Evaluation of Double Lumen Tube Intubation Difficulty With Photo-Based Artificial Intelligence Algorithms
NCT ID: NCT06839261
Last Updated: 2025-09-08
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
260 participants
OBSERVATIONAL
2024-12-01
2025-03-30
Brief Summary
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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.
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Intubation - Difficult
According to the Intubation Difficulty Scale (IDS), a score of \> 5 was defined as difficult intubation.
Intubation Difficulty Scale
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 Intelligence
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.
Intubation - Easy
According to the Intubation Difficulty Scale (IDS), a score of ≤ 5 was defined as easy intubation.
Intubation Difficulty Scale
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 Intelligence
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.
Interventions
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Intubation Difficulty Scale
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 Intelligence
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.
Eligibility Criteria
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Inclusion Criteria
* Giving informed consent
* Over 18 years of age
* Double lumen tube used for intubation
* ASA (American Society of Anesthesiologist)1-2-3
Exclusion Criteria
* ASA 4 and above
* Head and neck tumor, history of surgery/RT related to tumor
* Presence of syndrome that will cause difficult intubation
18 Years
ALL
No
Sponsors
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Ankara Ataturk Sanatorium Training and Research Hospital
OTHER_GOV
Responsible Party
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Onur Kucuk
Principal Investigator
Principal Investigators
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Onur Küçük, Specialist
Role: PRINCIPAL_INVESTIGATOR
Ankara Atatürk Sanatoryum Training and Research Hospital
Locations
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Ankara Atatürk Sanatoryum Training and Research Hospital
Ankara, Keçiören, Turkey (Türkiye)
Countries
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Other Identifiers
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2024-KAEK-24/07
Identifier Type: -
Identifier Source: org_study_id
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