Kaladerhan O Agbontaen, Kristoffer M Cold, David Woods, Vimal Grover, Hatem Soliman Aboumarie, Sundeep Kaul, Lars Konge, Suveer Singh
{"title":"一项随机对照试验：人工智能引导支气管镜检查对重症监护医生的表现优于人类专家指导。","authors":"Kaladerhan O Agbontaen, Kristoffer M Cold, David Woods, Vimal Grover, Hatem Soliman Aboumarie, Sundeep Kaul, Lars Konge, Suveer Singh","doi":"10.1097/CCM.0000000000006629","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>Bronchoscopy in the mechanically ventilated patient is an important skill for critical-care physicians. However, training opportunity is heterogenous and limited by infrequent caseload or inadequate instructor feedback for satisfactory competencies. A new artificial intelligence (AI) navigational system using augmented reality - the Ambu Broncho Simulator - can guide bronchoscopy training. Is training with the AI system comparable to bedside, expert tutor instruction in improving bronchoscopy performance?</p><p><strong>Design: </strong>A nonblinded, parallel group randomized controlled trial was conducted.</p><p><strong>Setting: </strong>The study was conducted in a simulated setting at an academic university hospital.</p><p><strong>Subjects: </strong>Critical-care physicians were invited to take part in the study.</p><p><strong>Interventions: </strong>Forty participants received 30 minutes of bronchoscopy training, either guided by AI only (artificial intelligence group [AIG]) or by expert tutor feedback (expert tutor group [ETG]). All participants performed a final full navigation bronchoscopy performance test and completed a cognitive load questionnaire, the NASA Task Load Index .</p><p><strong>Measurements and main results: </strong>Mean intersegmental time (MIT = PT/DC), diagnostic completeness (DC), procedure time (PT), structured progress (SP), and number of segments revisited (SR) were measured. The primary outcome measure assessed was MIT, a measure of bronchoscopic performance efficiency. The secondary outcome measures were DC, PT, SP, and SR. Nineteen participants were randomized to the AIG and 21 participants to the ETG. MIT, PT, and SR were significantly better in the AIG compared to the ETG (median difference, p): MIT (-7.9 s, 0.027), PT (-77 s, 0.022), SR (-7 segments, 0.019); all showing moderate effect sizes (0.35, 0.36, and 0.37, respectively) as per Cohen's classification.There was no significant difference between the groups for all other final test measures.</p><p><strong>Conclusions: </strong>Training using an AI system resulted in faster and more efficient bronchoscopy performance by critical-care physicians when compared to expert human tutor instruction. This could change the future of bronchoscopy training in critical care and warrants validation in patients through clinical studies.</p>","PeriodicalId":10765,"journal":{"name":"Critical Care Medicine","volume":" ","pages":""},"PeriodicalIF":7.7000,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial Intelligence Guided Bronchoscopy is Superior to Human Expert Instruction for the Performance of Critical-Care Physicians: A Randomized Controlled Trial.\",\"authors\":\"Kaladerhan O Agbontaen, Kristoffer M Cold, David Woods, Vimal Grover, Hatem Soliman Aboumarie, Sundeep Kaul, Lars Konge, Suveer Singh\",\"doi\":\"10.1097/CCM.0000000000006629\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>Bronchoscopy in the mechanically ventilated patient is an important skill for critical-care physicians. However, training opportunity is heterogenous and limited by infrequent caseload or inadequate instructor feedback for satisfactory competencies. A new artificial intelligence (AI) navigational system using augmented reality - the Ambu Broncho Simulator - can guide bronchoscopy training. Is training with the AI system comparable to bedside, expert tutor instruction in improving bronchoscopy performance?</p><p><strong>Design: </strong>A nonblinded, parallel group randomized controlled trial was conducted.</p><p><strong>Setting: </strong>The study was conducted in a simulated setting at an academic university hospital.</p><p><strong>Subjects: </strong>Critical-care physicians were invited to take part in the study.</p><p><strong>Interventions: </strong>Forty participants received 30 minutes of bronchoscopy training, either guided by AI only (artificial intelligence group [AIG]) or by expert tutor feedback (expert tutor group [ETG]). All participants performed a final full navigation bronchoscopy performance test and completed a cognitive load questionnaire, the NASA Task Load Index .</p><p><strong>Measurements and main results: </strong>Mean intersegmental time (MIT = PT/DC), diagnostic completeness (DC), procedure time (PT), structured progress (SP), and number of segments revisited (SR) were measured. The primary outcome measure assessed was MIT, a measure of bronchoscopic performance efficiency. The secondary outcome measures were DC, PT, SP, and SR. Nineteen participants were randomized to the AIG and 21 participants to the ETG. MIT, PT, and SR were significantly better in the AIG compared to the ETG (median difference, p): MIT (-7.9 s, 0.027), PT (-77 s, 0.022), SR (-7 segments, 0.019); all showing moderate effect sizes (0.35, 0.36, and 0.37, respectively) as per Cohen's classification.There was no significant difference between the groups for all other final test measures.</p><p><strong>Conclusions: </strong>Training using an AI system resulted in faster and more efficient bronchoscopy performance by critical-care physicians when compared to expert human tutor instruction. 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Artificial Intelligence Guided Bronchoscopy is Superior to Human Expert Instruction for the Performance of Critical-Care Physicians: A Randomized Controlled Trial.
Objectives: Bronchoscopy in the mechanically ventilated patient is an important skill for critical-care physicians. However, training opportunity is heterogenous and limited by infrequent caseload or inadequate instructor feedback for satisfactory competencies. A new artificial intelligence (AI) navigational system using augmented reality - the Ambu Broncho Simulator - can guide bronchoscopy training. Is training with the AI system comparable to bedside, expert tutor instruction in improving bronchoscopy performance?
Design: A nonblinded, parallel group randomized controlled trial was conducted.
Setting: The study was conducted in a simulated setting at an academic university hospital.
Subjects: Critical-care physicians were invited to take part in the study.
Interventions: Forty participants received 30 minutes of bronchoscopy training, either guided by AI only (artificial intelligence group [AIG]) or by expert tutor feedback (expert tutor group [ETG]). All participants performed a final full navigation bronchoscopy performance test and completed a cognitive load questionnaire, the NASA Task Load Index .
Measurements and main results: Mean intersegmental time (MIT = PT/DC), diagnostic completeness (DC), procedure time (PT), structured progress (SP), and number of segments revisited (SR) were measured. The primary outcome measure assessed was MIT, a measure of bronchoscopic performance efficiency. The secondary outcome measures were DC, PT, SP, and SR. Nineteen participants were randomized to the AIG and 21 participants to the ETG. MIT, PT, and SR were significantly better in the AIG compared to the ETG (median difference, p): MIT (-7.9 s, 0.027), PT (-77 s, 0.022), SR (-7 segments, 0.019); all showing moderate effect sizes (0.35, 0.36, and 0.37, respectively) as per Cohen's classification.There was no significant difference between the groups for all other final test measures.
Conclusions: Training using an AI system resulted in faster and more efficient bronchoscopy performance by critical-care physicians when compared to expert human tutor instruction. This could change the future of bronchoscopy training in critical care and warrants validation in patients through clinical studies.
期刊介绍：
Critical Care Medicine is the premier peer-reviewed, scientific publication in critical care medicine. Directed to those specialists who treat patients in the ICU and CCU, including chest physicians, surgeons, pediatricians, pharmacists/pharmacologists, anesthesiologists, critical care nurses, and other healthcare professionals, Critical Care Medicine covers all aspects of acute and emergency care for the critically ill or injured patient.
Each issue presents critical care practitioners with clinical breakthroughs that lead to better patient care, the latest news on promising research, and advances in equipment and techniques.
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