Artificial intelligence in otorhinolaryngology: current trends and application areas

European Archives of Oto-Rhino-Laryngology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

Abstract Purpose This study aims to perform a bibliometric analysis of scientific research on the use artificial intelligence (AI) in field Otorhinolaryngology (ORL), with specific focus identifying emerging AI trend topics within this discipline. Methods A total 498 articles ORL, published between 1982 and 2024, were retrieved from Web Science database. Various techniques, including keyword factor analysis, applied analyze data. Results The most prolific journal was European Archives Oto-Rhino-Laryngology ( n = 67). USA 200) China 61) productive countries AI-related ORL research. institutions Harvard University / Medical School 71). leading authors Lechien JR. 18) Rameau A. 17). frequently used keywords cochlear implant, head neck cancer, magnetic resonance imaging (MRI), hearing loss, patient education, diagnosis, radiomics, surgery, aids, laryngology ve otitis media. Recent trends otorhinolaryngology reflect dynamic focus, progressing hearing-related technologies such as aids implants earlier years, diagnostic innovations like audiometry, psychoacoustics, narrow band imaging. emphasis has recently shifted toward advanced applications MRI, computed tomography (CT) for conditions chronic rhinosinusitis, laryngology, Additionally, increasing attention been given quality life, prognosis, underscoring holistic approach treatment otorhinolaryngology. Conclusion significantly impacted especially therapeutic planning. With advancements MRI CT-based technologies, proven enhance disease detection management. future suggests promising path improving clinical decision-making, care, healthcare efficiency.

Language: Английский

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Large Language Models for Pediatric Differential Diagnoses in Rural Health Care: Multicenter Retrospective Cohort Study Comparing GPT-3 With Pediatrician Performance

JMIRx Med, Journal Year: 2025, Volume and Issue: 6, P. e65263 - e65263

Published: March 19, 2025

Rural health care providers face unique challenges such as limited specialist access and high patient volumes, making accurate diagnostic support tools essential. Large language models like GPT-3 have demonstrated potential in clinical decision but remain understudied pediatric differential diagnosis. This study aims to evaluate the accuracy reliability of a fine-tuned model compared board-certified pediatricians rural settings. multicenter retrospective cohort analyzed 500 encounters (ages 0-18 years; n=261, 52.2% female) from organizations Central Louisiana between January 2020 December 2021. The (DaVinci version) was using OpenAI application programming interface trained on 350 encounters, with 150 reserved for testing. Five (mean experience: 12, SD 5.8 years) provided reference standard diagnoses. Model performance assessed accuracy, sensitivity, specificity, subgroup analyses. achieved an 87.3% (131/150 cases), sensitivity 85% (95% CI 82%-88%), specificity 90% 87%-93%), comparable pediatricians' 91.3% (137/150 cases; P=.47). Performance consistent across age groups (0-5 years: 54/62, 87%; 6-12 47/53, 89%; 13-18 30/35, 86%) common complaints (fever: 36/39, 92%; abdominal pain: 20/23, 87%). For rare diagnoses (n=20), slightly lower (16/20, 80%) (17/20, 85%; P=.62). demonstrates that can provide pediatricians, particularly presentations, care. Further validation diverse populations is necessary before implementation.

Language: Английский

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Artificial intelligence based surgical support for experimental laparoscopic Nissen fundoplication

Frontiers in Pediatrics, Journal Year: 2025, Volume and Issue: 13

Published: May 23, 2025

Background Computer vision (CV), a subset of artificial intelligence (AI), enables deep learning models to detect specific events within digital images or videos. Especially in medical imaging, AI/CV holds significant promise analyzing data from x-rays, CT scans, and MRIs. However, the application support surgery has progressed more slowly. This study presents development first image-based model classifying quality indicators laparoscopic Nissen fundoplication (LNF). Materials methods Six visible (VQIs) for were predefined as parameters build datasets including correct (360° fundoplication) incorrect configurations (incomplete, twisted wraps, too long (>four knots), loose, long, malpositioning (at/below gastroesophageal junction). In porcine model, multiple iterations each VQI performed. A total 57 video sequences processed, extracting 3,138 at 0.5-second intervals. These annotated corresponding their respective VQIs. The EfficientNet architecture, typical was employed train an ensemble image classifiers, well multi-class classifier, distinguish between wraps. Results demonstrated strong performance predicting VQIs fundoplication. individual classifiers achieved average F1-Score 0.9738 ± 0.1699 when adjusted optimal Equal Error Rate (EER) decision boundary. similar observed using classifier. results remained robust despite extensive augmentation. For 3/5 identical; detection incomplete loose LNFs showed slight decline predictive power. Conclusion experimental demonstrates that algorithm can effectively fundoplications. proof concept does not aim test clinical fundoplication, but provides evidence be trained classify various surgical configurations. future, this could developed into AI based real-time enhance outcome patient safety.

Language: Английский

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“Artificial intelligence and pediatric surgery: where are we?’’. Commentary

Pediatric Surgery International, Journal Year: 2024, Volume and Issue: 41(1)

Published: Dec. 15, 2024

Language: Английский

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