Classifying voice disorders for machine learning: a pilot study using the USVAC-C2025 diagnostic framework - Top-Institutions - MDSpire

Classifying voice disorders for machine learning: a pilot study using the USVAC-C2025 diagnostic framework

  • By

  • Catherine Madill

  • Zhou Hao Leong

  • Dharshini Manoharan

  • Dhanshree Gunjawate

  • Charu Grover

  • Katrina Sandham

  • Rijul Gupta

  • Craig Jin

  • Duy Duong Nguyen

  • James Jordan Johnson

  • Daniel Novakovic

  • June 23, 2026

  • 0 min

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Top Institutions in Otolaryngology

Brief introduction explaining scope and methodology.

  • #1

    tertiary voice clinic in Sydney, Australia
    tertiary voice clinic in Sydney, Australia

    Sydney, NSW

    Key Differentiators

    • Otolaryngology

  • #2

    Bridge2AI-Voice consortium
    Bridge2AI-Voice consortium

    N/A, N/A

    Key Differentiators

    • Otolaryngology

  • #3

    Seoul National University Hospital
    Seoul National University Hospital

    Seoul, Seoul

    Key Differentiators

    • Otolaryngology

  • #4

    University Hospital Magdeburg
    University Hospital Magdeburg

    Magdeburg, Saxony-Anhalt

    Key Differentiators

    • Otolaryngology

  • #5

    Swedish university hospitals
    Swedish university hospitals

    N/A, N/A

    Key Differentiators

    • Otolaryngology

Original Source(s)

Classifying voice disorders for machine learning: a pilot study using the USVAC-C2025 diagnostic framework

  • by Catherine Madill, Zhou Hao Leong, Dharshini Manoharan, Dhanshree Gunjawate, Charu Grover, Katrina Sandham, Rijul Gupta, Craig Jin, Duy Duong Nguyen, James Jordan Johnson, Daniel Novakovic

  • June 23, 2026

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