaluation is performed by anesthesiologists based on clinical history, physical examination, comorbidities, and laboratory findings.
This observational study aims to compare the clinical performance of a machine learning-based artificial intelligence system with anesthesiologist assessment during preoperative patient evaluation. The artificial intelligence system independently analyzes patient data and generates risk assessments, which are then compared with evaluations performed by anesthesiologists.
The primary objective of the study is to assess the level of agreement between the artificial intelligence system and anesthesiologists in preoperative risk assessment. Secondary objectives include evaluating the accuracy and consistency of the artificial intelligence system and exploring its potential role as a decision-support tool in preoperative clinical practice.
The findings of this study may contribute to understanding the potential benefits and limitations of artificial intelligence-assisted decision making in preoperative evaluation
Detailed description
Preoperative evaluation is a critical component of perioperative care, aimed at identifying patient-specific risks, optimizing patient safety, and guiding anesthetic planning prior to elective surgical procedures. This process traditionally relies on the clinical judgment of anesthesiologists, who integrate medical history, physical examination findings, comorbid conditions, and relevant laboratory data to assess perioperative risk.
Recent advances in artificial intelligence and machine learning have enabled the development of clinical decision-support systems capable of analyzing complex clinical data and generating predictive risk assessments. Despite increasing interest in these technologies, their clinical performance and reliability in real-world preoperative settings remain insufficiently evaluated.
This observational study is designed to compare preoperative risk assessments generated by a machine learning-based artificial intelligence system with routine anesthesiologist-led evaluations. Adult patients scheduled for elective surgery will undergo standard preoperative assessment performed by anesthesiologists as part of usual clinical care. Independently, anonymized patient data will be processed by the artificial intelligence system to produce preoperative risk assessments. The artificial intelligence output will not be available to clinicians and will not influence patient management.
The primary outcome of the study is the level of agreement between the artificial intelligence system and anesthesiologists in preoperative risk stratification. Secondary outcomes include the consistency, concordance, and overall performance of artificial intelligence-generated assessments compared with clinician evaluations.
This study involves no interventions and does not alter standard patient care. All anesthetic and perioperative management decisions will remain entirely under the responsibility of the treating anesthesiologist. By systematically comparing artificial intelligence-based assessments with clinician evaluations, this study aims to clarify the potential role, strengths, and limitations of artificial intelligence as a supportive tool in routine preoperative evaluation
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 to 99 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of adult patients aged 18 years and older who were scheduled for elective surgical procedures and underwent routine preoperative evaluation at a tertiary care university hospital. Preoperative risk assessments performed by anesthesiologists were compared with artificial intelligence-based risk assessments using the same clinical data.
Key inclusion criteria (4 listed)
- - Adult patients aged 18 years and older
- Patients scheduled for elective surgery under anesthesia
- Patients who underwent routine preoperative evaluation
- Availability of complete preoperative clinical data required for both anesthesiologist and artificial intelligence-based assessment
Key exclusion criteria (4 listed)
- - Patients younger than 18 years
- Emergency surgery cases
- Patients with incomplete or missing preoperative clinical data
- Patients who declined participation or whose data could not be evaluated
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Rate of Agreement Between Artificial Intelligence-Based and Anesthesiologist Preoperative Risk Assessments
Time frame: At the time of preoperative evaluation
This outcome measures the level of agreement between an artificial intelligence-based preoperative evaluation system and anesthesiologist assessment, including American Society of Anesthesiologists (ASA) physical status classification and overall perioperative risk stratification. Agreement will be evaluated using appropriate statistical measures.
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 23 Jan 2026.
- Individual participant dataSharing planned: No. Individual participant data will not be shared. The study has been completed, and no prospectively defined plan for data sharing was included in the study protocol or the ethics committee approval.
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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.
Gülgün E Aksoy, MD principal investigator
Trabzon Faculty of Medicine, Kanuni Training and Research Hospital, Turkey
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 Preoperative Risk Assessment, run by Gülgün Elif Aksoy 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 4 inclusion and 4 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 23 Jan 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?
Rate of Agreement Between Artificial Intelligence-Based and Anesthesiologist Preoperative Risk Assessments (time frame: At the time of preoperative evaluation). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 23 Jan 2026.