Machine Learning Models for Predicting Hearing Prognosis in Unilateral Idiopathic Sudden Sensorineural Hearing Loss

Evaluating Prediction Models with Hearing Handicap Inventory for the Elderly in Chronic Otitis Media Patients

BACKGROUND

This retrospective, cross-sectional study aimed to assess the functional hearing capacity of individuals with Chronic Otitis Media (COM) using prediction modeling techniques and the Hearing Handicap Inventory for the Elderly (HHIE) questionnaire. This study investigated the potential of predictive models to identify hearing levels in patients with COM.

METHODS

We comprehensively examined 289 individuals diagnosed with COM, of whom 136 reported tinnitus and 143 did not. This study involved a detailed analysis of various patient characteristics and HHIE questionnaire results. Logistic and Random Forest models were employed and compared based on key performance metrics.

RESULTS

The logistic model demonstrated a slightly higher accuracy (73.56%), area under the curve (AUC; 0.73), Kappa value (0.45), and F1 score (0.78) than the Random Forest model. These findings suggest the superior predictive performance of the logistic model in identifying hearing levels in patients with COM.

CONCLUSIONS

Although the AUC for the logistic regression did not meet the benchmark, this study highlights the potential for enhanced reliability and improved performance metrics using a larger dataset. The integration of prediction modeling techniques and the HHIE questionnaire shows promise for achieving greater diagnostic accuracy and refining intervention strategies for individuals with COM.

Prediction of hearing recovery with deep learning algorithm in sudden sensorineural hearing loss

This study aimed to establish a deep learning-based predictive model for the prognosis of idiopathic sudden sensorineural hearing loss (SSNHL). Data from 1108 patients with SSNHL between January 2015 and May 2023 were retrospectively analyzed. Patients underwent standardized treatment protocols including high-dose steroid therapy and hearing outcomes were assessed after three months using Siegel's criteria and the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) classification. For predicting patient recovery, a two-layered classification process was implemented. Initially, a set of 22 Multilayer Perceptrons (MLP) networks was employed to categorize the patients. The outcomes from this initial categorization were subsequently relayed to a second-layer meta-classifier for final prognosis determination. The validity of this methodology was ascertained through a K-fold cross-validation procedure executed with 10 distinct splits. The prediction model for complete recovery, based on Siegel's criteria, demonstrated an accuracy of 0.892 and area under the curve (AUC) of 0.922. For the class A prediction, according to AAO-HNS classification, the model showed an accuracy of 0.847 and AUC of 0.918. These results suggest that the model may have the potential to contribute to the establishment of tailored patient management strategies by predicting hearing recovery in patients with SSNHL.

Applications of Machine Learning in Meniere's Disease Assessment Based on Pure-Tone Audiometry

OBJECTIVE

To apply machine learning models based on air conduction thresholds of pure-tone audiometry for automatic diagnosis of Meniere's disease (MD) and prediction of endolymphatic hydrops (EH).

STUDY DESIGN

Retrospective study.

SETTING

Tertiary medical center.

METHODS

Gadolinium-enhanced magnetic resonance imaging sequences and pure-tone audiometry data were collected. Subsequently, basic and multiple analytical features were engineered based on the air conduction thresholds of pure-tone audiometry. Later, 5 classical machine learning models were trained to diagnose MD using the engineered features. The models demonstrating excellent performance were also selected to predict EH. The model's effectiveness in MD diagnosis was compared with experienced otolaryngologists.

RESULTS

First, the winning light gradient boosting (LGB) machine learning model trained by multiple features demonstrates a remarkable performance on the diagnosis of MD, achieving an accuracy rate of 87%, sensitivity of 83%, specificity of 90%, and a robust area under the receiver operating characteristic curve of 0.95, which compares favorably with experienced clinicians. Second, the LGB model, with an accuracy of 78% on EH prediction, outperformed the other 3 machine learning models. Finally, a feature importance analysis reveals a pivotal role of the specific pure-tone audiometry features that are essential for both MD diagnosis and EH prediction. Highlighted features include standard deviation and mean of the whole-frequency hearing, the peak of the audiogram, and hearing at low frequencies, notably at 250 Hz.

CONCLUSION

An efficient machine learning model based on pure-tone audiometry features was produced to diagnose MD, which also showed the potential to predict the subtypes of EH. The innovative approach demonstrated a game-changing strategy for MD screening and promising cost-effective benefits for the health care enterprise.

Prognosis Prediction of Sudden Sensorineural Hearing Loss Using Ensemble Artificial Intelligence Learning Models

OBJECTIVE

We used simple variables to construct prognostic prediction ensemble learning models for patients with sudden sensorineural hearing loss (SSNHL).

STUDY DESIGN

Retrospectively study.

SETTING

Tertiary medical center.

PATIENTS

1,572 patients with SSNHL.

INTERVENTION

Prognostic.

MAIN OUTCOME MEASURES

We selected four variables, namely, age, days after onset of hearing loss, vertigo, and type of hearing loss. We also compared the accuracy between different ensemble learning models based on the boosting, bagging, AdaBoost, and stacking algorithms.

RESULTS

We enrolled 1,572 patients with SSNHL; 73.5% of them showed improving and 26.5% did not. Significant between-group differences were noted in terms of age ( p = 0.011), days after onset of hearing loss ( p < 0.001), and concurrent vertigo ( p < 0.001), indicating that the patients who showed improving to treatment were younger and had fewer days after onset and fewer vertigo symptoms. Among ensemble learning models, the AdaBoost algorithm, compared with the other algorithms, achieved higher accuracy (82.89%), higher precision (86.66%), a higher F1 score (89.20), and a larger area under the receiver operating characteristics curve (0.79), as indicated by test results of a dataset with 10 independent runs. Furthermore, Gini scores indicated that age and days after onset are two key parameters of the predictive model.

CONCLUSIONS

The AdaBoost model is an effective model for predicting SSNHL. The use of simple parameters can increase its practicality and applicability in remote medical care. Moreover, age may be a key factor influencing prognosis.

The inflammatory and metabolic status of patients with sudden-onset sensorineural hearing loss

Introduction

Sudden sensorineural hearing loss (SSNHL) is a common emergency symptom in otolaryngology that requires immediate diagnosis and treatment. SSNHL has a multifactorial etiology, and its pathophysiologic mechanisms may be associated with inflammatory and metabolic changes that may affect the cochlear microenvironment or its nervous component, thus triggering the process or hindering hearing recovery. Therefore, the aim of this study was to assess metabolic and inflammatory changes to identify systemic parameters that could serve as prognostic factors for hearing recovery in patients with SSNHL.

Materials and methods

Thirty patients with a sudden hearing loss of at least 30 dB in three contiguous frequencies were enrolled in this study. Patients were followed up for 4 months and peripheral blood samples were collected at 7 days (V1), 30 days (V2) and 120 days (V3). Interleukins (IL)-1F7, IL-2, IL-4, IL-5, IL-6, IL-10, interferon γ (IFN-γ), tumor necrosis factor α (TNF-α) and adiponectin were quantified in serum. In addition, lipid and glycemic profiles as well as concentration of creatinine, uric acid, fructosamine, peroxide, total proteins and albumin were analyzed. Patients underwent weekly ear-specific hearing tests with standard pure tone thresholds for frequencies of 250-8,000 Hz, speech recognition threshold and word recognition score.

Results

Patients with SSNHL were divided into a group of patients who did not achieve hearing recovery (n = 14) and another group who achieved complete and significant recovery (n = 16). Most serologic parameters showed no significant changes or values indicating clinical changes. However, IFN-γ levels decreased by 36.3% between V1 and V2. The cytokine TNF-α showed a statistically significant decrease from V1 to V3 (from 22.91 to 10.34 pg./mL). Adiponectin showed a decrease from 553.7 ng/mL in V1 to 454.4 ng/mL in V3.

Discussion

Our results show that serologic cytokine levels change in the acute phase of manifestation of SSNHL and establish a parallel between systemic changes and improvements in hearing, especially TNF-α, which showed differences in hearing recovery. The use of IFN-γ, TNF-α and adiponectin may elucidate the clinical improvement in these patients.

Predicting Sudden Sensorineural Hearing Loss Recovery with Patient-Personalized Seigel's Criteria Using Machine Learning

BACKGROUND

Accurate prognostic prediction is crucial for managing Idiopathic Sudden Sensorineural Hearing Loss (ISSHL). Previous studies developing ISSHL prognosis models often overlooked individual variability in hearing damage by relying on fixed frequency domains. This study aims to develop models predicting ISSHL prognosis one month after treatment, focusing on patient-specific hearing impairments.

METHODS

Patient-Personalized Seigel's Criteria (PPSC) were developed considering patient-specific hearing impairment related to ISSHL criteria. We performed a statistical test to assess the shift in the recovery assessment when applying PPSC. The utilized dataset of 581 patients comprised demographic information, health records, laboratory testing, onset and treatment, and hearing levels. To reduce the model's reliance on hearing level features, we used only the averages of hearing levels of the impaired frequencies. Then, model development, evaluation, and interpretation proceeded.

RESULTS

The chi-square test (p-value: 0.106) indicated that the shift in recovery assessment is not statistically significant. The soft-voting ensemble model was most effective, achieving an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.864 (95% CI: 0.801-0.927), with model interpretation based on the SHapley Additive exPlanations value.

CONCLUSIONS

With PPSC, providing a hearing assessment comparable to traditional Seigel's criteria, the developed models successfully predicted ISSHL recovery one month post-treatment by considering patient-specific impairments.

Machine learning-based longitudinal prediction for GJB2-related sensorineural hearing loss

BACKGROUND

Recessive GJB2 variants, the most common genetic cause of hearing loss, may contribute to progressive sensorineural hearing loss (SNHL). The aim of this study is to build a realistic predictive model for GJB2-related SNHL using machine learning to enable personalized medical planning for timely intervention.

METHOD

Patients with SNHL with confirmed biallelic GJB2 variants in a nationwide cohort between 2005 and 2022 were included. Different data preprocessing protocols and computational algorithms were combined to construct a prediction model. We randomly divided the dataset into training, validation, and test sets at a ratio of 72:8:20, and repeated this process ten times to obtain an average result. The performance of the models was evaluated using the mean absolute error (MAE), which refers to the discrepancy between the predicted and actual hearing thresholds.

RESULTS

We enrolled 449 patients with 2184 audiograms available for deep learning analysis. SNHL progression was identified in all models and was independent of age, sex, and genotype. The average hearing progression rate was 0.61 dB HL per year. The best MAE for linear regression, multilayer perceptron, long short-term memory, and attention model were 4.42, 4.38, 4.34, and 4.76 dB HL, respectively. The long short-term memory model performed best with an average MAE of 4.34 dB HL and acceptable accuracy for up to 4 years.

CONCLUSIONS

We have developed a prognostic model that uses machine learning to approximate realistic hearing progression in GJB2-related SNHL, allowing for the design of individualized medical plans, such as recommending the optimal follow-up interval for this population.

Machine Learning Models for Predicting Sudden Sensorineural Hearing Loss Outcome: A Systematic Review

BACKGROUND

Machine Learning models have been applied in various healthcare fields, including Audiology, to predict disease outcomes. The prognosis of sudden sensorineural hearing loss is difficult to predict due to the variable course of the disease. Hence, researchers have attempted to utilize ML models to predict the outcome of patients with sudden sensorineural hearing loss. The objectives of this study were to review the performance of these machine learning models and assess their applicability in real-world settings.

METHODS

A systematic search was conducted in PubMed, Web of Science and Scopus. Only studies that built machine learning prediction models were included, and studies that used algorithms such as logistic regression only for the purpose of adjusting for confounding variables were excluded. The risk of bias was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST).

RESULTS

After screening, a total of 7 papers were eligible for synthesis. In total, these studies built 48 ML models. The most common utilized algorithms were Logistic Regression, Support Vector Machine (SVM) and boosting. The area under the curve of the receiver operating characteristic curve ranged between 0.59 and 0.915. All of the included studies had a high risk of bias; hence there are concerns regarding their applicability.

CONCLUSION

Although these models showed great performance and promising results, future studies are still needed before these models can be applied in a real-world setting. Future studies should employ multiple cohorts, different feature selection methods, and external validation to further validate the models' applicability.

NcRNA: key and potential in hearing loss

Hearing loss has an extremely high prevalence worldwide and brings incredible economic and social burdens. Mechanisms such as epigenetics are profoundly involved in the initiation and progression of hearing loss and potentially yield definite strategies for hearing loss treatment. Non-coding genes occupy 97% of the human genome, and their transcripts, non-coding RNAs (ncRNAs), are widely participated in regulating various physiological and pathological situations. NcRNAs, mainly including micro-RNAs (miRNAs), long-stranded non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), are involved in the regulation of cell metabolism and cell death by modulating gene expression and protein-protein interactions, thus impacting the occurrence and prognosis of hearing loss. This review provides a detailed overview of ncRNAs, especially miRNAs and lncRNAs, in the pathogenesis of hearing loss. We also discuss the shortcomings and issues that need to be addressed in the study of hearing loss ncRNAs in the hope of providing viable therapeutic strategies for the precise treatment of hearing loss.

Prediction of Hearing Prognosis After Intact Canal Wall Mastoidectomy With Tympanoplasty Using Artificial Intelligence

OBJECTIVE

To evaluate the performance of a machine learning model and the effects of major prognostic factors on hearing outcomes following intact canal wall (ICW) mastoidectomy with tympanoplasty.

STUDY DESIGN

Retrospective cross-sectional study.

SETTING

Tertiary hospital.

METHODS

A total of 484 patients with chronic otitis media who underwent ICW tympanomastoidectomy between January 2007 and December 2020 were included in this study. Successful hearing outcomes were defined by a postoperative air-bone gap (ABG) of ≤20 dB and preoperative air conduction (AC)-postoperative AC value of ≥15 dB according to the Korean Otological Society guidelines for outcome reporting after chronic otitis media surgery. The light gradient boosting machine (LightGBM) and multilayer perceptron (MLP) models were tested as artificial intelligence models and compared using logistic regression. The main outcome assessed was the successful hearing outcome after surgery, measured using the area under the receiver operating characteristic curve (AUROC).

RESULTS

In the analysis using the postoperative ABG criterion, the LightGBM exhibited a significantly higher AUROC compared to those of the baseline model (mean, 0.811). According to the difference between preoperative and postoperative AC, the MLP showed a significantly higher AUROC than those of the baseline model (mean, 0.795).

CONCLUSION

This study analyzed multiple factors that could affect the hearing outcome using different artificial intelligence models and found that preoperative hearing status was the most important factor. Our findings provide additional information regarding postoperative hearing for clinicians.

Endoscopic sleeve gastroplasty reintervention score using supervised machine learning

BACKGROUND AND AIMS

Reintervention after endoscopic sleeve gastroplasty (ESG) can be indicated because of postprocedural adverse events from various preinterventional or postprocedural comorbidities. We developed and internally validated an ESG reintervention score (ESG-RS) that determines the individualized risk of reintervention within the first 30 days after ESG.

METHODS

We used data from a sample of 3583 patients who underwent ESG in the Metabolic and Bariatric Surgery Accreditation Quality Improvement Program database (2016-2021). The least absolute shrinkage and selection operator (LASSO)-penalized regression was used to select the most promising predictors of reintervention after ESG within 30 days. The predictive variables extracted by LASSO regression were entered into multivariate analysis to generate an ESG-RS by using the coefficients of the statistically significant variables. The model performance was assessed using receiver-operator curves by 10-fold cross-validation.

RESULTS

Eleven variables were selected by LASSO regression and used in the final multivariate analysis. The ESG-RS was inferred using 5 factors (history of previous foregut surgery, preoperative anticoagulation use, female gender, American Society of Anesthesiologists class ≥II, and hypertension) weighted by their regression coefficients in the multivariable logistic regression model. The area under the curve of the ESG-RS was .74 (95% confidence interval, .70-.78). For the ESG-RS, the optimal cutpoint was 67.9 (high risk vs low risk), with a sensitivity of .76 and specificity of .71.

CONCLUSIONS

The ESG-RS aids clinicians in preoperative risk stratification of patients undergoing ESG while clarifying factors contributing to a higher risk of reintervention.

Noninvasive prognostic factors and web predictive tools for idiopathic sudden sensorineural hearing loss

PURPOSE

To friendly predict a reference prognostic outcome for idiopathic sudden sensorineural hearing loss (ISSNHL) patients with or without anxiety, we identified independent prognostic factors and developed practical predictive tools without invasive tests.

METHODS

Patients with ISSNHL in our center were enrolled from June 2013 to December 2018. Univariate and multivariate logistic regression analyses were performed to identify independent prognostic factors of the complete recovery and the overall recovery for ISSNHL, which were subsequently utilized to develop the web nomograms. The discrimination, calibration, and clinical benefit were used to evaluate the performance of nomograms for ISSNHL.

RESULTS

704 ISSNHL patients were finally enrolled in this study. Multivariate logistic regression analysis showed that age, time of onset, gender, affected ear, degree, and type of hearing loss were independent prognostic factors of complete recovery. Age, time of onset, affected ear, and type of hearing loss were independent prognostic factors of overall recovery. Web predictive nomograms were developed with excellent discrimination, calibration, and clinical value.

CONCLUSION

Based on the patients' data with a considerable size, independent noninvasive prognostic factors of complete recovery and overall recovery of ISSNHL were identified. Integrating these prognostic factors without invasive tests, practical web predictive nomograms were developed. Using web nomograms, clinical doctors could provide reference data (the predicted recovery rate) for supporting prognostic consultation of ISSNHL patients, especially those with anxiety.

Hearing recovery prediction and prognostic factors of idiopathic sudden sensorineural hearing loss: a retrospective analysis with a deep neural network model

OBJECTIVE

Idiopathic Sudden Sensorineural Hearing Loss (ISSHL) is an otologic emergency, and an early prediction of prognosis may facilitate proper treatment. Therefore, we investigated the prognostic factors for predicting the recovery in patients with ISSHL treated with combined treatment method using machine learning models.

METHODS

We retrospectively reviewed the medical records of 298 patients with ISSHL at a tertiary medical institution between January 2015 and September 2020. Fifty-two variables were analyzed to predict hearing recovery. Recovery was defined using Siegel's criteria, and the patients were categorized into recovery and non-recovery groups. Recovery was predicted by various machine learning models. In addition, the prognostic factors were analyzed using the difference in the loss function.

RESULTS

There were significant differences in variables including age, hypertension, previous hearing loss, ear fullness, duration of hospital admission, initial hearing level of the affected and unaffected ears, and post-treatment hearing level between recovery and non-recovery groups. The deep neural network model showed the highest predictive performance (accuracy, 88.81%; area under the receiver operating characteristic curve, 0.9448). In addition, initial hearing level of affected and non-affected ear, post-treatment (2-weeks) hearing level of affected ear were significant factors for predicting the prognosis.

CONCLUSION

The deep neural network model showed the highest predictive performance for recovery in patients with ISSHL. Some factors with prognostic value were identified. Further studies using a larger patient population are warranted.

LEVEL OF EVIDENCE

Level 4.

Differentially expressed miRNA profiles of serum-derived exosomes in patients with sudden sensorineural hearing loss

Objectives

This study aimed to compare the expressed microRNA (miRNA) profiles of serum-derived exosomes of patients with sudden sensorineural hearing loss (SSNHL) and normal hearing controls to identify exosomal miRNAs that may be associated with SSNHL or serve as biomarkers for SSNHL.

Methods

Peripheral venous blood of patients with SSNHL and healthy controls was collected to isolate exosomes. Nanoparticle tracking analysis, transmission electron microscopy, and Western blotting were used to identify the isolated exosomes, after which total RNA was extracted and used for miRNA transcriptome sequencing. Differentially expressed miRNAs (DE-miRNAs) were identified based on the thresholds of P < 0.05 and |log₂fold change| > 1 and subjected to functional analyses. Finally, four exosomal DE-miRNAs, including PC-5p-38556_39, PC-5p-29163_54, PC-5p-31742_49, and hsa-miR-93-3p_R+1, were chosen for validation using quantitative real-time polymerase chain reaction (RT-qPCR).

Results

Exosomes were isolated from serum and identified based on particle size, morphological examination, and expression of exosome-marker proteins. A total of 18 exosomal DE-miRNAs, including three upregulated and 15 downregulated miRNAs, were found in SSNHL cases. Gene ontology (GO) functional annotation analysis revealed that target genes in the top 20 terms were mainly related to "protein binding," "metal ion binding," "ATP binding," and "intracellular signal transduction." Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that these target genes were functionally enriched in the "Ras," "Hippo," "cGMP-PKG," and "AMPK signaling pathways." The expression levels of PC-5p-38556_39 and PC-5p-29163_54 were significantly downregulated and that of miR-93-3p_R+1 was highly upregulated in SSNHL. Consequently, the consistency rate between sequencing and RT-qPCR was 75% and sequencing results were highly reliable.

Conclusion

This study identified 18 exosomal DE-miRNAs, including PC-5p-38556_39, PC-5p-29163_54, and miR-93-3p, which may be closely related to SSNHL pathogenesis or serve as biomarkers for SSNHL.

Nomogram for predicting the prognosis of sudden sensorineural hearing loss patients based on clinical characteristics: a retrospective cohort study

Background

Based on the clinical characteristics of patients, a nomogram predicting the prognosis of patients suffering from sudden sensorineural hearing loss (SSNHL) was constructed, which could aid in personalized treatment.

Methods

Data on the clinical characteristics of patients with SSNHL were collected and statistically analyzed. A nomogram for predicting the hearing prognosis of SSNHL patients were then constructed.

Results

A total of 356 patients were included in this study, including 227 and 129 in the recovery group (63.76%) and non-recovery group (36.24%), respectively. Univariable logistic regression demonstrated that age, gender, body mass index (BMI), marital, Audiogram curve, vertigo, hearing loss degree, and time to initial treatment were associated with hearing outcomes. Multivariate logistic models showed that age [odds ratio (OR): 0.479, 95% confidence interval (CI): 0.301-0.748, P<0.001], descending (OR: 0.116, 95% CI: 0.047-0.275, P<0.001) and flat audiogram curves (OR: 0.397, 95% CI: 0.159-0.979, P=0.045), profound hearing loss (OR: 0.047, 95% CI: 0.013-0.152, P<0.001), and treatment initiation after 1 week (8-14 days: OR: 0.047, 95% CI: 0.013-0.152, P<0.001; >14 days: OR: 0.131, 95% CI: 0.039-0.413) were risk factors for the hearing recovery. Logistic regression analyses were conducted to construct the prognostic nomogram. As estimated by the area under the receiver operating characteristic curve (ROC), the model had an accuracy of 0.867 (95% CI: 0.709-0.747). The validation analysis confirmed the high accuracy of the nomogram, and the decision curve showed that the model has potential clinical application value.

Conclusions

This study demonstrated that age, descending and flat audiogram curves, profound hearing loss, and initiating treatment after 1 week of SSNHL onset were independent risk factors associated with a worse hearing recovery prognosis. Using these factors, a nomogram with a high prediction accuracy was developed to predict the hearing recovery rate of SSNHL patients.

Models of ultrasonic radiomics and clinical characters for lymph node metastasis assessment in thyroid cancer: a retrospective study

Background

Preoperative prediction of cervical lymph node metastasis in papillary thyroid carcinoma provided a basis for tumor staging and treatment decision. This study aimed to investigate the utility of machine learning and develop different models to preoperatively predict cervical lymph node metastasis based on ultrasonic radiomic features and clinical characteristics in papillary thyroid carcinoma nodules.

Methods

Data from 400 papillary thyroid carcinoma nodules were included and divided into training and validation group. With the help of machine learning, clinical characteristics and ultrasonic radiomic features were extracted and selected using randomforest and least absolute shrinkage and selection operator regression before classified by five classifiers. Finally, 10 models were built and their area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, positive predictive value and negative predictive value were measured.

Results

Among the 10 models, RF-RF model revealed the highest area under curve (0.812) and accuracy (0.7542) in validation group. The top 10 variables of it included age, seven textural features, one shape feature and one first-order feature, in which eight were high-dimensional features.

Conclusions

RF-RF model showed the best predictive performance for cervical lymph node metastasis. And the importance features selected by it highlighted the unique role of higher-dimensional statistical methods for radiomics analysis.

Prediction of hearing recovery in unilateral sudden sensorineural hearing loss using artificial intelligence

Despite the significance of predicting the prognosis of idiopathic sudden sensorineural hearing loss (ISSNHL), no predictive models have been established. This study used artificial intelligence to develop prognosis models to predict recovery from ISSNHL. We retrospectively reviewed the medical data of 453 patients with ISSNHL (men, 220; women, 233; mean age, 50.3 years) who underwent treatment at a tertiary hospital between January 2021 and December 2019 and were followed up after 1 month. According to Siegel's criteria, 203 patients recovered in 1 month. Demographic characteristics, clinical and laboratory data, and pure-tone audiometry were analyzed. Logistic regression (baseline), a support vector machine, extreme gradient boosting, a light gradient boosting machine, and multilayer perceptron were used. The outcomes were the area under the receiver operating characteristic curve (AUROC) primarily, area under the precision-recall curve, Brier score, balanced accuracy, and F1 score. The light gradient boosting machine model had the best AUROC and balanced accuracy. Together with multilayer perceptron, it was also significantly superior to logistic regression in terms of AUROC. Using the SHapley Additive exPlanation method, we found that the initial audiogram shape is the most important prognostic factor. Machine/deep learning methods were successfully established to predict the prognosis of ISSNHL.

Association of sudden sensorineural hearing loss with asthma: a longitudinal follow-up study using a national sample cohort

OBJECTIVE

To investigate the risk of sudden sensorineural hearing loss (SSNHL) in asthma patients.

DESIGN

A longitudinal follow-up study using a retrospective cohort SETTING: The 2002-2013 Korean National Health Insurance Service-Health Screening Cohort PARTICIPANTS AND INTERVENTIONS: The ≥40 years old Korean population were enrolled. The asthma patients were 1:1 matched with the control group for age, sex, income and region of residence.

MAIN OUTCOME MEASURE

The occurrence of SSNHL was followed in both asthma and control groups. The stratified Cox proportional hazard model was used. Age, sex, income and region of residence were stratified, and Charlson Comorbidity Index scores, obesity, smoking, alcohol consumption and atopic dermatitis histories were adjusted. Subgroup analysis was performed according to age, sex, obesity, smoking and alcohol consumption.

RESULTS

The results showed that 1.0% (877/90 564) of the asthma group and 0.8% (706/90,564) of the control group exhibited SSNHL (p<0.001). The asthma group demonstrated a higher HR for SSNHL than the control group (adjusted HR 1.23, 95% CI 1.11 to 1.36, p<0.001). According to age and sex, the female subgroup showed elevated HRs for SSNHL in asthma patients. Both the non-smoker and current smoker groups demonstrated higher HRs for SSNHL in asthma patients than in controls. According to alcohol consumption or obesity, the <1 time a week alcohol consumption group and normal weight and severe obesity groups showed higher HRs for SSNHL in asthma patients than in the controls.

CONCLUSIONS

Adult asthma patients had a higher risk of SSNHL than the control participants matched for demographic and socioeconomic factors.

Prognosis of Idiopathic Sudden Sensorineural Hearing Loss: The Nomogram Perspective

OBJECTIVE

The aim of this study is to create a nomogram for accurately predicting the prognosis of idiopathic sudden sensorineural hearing loss (ISSNHL) and provide a reference for clinical treatment.

METHODS

Three hundred and twenty-three patients with ISSNHL were admitted from September 2014 to November 2020. The clinical data were retrospectively reviewed. Prognostic factors for ISSNHL were assessed based on univariate and multivariate logistic regression analysis and used to create a nomogram. Nomogram performance in terms of predictive and discriminatory ability was evaluated by calculating the concordance index (C-index) and generating calibration plots.

RESULTS

The overall hearing improvement rate was 41.4%, comprising complete recovery (13.3%), marked recovery (17.0%), and slight recovery (11.1%). Multivariate logistic regression analysis showed that age, symptoms of vertigo, interval between onset and treatment, low-density lipoprotein, and type of hearing loss were independent predictors of ISSNHL. A nomogram based on these 5 factors had a C index of 0.798 (95% confidence interval 0.750-0.845).

CONCLUSIONS

Age, vertigo, interval between onset and treatment, low-density lipoprotein level, and type of hearing loss are closely associated with hearing recovery. The nomogram may enable prediction of the prognosis of ISSNHL and facilitate clinical decision-making.

Frequency-specific prediction model of hearing outcomes in patients with idiopathic sudden sensorineural hearing loss

PURPOSE

The hearing outcome of idiopathic sudden sensorineural hearing loss (ISSNHL) is hard to predict. We herein constructed a multiple regression model for hearing outcomes in each frequency separately in an attempt to achieve practical prediction in ISSNHL.

METHODS

We enrolled 235 consecutive in-patients with ISSNHL who were treated in our department from 2015 to 2020 (average hearing level at 250-4000 Hz ≥ 40 dB; time from onset to treatment ≤ 14 days; 126 males/109 females; age range 17-87 years (average 61.0 years)). All patients received systemic prednisolone administration combined with intratympanic dexamethasone injection. The pure-tone hearing threshold of 125-8000 Hz was measured at every octave before (HL_pre) and after (HL_post) treatment. A multiple regression model was constructed for HL_post (dependent variable) using five explanatory variables (age, days from onset to treatment, presence of vertigo, HL_pre, and hearing level of the contralateral ear).

RESULTS

The multiple correlation coefficient increased as the frequency increased. Strong correlations were seen in high frequencies, with multiple correlation coefficients of 0.784/0.830 for 4000/8000 Hz. The width of the 70% prediction interval was narrower for 4000/8000 Hz (± 18.2/16.3 dB) than for low to mid-frequencies. Among the five explanatory variables, HL_pre showed the largest partial correlation coefficient for any frequency. The partial correlation coefficient for HL_pre increased as the frequency increased, which may partially explain the high multiple correlation coefficients for high frequencies.

CONCLUSION

The present model would be of practical use for predicting hearing outcomes in high frequencies in patients with ISSNHL.

[Construction and evaluation of a model for predicting ischemic stroke risk in patients with sudden sensorineural hearing loss]

Objective:To explore the related factors of sudden sensorineural hearing loss complicated with ischemic stroke, construct the risk prediction model, and verify the prediction effect of the model. Methods:A retrospective analysis was performed on 901 sudden sensorineural hearing loss patients hospitalized from January 2017 to December 2020, The patients were divided into the ischemic stroke group（100 cases） and the sudden deafness group（801 cases） according to whether they were complicated with ischemic stroke, The independent correlation factors of sudden deafness complicated with ischemic stroke were screened by univariate analysis and multivariate Logistic regression model, and the risk prediction model and internal verification were established. The original data were randomly divided into the modeling group（631 cases） and the validation group（270 cases） at a 7∶3 ratio. Hosmer-Lemeshow and receiver operating characteristic curve were used to test the goodness of fit and predictive effect of the model, and 270 patients were included again in the application research of the model and to test the prediction effect of the model. Results:The results of single factor analysis showed that age, NEUR, NC, NLR, PLR, TC, HDL-C, BUN, TC-HDL-C, TG/HDL-C, LDL-C/HDL-C, Hcy, FIB and cervical vascular plaque were related factors of sudden sensorineural hearing loss complicated with ischemic stroke（P<0.05）. Age（OR=2.816）, NEUR（OR=2.707）, Hcy（OR=88.833）, FIB（OR=1.389）, TC-HDL-C（OR=1.613）, cervical vascular plaque（OR=2.862） are the independent risk factors of SNHL complicated with ischemic stroke. These 6 factors are used to construct a prediction model. Hosmer-lemeshow test results, the area under the ROC curve of the modeling group was 0.846, P=0.555, Youden index was 0.564, sensitivity was 0.820, and specificity was 0.744. In the validation group, the area under ROC curve was 0.847, P=0.288, Youden index was 0.432, sensitivity was 0.783, and specificity was 0.649. Conclusion:The risk prediction model constructed in this study shows good prediction efficiency. which can provide references for the clinical screening of ischemic stroke risks in patients with sudden sensorineural hearing loss and early interventions in early stage.

The emergence of machine learning in auditory neural impairment: A systematic review

Hearing loss is a common neurodegenerative disease that can start at any stage of life. Misalignment of the auditory neural impairment may impose challenges in processing incoming auditory stimulus that can be measured using electroencephalography (EEG). The electrophysiological behaviour response emanated from EEG auditory evoked potential (AEP) requires highly trained professionals for analysis and interpretation. Reliable automated methods using techniques of machine learning would assist the auditory assessment process for informed treatment and practice. It is thus highly required to develop models that are more efficient and precise by considering the characteristics of brain signals. This study aims to provide a comprehensive review of several state-of-the-art techniques of machine learning that adopt EEG evoked response for the auditory assessment within the last 13 years. Out of 161 initially screened articles, 11 were retained for synthesis. The outcome of the review presented that the Support Vector Machine (SVM) classifier outperformed with over 80% accuracy metric and was recognized as the best suited model within the field of auditory research. This paper discussed the comprehensive iterative properties of the proposed computed algorithms and the feasible future direction in hearing impaired rehabilitation.

Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques

Physicians depend on their insight and experience and on a fundamentally indicative or symptomatic approach to decide on the possible ailment of a patient. However, numerous phases of problem identification and longer strategies can prompt a longer time for consulting and can subsequently cause other patients that require attention to wait for longer. This can bring about pressure and tension concerning those patients. In this study, we focus on developing a decision-support system for diagnosing the symptoms as a result of hearing loss. The model is implemented by utilizing machine learning techniques. The Frequent Pattern Growth (FP-Growth) algorithm is used as a feature transformation method and the multivariate Bernoulli naïve Bayes classification model as the classifier. To find the correlation that exists between the hearing thresholds and symptoms of hearing loss, the FP-Growth and association rule algorithms were first used to experiment with small sample and large sample datasets. The result of these two experiments showed the existence of this relationship, and that the performance of the hybrid of the FP-Growth and naïve Bayes algorithms in identifying hearing-loss symptoms was found to be efficient, with a very small error rate. The average accuracy rate and average error rate for the multivariate Bernoulli model with FP-Growth feature transformation, using five training sets, are 98.25% and 1.73%, respectively.

Predicting hearing recovery following treatment of idiopathic sudden sensorineural hearing loss with machine learning models

PURPOSE

Idiopathic sudden sensorineural hearing loss (ISSHL) is an emergency otological disease, and its definite prognostic factors remain unclear. This study applied machine learning methods to develop a new ISSHL prognosis prediction model.

MATERIALS AND METHODS

This retrospective study reviewed the medical data of 244 patients who underwent combined intratympanic and systemic steroid treatment for ISSHL at a tertiary referral center between January 2015 and October 2019. We used 35 variables to predict hearing recovery based on Siegel's criteria. In addition to performing an analysis based on the conventional logistic regression model, we developed prediction models with five machine learning methods: least absolute shrinkage and selection operator, decision tree, random forest (RF), support vector machine, and boosting. To compare the predictive ability of each model, the accuracy, precision, recall, F-score, and the area under the receiver operator characteristic curves (ROC-AUC) were calculated.

RESULTS

Former otological history, ear fullness, delay between symptom onset and treatment, delay between symptom onset and intratympanic steroid injection (ITSI), and initial hearing thresholds of the affected and unaffected ears differed significantly between the recovery and non-recovery groups. While the RF method (accuracy: 72.22%, ROC-AUC: 0.7445) achieved the highest predictive power, the other methods also featured relatively good predictive power. In the RF model, the following variables were identified to be important for hearing-recovery prediction: delay between symptom onset and ITSI or the initial treatment, initial hearing levels of the affected and non-affected ears, body mass index, and a previous history of hearing loss.

CONCLUSIONS

The machine learning models predictive of hearing recovery following treatment for ISSHL showed superior predictive power relative to the conventional logistic regression method, potentially allowing for better patient treatment outcomes.

Machine Learning Models for Predicting Facial Nerve Palsy in Parotid Gland Surgery for Benign Tumors

BACKGROUND

Despite the increasing use of intraoperative facial nerve monitoring during parotid gland surgery (PGS) and the improvement in the preoperative radiological assessment, facial nerve injury (FNI) remains the most severe complication after PGS. Until now, no studies have been published regarding the application of machine learning (ML) for predicting FNI after PGS. We hypothesize that ML would improve the prediction of patients at risk.

METHODS

Patients who underwent PGS for benign tumors between June 2010 and June 2019 were included.

RESULTS

Regarding prediction accuracy and performance of each ML algorithm, the K-nearest neighbor and the random forest achieved the highest sensitivity, specificity, positive predictive value, negative predictive value F-score, receiver operating characteristic (ROC)-area under the ROC curve, and accuracy globally. The K-nearest neighbor algorithm achieved performance values above 0.9 for specificity, negative predictive value, F-score and ROC-area under the ROC curve, and the highest sensitivity and positive predictive value.

CONCLUSIONS

This study demonstrates that ML prediction models can provide evidence-based predictions about the risk of FNI to otolaryngologists and patients. It is hoped that such algorithms, which use clinical, radiological, histological, and cytological information, can improve the information given to patients before surgery so that they can be better informed of any potential complications.

Contributions and limitations of using machine learning to predict noise-induced hearing loss

Purpose

Noise-induced hearing loss (NIHL) is a global issue that impacts people’s life and health. The current review aims to clarify the contributions and limitations of applying machine learning (ML) to predict NIHL by analyzing the performance of different ML techniques and the procedure of model construction.

Methods

The authors searched PubMed, EMBASE and Scopus on November 26, 2020.

Results

Eight studies were recruited in the current review following defined inclusion and exclusion criteria. Sample size in the selected studies ranged between 150 and 10,567. The most popular models were artificial neural networks (n = 4), random forests (n = 3) and support vector machines (n = 3). Features mostly correlated with NIHL and used in the models were: age (n = 6), duration of noise exposure (n = 5) and noise exposure level (n = 4). Five included studies used either split-sample validation (n = 3) or ten-fold cross-validation (n = 2). Assessment of accuracy ranged in value from 75.3% to 99% with a low prediction error/root-mean-square error in 3 studies. Only 2 studies measured discrimination risk using the receiver operating characteristic (ROC) curve and/or the area under ROC curve.

Conclusion

In spite of high accuracy and low prediction error of machine learning models, some improvement can be expected from larger sample sizes, multiple algorithm use, completed reports of model construction and the sufficient evaluation of calibration and discrimination risk.

Biochemical and Pain Comparisons Between the Laser Lancing Device and Needle Lancets for Capillary Blood Sampling: A Randomized Control Trial

BACKGROUND AND OBJECTIVES

Patients around the world use a lancing device to perform self-monitoring of blood sugar (SMBG). However, there are always fears of needles and pain. Therefore, less painful devices are being developed. The purpose of this study was to compare the usefulness and safety of a laser lancing device (without a needle) to a conventional needle lancet (with a needle) for capillary blood sampling.

STUDY DESIGN/MATERIALS AND METHODS

A total of 40 healthy subjects were enrolled in the study. Capillary blood was collected from a laser lancing device (without a needle) and a conventional needle lancet (with a needle) on opposite fingers, the choice of which was randomly selected. The laser lancing device (LMT-3000) uses a 2940 nm mono-pulse laser, a radiation field of 350 μm, laser energy of 210 mJ, and a 3.7 V battery. One week later, capillary blood was obtained by switching the devices and fingers. The biochemical measurements and pain were compared between the two groups. Puncture pain was measured on a pain scale from 0 to 10.

RESULT

All patients were tested with both a laser lancing device and a conventional needle lancet. In the biochemical analysis, the blood glucose level was 103.21 ± 17.20 mg/dl in laser lancing device group and 102.25 ± 22.44 mg/dl in the conventional needle lancet group, and there were no significant differences between the two groups (P = 0.940). The pH, CO₂ , O₂ , lactate and hematocrit levels of the blood were no significant differences between the two groups. In the first trial, the median pain score (interquartile range) of patients using laser lancing device was 2.0 (1.0-3.0), whereas it was 2.5 (2.0-4.0) in patients using a conventional needle lancet (P = 0.029). In the second trial, one week later, the median pain score in the laser lancing device group was 2.5 (1.0-4.0), whereas it was 3.5 (2.25-5.0) in the conventional needle lancet group (P = 0.001). The difference in pain scores between the first and second trials was significant in the conventional needle lancet group (P = 0.007), but not in the laser lancing device group (P = 0.150).

CONCLUSION

There was no difference in biochemical results between the laser lancing device group and the conventional needle lancet group. The laser lancing device demonstrated comparatively lower pain than the conventional needle lancet. Lasers Surg. Med. © 2020 Wiley Periodicals LLC.