Intelligent Evaluation Research Articles

Responsive Versus Continuous Deep Brain Stimulation for Speech in Essential Tremor: A Pilot Study.

Responsive deep brain stimulation (rDBS) uses physiological signals to deliver stimulation when needed. rDBS is hypothesized to reduce stimulation-induced speech effects associated with continuous DBS (cDBS) in patients with essential tremor (ET). To determine if rDBS reduces cDBS speech-related side effects while maintaining tremor suppression. Eight ET participants with thalamic DBS underwent unilateral rDBS. Both speech evaluations and tremor severity were assessed across three conditions (DBS OFF, cDBS ON, and rDBS ON). Speech was analyzed using intelligibility ratings. Tremor severity was scored using the Fahn-Tolosa-Marin Tremor Rating Scale (TRS). During unilateral cDBS, participants experienced reduced speech intelligibility (P = 0.025) compared to DBS OFF. rDBS was not associated with a deterioration of intelligibility. Both rDBS (P = 0.026) and cDBS (P = 0.038) improved the contralateral TRS score compared to DBS OFF. rDBS maintained speech intelligibility without loss of tremor suppression. A larger prospective chronic study of rDBS in ET is justified. © 2024 International Parkinson and Movement Disorder Society.

Effects of SPEAK OUT! & LOUD Crowd on Functional Speech Measures in Parkinson's Disease.

This study investigated the effects of the SPEAK OUT! & LOUD Crowd therapy program on speaking rate, percent pause time, intelligibility, naturalness, and communicative participation in individuals with Parkinson's disease (PD). Six adults with PD completed 12 individual SPEAK OUT! sessions across four consecutive weeks followed by group-based LOUD Crowd sessions for five consecutive weeks. Most therapy sessions were conducted via telehealth, with two participants completing the SPEAK OUT! portion in person. Speech samples were recorded at six time points: three baseline time points prior to SPEAK OUT!, two post-SPEAK OUT! time points, and one post-LOUD Crowd time point. Acoustic measures of speaking rate and percent pause time and listener ratings of speech intelligibility and naturalness were obtained for each time point. Participant self-ratings of communicative participation were also collected at pre- and posttreatment time points. Results showed significant improvement in communicative participation scores at a group level following completion of the SPEAK OUT! & LOUD Crowd treatment program. Two participants showed a significant decrease in speaking rate and increase in percent pause time following treatment. Changes in intelligibility and naturalness were not statistically significant. These findings provide preliminary support for the effectiveness of the SPEAK OUT! & LOUD Crowd treatment program in improving communicative participation for people with mild-to-moderate hypokinetic dysarthria secondary to PD. This study is also the first to demonstrate positive effects of this treatment program for people receiving the therapy via telehealth.

THE BENEFITS OF EARLY COCHLEAR IMPLANTATION FOR SPEECH DEVELOPMENT IN CHILDREN WITH USHER SYNDROME: LITERATURE REVIEW

IntroductionUsher syndrome (USH) is a rare autosomal recessive genetic disorder characterized by sensorineural hearing loss (SNHL), vision loss (retinitis pigmentosa), and occasional balance impairment. Depending on the severity and onset of hearing loss and coexisting vestibular dysfunction, USH is divided into three clinical types – USH1, USH2, USH3 – as well as atypical USH which combines features of all these three. The purpose of this review is to present the impact of cochlear implantation on speech development in children diagnosed with all types of Usher syndrome.Material and methodsAll relevant publications published in from 2013 to 2023 were retrieved from PubMed based on the keywords <i>Usher syndrome</i>, <i>Usher syndrome diagnostics</i>, <i>Usher syndrome hearing loss</i>, <i>Usher syndrome cochlear</i>, and <i>Usher syndrome speech</i>. Exactly 67 papers were selected.ResultsBilateral cochlear implantation in children with Usher syndrome is beneficial for audiological and verbal development provided that hearing loss is detected early and implantation done promptly. In USH1 the preferred age is before 3 in the case of severe to profound congenital SNHL; in USH2 and USH3 the optimal time for implantation is hampered by the difficulty of estimating when hearing loss occurred and its rate of progression. After bilateral cochlear implantation, studies showed improvements in the categories of auditory performance (CAP), speech intelligibility rate (SIR), meaningful auditory integration scale (MAIS), meaningful use of speech scale (MUSS), and speech reception score (SRS), together with good development of speech perception and verbal communication.ConclusionsEarly diagnosis (before the onset of vision loss) and early bilateral cochlear implantation in children who have suffered severe to profound SNHL due to Usher syndrome reduces disability and maximizes auditory–oral communication skills, significantly increasing their quality of life.

A comprehensive evaluation method for frailty based on semi-supervised learning and transfer-learning

Frailty evaluation is of great significance for the specific population, which can speed up the treatment process and reduce the adverse effects after treatment. In this article, in order to make up for the shortcomings of the traditional evaluation methods, a comprehensive intelligent evaluation method that integrates physiological data from multiple perspectives, such as mobility, muscle level, and body composition data has been proposed. The multi-source physiological data of 174 participants has been collected, and a series of features related to frailty have been extracted. A recurrent stepping semi-supervised learning (RSSSL) framework is proposed to transmit the knowledge from the annotated data to the unannotated gait sequence utilizing various networks with varying architectures. Moreover, in order to enhance the frailty evaluation’s performance, an approach based on subspace transfer learning integrating with heterogeneous features (STL-IHF) is proposed. The mean segmentation accuracy on the unannotated gait sequence reaches over 97.10% across recruited subjects with distinct gait patterns, which is significantly superior when compared with classical deep learning methods. And the recognition accuracy of the STL-IHF method for frailty evaluation reaches over 92.9%. The overall experimental results demonstrate that the proposed methods are effective in frailty evaluation tasks in clinics.

Clinical characteristics and hearing loss etiology of cochlear implantees undergoing surgery in their teens, 20s, and 30s

PurposeThis study aimed to investigate the etiology of hearing loss, including genetic variants, in individuals who underwent cochlear implantation (CI) in their teens to thirties. It also sought to analyze post-CI speech performance and identify prognostic factors affecting CI outcomes in this age group.MethodsWe conducted a retrospective review of 421 cochlear implant patients at Seoul National University Bundang Hospital, focusing on 63 subjects aged 10–39 years who underwent their first CI by a single surgeon between July 2018 and June 2022. The study included audiologic evaluation, molecular genetic testing, and analysis of speech performance post-CI. Statistical analyses were performed using SPSS 25 and GraphPad Prism 7.ResultsAmong 63 participants (M:F, 24:39), nine underwent CI in their teens, 24 in their 20 s, and 30 in their 30 s. Most of them (40, 63.5%) had postlingual deafness. The study found that 65.2% (40/63) of subjects received a genetic diagnosis, with DFNB4 being the most common etiology (37.5%, 15/40). Post-CI speech evaluation showed an average sentence score of 80% across all subjects. Factors such as the onset of hearing loss, duration of deafness (DoD), and preoperative Speech Intelligibility Rating (SIR) significantly influenced CI outcomes. Notably, longer DoD was associated with poorer CI outcomes, but this did not affect individuals with postlingual hearing loss as much.ConclusionThe study concludes that in individuals aged 10–39 undergoing CI, the onset of hearing loss and preoperative SIR are critical predictors of postoperative outcomes. CI is recommended for those with postlingual hearing loss in this age group, irrespective of the DoD. The study highlights the importance of genetic factors especially DFNB4 in hearing loss etiology and underscores the value of the relatively easy-to-evaluate factor, preoperative SIR in predicting CI outcomes.

Implementing recommended approaches to assessing multilingual children’s speech

Traditional approaches to assessing speech production are not appropriate for the diagnosis of speech sound disorders (SSD) in multilingual children. This study explored the implementation of recommended approaches to assessing the speech production of multilingual children in a French-speaking context. The speech production of 20 multilingual and 20 monolingual children aged 4-6 years was examined using a standardised naming task, parental ratings of intelligibility, and parental concern about speech development. We compared the diagnostic outcomes for monolingual and multilingual children using the norm-referenced approach and a criterion-referenced approach, based on percentage of consonant correct (PCC). We then examined the diagnostic outcomes for the 20 multilingual children using the recommended converging evidence approach (considering multiple measures). Results confirmed that the norm-referenced approach is not appropriate for French-speaking multilingual children, leading to an overdiagnosis of SSD. The converging evidence approach allowed for more informed and more nuanced diagnostic decisions for multilingual children.

A New Machine-Learning-Driven Grade-Point Average Prediction Approach for College Students Incorporating Psychological Evaluations in the Post-COVID-19 Era

With the rapid development of artificial intelligence in recent years, intelligent evaluation of college students’ growth by means of the monitoring data from training processes is becoming a promising technique in the field intelligent education. Current studies, however, tend to utilize course grades, which are objective, to predict students’ grade-point averages (GPAs), but usually neglect subjective factors like psychological resilience. To solve this problem, this paper takes mechanical engineering as the research object, and proposes a new machine-learning-driven GPA prediction approach to evaluate the academic performance of engineering students by incorporating psychological evaluation data into basic course scores. Specifically, this paper adopts SCL-90 psychological assessment data collected in the freshman year, including key mental health indicators such as somatization, depression, hostility, and interpersonal sensitivity indicators, as well as professional basic course scores, including mechanical principles, mechanical design, advanced mathematics, and engineering drawing. Four representative machine learning algorithms, Support Vector Machine (SVM), CNN-CBAM, Extreme Gradient Boosting (XGBoost) and Classification and Regression Tree (CART) that include deep and shallow models, respectively, are then employed to build a classification model for GPA prediction. This paper designs a validation experiment by tracking 229 students from the 2020 class from the School of Mechanical and Electrical Engineering of Henan University of Science and Technology, China. The students’ academic performance in senior grades is divided into five classes to use as the prediction labels. It is verified that psychological data and course data can be effectively integrated into GPA prediction for college students, with an accuracy rate of 83.64%. Meanwhile, this paper also reveals that anxiety indicators in the psychological assessment data have the greatest impact on college students’ academic performance, followed by interpersonal sensitivity. The experimental results also show that, for predicting junior year GPAs, psychological factors play more important role than they do in predicting sophomore GPAs. Suggestions are therefore given: the current practice in existing undergraduate teaching, i.e., only conducting psychological assessments in the initial freshman year, should be updated by introducing follow-up psychological assessments in each academic year.

A comprehensive research on measurement and evaluation of intelligent meter reading systems

Based on the machine vision and artificial intelligence technologies, the reading and acquisition of meter values have become the mainstream technical solutions for real-time monitoring of production data in the current industrial field. At the same time, the number of intelligent instruments and meters is increasing, and the quality varies. There is a lack of scientifically measurement methods and quality trust for intelligent meter reading algorithms. To promote their reliable and widespread application in various fields, it is necessary to conduct in-depth research on the assessment system of artificial intelligence in intelligent meter reading systems. This paper comprehensively studies various aspects involved in the emerging measurement field of artificial intelligence evaluation at the current stage, providing scientific measurement and evaluation references for the quality trust of artificial intelligence in the field of meters.

TinyM 2 Net-V2: A Compact Low-power Software Hardware Architecture for M ulti m odal Deep Neural Networks

With the evaluation of Artificial Intelligence (AI), there has been a resurgence of interest in how to use AI algorithms on low-power embedded systems to broaden potential use cases of the Internet of Things (IoT). To mimic multimodal human perception, multimodal deep neural networks (M-DNN) have recently become very popular with the classification task due to their impressive performance for computer vision and audio processing tasks. This article presents TinyM 2 Net-V2 —a compact low-power software hardware architecture for m ulti m odal deep neural networks for resource-constrained tiny devices. To compress the models to implement on tiny devices, cyclicly sparsification and hybrid quantization (4-bits weights and 8-bits activations) methods are used. Although model compression techniques are an active research area, we are the first to demonstrate their efficacy for multimodal deep neural networks, using cyclicly sparsification and hybrid quantization of weights/activations. TinyM 2 Net-V2 shows that even a tiny multimodal deep neural network model can improve the classification accuracy more than that of any unimodal counterparts. Parameterized M-DNN model architecture was designed to be evaluated in two different case-studies: vehicle detection from multimodal images and audios and COVID-19 detection from multimodal audio recordings. The most compressed TinyM 2 Net-V2 achieves 92.5% COVID-19 detection accuracy (6.8% improvement from the unimodal full precision model) and 90.6% vehicle classification accuracy (7.7% improvement from the unimodal full precision model). A parameterized and flexible FPGA hardware accelerator was designed as well for TinyM 2 Net-V2 models. To the best of our knowledge, this is the first work accelerating multimodal deep neural network models on low-power Artix-7 FPGA hardware. We achieved energy efficiency of 9.04 GOP/s/W and 15.38 GOP/s/W for case-study 1 and case-study 2, respectively, which is comparable to the state-of-the-art results. Finally, we compared our tiny FPGA hardware implementation results with off-the-shelf resource-constrained devices and showed our implementation is faster and consumed less power compared to the off-the-shelf resource-constrained devices.

Multi-index fusion database and intelligent evaluation modelling for geostress classification

Accurate evaluation of geostress levels is crucial for the investigation, design, and construction of underground engineering. However, existing methods for geostress classification are limited by their reliance on singular indexes, leading to discrepancies between the evaluation results and the actual observations in engineering. The discrepancies have resulted in casualties and economic losses. To address the above issues, 204 sets of geostress levels data were collected from numerous documents in the study. Six indexes related to geostress levels have been identified by analyzing the collected data and traditional criteria. Additionally, an orthogonal experimental design was carried out on the range-based indexes to obtain a multi-index fusion database. Furthermore, an intelligent evaluation model for the geostress levels was established in combination with the AutoGluon automatic machine learning framework. Subsequently, software has been developed from this model for ease of use in practical engineering. After on-site data verification, the proposed model demonstrated an accuracy of 95%, outperforming five traditional criteria, with the highest accuracy among them being 70%. The research could provide an effective basis for geostress classification, thereby enhancing the safety of on-site construction personnel.

An Evaluation Method of Dental Treatment Quality Combined with Deep Learning and Multi-index Decomposition

ABSTRACT Dentists judge that the quality of dental treatment for each patient is very time-consuming and inefficient, lacks quantitative evaluation criteria, and is easy to cause errors. At the same time, the traditional method of extracting tooth and root canal image features based on experience is difficult to accurately extract the tooth area and root canal filling area, resulting in low accuracy of tooth and root canal segmentation, which in turn affects the accuracy of tooth treatment quality evaluation. In this paper, a deep learning convolutional neural network is used to segment the root canal filling area, tooth boundary, and the boundary between tooth and soft tissue for the real patient ‘s root canal treatment and filling image. Finally, the segmented image is quantitatively evaluated according to the multi-evaluation index of professional doctors. The experimental results show that the intelligent evaluation method of dental treatment quality combined with deep learning and multi-index decomposition proposed in this paper not only unifies the evaluation criteria of dental treatment quality but also the therapeutic effect of quantitative scoring can effectively improve the work efficiency of doctors, which has reference significance for the application of artificial intelligence in the medical field.

Digital twin‐driven online intelligent assessment of wind turbine gearbox

AbstractRemaining useful fatigue life monitoring of wind turbine drivetrains is important. However, the implementation of real‐time monitoring often faces efficiency and accuracy challenges. In order to resolve this, this paper proposes a vibration‐based damage monitoring digital twin (VBDM‐DT) that enables the online intelligent evaluation of wind turbine gearboxes, using gear tooth surface durability as an example fatigue mode. The VBDM‐DT integrates a random wind load model, a high‐fidelity dynamics model, and a fatigue damage model. The random wind load model takes the wind speed from the supervisory control and data acquisition (SCADA) as input to estimate the input torque of the drivetrain in real time. Simultaneously, VBDM‐DT uses the vibration signals from the condition monitoring system (CMS) to intelligently calibrate the dynamics model, allowing it to be continuously adjusted and optimized in response to actual vibrations. The fatigue damage model takes the real‐time dynamic loads estimated by the high‐fidelity dynamic model as input to achieve real‐time fatigue damage monitoring of key components in the wind turbine gearbox. Applying the VBDM‐DT model to a 2 MW wind turbine gearbox, the results indicate that the model provides satisfactory accuracy in estimating input loads and good adaptability in intelligent calibration of the dynamic model. Based on this model, the fatigue life estimation for gears and bearings is more credible and reliable.

Evaluation of the outcomes of cochlear implant in children with auditory neuropathy

Objective: To investigate the auditory and speech abilities of children with congenital auditory neuropathy (AN) after cochlear implant (CI), and to analyze the role of genetic testing in predicting the postoperative outcomes of CI in AN patients. Methods: Fourteen children diagnosed with AN by audiological battery test and underwent CI surgery in Xijing Hospital of the Air Force Medical University from 2002 to 2021 were included in this study (9 males and 5 females), with an implantation age of (3.1±1.7) years (mean±standard deviation, the same as follows). The preoperative audiological results and deafness gene results were analyzed. Another 52 children with ordinary sensorineural hearing loss (SNHL) were selected as the control group (30 males and 22 females), with an implantation age of (2.2±0.9) years. The demographic factors such as age and gender were matched with those of the AN group. The modified Category Auditory Performance (CAP-Ⅱ) and Speech Intelligence Rate (SIR) were used to evaluate the development of postoperative auditory and speech abilities in two groups. The Mandarin Speech Test System was used to test the speech recognition rate of monosyllabic and disyllabic words and sentences. Matlab 2022 software was used to analyze the data. Results: The results of gene in 14 children with AN showed that 6 cases had OTOF gene mutations, 2 cases (siblings) were confirmed to have TNN gene mutations through whole exome sequencing, and the remaining 6 cases were not find any clear pathogenic gene mutations. All subjects underwent CI surgery with electrodes implanted into the cochlea smoothly, and there were no postoperative complications. After surgery, all AN children had improved auditory and speech abilities, but only 64% (9/14) of AN children with CI had auditory ability scores comparable to the control group of SNHL children (including 2 children with TNN gene mutations), and 36% (5/14) of AN children had lower scores than the control group of SNHL children.The average speech recognition rate of two children with TNN gene mutations was 86.5%, and of two children with OTOF gene mutations was 83.2%. Conclusions: AN children achieved varying degrees of auditory and speech abilities after CI, but the postoperative effects varied greatly. Some children achieved similar results as ordinary SNHL children, but there were still some children whose effects were worse than those of ordinary SNHL children. The postoperative efficacy of CI in two children with AN caused by TNN pathogenic genes were comparable to that of ordinary SNHL in children. Genetic testing had certain reference value for predicting the postoperative effect of CI in AN children.

Evolving testing scenario generation and intelligence evaluation for automated vehicles

Interaction between the background vehicles (BVs) and automated vehicles (AVs) in scenario-based testing plays a critical role in evaluating the intelligence of the AVs. Current testing scenarios typically employ predefined or scripted BVs, which inadequately reflect the complexity of human-like social behaviors in real-world driving scenarios, and also lack a systematic metric for evaluating the comprehensive intelligence of AVs. Therefore, this paper proposes an evolving scenario generation method, employing deep reinforcement learning (DRL) to construct human-like BVs that interact with AVs, and this evolving scenario is designed to test and evaluate the intelligence of AVs. Firstly, a class of BV driver models with human-like competitive, mutual, and cooperative driving motivations is designed. Then, utilizing the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and an improved level-k training procedure, the three distinct driver models acquire game-based interactive driving policies. And these driver models are combined to generate evolving scenarios in which they can interact continuously and evolve diverse contents. Next, a framework including safety, driving efficiency, and interaction utility are presented to evaluate and quantify the intelligence performance of 3 systems under test (SUTs), indicating the effectiveness of the evolving scenario for intelligence testing. Finally, the complexity and fidelity of the proposed evolving testing scenario are validated. The results demonstrate that the proposed evolving scenario exhibits the highest level of complexity compared to other baseline scenarios and has more than 85% similarity to naturalistic driving data. This highlights the potential of the proposed method to facilitate the development and evaluation of high-level AVs in a realistic and challenging environment.

Research on the Construction of Digital Development Model for Higher Vocational Education and Influencing Factors

Nowadays digital transformation has become the focus of vocational education reform and development. Starting from the background of the digitalization situation in higher vocational education and the analysis of the "1+5" system connotation elements, the paper attempts to construct an evaluation model for the digitalization development of higher vocational education based on research on the current situation and interpretation of the digitalization development process of several vocational colleges in the Yangtze River Delta region. It includes dimensions such as the connotation and characteristics of digital transformation, the meaning and direction of digital transformation, digital technology and application, digital strategies and paths, etc. It explores the main influencing factors of the digitalization development of higher vocational education from five dimensions: educational and teaching concepts, teaching resource construction, intelligent evaluation technology, intelligent teaching environment, and digital service capabilities. It proposes strategies to promote the digitalization development of higher vocational education, such as reshaping digital concepts, strengthening hardware infrastructure construction, and upgrading digital software.

Cochlear implantation through retro-facial approach with congenital microtia malformation with facial nerve deformity: a case report

The difficulty of cochlear implantation in patients with congenital microtia is usually increased due to the vague anatomical marks and facial nerve malformation. The common types of facial nerve malformation include facial nerve bony cover loss, aberrant position, and bifurcation malformation. Bifurcation malformation may obscure the oval window, press against stapes, and bifurcate in the vestibular window while obscuring the round window. It is important to correctly identify the facial nerve and choose a reasonable surgical approach to avoid postoperative complications. This article describes a case of profound sensorineural hearing loss due to facial nerve malformation in our institution. The patient underwent cochlear implantation through the retro-facial approach. There was no facial nerve injury or dysfunction symptoms such as facial paralysis and hemifacial spasm 2 years after the operation, and the cochlear implant works well. The score of the categories of the auditory performance（CAP） questionnaire was 7, and the score of the speech intelligibility rating（SIR） questionnaire was 4. When the round window cannot be exposed through the facial recess approach during surgery, the retro-facial approach is a feasible method. To avoid facial nerve injury, a thin-section CT of the temporal bone should be performed before the middle and inner ear surgery for patients with facial nerve malformation, and the intraoperative facial nerve monitor should be used to clarify the course of the facial nerve to avoid injury.

Autism's Impact on Cochlear Implantation Surgery Outcomes in Deaf Children.

Cochlear implants (CI) provide a hearing sense for severe to profound hearing-impaired patients, both adults and children, and they are a broadly effective and accepted therapeutic method for those patients. Also, Deaf children with comorbidities, including autism spectrum disorders (ASDs), undergo cochlear implantation. ASDs are a group of developing disorders characterized by abnormalities in social interaction and communication with limited repetitive patterns of behavior. This study aimed to assess the effect of Autism on CI surgery outcomes in Deaf Children. We followed 12 autistic patients with cochlear implantation and 12 non-autistic cochlear-implanted patients for two years. The Categories of Auditory Performance (CAP) and Speech Intelligibility Rating (SIR) scores were used to assess 6, 12, and 24 months after cochlear implantation surgery. During the 24-month follow-up, the CAP means scores increased in both groups, and SIR and CAP progresses were considerably greater in non-ASD children (P<0.001). However, in ASD children, the progress of CAP and SIR variables were significant, with 99% and 95% confidence, respectively, at 24 months after surgery. Although the CIs could improve hearing performance in autistic patients, speech development after CIs in autistic children could affected by several factors, including the severity of autism, and this can be effective in providing pre-implant counseling to parents. The application of the alternative communication methods could be taken into account as a potential rehab technique.

Evaluation of moral distress and moral intelligence among medical residents

Background: Medical ethics is necessary in the medical field to protect patient rights, enhance professional conduct, manage complex situations, prevent malpractice, and advance healthcare policy. Aim: This study aimed to examine the levels of moral distress and moral intelligence among medical residents and explore the relationship between these two concepts. Methods: This descriptive-analytical cross-sectional study was performed on 221 medical residents in 2021. The participants were recruited by census sampling method. Data were collected by two standard questionnaires, including Corley’s moral distress scale (2001) and Lennick and Kiel’s moral intelligence scale (2005). Statistical analyses were conducted using both parametric and non-parametric methods. All analyses were performed by SPSS-22 software. Results: Of participants, 140 (63.4%) were non-surgical and 81 (36.6%) were surgical assistants. Moral distress was significantly higher in surgical than non-surgical disciplines, (P&lt;0.05). Further, according to the moral distress classification, 51.8% of surgical residents and 15.8% of non-surgical residents had severe moral distress. Moral intelligence was also higher in surgical than in non-surgical disciplines, but the difference was not statistically significant (P&gt;0.05), and moral intelligence was moderate to high. There was a significantly negative correlation between moral intelligence (and its four subscales) and moral distress (P &lt;0.001, r =-0.410). Conclusion: The results showed high moral distress and an inverse correlation between moral distress and moral intelligence among medical residents. The study highlights the importance of addressing issues related to moral distress and moral intelligence in medical education and training programs

Factors Affecting the Development of Speech in Children After Cochlear Implant

Objective: To determine the odds of different factors for developing poor speech intelligibility in pre-lingually deaf children who have undergone cochlear implantation. Study Design: Case-control study. Place and Duration of Study: Department of ENT, Combined Military Hospital, Rawalpindi Pakistan, Jan 2020 to Aug 2022. Methodology: This study was based on a sample of 60 patients: 30 cases with cochlear implants placed before two years of age, with a total time of using a cochlear implant of ≥1 year, and 30 controls. Both groups consisted of both genders aged between 3 and 12 years. All patients underwent assessment using the Speech Intelligibility Rating system and were documented for var-ious factors influencing speech development. Results: The mean age of the sample at the time of the study was 6.37±3.17 years. A total of 34(56.7%) participants were male. Factors associated with poorer speech outcomes were male gender (OR0.44[CI 95% 0.15–1.30]), no peers in the household (OR 0.53 [CI 95% 0.15–1.84]), higher maternal literacy (OR 0.42[CI 95% 0.13–1.30]). Factors associated with improved speech devel-opment included primarily oral form of communication (OR 2.14[CI 95%0.41–11.17]), less than one-year age at implantation (OR 1.29 [CI 95% 0.24–6.96]), a total duration of speech therapy less than two years (OR 1.70[CI 95% 0.32–8.74]) and the pres-ence of unilateral implants, (OR 6.33[CI 95% 1.00–40.07]). Conclusion: Earlier cochlear implantation with frequent verbal skill exercise appears essential to developing good speech in-telligibility in pre-lingually deaf children. Keywords: Children, Cochlear Implantation, Pre-Lingual Deafness, Speech intelligibility

Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

Surface roughness is of great significance in maintaining mechanical performance and improving the reliability of the equipment. However, fast surface roughness evaluations that are sufficiently stable and efficient for engineering in situ use have not yet been realized. To address this issue, an image-driven roughness intelligent method is proposed in this research. By evaluating the texture similarity intelligently between the testing image and the reference image, the surface roughness of the testing image can be acquired. Firstly, with a proposed adaptive texture extraction method, the texture feature of an image can be extracted even under a complex background. Secondly, by establishing the graph structure of the texture grayscale features, the similarity between different images is evaluated. Finally, by establishing a sparrow-optimized support vector machine regression method, the mapping relationship between the similarity and the surface roughness can be acquired. The experimental results indicate that the proposed method for intelligent evaluation of roughness has superior prediction performance (the average relative prediction error of Ra and Rz are 8.8156% and 8.0571%, respectively). Therefore, this work provides a useful tool for non-contact detection of workpiece surface roughness.