Intubation; DifficultNCT06839261
Evaluation of Double Lumen Tube Intubation Difficulty With Photo-Based Artificial Intelligence Algorithms
An observational study of Intubation; Difficult and Artificial Intelligence (AI), run by Ankara Ataturk Sanatorium Training and Research Hospital at 1 site in Turkey (Türkiye).
Trial summary
The complexity and difficulty of intubation with double lumen tubes requires the use of advanced technologies in the management of this procedure. The potential of photo-based artificial intelligence algorithms to predict and minimize the difficulties encountered during intubation is the main motivation for this… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Intubation; Difficult, Artificial Intelligence (AI), Double Lumen Tube Intubation
- What is being tested?
- Intubation Difficulty Scale and Artificial Intelligence
- How many people
- 260 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 Dec 2024to 30 Mar 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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The sample size was calculated as 214 patients with a power of 0.90, 5% type-1 error, 81.8% sensitivity and 26.7% prevalence (AUC=0.864) with a power of 0.90, 5% type-1 error, 81.8% sensitivity and (AUC=0.864) 26.7% prevalence in the Sample Size Estimation for Diagnostic Accuracy Studies calculated for the study considering the literature data. Since 214 patients would be used to teach the AI easy and difficult intubation and 46 patients would be used to test the AI, a total of 260 patients were recruited.
Who may be able to join
- Undergoing thoracic surgery
- Giving informed consent
- Over 18 years of age
- Double lumen tube used for intubation
- ASA (American Society of Anesthesiologist)1-2-3
Who cannot join
- Emergency surgeries
- ASA 4 and above
- Head and neck tumor, history of surgery/RT related to tumor
- Presence of syndrome that will cause difficult intubation
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site
Ankara Atatürk Sanatoryum Training and Research Hospital
Ankara, Keçiören, 06290
About this study
Official title: Efficacy and Reliability of Photo-Based Artificial Intelligence Algorithms in Assessing Difficulty of Intubation With a Double-Lumen Tube
The complexity and difficulty of intubation with double lumen tubes requires the use of advanced technologies in the management of this procedure. The potential of photo-based artificial intelligence algorithms to predict and minimize the difficulties encountered during intubation is the main motivation for this study.
The utilization of artificial intelligence algorithms within the domain of airway management holds considerable promise in providing real-time feedback to anesthesiologists, enhancing the efficacy of intubation procedures, and reducing the occurrence of complications. Specifically, photo-based AI systems can facilitate a more comprehensive understanding of airway anatomy by analyzing images captured prior to and during intubation, thereby enhancing the management of complex cases.The objective of this study is to examine the efficacy and reliability of photo-based artificial intelligence algorithms in evaluating the complexity of intubation with a double lumen tube.The integration of artificial intelligence into the intubation process is intended to enhance patient outcomes and establish a new benchmark for anesthesia practice. This study aims to address the existing gap in the literature and provide innovative approaches to clinical practice.
Read more
Informed consent was obtained from patients undergoing thoracic surgery operations, and demographic data (age, height, body weight, body mass index, gender), American Society of Anesthesiologists (ASA) score, type of operation, and comorbid diseases (diabetes mellitus, hypertension, coronary artery disease, chronic kidney disease, chronic obstructive pulmonary disease, asthma, obstructive sleep apnea) were obtained. Thoracic and/or extrath oracic malignancy history), parameters considered as risk factors for difficult intubation (history of previous difficult intubation, LEMON criteria (look externally, evaluate, mallampathy, obstruction, neck mobility), upper lip bite test) and photographs of the patients (including head and neck region) will be recorded in six different directions and ways with a professional camera (actively used in our hospital) in the preoperative period. During the intraoperative phase, the Cormack-Lehane scoring system will be employed, and the intubation process with a double-lumen tube will be evaluated for ease or difficulty. Intraoperative complications related to the operation will also be documented.The data will then be processed using Python 3 programming language and open-source libraries to calculate artificial intelligence algorithms. In the event of incomplete patient data, data imputation techniques will be employed to supplement the artificial intelligence program.
The primary outcome variable of the study is the rate at which the photo-based artificial intelligence algorithm predicts whether intubation with a double lumen tube is easy or difficult.The secondary outcome variable is the comparison of the rate of prediction of intubation with double lumen tube by photo-based artificial intelligence algorithms and the rate of prediction of intubation with double lumen tube by conventional methods.
- Being tested
- Intubation Difficulty Scale, Artificial Intelligence
- Keywords
- airway management, Double lumen tube, Difficult intubation, artificial intelligence, thoracic surgery
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Intubation Difficulty ScaleDiagnostic test
The Intubation Difficulty Scale (IDS) is an objective way to classify easy and difficult intubation. A score ≤ 5 indicates an easy or mildly difficult intubation, while IDS > 5 suggests difficult intubation, requiring additional techniques or attempts.
Artificial IntelligenceOther
The program made with Python 3 programming language using open source libraries. It will be developed to predict difficult intubation with 6 different photo data of patients, this process will be taught with a learning process and then tested.
| Study group | Role | What they receive |
|---|---|---|
| Intubation - Difficult | According to the Intubation Difficulty Scale (IDS), a score of > 5 was defined as difficult intubation. Receives: Diagnostic Test: Intubation Difficulty Scale; Other: Artificial Intelligence | |
| Intubation - Easy | According to the Intubation Difficulty Scale (IDS), a score of ≤ 5 was defined as easy intubation. Receives: Diagnostic Test: Intubation Difficulty Scale; Other: Artificial Intelligence |
What is being measured
The primary outcome is the main question the study is designed to answer.
Intubation Difficulty Scale (IDS)
The Intubation Difficulty Scale (IDS) is an objective way to classify easy and difficult intubation. A score ≤ 5 indicates an easy or mildly difficult intubation, while IDS > 5 suggests difficult intubation, requiring additional techniques or attempts.
Measured: During the operation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Feb 2025
- Study start1 Dec 2024
- Primary completion1 Mar 2025
- Study completion30 Mar 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Intubation; DifficultStudies 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
- 260 participants (actual)
Sponsor and investigators
- Lead sponsor
- Ankara Ataturk Sanatorium Training and Research Hospital
- Sponsor type
- Other government
- Responsible party
- Principal investigator, Onur Kucuk, Ankara Ataturk Sanatorium Training and Research Hospital
- Investigators
- Onur Küçük, Specialist, Principal investigator, Ankara Atatürk Sanatoryum Training and Research Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06839261
- Sponsor's protocol ID
- 2024-KAEK-24/07
- Status last verified
- Feb 2025
- First submitted
- 17 Feb 2025
- Last updated
- 8 Sep 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 8 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Efficacy and Reliability of Photo-Based Artificial Intelligence Algorithms in Assessing Difficulty of Intubation With a Double-Lumen Tube
- ClinicalTrials.gov ID
- NCT06839261
- Acronym / sponsor ID
- 2024-KAEK-24/07
- Keywords
- airway management, Double lumen tube, Difficult intubation, artificial intelligence, thoracic surgery
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 260 participants (actual)
- Lead sponsor
- Ankara Ataturk Sanatorium Training and Research HospitalOther government
- Study start
- 1 Dec 2024 (actual)
- Primary completion
- 1 Mar 2025 (actual)
- Study completion
- 30 Mar 2025 (actual)
- First posted
- 21 Feb 2025 (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.
The complexity and difficulty of intubation with double lumen tubes requires the use of advanced technologies in the management of this procedure. The potential of photo-based artificial intelligence algorithms to predict and minimize the difficulties encountered during intubation is the main motivation for this study.
The utilization of artificial intelligence algorithms within the domain of airway management holds considerable promise in providing real-time feedback to anesthesiologists, enhancing the efficacy of intubation procedures, and reducing the occurrence of complications. Specifically, photo-based AI systems can facilitate a more comprehensive understanding of airway anatomy by analyzing images captured prior to and during intubation, thereby enhancing the management of complex cases.The objective of this study is to examine the efficacy and reliability of photo-based artificial intelligence algorithms in evaluating the complexity of intubation with a double lumen tube.The integration of artificial intelligence into the intubation process is intended to enhance patient outcomes and establish a new benchmark for anesthesia practice. This study aims to address the existing gap in the literature and provide innovative approaches to clinical practice.
Informed consent was obtained from patients undergoing thoracic surgery operations, and demographic data (age, height, body weight, body mass index, gender), American Society of Anesthesiologists (ASA) score, type of operation, and comorbid diseases (diabetes mellitus, hypertension, coronary artery disease, chronic kidney disease, chronic obstructive pulmonary disease, asthma, obstructive sleep apnea) were obtained. Thoracic and/or extrath oracic malignancy history), parameters considered as risk factors for difficult intubation (history of previous difficult intubation, LEMON criteria (look externally, evaluate, mallampathy, obstruction, neck mobility), upper lip bite test) and photographs of the patients (including head and neck region) will be recorded in six different directions and ways with a professional camera (actively used in our hospital) in the preoperative period. During the intraoperative phase, the Cormack-Lehane scoring system will be employed, and the intubation process with a double-lumen tube will be evaluated for ease or difficulty. Intraoperative complications related to the operation will also be documented.The data will then be processed using Python 3 programming language and open-source libraries to calculate artificial intelligence algorithms. In the event of incomplete patient data, data imputation techniques will be employed to supplement the artificial intelligence program.
The primary outcome variable of the study is the rate at which the photo-based artificial intelligence algorithm predicts whether intubation with a double lumen tube is easy or difficult.The secondary outcome variable is the comparison of the rate of prediction of intubation with double lumen tube by photo-based artificial intelligence algorithms and the rate of prediction of intubation with double lumen tube by conventional methods.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Intubation - Difficult | – | Diagnostic Test: Intubation Difficulty Scale, Other: Artificial IntelligenceAccording to the Intubation Difficulty Scale (IDS), a score of > 5 was defined as difficult intubation. |
| Intubation - Easy | – | Diagnostic Test: Intubation Difficulty Scale, Other: Artificial IntelligenceAccording to the Intubation Difficulty Scale (IDS), a score of ≤ 5 was defined as easy intubation. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The sample size was calculated as 214 patients with a power of 0.90, 5% type-1 error, 81.8% sensitivity and 26.7% prevalence (AUC=0.864) with a power of 0.90, 5% type-1 error, 81.8% sensitivity and (AUC=0.864) 26.7% prevalence in the Sample Size Estimation for Diagnostic Accuracy Studies calculated for the study considering the literature data. Since 214 patients would be used to teach the AI easy and difficult intubation and 46 patients would be used to test the AI, a total of 260 patients were recruited.
Key inclusion criteria (5 listed)
- Undergoing thoracic surgery
- Giving informed consent
- Over 18 years of age
- Double lumen tube used for intubation
- ASA (American Society of Anesthesiologist)1-2-3
Key exclusion criteria (4 listed)
- Emergency surgeries
- ASA 4 and above
- Head and neck tumor, history of surgery/RT related to tumor
- Presence of syndrome that will cause difficult intubation
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 |
|---|---|---|
| Intubation Difficulty ScaleThe Intubation Difficulty Scale (IDS) is an objective way to classify easy and difficult intubation. A score ≤ 5 indicates an easy or mildly difficult intubation, while IDS > 5 suggests difficult intubation, requiring additional techniques or attempts. | Diagnostic test | Intubation - Difficult, Intubation - Easy |
| Artificial IntelligenceThe program made with Python 3 programming language using open source libraries. It will be developed to predict difficult intubation with 6 different photo data of patients, this process will be taught with a learning process and then tested. | Other | Intubation - Difficult, Intubation - Easy |
Primary outcome
Intubation Difficulty Scale (IDS)
Time frame: During the operation
The Intubation Difficulty Scale (IDS) is an objective way to classify easy and difficult intubation. A score ≤ 5 indicates an easy or mildly difficult intubation, while IDS > 5 suggests difficult intubation, requiring additional techniques or attempts.
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 8 Sep 2025.
- Individual participant dataSharing planned: No
Related trials for Intubation; Difficult
Enrolling by invitation
Pediatric Intubation Simulations With Bougie
Difficult Airway Intubation
Not yet recruitingObservational study
Sniper Intubation for Training of Undergraduate Medical Student
Intubation;Difficult
RecruitingObservational study
Airway Ultrasound in Oncologic Maxillofacial Patients
Head and Neck Cancer
Not yet recruitingObservational study
Sniffing Position vs Left Head Rotation (LeHeR) Maneuver for Glottic View During Direct Laryngoscopy
Difficult Intubation, Laryngoscopy
Not yet recruitingObservational study
LeHeR Maneuver vs Sniffing Position During Video Laryngoscopy in Predicted Difficult Airway
Difficult Airway, Difficult Intubation
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.
Onur Küçük, Specialist principal investigator
Ankara Atatürk Sanatoryum Training and Research Hospital
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 Intubation; Difficult and Artificial Intelligence (AI), run by Ankara Ataturk Sanatorium Training and Research Hospital 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 5 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 8 Sep 2025. 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?
Intubation Difficulty Scale (IDS) (time frame: During the operation). 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 8 Sep 2025.