Use of data mining to predict significant factors and benefits of bilateral cochlear implantation

Prediction of Cochlear Implant Fitting by Machine Learning Techniques

OBJECTIVE

This study aimed to evaluate the differences in electrically evoked compound action potential (ECAP) thresholds and postoperative mapping current (T) levels between electrode types after cochlear implantation, the correlation between ECAP thresholds and T levels, and the performance of machine learning techniques in predicting postoperative T levels.

STUDY DESIGN

Retrospective case review.

SETTING

Tertiary hospital.

PATIENTS

We reviewed the charts of 124 ears of children with severe-to-profound hearing loss who had undergone cochlear implantation.

INTERVENTIONS

We compared ECAP thresholds and T levels from different electrodes, calculated correlations between ECAP thresholds and T levels, and created five prediction models of T levels at switch-on and 6 months after surgery.

MAIN OUTCOME MEASURES

The accuracy of prediction in postoperative mapping current (T) levels.

RESULTS

The ECAP thresholds of the slim modiolar electrodes were significantly lower than those of the straight electrodes on the apical side. However, there was no significant difference in the neural response telemetry thresholds between the two electrodes on the basal side. Lasso regression achieved the most accurate prediction of T levels at switch-on, and the random forest algorithm achieved the most accurate prediction of T levels 6 months after surgery in this dataset.

CONCLUSION

Machine learning techniques could be useful for accurately predicting postoperative T levels after cochlear implantation in children.

Artificial Intelligence in Otology and Neurotology

Clinical applications of artificial intelligence (AI) have grown exponentially with increasing computational power and Big Data. Data rich fields such as Otology and Neurotology are still in the infancy of harnessing the power of AI but are increasingly involved in training and developing ways to incorporate AI into patient care. Current studies involving AI are focused on accessible datasets; health care wearables, tabular data from electronic medical records, electrophysiologic measurements, imaging, and "omics" provide huge amounts of data to utilize. Health care wearables, such as hearing aids and cochlear implants, are a ripe environment for AI implementation.

Predicting synkinesis caused by Bell's palsy or Ramsay Hunt syndrome using machine learning-based logistic regression

Objective

To investigate whether machine learning (ML)-based algorithms, namely logistic regression (LR), random forest (RF), k-nearest neighbor (k-NN), and gradient-boosting decision tree (GBDT), utilizing early post-onset parameters can predict facial synkinesis resulting from Bell's palsy or Ramsay Hunt syndrome more accurately than the conventional statistics-based LR.

Methods

This retrospective study included 362 patients who presented to a facial palsy outpatient clinic. Median follow-up of synkinesis-positive and -negative patients was 388 (range, 177-1922) and 198 (range, 190-3021) days, respectively. Electrophysiological examinations were performed, and the rate of synkinesis in Bell's palsy and Ramsay Hunt syndrome was evaluated. Sensitivity and specificity were assessed using statistics-based LR; and electroneurography (ENoG) value, the difference in the nerve excitability test (NET), and scores of the subjective Yanagihara scaling system were evaluated using early post-onset parameters with ML-based LR, RF, k-NN, and GBDT.

Results

Synkinesis rate in Bell's palsy and Ramsay Hunt syndrome was 20.2% (53/262) and 40.0% (40/100), respectively. Sensitivity and specificity obtained with statistics-based LR were 0.796 and 0.806, respectively, and the area under the receiver operating characteristic curve (AUC) was 0.87. AUCs measured using ML-based LR of "ENoG," "difference in NET," "Yanagihara," and all three components ("all") were 0.910, 0.834, 0.711, and 0.901, respectively.

Conclusion

ML-based LR model shows potential in predicting facial synkinesis probability resulting from Bell's palsy or Ramsay Hunt syndrome and has comparable reliability to the conventional statistics-based LR.

Level of Evidence

3.

Application of Machine Learning to Predict Hearing Outcomes of Tympanoplasty

OBJECTIVE

This retrospective study aimed to evaluate the performance of machine learning techniques in predicting air-bone gap after tympanoplasty compared with conventional scoring models and to identify the influential factors.

METHODS

We reviewed the charts of 105 patients (114 ears) with chronic otitis media who underwent tympanoplasty. Two numerical scoring systems (middle ear risk index [MERI] and ossiculoplasty outcome parameter staging [OOPS]) and three algorithms (random forest [RF], support vector machine [SVM], and k nearest neighbor [kNN]) were created. Experimental variables included age, preoperative air-bone gap, soft-tissue density lesion in the tympanic cavity in CT, otorrhea, surgical history, ossicular bone problems in CT, tympanic perforation location, perforation type (central or marginal), grafting material, smoking history, endoscopy use, and the operator whose experience was 20 years or longer, or shorter. Binary classification, postoperative air-bone gap ≤15 or >15 dB, and multiclass classification, classification into seven categories by 10 dB, were performed, and the percentages of correct prediction were calculated. The importance of features in the RF model was calculated to identify influential factors.

RESULTS

The percentages of correct prediction in binary classification were 62.3%, 72.8%, 81.5%, 81.5%, and 81.5% in MERI, OOPS, RF, SVM, and kNN, respectively, and those in multiclass classification were 29.8%, 21.9%, 63.1%, 44.7%, and 50% in the same order. The RF model suggested larger preoperative air-bone gap, and older age could make the postoperative air-bone gap larger.

CONCLUSION

The machine learning techniques, especially the RF model, are promising methods for precise postoperative air-bone gap prediction.

LEVEL OF EVIDENCE

4 Laryngoscope, 133:2371-2378, 2023.

Exploring neurocognitive factors and brain activation in adult cochlear implant recipients associated with speech perception outcomes-A scoping review

Background

Cochlear implants (CIs) are considered an effective treatment for severe-to-profound sensorineural hearing loss. However, speech perception outcomes are highly variable among adult CI recipients. Top-down neurocognitive factors have been hypothesized to contribute to this variation that is currently only partly explained by biological and audiological factors. Studies investigating this, use varying methods and observe varying outcomes, and their relevance has yet to be evaluated in a review. Gathering and structuring this evidence in this scoping review provides a clear overview of where this research line currently stands, with the aim of guiding future research.

Objective

To understand to which extent different neurocognitive factors influence speech perception in adult CI users with a postlingual onset of hearing loss, by systematically reviewing the literature.

Methods

A systematic scoping review was performed according to the PRISMA guidelines. Studies investigating the influence of one or more neurocognitive factors on speech perception post-implantation were included. Word and sentence perception in quiet and noise were included as speech perception outcome metrics and six key neurocognitive domains, as defined by the DSM-5, were covered during the literature search (Protocol in open science registries: 10.17605/OSF.IO/Z3G7W of searches in June 2020, April 2022).

Results

From 5,668 retrieved articles, 54 articles were included and grouped into three categories using different measures to relate to speech perception outcomes: (1) Nineteen studies investigating brain activation, (2) Thirty-one investigating performance on cognitive tests, and (3) Eighteen investigating linguistic skills.

Conclusion

The use of cognitive functions, recruiting the frontal cortex, the use of visual cues, recruiting the occipital cortex, and the temporal cortex still available for language processing, are beneficial for adult CI users. Cognitive assessments indicate that performance on non-verbal intelligence tasks positively correlated with speech perception outcomes. Performance on auditory or visual working memory, learning, memory and vocabulary tasks were unrelated to speech perception outcomes and performance on the Stroop task not to word perception in quiet. However, there are still many uncertainties regarding the explanation of inconsistent results between papers and more comprehensive studies are needed e.g., including different assessment times, or combining neuroimaging and behavioral measures.

Systematic review registration

https://doi.org/10.17605/OSF.IO/Z3G7W.

Machine Learning Technique Reveals Prognostic Factors of Vibrant Soundbridge for Conductive or Mixed Hearing Loss Patients

OBJECTIVES

Vibrant Soundbridge (VSB) was developed for treatment of hearing loss, but clinical outcomes vary and prognostic factors predicting the success of the treatment remain unknown. We examined clinical outcomes of VSB for conductive or mixed hearing loss, prognostic factors by analyzing prediction models, and cut-off values to predict the outcomes.

STUDY DESIGN

Retrospective chart review.

SETTING

Tertiary care hospital.

PATIENTS

Thirty patients who underwent VSB surgery from January 2017 to December 2019 at our hospital.

INTERVENTION

Audiological tests were performed prior to and 3 months after surgery; patients completed questionnaires 3 months after surgery.

MAIN OUTCOME MEASURES

We used a multiregression and the random forest algorithm for predictions. Mean absolute errors and coefficient of determinations were calculated to estimate prediction accuracies. Coefficient values in the multiregression model and the importance of features in the random forest model were calculated to clarify prognostic factors. Receiver operation characteristic curves were plotted.

RESULTS

All audiological outcomes improved after surgery. The random forest model (mean absolute error: 0.06) recorded more accuracy than the multiregression model (mean absolute error: 0.12). Speech discrimination score in a silent context in patients with hearing aids was the most influential factor (coefficient value: 0.51, featured value: 0.71). The candidate cut-off value was 36% (sensitivity: 89%, specificity: 75%).

CONCLUSIONS

VSB is an effective treatment for conductive or mixed hearing loss. Machine learning demonstrated more precise predictions, and speech discrimination scores in a silent context in patients with hearing aids were the most important factor in predicting clinical outcomes.

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

This study presents an up-to-date survey of the use of artificial intelligence (AI) in the field of otorhinolaryngology, considering opportunities, research challenges, and research directions. We searched PubMed, the Cochrane Central Register of Controlled Trials, Embase, and the Web of Science. We initially retrieved 458 articles. The exclusion of non-English publications and duplicates yielded a total of 90 remaining studies. These 90 studies were divided into those analyzing medical images, voice, medical devices, and clinical diagnoses and treatments. Most studies (42.2%, 38/90) used AI for image-based analysis, followed by clinical diagnoses and treatments (24 studies). Each of the remaining two subcategories included 14 studies. Machine learning and deep learning have been extensively applied in the field of otorhinolaryngology. However, the performance of AI models varies and research challenges remain.

Prediction of the Functional Status of the Cochlear Nerve in Individual Cochlear Implant Users Using Machine Learning and Electrophysiological Measures

OBJECTIVES

This study aimed to create an objective predictive model for assessing the functional status of the cochlear nerve (CN) in individual cochlear implant (CI) users.

DESIGN

Study participants included 23 children with cochlear nerve deficiency (CND), 29 children with normal-sized CNs (NSCNs), and 20 adults with various etiologies of hearing loss. Eight participants were bilateral CI users and were tested in both ears. As a result, a total of 80 ears were tested in this study. All participants used Cochlear Nucleus CIs in their test ears. For each participant, the CN refractory recovery function and input/output (I/O) function were measured using electrophysiological measures of the electrically evoked compound action potential (eCAP) at three electrode sites across the electrode array. Refractory recovery time constants were estimated using statistical modeling with an exponential decay function. Slopes of I/O functions were estimated using linear regression. The eCAP parameters used as input variables in the predictive model were absolute refractory recovery time estimated based on the refractory recovery function, eCAP threshold, slope of the eCAP I/O function, and negative-peak (i.e., N1) latency. The output variable of the predictive model was CN index, an indicator for the functional status of the CN. Predictive models were created by performing linear regression, support vector machine regression, and logistic regression with eCAP parameters from children with CND and the children with NSCNs. One-way analysis of variance with post hoc analysis with Tukey's honest significant difference criterion was used to compare study variables among study groups.

RESULTS

All three machine learning algorithms created two distinct distributions of CN indices for children with CND and children with NSCNs. Variations in CN index when calculated using different machine learning techniques were observed for adult CI users. Regardless of these variations, CN indices calculated using all three techniques in adult CI users were significantly correlated with Consonant-Nucleus-Consonant word and AzBio sentence scores measured in quiet. The five oldest CI users had smaller CN indices than the five youngest CI users in this study.

CONCLUSIONS

The functional status of the CN for individual CI users was estimated by our newly developed analytical models. Model predictions of CN function for individual adult CI users were positively and significantly correlated with speech perception performance. The models presented in this study may be useful for understanding and/or predicting CI outcomes for individual patients.

Structural neuroimaging of the altered brain stemming from pediatric and adolescent hearing loss-Scientific and clinical challenges

There has been a spurt in structural neuroimaging studies of the effect of hearing loss on the brain. Specifically, magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) technologies provide an opportunity to quantify changes in gray and white matter structures at the macroscopic scale. To date, there have been 32 MRI and 23 DTI studies that have analyzed structural differences accruing from pre- or peri-lingual pediatric hearing loss with congenital or early onset etiology and postlingual hearing loss in pre-to-late adolescence. Additionally, there have been 15 prospective clinical structural neuroimaging studies of children and adolescents being evaluated for cochlear implants. The results of the 70 studies are summarized in two figures and three tables. Plastic changes in the brain are seen to be multifocal rather than diffuse, that is, differences are consistent across regions implicated in the hearing, speech and language networks regardless of modes of communication and amplification. Structures in that play an important role in cognition are affected to a lesser extent. A limitation of these studies is the emphasis on volumetric measures and on homogeneous groups of subjects with hearing loss. It is suggested that additional measures of morphometry and connectivity could contribute to a greater understanding of the effect of hearing loss on the brain. Then an interpretation of the observed macroscopic structural differences is given. This is followed by discussion of how structural imaging can be combined with functional imaging to provide biomarkers for longitudinal tracking of amplification. This article is categorized under: Developmental Biology > Developmental Processes in Health and Disease Translational, Genomic, and Systems Medicine > Translational Medicine Laboratory Methods and Technologies > Imaging.

Machine Learning and Cochlear Implantation-A Structured Review of Opportunities and Challenges

OBJECTIVE

The use of machine learning technology to automate intellectual processes and boost clinical process efficiency in medicine has exploded in the past 5 years. Machine learning excels in automating pattern recognition and in adapting learned representations to new settings. Moreover, machine learning techniques have the advantage of incorporating complexity and are free from many of the limitations of traditional deterministic approaches. Cochlear implants (CI) are a unique fit for machine learning techniques given the need for optimization of signal processing to fit complex environmental scenarios and individual patients' CI MAPping. However, there are many other opportunities where machine learning may assist in CI beyond signal processing. The objective of this review was to synthesize past applications of machine learning technologies for pediatric and adult CI and describe novel opportunities for research and development.

DATA SOURCES

The PubMed/MEDLINE, EMBASE, Scopus, and ISI Web of Knowledge databases were mined using a directed search strategy to identify the nexus between CI and artificial intelligence/machine learning literature.

STUDY SELECTION

Non-English language articles, articles without an available abstract or full-text, and nonrelevant articles were manually appraised and excluded. Included articles were evaluated for specific machine learning methodologies, content, and application success.

DATA SYNTHESIS

The database search identified 298 articles. Two hundred fifty-nine articles (86.9%) were excluded based on the available abstract/full-text, language, and relevance. The remaining 39 articles were included in the review analysis. There was a marked increase in year-over-year publications from 2013 to 2018. Applications of machine learning technologies involved speech/signal processing optimization (17; 43.6% of articles), automated evoked potential measurement (6; 15.4%), postoperative performance/efficacy prediction (5; 12.8%), and surgical anatomy location prediction (3; 7.7%), and 2 (5.1%) in each of robotics, electrode placement performance, and biomaterials performance.

CONCLUSION

The relationship between CI and artificial intelligence is strengthening with a recent increase in publications reporting successful applications. Considerable effort has been directed toward augmenting signal processing and automating postoperative MAPping using machine learning algorithms. Other promising applications include augmenting CI surgery mechanics and personalized medicine approaches for boosting CI patient performance. Future opportunities include addressing scalability and the research and clinical communities' acceptance of machine learning algorithms as effective techniques.

Predicting and Weighting the Factors Affecting Workers' Hearing Loss Based on Audiometric Data Using C5 Algorithm

Introduction:

With the extensively spread of industrialization in the world, noise exposure is becoming more prevalent in the industrial settings. The most important and definite harmful effects of sound include hearing loss, both permanent and temporary.

Objective:

This study was designed aimed to use the C5 algorithm to determine the weight of factors affecting the workers’ hearing loss based on the audiometric data.

Methods:

This cross-sectional, descriptive, analytical study was conducted in 2018 in a mining industry in southeastern Iran. In this study, workers were divided into three exposed groups with different sound pressure levels (one control group and two case groups). Audiometry was conducted for each group of 50 persons; hence, the total number of subjects was 150. The stages of this study include: 1) selecting factors (predictive) to check and weigh them; 2) conducting the audiometry for both ears; 3) calculating the permanent hearing loss in each ear and permanent hearing loss of both ears; 4) classifying the types of hearing loss; and 5) investigating and determining the weight of factors affecting the hearing loss and their classification based on the C5 algorithm and determining the error and accuracy rate of each model. To assess and determine the factors affecting the hearing loss of workers, the C5 algorithm and IBM SPSS Modeler 18.0 were used. SPSS V.18 was used to analyze the linear regression and paired t-test tests, too.

Results:

The results showed that in the first model (SPL <70 dBA), the 8KHz frequency with the weight of 31% had the highest effect, the factors of work experience and the frequency of 250Hz each with the weight of 3%, had the least effect, and the accuracy of the model was 100%. In the second model (SPL 70–80 dBA) the frequency of 8KHz with the weight of 21% had the highest effect, the frequency of 250Hz and the working experience each had the lowest effect with the weight of 7% and the accuracy of the model was calculated as 100%. In the third model (SPL >85 dBA), the 4KHz frequency with the weight of 31% had the highest effect, and the work experience with a weight of 1% had the lowest effect, and the accuracy of the model was 94%. In the fourth model, the 4KHz frequency with the weight of 22% had the highest effect and 250Hz and age each with the weight of 8% had the lowest effects; the accuracy of this model was calculated to be 99.05%.

Conclusions:

During investigating and determining the weight of the factors affecting hearing loss by the C5 algorithm, the high weight and effect of the 4KHz frequency were predicted in hearing loss changes. Considering the high accuracy obtained in this modeling, this algorithm is a suitable and powerful tool for predicting and modeling the hearing loss.

Roles of triglyceride and phosphate in atherosclerosis of diabetic hemodialysis patients

Background: A growing number of patients with End-Stage Renal Disease (ESRD) are undergoing long-term hemodialysis (HD). HD needs a vascular access (VA) and complications of VA account for a sizable proportion of its costs. One of the important cardiovascular diseases (CVD) is atherosclerosis, which is a major cause of premature deaths in the world. So, it is essential to find the risk factors to treat them before they cause an obvious CVD. Methods: We analyzed data from 174 ESRD patients who were candidate for Arterio Venous Fistula (AVF) creation from April 2008 to March 2009 in Hasheminejad Kidney Center by convenient sampling. X-ray images were used and C 4.5 algorithm of data mining techniques revealed the roles of two risk factors for atherosclerosis of diabetic ESRD patients. Pearson coefficient was also used to measure the correlation between the parameters. Results: Diabetic patients had significantly more calcified arteries in their forearm X-ray than other patients (p<0.001). Occurrence of atherosclerotic CVD in diabetic HD patients has an adverse relation compared with the controlled levels of their plasma levels of Triglyceride (TG) and Phosphorus. We found an inverse effect of TG and phosphorus plasma levels on the atherosclerotic involvement of radial and ulnar arteries in diabetic HD patients. We observed that the prevalence of radial and ulnar arteries calcification in these patients is lower when they have higher plasma levels of TG and phosphorous. Conclusion: This study investigates the role of high plasma levels of TG and phosphorous in the development of atherosclerosis in diabetic HD patients. Although many studies showed that hypertriglyceridemia plays a promoting role in the development of CVD, our study also found an inverse effect of plasma levels of TG on the atherosclerotic involvement of radial and ulnar arteries in diabetic patients, and therefore our results support this suspicion that hypertriglyceridemia plays a significant role in developing atherosclerosis.