Work

Analysis of genetic polymorphisms in sudden sensorineural hearing loss and artificial intelligence-supported individualized precision therapy

Year: 2026

Type: article

Abstract: Background Sudden sensorineural hearing loss (SSNHL) is characterized by acute onset, complex pathogenesis, and visible variability in prognosis, making precise treatment challenging. This article focuses on identifying key factors influencing the treatment outcomes of SSNHL. Methods Clinical data were collected from 200 SSNHL patients, recording treatment regimens including systemic and intratympanic steroid administration. Genetic polymorphisms were analyzed via blood testing, and a personalized treatment prediction model was constructed and validated. An independent external validation set of 100 cases was included to assess model generalizability. Comparative efficacy predictions were performed among multifactorial logistic regression, convolutional neural network (CNN), random forest, and support vector machine models. Results As against systemic steroid therapy, intratympanic injection, and combination treatment (corticosteroids combined with retroauricular subtympanic membrane and intratympanic injections) showed superior recovery rates. The distinction between combination treatment and monotherapy was visible ( p < 0.01). At the level of key genetic polymorphisms, specific single-nucleotide polymorphism sites in genes such as GJB2, SLC26A4, TNF- α , and CYP3A4 were closely associated with treatment responses, with different genetic profiles corresponding to distinct treatment recommendations. In AI-based treatment efficacy prediction, the CNN model demonstrated significantly higher sensitivity, specificity, and accuracy compared to random forest, support vector machine, and other models ( p < 0.05). It consistently outperformed traditional multifactorial logistic regression in both internal and external validation sets, particularly in identifying poor-recovery cases ( p < 0.05). Conclusion In SSNHL treatment, the combined approach of postauricular subperiosteal and intratympanic steroid injections was significantly more effective than systemic steroid therapy, representing the optimal treatment choice. Specific genetic polymorphisms were closely associated with treatment response and may serve as molecular biomarkers for personalized therapy. The deep learning CNN model exhibited superior performance in efficacy prediction, surpassing conventional models, and could assist in precision treatment decision-making.

Source: Frontiers in Neurology

DOI: 10.3389/fneur.2025.1643435

Authors: Xin Li, Dong Yang

Institutions: Beijing Tsinghua Chang Gung Hospital, Tsinghua University, Tianjin University, Tianjin Third Central Hospital

Language: English

Topic: Vestibular and auditory disorders

Subfield: Neurology

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,801