Discover the newest research about AI innovations in 👂 Audiology.
Machine learning predicts occupational noise-induced hearing loss using blood indicators. AUC 0.942, sensitivity 0.875, specificity 0.936. 📊🔍
Deep learning enhances pediatric audiometry accuracy: sensitivity up to 0.943, specificity 0.947. Real-time monitoring improves hearing assessments. 📊👶
New study reveals TUG test with IMU sensors effectively detects vestibular impairments. Sensitivity up to 95%! 🧠⚖️
New tech aids hearing loss research! 🦻 FAVE estimates thresholds from handwritten audiograms, enhancing data accessibility. 📊
Exploring the fusion of machine learning and physics in physiology. 🧠🔬 Insights from a recent PubMed article.
Exploring AI in language disorder detection 🤖📚: Recent advances enhance assessment efficiency for children’s language disorders.
Exploring glottis segmentation’s challenges and advancements in voice physiology research. 🤔🔍 Key insights from a recent PubMed article. 📚
AI tool assesses velopharyngeal competence in children with cleft palate. Results show limited accuracy for clinical use. 🤖👶
