Robot-assisted perception augmentation for online detection of insertion failure during cochlear implant surgery

Pile, Jason; Wanna, George B.; Simaan, Nabil

doi:10.1017/s0263574716000333

Cited by 16 publications

(7 citation statements)

References 35 publications

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“…Other researchers have recently developed motorized insertion tools by reducing the critical peak insertion forces to reduce insertion trauma. [20][21][22] Using automated setups, different insertion speeds have been investigated, and the majority of data suggested that lower insertion speed resulted in lower insertion forces. 23 Furthermore, according to Kesler et al, 24 these automatic insertions can be engineered to occur at speeds lower than 0.9 mm/s, which are not possible to do manually as a consistent, steady movement.…”

Section: Introductionmentioning

confidence: 99%

Development of Automated Tool for Electrode Array Insertion and its Study on Intracochlear Pressure

Zagabathuni,

Padi,

Kameswaran

et al. 2023

The Laryngoscope

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Cochlear implantation is the most successful approach for people with profound sensorineural hearing loss. Manual insertion of the electrode array may result in damaging the soft tissue structures and basilar membrane. An automated electrode array insertion device is reported to be less traumatic in cochlear implant surgery.ObjectivesThe present work develops a simple, reliable, and compact device for automatically inserting the electrode array during cochlear implantation and test the device to observe intracochlear pressure during simulated electrode insertion.MethodsThe device actuates the electrode array by a roller mechanism. For testing the automated device, a straight cochlea having the dimension of the scala tympani and a model electrode is developed using a 3D printer. A pressure sensor is utilized to observe the pressure change at different insertional conditions.ResultsThe electrode is inserted into a prototype cochlea at different speeds without any pause, and it is noticed that the pressure is increased with the depth of insertion of the electrode irrespective of the speed of electrode insertion. The rate of pressure change is observed to be increased exponentially with the speed of insertion.ConclusionAt an insertion speed of 0.15 mm/s, the peak pressure is observed to be 133 Pa, which can be further evaluated in anatomical models for clinical scenarios.Level of EvidenceN/A Laryngoscope, 2023

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Section: Introductionmentioning

confidence: 99%

Development of Automated Tool for Electrode Array Insertion and its Study on Intracochlear Pressure

Zagabathuni,

Padi,

Kameswaran

et al. 2023

The Laryngoscope

View full text Add to dashboard Cite

show abstract

“…In the recent decade, AI based approaches have demonstrated their supreme capability over traditional signal processing algorithms in computer vision, natural language processing, gaming, and biomedical engineering, including hearing healthcare [8]. Machine learning and deep learning have changed various aspects of the 'AI + CI' research [9], including prognosis estimation [10], electrode placement [11], robotic surgery [12], mapping [13], and sound signal processing [14], [15]. In addition, the success of deep learning in general audio processing [16], [17] and hearing aids [18], [19] may also change CI sound processing.…”

Section: Introductionmentioning

confidence: 99%

ElectrodeNet – A Deep Learning Based Sound Coding Strategy for Cochlear Implants

Huang¹,

Chao²,

Yu³

et al. 2022

Preprint

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<p>ElectrodeNet, a deep-learning based sound coding strategy for the cochlear implant (CI), is proposed in this study. </p> <p>ElectrodeNet emulates the ACE strategy by replacing the conventional envelope detection using various artificial neural networks, and the extended ElectrodeNet-CS strategy further incorporates the channel selection (CS) in the network. Network models of deep neural network (DNN), convolutional neural network (CNN), and long short-term memory (LSTM) were trained using the Fast Fourier Transformed bins and electrode stimulation patterns from the processing of the ACE strategy for clean speech. Objective speech understanding using short-time objective intelligibility (STOI) and normalized covariance metric (NCM) was estimated for ElectrodeNet with the factors of network architecture, dataset language, and noise type using CI simulations. Subjective listening tests for vocoded Mandarin speech were conducted with normal-hearing listeners to measure sentence recognition scores. DNN, CNN, and LSTM based ElectrodeNets exhibited strong correlations to ACE in objective and subjective scores using mean squared error (MSE), linear correlation coefficient (LCC) and Spearman’s rank correlation coefficient (SRCC). The ElectrodeNet-CS strategy was capable of producing N-of-M compatible electrode patterns using a modified DNN network to embed maxima selection, and to perform in similar or even slightly above average in STOI and sentence recognition compared to ACE. The methods and findings in this study demonstrated the feasibility and potential of using deep learning in CI sound coding strategy.</p>

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“…In the recent decade, AI based approaches have demonstrated their supreme capability over traditional signal processing algorithms in computer vision, natural language processing, gaming, and biomedical engineering, including hearing healthcare [9]. Machine learning and deep learning have changed various aspects of the 'AI + CI' research [10], including prognosis estimation [11], electrode placement [12], robotic surgery [13], mapping [14], [15], and sound signal processing [16], [17]. In addition, the successful stories of deep learning in general audio processing [18], [19] and hearing aids [20], [21] may also significant change the related field of CI sound processing.…”

Section: Introductionmentioning

confidence: 99%

ElectrodeNet – A Deep Learning Based Sound Coding Strategy for Cochlear Implants

Huang¹,

Chao²,

Yu³

et al. 2022

Preprint

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<div> <div> <div> <div> <p>Objective: ElectrodeNet, a deep-learning based sound coding strategy for the cochlear implant (CI), is proposed in this study. The performance between ElectrodeNet and the advanced combination encoder (ACE) coding strategy in speech intelligibility is compared. Methods: ElectrodeNet emulates the ACE strategy and replaces the conventional envelope detection using various forms of artificial neural networks. Network models of deep neural network (DNN), convolutional neural network (CNN), and long short-term memory (LSTM) were trained using the fast Fourier transformed clean speech and the corresponding electrode stimulation patterns. Objective speech intelligibility was estimated for ElectrodeNets for the factors of loss function, network architecture, language, and noise type. Subjective listening tests for vocoded Mandarin speech were conducted with 40 normal-hearing listeners. Results: DNN, CNN, and LSTM based ElectrodeNets exhibited strong correlations with the ACE strategy in short-time objective intelligibility (STOI) and normalized covariance metric (NCM) scores. For objective evaluations, small mean squared error (MSE) scores between ACE and ElectrodeNets were less than 0.01 under all experimental conditions, whereas linear correlation coefficient (LCC) and Spearman’s rank correlation coefficient (SRCC) were obtained in large values greater than 0.97 and 0.96, respectively. According to the listening test results, substantial positive relationships were also observed between ACE and both DNN and CNN based ElectrodeNets with MSEs smaller than 0.02, and LCCs and SRCCs greater than 0.9. Significance: This study demonstrates the feasibility of using deep learning to encode sound into meaningful patterns for CI listening. </p> </div> </div> </div> </div>

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Robot-assisted perception augmentation for online detection of insertion failure during cochlear implant surgery

Cited by 16 publications

References 35 publications

Development of Automated Tool for Electrode Array Insertion and its Study on Intracochlear Pressure

Development of Automated Tool for Electrode Array Insertion and its Study on Intracochlear Pressure

ElectrodeNet – A Deep Learning Based Sound Coding Strategy for Cochlear Implants

ElectrodeNet – A Deep Learning Based Sound Coding Strategy for Cochlear Implants

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