A Deep-Learning Model For Multi-class Audio Classification of Vocal Fold Pathologies in Office Stroboscopy.

Details

• Alternative Title:
  The Laryngoscope, 2025, v. 135, no. 7: A Deep-Learning Model For Multi-class Audio Classification of Vocal Fold Pathologies in Office Stroboscopy.
• Journal Article:
  The Laryngoscope
• Personal Author:
  Kim, Yeo Eun ; Dobko, Maria ; Li, Haomiao ; Shao, Tianlan ; Periyakoil, Preethi ; Tipton, Courtney ; Colasacco, Christine ; Serpedin, Aisha ; Elemento, Olivier ; Sabuncu, Mert ; Pitman, Michael ; Sulica, Lucian ; Rameau, Anaïs
• Description:
  To develop and validate a deep-learning classifier trained on voice data extracted from videolaryngostroboscopy recordings, differentiating between three different vocal fold (VF) states: healthy (HVF), unilateral paralysis (UVFP), and VF lesions, including benign and malignant pathologies.
  
  Patients with UVFP (n = 105), VF lesions (n = 63), and HVF (n = 41) were retrospectively identified. Voice samples were extracted from stroboscopic videos (Pentax Laryngeal Strobe Model 9400), including sustained /i/ phonation, pitch glide, and /i/ sniff task. Extracted audio files were converted into Mel-spectrograms. Voice samples were independently divided into training (80%), validation (10%), and test (10%) by patient. Pretrained ResNet18 models were trained to classify (1) HVF and pathological VF (lesions and UVFP), and (2) HVF, UVFP, and VF lesions. Both classifiers were further validated on an external dataset consisting of 12 UVFP, 13 VF lesions, and 15 HVF patients. Model performances were evaluated by accuracy and F1-score.
  
  When evaluated on a hold-out test set, the binary classifier demonstrated stronger performance compared to the multi-class classifier (accuracy 83% vs. 40%; F1-score 0.90 vs. 0.36). When evaluated on an external dataset, the binary classifier achieved an accuracy of 63% and F1-score of 0.48, compared to 35% and 0.25 for the multi-class classifier.
  
  Deep-learning classifiers differentiating HVF, UVFP, and VF lesions were developed using voice data from stroboscopic videos. Although healthy and pathological voice were differentiated with moderate accuracy, multi-class classification lowered model performance. The model performed poorly on an external dataset. Voice captured in stroboscopic videos may have limited diagnostic value, though further studies are needed.
  
  4 Laryngoscope, 135:2428-2436, 2025.
• Content Notes:
  Author manuscript
• Subjects:
  • Humans
  • Deep Learning
  • Vocal Cords
  • Stroboscopy
  • Retrospective Studies
  • Male
  • Female
  • Middle Aged
  • Video Recording
  • Adult
  More +
• Keywords:
  • Deep learning
  • artificial intelligence
  • convolutional neural network
  • computer-aided diagnosis
  • laryngology
• Source:
  The Laryngoscope, 2025, v. 135, no. 7
• DOI:
  https://doi.org/10.1002/lary.32036
• ISSN:
  0023-852X ; 1531-4995
• Pubmed ID:
  39907244
• Pubmed Central ID:
  PMC12234251
• Document Type:
  Text
• Funding:
  T32 GM152349/GM/NIGMS NIH HHSUnited States/ ; K76 AG079040/AG/NIA NIH HHSUnited States/ ; OT2 OD032720/CD/ODCDC CDC HHSUnited States/ ; K76AG079040/AG/NIA NIH HHSUnited States/ ; OT2 OD032720/OD/NIH HHSUnited States/
• Genre:
  Journal Article
• Pages in Document:
  2428-2436 (18 pdf pages)
• Volume:
  135
• Issue:
  7
• Download URL:
  https://stacks.cdc.gov/view/cdc/261706/cdc_261706_DS1.pdf
• File Type:
  [PDF - 272.93 KB]
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha-512:b04cce2f533255b2c8c25c463601deddac1ce384597cb14292dd778afb4fbfc602ae479c4f66cc0af045319c6af9d818db66b4c7f8fdc9d098abecd05a1a6d43