
Current insights on predicting vestibular diseases using machine learning.
Exploring machine learning’s role in predicting vestibular diseases: a transformative approach for early diagnosis and personalized care. ๐ค๐ฉบ
Discover the newest research about AI innovations in ๐ Audiology.

Exploring machine learning’s role in predicting vestibular diseases: a transformative approach for early diagnosis and personalized care. ๐ค๐ฉบ

Parental insights on AI in childhood ear health reveal concerns and potential benefits. Key themes identified in recent study. ๐๐ค

AI tools in audiology & speech therapy: 68% usage among academicians, yet concerns on data authenticity persist. ๐๐ค

AI in Thyroid Ultrasound Training: 70% Accuracy Achieved! ๐ค๐ Automated assessments enhance skill evaluation in medical simulations.

DNN noise reduction boosts speech recognition in bimodal CI users by 19% in noise. ๐๐

AI enhances laryngeal endoscopy image analysis, achieving segmentation quality comparable to human raters. ๐ฆ๐

Machine learning predicts occupational noise-induced hearing loss using blood indicators. AUC 0.942, sensitivity 0.875, specificity 0.936. ๐๐

Deep learning enhances pediatric audiometry accuracy: sensitivity up to 0.943, specificity 0.947. Real-time monitoring improves hearing assessments. ๐๐ถ

New study reveals TUG test with IMU sensors effectively detects vestibular impairments. Sensitivity up to 95%! ๐ง โ๏ธ

New tech aids hearing loss research! ๐ฆป FAVE estimates thresholds from handwritten audiograms, enhancing data accessibility. ๐