{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T09:00:54Z","timestamp":1789203654940,"version":"build-2803163510"},"reference-count":29,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T00:00:00Z","timestamp":1747872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)","award":["390895286"],"award-info":[{"award-number":["390895286"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objectives: Cochlear implantation (CI) significantly enhances speech perception and quality of life in patients with severe-to-profound sensorineural hearing loss, yet outcomes vary substantially. Accurate preoperative prediction of CI outcomes remains challenging. This study aimed to develop and validate a machine learning model predicting postoperative speech recognition using a large, single-center dataset. Additionally, we compared model performance with expert clinical predictions to evaluate potential clinical utility. Methods: We retrospectively analyzed data from 2571 adult patients with postlingual hearing loss who received their cochlear implant between 2000 and 2022 at Hannover Medical School, Germany. A decision tree regression model was trained to predict monosyllabic (MS) word recognition score one to two years post-implantation using preoperative clinical variables (age, duration of deafness, preoperative MS score, pure tone average, onset type, and contralateral implantation status). Model evaluation was performed using a random data split (10%), a chronological future cohort (patients implanted after 2020), and a subset where experienced audiologists predicted outcomes for comparison. Results: The model achieved a mean absolute error (MAE) of 17.3% on the random test set and 17.8% on the chronological test set, demonstrating robust predictive performance over time. Compared to expert audiologist predictions, the model showed similar accuracy (MAE: 19.1% for the model vs. 18.9% for experts), suggesting comparable effectiveness. Conclusions: Our machine learning model reliably predicts postoperative speech outcomes and matches expert clinical predictions, highlighting its potential for supporting clinical decision-making. Future research should include external validation and prospective trials to further confirm clinical applicability.<\/jats:p>","DOI":"10.3390\/jcm14113625","type":"journal-article","created":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T07:04:28Z","timestamp":1747897468000},"page":"3625","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Machine Learning Model to Predict Postoperative Speech Recognition Outcomes in Cochlear Implant Recipients: Development, Validation, and Comparison with Expert Clinical Judgment"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7627-7733","authenticated-orcid":false,"given":"Alexey","family":"Demyanchuk","sequence":"first","affiliation":[{"name":"Department of Otorhinolaryngology, Hannover Medical School, Carl-Neuberg-Stra\u00dfe 1, 30625 Hannover, Germany"},{"name":"Hearing4all Cluster of Excellence, Hannover Medical School, 30625 Hannover, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eugen","family":"Kludt","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology, Hannover Medical School, Carl-Neuberg-Stra\u00dfe 1, 30625 Hannover, Germany"},{"name":"Hearing4all Cluster of Excellence, Hannover Medical School, 30625 Hannover, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9307-5989","authenticated-orcid":false,"given":"Thomas","family":"Lenarz","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology, Hannover Medical School, Carl-Neuberg-Stra\u00dfe 1, 30625 Hannover, Germany"},{"name":"Hearing4all Cluster of Excellence, Hannover Medical School, 30625 Hannover, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"B\u00fcchner","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology, Hannover Medical School, Carl-Neuberg-Stra\u00dfe 1, 30625 Hannover, Germany"},{"name":"Hearing4all Cluster of Excellence, Hannover Medical School, 30625 Hannover, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"367","DOI":"10.2471\/BLT.13.128728","article-title":"The Global Burden of Disabling Hearing Impairment: A Call to Action","volume":"92","author":"Olusanya","year":"2014","journal-title":"Bull. World Health Organ."},{"key":"ref_2","unstructured":"(2025, February 04). World Report on Hearing. Available online: https:\/\/www.who.int\/publications\/i\/item\/9789240020481."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1001\/jamainternmed.2013.1868","article-title":"Hearing Loss and Cognitive Decline in Older Adults","volume":"173","author":"Lin","year":"2013","journal-title":"JAMA Intern. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1159\/000113510","article-title":"Speech Recognition Materials and Ceiling Effects: Considerations for Cochlear Implant Programs","volume":"13","author":"Gifford","year":"2008","journal-title":"Audiol. Neurootol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Boisvert, I., Reis, M., Au, A., Cowan, R., and Dowell, R.C. (2020). Cochlear Implantation Outcomes in Adults: A Scoping Review. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0232421"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.wjorl.2017.12.010","article-title":"Three Challenges for Future Research on Cochlear Implants","volume":"03","author":"Pisoni","year":"2017","journal-title":"World J. Otorhinolaryngol.\u2014Head Neck Surg."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2475","DOI":"10.1016\/j.neuropsychologia.2011.04.025","article-title":"Evolution of Non-Speech Sound Memory in Postlingual Deafness: Implications for Cochlear Implant Rehabilitation","volume":"49","author":"Lazard","year":"2011","journal-title":"Neuropsychologia"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.jclinepi.2021.04.005","article-title":"Prediction Models for Clinical Outcome after Cochlear Implantation: A Systematic Review","volume":"137","author":"Velde","year":"2021","journal-title":"J. Clin. Epidemiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1097\/AUD.0b013e3182741aa7","article-title":"Factors Affecting Open-Set Word Recognition in Adults With Cochlear Implants","volume":"34","author":"Holden","year":"2013","journal-title":"Ear Hear."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hoppe, U., Hast, A., Hornung, J., and Hocke, T. (2023). Evolving a Model for Cochlear Implant Outcome. J. Clin. Med., 12.","DOI":"10.20944\/preprints202307.2045.v1"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e1013","DOI":"10.1097\/MAO.0000000000002710","article-title":"Predicting Postoperative Cochlear Implant Performance Using Supervised Machine Learning","volume":"41","author":"Crowson","year":"2020","journal-title":"Otol. Neurotol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"23312165211066174","DOI":"10.1177\/23312165211066174","article-title":"Predictive Models for Cochlear Implant Outcomes: Performance, Generalizability, and the Impact of Cohort Size","volume":"25","author":"Shafieibavani","year":"2021","journal-title":"Trends Hear."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kim, H., Kang, W.S., Park, H.J., Lee, J.Y., Park, J.W., Kim, Y., Seo, J.W., Kwak, M.Y., Kang, B.C., and Yang, C.J. (2018). Cochlear Implantation in Postlingually Deaf Adults Is Time-Sensitive Towards Positive Outcome: Prediction Using Advanced Machine Learning Techniques. Sci. Rep., 8.","DOI":"10.1038\/s41598-018-36404-1"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1097\/MAO.0b013e31819d3480","article-title":"A Predictive Model of Cochlear Implant Performance in Postlingually Deafened Adults","volume":"30","author":"Roditi","year":"2009","journal-title":"Otol. Neurotol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1097\/AUD.0000000000000233","article-title":"Factors Predicting Postoperative Unilateral and Bilateral Speech Recognition in Adult Cochlear Implant Recipients with Acoustic Hearing","volume":"37","author":"Plant","year":"2016","journal-title":"Ear Hear."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3089","DOI":"10.1007\/s00405-019-05613-3","article-title":"Cochlear Implant Outcomes in the Elderly: A Uni- and Multivariate Analyses of Prognostic Factors","volume":"276","author":"Favaretto","year":"2019","journal-title":"Eur. Arch. Otorhinolaryngol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1097\/AUD.0000000000000670","article-title":"Early Sentence Recognition in Adult Cochlear Implant Users","volume":"40","author":"James","year":"2019","journal-title":"Ear Hear."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI Statement: Updated Guidance for Reporting Clinical Prediction Models That Use Regression or Machine Learning Methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1093\/bioinformatics\/17.6.520","article-title":"Missing Value Estimation Methods for DNA Microarrays","volume":"17","author":"Troyanskaya","year":"2001","journal-title":"Bioinformatics"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Breiman, L., Friedman, J., Olshen, R.A., and Stone, C.J. (2017). Classification and Regression Trees, Chapman and Hall\/CRC.","DOI":"10.1201\/9781315139470"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). Additive Models, Trees, and Related Methods. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer.","DOI":"10.1007\/b94608_9"},{"key":"ref_22","first-page":"1137","article-title":"A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection","volume":"14","author":"Kohavi","year":"2001","journal-title":"IJCAL"},{"key":"ref_23","unstructured":"Mckinney, W. (July, January 28). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference, Austin, TX, USA."},{"key":"ref_24","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D Graphics Environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_26","unstructured":"Buhl, M., Kludt, E., Schell-Majoor, L., Avan, P., and Campi, M. (2025). Discrimination Loss vs. SRT: A Model-Based Approach towards Harmonizing Speech Test Interpretations. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Saak, S., Huelsmeier, D., Kollmeier, B., and Buhl, M. (2022). A Flexible Data-Driven Audiological Patient Stratification Method for Deriving Auditory Profiles. Front. Neurol., 13.","DOI":"10.3389\/fneur.2022.959582"},{"key":"ref_28","unstructured":"Systems, S.G. (2025, April 22). Ocean Health Clinical Knowledge Manager. Available online: https:\/\/ckm.highmed.org\/ckm\/projects\/1246.152.56."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1080\/14992027.2023.2256973","article-title":"Variability in Clinicians\u2019 Prediction Accuracy for Outcomes of Adult Cochlear Implant Users","volume":"63","author":"Philpott","year":"2024","journal-title":"Int. J. Audiol."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/11\/3625\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:38:16Z","timestamp":1760031496000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/11\/3625"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,22]]},"references-count":29,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["jcm14113625"],"URL":"https:\/\/doi.org\/10.3390\/jcm14113625","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,22]]}}}