University of Ioannina

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Neuroscience Research Notes Sep 28, 2026
Greek trisyllabic word list developed: 40 words retained after testing 20 children aged 6 to 12 for speech recognition threshold
Addressing the gap in Speech Recognition Threshold (SRT) materials for Greek-speaking school-aged children (6 to 12 years), this study selected words on four criteria—syllabic structure (trisyllabics), age-appropriate vocabulary familiarity, phonemic differentiation, and homogeneity in audibility—recorded and processed them per ISO 8253-3:2022, had 20 children take part in the word homogeneity evaluation, determined for each word the presentation level needed for 50% correct recognition, and measured recognition rates across intensities to locate the steepest rise of the Performance-Intensity (PI) function between 20% and 80% recognition; it found that words homogeneous in recognition rate are not homogeneous in recognition threshold and vice versa, so only words within +1 Standard Deviati
Addressing the gap in Speech Recognition Threshold (SRT) materials for Greek-speaking school-aged children (6 to 12 years), this study selected words on four criteria—syllabic structure (trisyllabics), age-appropriate vocabulary familiarity, phonemic differentiation, and homogeneity in audibility—recorded and processed them per ISO 8253-3:2022, had 20 children take part in the word homogeneity evaluation, determined for each word the presentation level needed for 50% correct recognition, and measured recognition rates across intensities to locate the steepest rise of the Performance-Intensity (PI) function between 20% and 80% recognition; it found that words homogeneous in recognition rate are not homogeneous in recognition threshold and vice versa, so only words within +1 Standard Deviati
University of Patras University of Ioannina
Nikolaos Trimmis（First author）
Speech Recognition Threshold Speech Audiometry
European Archives of Oto-Rhino-Laryngology Sep 16, 2026
Artificial Intelligence in Otolaryngology: Current Applications, Limitations, and Future Perspectives
This narrative review, based on a structured search of PubMed/MEDLINE, Scopus, and Web of Science, maps the clinical applications of artificial intelligence across otolaryngology subspecialties, noting that deep learning shows potential in sinonasal disease detection, automated image segmentation, lymph node metastasis prediction, thyroid nodule classification, and prognostic modeling in head and neck cancer, while multimodal systems and generative large language models are emerging in medical education, image interpretation, differential diagnosis, and clinical decision support; however, limited external validation, retrospective designs, dataset heterogeneity, algorithmic bias, lack of transparency, privacy concerns, medico-legal uncertainty, and automation bias still constrain broad imp
This narrative review, based on a structured search of PubMed/MEDLINE, Scopus, and Web of Science, maps the clinical applications of artificial intelligence across otolaryngology subspecialties, noting that deep learning shows potential in sinonasal disease detection, automated image segmentation, lymph node metastasis prediction, thyroid nodule classification, and prognostic modeling in head and neck cancer, while multimodal systems and generative large language models are emerging in medical education, image interpretation, differential diagnosis, and clinical decision support; however, limited external validation, retrospective designs, dataset heterogeneity, algorithmic bias, lack of transparency, privacy concerns, medico-legal uncertainty, and automation bias still constrain broad imp
University of Ioannina University Hospital of Ioannina
Eleni V Litsou（First author, Corresponding author）
Artificial Intelligence applications Narrative Review

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Greek trisyllabic word list developed: 40 words retained after testing 20 children aged 6 to 12 for speech recognition threshold

Artificial Intelligence in Otolaryngology: Current Applications, Limitations, and Future Perspectives