IP Requirements:

• Emory IP

Experience Requirements:

• AI/ML Algorithm Development
  

Background: A cochlear implant (CI) is a surgically implanted medical device that can restore hearing to individuals with severe hearing loss.  Overall, cochlear implants are highly successful in achieving this goal; however, there is a wide range of hearing outcomes in CI patients.  Not surprisingly, it is difficult for physicians to predict how well a patient will perform with a CI.  Having some ability to predict CI performance will help physicians to better counsel patients about the decision to pursue cochlear implantation.  This is particularly relevant in cases of patients who are on the fence about surgery or patients who are medically-frail and at high risk for surgical complications.

There is currently limited information on this topic, with only a handful of published studies, and only one study (Crowson 2020) evaluating CI speech understanding outcomes in adults with anatomically-normal inner ears.   

Goal:  I would like to create a LLM that works to predict CI speech understanding outcomes, first using Emory’s CI patient data in a retrospective fashion to create and train a model, then use the LLM to prospectively predict outcomes.

https://chatgpt.com/share/d27a8132-6d5e-4295-83f8-37ca00f8032e

Existing Literature:

1: Crowson MG, Dixon P, Mahmood R, Lee JW, Shipp D, Le T, Lin V, Chen J, Chan TCY. Predicting Postoperative Cochlear Implant Performance Using Supervised Machine Learning. Otol Neurotol. 2020 Sep;41(8):e1013-e1023. doi: 10.1097/MAO.0000000000002710. PMID: 32558750.

2: Weng J, Xue S, Wei X, Lu S, Xie J, Kong Y, Shen M, Chen B, Chen J, Zou X, Zhang X, Gao Z, Liu P, Shi Y, Cui D, Li Y, Wang H. Machine learning-based prediction of the outcomes of cochlear implantation in patients with inner ear malformation. Eur Arch Otorhinolaryngol. 2024 Jul;281(7):3535-3545. doi: 10.1007/s00405-024-08463-w. Epub 2024 Feb 14. PMID: 38353769.

3: Shafieibavani E, Goudey B, Kiral I, Zhong P, Jimeno-Yepes A, Swan A, Gambhir M, Buechner A, Kludt E, Eikelboom RH, Sucher C, Gifford RH, Rottier R, Plant K, Anjomshoa H. Predictive models for cochlear implant outcomes: Performance, generalizability, and the impact of cohort size. Trends Hear. 2021 Jan-Dec;25:23312165211066174. doi: 10.1177/23312165211066174. PMID: 34903103; PMCID: PMC8764462.

4: Patro A, Perkins EL, Ortega CA, Lindquist NR, Dawant BM, Gifford R, Haynes DS, Chowdhury N. Machine Learning Approach for Screening Cochlear Implant Candidates: Comparing With the 60/60 Guideline. Otol Neurotol. 2023 Aug 1;44(7):e486-e491. doi: 10.1097/MAO.0000000000003927. Epub 2023 Jun 29. PMID: 37400135; PMCID: PMC10524241.

5: Lu S, Xie J, Wei X, Kong Y, Chen B, Chen J, Zhang L, Yang M, Xue S, Shi Y, Liu S, Xu T, Dong R, Chen X, Li Y, Wang H. Machine Learning-Based Prediction of the Outcomes of Cochlear Implantation in Patients With Cochlear Nerve Deficiency and Normal Cochlea: A 2-Year Follow-Up of 70 Children. Front Neurosci. 2022 Jun 23;16:895560. doi: 10.3389/fnins.2022.895560. PMID: 35812216; PMCID: PMC9260115.

6: Koyama H, Kashio A, Yamasoba T. Prediction of Cochlear Implant Fitting by Machine Learning Techniques. Otol Neurotol. 2024 Jul 1;45(6):643-650. doi: 10.1097/MAO.0000000000004205. Epub 2024 May 21. PMID: 38769101.

7: Paquette S, Gouin S, Lehmann A. Improving emotion perception in cochlear implant users: insights from machine learning analysis of EEG signals. BMC Neurol. 2024 Apr 8;24(1):115. doi: 10.1186/s12883-024-03616-0. PMID: 38589815; PMCID: PMC11000345.

8: Tan L, Holland SK, Deshpande AK, Chen Y, Choo DI, Lu LJ. A semi-supervised Support Vector Machine model for predicting the language outcomes following cochlear implantation based on pre-implant brain fMRI imaging. Brain Behav. 2015 Oct 12;5(12):e00391. doi: 10.1002/brb3.391. PMID: 26807332; PMCID: PMC4714644.

9: Kyong JS, Suh MW, Han JJ, Park MK, Noh TS, Oh SH, Lee JH. Cross-Modal Cortical Activity in the Brain Can Predict Cochlear Implantation Outcome in Adults: A Machine Learning Study. J Int Adv Otol. 2021 Sep;17(5):380-386. doi: 10.5152/iao.2021.9337. PMID: 34617886; PMCID: PMC8975390.10: Skidmore J, Xu L, Chao X, Riggs WJ, Pellittieri A, Vaughan C, Ning X, Wang R, Luo J, He S. Prediction of the Functional Status of the Cochlear Nerve in Individual Cochlear Implant Users Using Machine Learning and Electrophysiological Measures. Ear Hear. 2021 Jan/Feb;42(1):180-192. doi: 10.1097/AUD.0000000000000916. PMID: 32826505; PMCID: PMC8156737.

11: Kim H, Kang WS, Park HJ, Lee JY, Park JW, Kim Y, Seo JW, Kwak MY, Kang BC, Yang CJ, Duffy BA, Cho YS, Lee SY, Suh MW, Moon IJ, Ahn JH, Cho YS, Oh SH, Chung Cochlear Implantation in Postlingually Deaf Adults is Time-sensitive Towards Positive Outcome: Prediction using Advanced Machine Learning Techniques. Sci Rep. 2018 Dec 20;8(1):18004. doi: 10.1038/s41598-018-36404-1. PMID: 30573747; PMCID: PMC6301958.

12: Song Q, Qi S, Jin C, Yang L, Qian W, Yin Y, Zhao H, Yu H. Functional Brain Connections Identify Sensorineural Hearing Loss and Predict the Outcome of Cochlear Implantation. Front Comput Neurosci. 2022 Mar 30;16:825160. doi: 10.3389/fncom.2022.825160. PMID: 35431849; PMCID: PMC9005839.