May 15, 2020 Mary Gates Hall

us understand how evaluations for hoarseness are done and SESSION O-2F can be improved.

TOPICSIN GENOMICAND DIGITAL SESSION T-2G HEALTH Session Moderator: Emily Godfrey, Family Medicine PEDIATRICS,PHARMACOLOGY, 1:00 PM to 2:30 PM * Note: Titles in order of presentation. NEUROLOGICAL SURGERY, OTOLARYNGOLOGY Understanding Clinician Evaluations of Hoarseness via 10:05 AM to 10:50 AM an Online Survey Vivian T Ha, Senior, Biology (Physiology) * Note: Titles in order of presentation. Mentor: Tanya Meyer, Otolaryngology Development of a Database for Creation and Testing of Mentor: GRACE WANDELL, Otolaryngology-Head & Neck Machine Learning Algorithms That Analyze Voice Surgery Anthony J Maxin, Junior, Biochemistry Hoarseness is a common symptom of multiple laryngeal dis- Mentor: Tanya Meyer, Otolaryngology eases such as inflammation, paralysis, neurologic disease, or Mentor: GRACE WANDELL, Otolaryngology-Head & Neck laryngeal cancer. Many patients with these diseases are not Surgery diagnosed with the correct underlying cause of the hoarseness Hoarseness is a common symptom reported to generalist early enough. Therefore, healthcare providers need better healthcare providers, with approximately 1% of the clinical methods to screen for and evaluate different types of hoarse- population being affected by it each year. It can be caused by ness. Currently, a combination of tools are used to evalu- multiple etiologies, such as hoarseness due to a cold, acid re- ate voice disorders in specialty clinics such as patient history, flux, or laryngeal cancer. Perceptual evaluation of the voice is perceptual voice evaluation, and laryngoscopy. We want to inaccurate, and it is therefore difficult to differentiate between better understand how providers with different medical back- hoarseness requiring urgent referral for specialty evaluation grounds evaluate patients with voice complaints. We are most (i.e. laryngeal cancer) versus a disorder that could be man- interested in seeing how history, perceptual voice evaluation, aged without specialty care (i.e. acute laryngitis). The current and laryngoscopy impact decision-making and diagnosis. In gold standard of diagnosis for hoarseness is laryngoscopy, an addition, our group has developed a machine learning algo- in-clinic endoscopy recording of the larynx performed by an rithm that analyzes voice to detect the presence or absence otolaryngologist specialist. Our research team seeks to im- of a laryngeal mass. We want to see if this algorithm could prove perceptual voice evaluation by developing and testing be clinically useful for generalist providers. To address these machine learning algorithms which analyze voice for under- questions, a group of clinician evaluators including general lying pathology, beginning with an algorithm which screens practitioners, otolaryngologists, and speech language pathol- voice for laryngeal masses. We hypothesize that our algo- ogists, will be recruited remotely. Subjects will be asked to rithm will have greater than 80% sensitivity and specificity in complete an electronic questionnaire with patient case scenar- the classification of voice samples from patients with laryn- ios, asking them to evaluate hoarse voice samples and laryn- geal masses. To test this, we are developing a large, prospec- goscopy exams, with and without case history. For percep- tive database of voice samples from a laryngology clinic us- tual voice sample evaluations, clinician performance will be ing a smartphone application. Subjects are adult patients pre- compared to the algorithm’s classification of whether a hoarse senting to the laryngology clinic, with and without voice dis- voice is from someone with a laryngeal mass. From there we orders, who have had a recent laryngoscopy exam and no la- will see if clinician detection of laryngeal masses from voice ryngeal surgery within the past three months. We are collect- could be improved with this algorithm. If the algorithm has ing patient history which could influence voice quality, such better performance than clinicians, then it may be clinically as age, gender, alcohol use, smoking history, and subject- useful as a screening tool in the future. Our results will help

Undergraduate Research Program 1 www.uw.edu/undergradresearch perceived voice disorder impact. After collection of the voice sample and patient history, cases are classified into under- lying pathologic categories. We see recruitment of a well- classified and prospective patient population with a range of voice disorders. This work could lead to improved screening of patients with hoarseness in underserved and primary care settings, and more appropriate and timelier specialist referrals and treatment.

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