Artificial Intelligence-based System Research Articles

Establishment of an automatic diagnosis system for corneal endothelium diseases using artificial intelligence

PurposeTo use artificial intelligence to establish an automatic diagnosis system for corneal endothelium diseases (CEDs).MethodsWe develop an automatic system for detecting multiple common CEDs involving an enhanced compact convolutional transformer (ECCT). Specifically, we introduce a cross-head relative position encoding scheme into a standard self-attention module to capture contextual information among different regions and employ a token-attention feed-forward network to place greater focus on valuable abnormal regions.ResultsA total of 2723 images from CED patients are used to train our system. It achieves an accuracy of 89.53%, and the area under the receiver operating characteristic curve (AUC) is 0.958 (95% CI 0.943–0.971) on images from multiple centres.ConclusionsOur system is the first artificial intelligence-based system for diagnosing CEDs worldwide. Images can be uploaded to a specified website, and automatic diagnoses can be obtained; this system can be particularly helpful under pandemic conditions, such as those seen during the recent COVID-19 pandemic.

Can Artificial Intelligence Identify Reading Fluency and Level? Comparison of Human and Machine Performance

This study examined whether an artificial intelligence-based automatic speech recognition system can accurately assess students’ reading fluency and reading level. Participants were 120 fourth-grade students attending public schools in Türkiye. Students read a grade-level text out loud while their voice was recorded. Two experts and the artificial intelligence-based automatic speech recognition system analyzed the recordings for reading errors. Following the analysis, a word error rate was calculated for both the experts and the artificial intelligence-based automatic speech recognition system. Word error rates were converted into reading accuracy rate scores. Inter-rater agreement and linear regression analyses were used to compare the raters’ reading fluency scores, and logistic regression analyses were used to compare the classification of readers according to their reading levels. Results showed that the difference between the scores of the artificial intelligence-based automatic speech recognition system and the expert scores was minimal. This is because there was a very high level of agreement between the artificial intelligence-based automatic speech recognition system and the experts scores. Linear regression analyses showed that the artificial intelligence-based automatic speech recognition system significantly predicted the scores of experts. According to the logistic regression analysis results, the artificial intelligence-based automatic speech recognition system was at least 93% as successful as human raters in classifying readers as poor and good. These results give us hope that reading assessments at classroom, school, regional, national, and even international levels can be conducted more accurately and economically by using artificial intelligence-based systems in the coming years.

Automated systems for diagnosis of dysgraphia in children: a survey and novel framework

AbstractLearning disabilities, which primarily interfere with basic learning skills such as reading, writing, and math, are known to affect around 10% of children in the world. The poor motor skills and motor coordination as part of the neurodevelopmental disorder can become a causative factor for the difficulty in learning to write (dysgraphia), hindering the academic track of an individual. The signs and symptoms of dysgraphia include but are not limited to irregular handwriting, improper handling of writing medium, slow or labored writing, unusual hand position, etc. The widely accepted assessment criterion for all types of learning disabilities including dysgraphia has traditionally relied on examinations conducted by medical expert. However, in recent years, artificial intelligence has been employed to develop diagnostic systems for learning disabilities, utilizing diverse modalities of data, including handwriting analysis. This work presents a review of the existing automated dysgraphia diagnosis systems for children in the literature. The main focus of the work is to review artificial intelligence-based systems for dysgraphia diagnosis in children. This work discusses the data collection method, important handwriting features, and machine learning algorithms employed in the literature for the diagnosis of dysgraphia. Apart from that, this article discusses some of the non-artificial intelligence-based automated systems. Furthermore, this article discusses the drawbacks of existing systems and proposes a novel framework for dysgraphia diagnosis and assistance evaluation.

Nutritional management recommendation systems in polycystic ovary syndrome: a systematic review

BackgroundPeople with polycystic ovary syndrome suffer from many symptoms and are at risk of developing diseases such as hypertension and diabetes in the future. Therefore, the importance of self-care doubles. It is mainly to modify the lifestyle, especially following the principles of healthy eating. The purpose of this study is to review artificial intelligence-based systems for providing management recommendations, especially food recommendations.Materials and methodsThis study started by searching three databases: PubMed, Scopus, and Web of Science, from inception until 6 June 2023. The result was the retrieval of 15,064 articles. First, we removed duplicate studies. After the title and abstract screening, 119 articles remained. Finally, after reviewing the full text of the articles and considering the inclusion and exclusion criteria, 20 studies were selected for the study. To assess the quality of articles, we used criteria proposed by Malhotra, Wen, and Kitchenham. Out of the total number of included studies, seventeen studies were high quality, while three studies were moderate quality.ResultsMost studies were conducted in India in 2021. Out of all the studies, diagnostic recommendation systems were the most frequently researched, accounting for 86% of the total. Precision, sensitivity, specificity, and accuracy were more common than other performance metrics. The most significant challenge or limitation encountered in these studies was the small sample size.ConclusionRecommender systems based on artificial intelligence can help in fields such as prediction, diagnosis, and management of polycystic ovary syndrome. Therefore, since there are no nutritional recommendation systems for these patients in Iran, this study can serve as a starting point for such research.

Analyzing the performance of a bio-sensor integrated improved blended learning model for accurate pneumonia prediction

Pneumonia has been considered a life-threatening disease for elderly human beings and those with weakened immune systems in the present medical era. The contemporary scenario highlights the significance of intelligent and automatic handheld devices to detect pneumonia and other pulmonary diseases. Hence, this research has designed an improved blended learning paradigm (IBLP) for real-time pneumonia detection from chest X-rays, and early detection of lung diseases from alveolar gas using biosensors with a graphical processing unit (GPU) developed to overcome and resolve such challenges. It emphasizes the medical applications of blended learning techniques, particularly for identifying pneumonia from chest X-ray images and other pulmonary diseases from exhaled breath using biosensors and support vector machine (SVM). The experimental findings indicate that the blended learning based VGG16 (91.99%) consistently outperforms the VGG19 (88.91%) and ResNet50 (87.02%) model in diagnostic accuracy. IBLP VGG16 provided 95.5% precision, 97.69% F1 score, and a 100% recall rate with no false-negative results. The future of X-ray classification in pneumonia diagnosis will likely involve using artificial intelligence-based systems that can provide accurate and timely analysis of X-ray images, thereby improving patient outcomes and reducing healthcare costs.

Assessing Diabetic Retinopathy Staging With AI: A Comparative Analysis Between Pseudocolor and LED Imaging.

To compare the diagnostic performance of artificial intelligence (AI)-based diabetic retinopathy (DR) staging system across pseudocolor, simulated white light (SWL), and light-emitting diode (LED) camera imaging modalities. A cross-sectional investigation involved patients with diabetes undergoing imaging with an iCare DRSplus confocal LED camera and an Optos confocal, ultra-widefield pseudocolor camera, with and without SWL. Macula-centered and optic nerve-centered 45 × 45-degree photographs were processed using EyeArt v2.1. Human graders established the ground truth (GT) for DR severity on dilated fundus exams. Sensitivity and weighted Cohen's weighted kappa (wκ) were calculated. An ordinal generalized linear mixed model identified factors influencing accurate DR staging. The study included 362 eyes from 189 patients. The LED camera excelled in identifying sight-threatening DR stages (sensitivity=0.83, specificity=0.95 for proliferative DR) and had the highest agreement with the GT (wκ=0.71). The addition of SWL to pseudocolor imaging resulted in decreased performance (sensitivity=0.33, specificity=0.98 for proliferative DR; wκ=0.55). Peripheral lesions reduced the likelihood of being staged in the same or higher DR category by 80% (P<0.001). Pseudocolor and LED cameras, although proficient, demonstrated non-interchangeable performance, with the LED camera exhibiting superior accuracy in identifying advanced DR stages. These findings underscore the importance of implementing AI systems trained for ultra-widefield imaging, considering the impact of peripheral lesions on correct DR staging. This study underscores the need for artificial intelligence-based systems specifically trained for ultra-widefield imaging in diabetic retinopathy assessment.

Data-driven system for intelligent monitoring and optimization of froth flotation circuits using Artificial Neural Networks and Genetic Algorithms

In minerals processing, the froth flotation is one of the widely used process that separates valuable mineral components from their associated gangue materials. The efficiency of this process relies on several factors, such as feed characteristics, particle size, pulp flow rate, pH, conditioning time, aeration, reagents system and many other affecting parameters. These processing parameters significantly impact the overall performance of the flotation process and influence the quality of the final concentrate. For instance, improper pulp flow and reagent dosing systems can result in metal loss and waste, particularly when dealing with frequently changing ore compositions. In this work, we established an Artificial Intelligence-based system which goal is to intelligently monitor flotation circuits and to recommend set-points for the process’s manipulated variables in order to achieve optimal performance.The system has been developed and evaluated within an industrial flotation plant that processes complex Pb-Cu-Zn sulfide ores. Leveraging an Artificial Neural Network-based Mixture of Experts (MoEs) predictive model, the system accurately estimates the mineral grades in the final concentrate and tailing of the flotation circuit. Moreover, using a Genetic Algorithms-based optimization pipeline, the system recommends set-points for the manipulated variables of the process for a maximum recovery and optimal product quality.The industrial validation of the predictive component demonstrated a 94% accuracy with a rapid 3s response time. Furthermore, the hypothetical simulation of the optimization component indicated a potential 5% increase in circuit recovery and a 4% increase of lead (Pb) grade in the circuit’s final concentrate. This developed system aims to enhance the control of froth flotation process, stabilize the product quality, and improve the overall economic benefits of production efficiency. This research contributes to the field of manufacturing systems by providing practical data-driven application for the advanced monitoring, optimization and control of industrial processes with a specific emphasis on the froth flotation process.

The role of artificial intelligence in modern ophthalmology

Currently, artificial intelligence is actively being introduced into various spheres of life, and medicine is no exception. In ophthalmology, the use of artificial intelligence is very promising, given that the diagnosis and therapeutic monitoring of eye diseases often depend heavily on the correct interpretation of images. The use of artificial intelligence in ophthalmology focuses on eye diseases that lead to vision loss, such as age-related macular degeneration, diabetic retinopathy, glaucoma and cataract. Over the past few years, artificial intelligence has reached tremendous successes in the practice of ophthalmology. Many studies have shown that artificial intelligence performance is equal to and even exceeds the capabilities of ophthalmologists in many diagnostic and prognostic tasks. However, there is still a lot of work to be done before introducing artificial intelligence into routine clinical practice. Issues such as real-world performance, generalizability, and interpretability of artificial intelligence systems are still poorly understood and will require more attention in future research. Most artificial intelligence-based systems are used in developed countries, and some require further study. High costs and a shortage in doctors and equipment in some regions of the Russian Federation and rural areas make it difficult to screen for eye diseases. Although the field of artificial intelligence is underdeveloped, we hope that artificial intelligence will play an important role in the future of ophthalmology by making healthcare more efficient, accurate and accessible, especially in regions where staffing problems exist.

Feature Extraction Based on Local Histogram with Unequal Bins and a Recurrent Neural Network for the Diagnosis of Kidney Diseases from CT Images.

Kidney disease remains one of the most common ailments worldwide, with cancer being one of its most common forms. Early diagnosis can significantly increase the good prognosis for the patient. The development of an artificial intelligence-based system to assist in kidney cancer diagnosis is crucial because kidney illness is a global health concern, and there are limited nephrologists qualified to evaluate kidney cancer. Diagnosing and categorising different forms of renal failure presents the biggest treatment hurdle for kidney cancer. Thus, this article presents a novel method for detecting and classifying kidney cancer subgroups in Computed Tomography (CT) images based on an asymmetric local statistical pixel distribution. In the first step, the input image is non-overlapping windowed, and a statistical distribution of its pixels in each cancer type is built. Then, the method builds the asymmetric statistical distribution of the image's gradient pixels. Finally, the cancer type is identified by applying the two built statistical distributions to a Deep Neural Network (DNN). The proposed method was evaluated using a dataset collected and authorised by the Dhaka Central International Medical Hospital in Bangladesh, which includes 12,446 CT images of the whole abdomen and urogram, acquired with and without contrast. Based on the results, it is possible to confirm that the proposed method outperformed state-of-the-art methods in terms of the usual correctness criteria. The accuracy of the proposed method for all kidney cancer subtypes presented in the dataset was 99.89%, which is promising.

An enumerative pre-processing approach for retinopathy severity grading using an interpretable classifier: a comparative study.

Diabetic retinopathy (DR) is a serious eye complication that results in permanent vision damage. As the number of patients suffering from DR increases, so does the delay in treatment for DR diagnosis. To bridge this gap, an efficient DR screening system that assists clinicians is required. Although many artificial intelligence (AI) screening systems have been deployed in recent years, accuracy remains a metric that can be improved. An enumerative pre-processing approach is implemented in the deep learning model to attain better accuracies for DR severity grading. The proposed approach is compared with various pre-trained models, and the necessary performance metrics were tabulated. This paper also presents the comparative analysis of various optimization algorithms that are utilized in the deep network model, and the results were outlined. The experimental results are carried out on the MESSIDOR dataset to assess the performance. The experimental results show that an enumerative pipeline combination K1-K2-K3-DFNN-LOA shows better results when compared with other combinations. When compared with various optimization algorithms and pre-trained models, the proposed model has better performance with maximum accuracy, precision, recall, F1 score, and macro-averaged metric of 97.60%, 94.60%, 98.40%, 94.60%, and 0.97, respectively. This study focussed on developing and implementing a DR screening system on color fundus photographs. This artificial intelligence-based system offers the possibility to enhance the efficacy and approachability of DR diagnosis.

A low-cost vision-based weld-line detection and measurement technique for robotic welding

ABSTRACT To make robotic welding more flexible and intelligent, artificial intelligence-based systems are one of the most important developments. This paper introduces a computer vision-based algorithm for weld path detection, gap measurement, and weld length calculation. The proposed innovative approach employs various image processing techniques and mathematical operations, accurately determining weld attributes at seam points. Using the YOLO-based object detection algorithm, the model attains a remarkable average precision of 99.5% in identifying atypical weld regions. The study also introduces an efficient boundary line elimination method based on the Probabilistic Hough transform and mathematical logic. Methodology for classifying weld lines with or without significant gaps has been proposed, followed by adapting distinct set of algorithms for weld line identification and gap measurement. Rigorous testing on butt joints of diverse shapes (e.g., straight, zig-zag, and curve) and sizes verifies the robustness of the algorithm, with errors well within ±1 mm for length measurements. In testing conducted at three different points along the individual weld profile, the maximum error in estimating the weld gap was observed to be 0.11 mm. Weld seam information can be extracted effectively with the proposed algorithm, which proves its viability for industrial applications.

Exploring content-based group recommendation for suggesting restaurants in Havana City

Recommender systems (RSs) are a relevant kind of artificial intelligence-based systems focused on providing users with the information that best fit their preferences and needs in a search space overloaded of possible options. Specifically, group recommender systems (GRSs) are a special type of RS centered on recommending items that are consumed in groups and not individually, being TV program and touristic packages key examples of such items. The current work is focused on proposing a content-based group recommendation approach (CB-GRS) contextualized to the restaurant recommendation domain. In contrast to previous content-based group recommendation models, the proposal incorporates novel stages such as restaurants feature imputation, the generation of a virtual group profile, the use of feature weighting, and the automatic selection of the most appropriate aggregation approach for composing group recommendations. The proposal is evaluated in an original recommendation scenario, related to restaurant from Havana City in Cuba, where several restaurant attributes are identified for applying the proposed CB-GRS approach. The experimental protocol evaluates individually each component of the proposal, evidencing their importance as part of the whole framework. Furthermore, the comparison with previous works has been also developed. The proposed approach can be applied in other recommendation scenarios, and in addition, the developed experimental protocol is generalizable for the evaluation of further content-based individual and group recommendation approaches in the tourism domain.

Detection of circulating plasma cells in peripheral blood using deep learning-based morphological analysis.

The presence of circulating plasma cells (CPCs) is an important laboratory indicator for the diagnosis, staging, risk stratification, and progression monitoring of multiple myeloma (MM). Early detection of CPCs in the peripheral blood (PB) followed by timely interventions can significantly improve MM prognosis and delay its progression. Although the conventional cell morphology examination remains the predominant method for CPC detection because of accessibility, its sensitivity and reproducibility are limited by technician expertise and cell quantity constraints. This study aims to develop an artificial intelligence (AI)-based automated system for a more sensitive and efficient CPC morphology detection. A total of 137 bone marrow smears and 72 PB smears from patients with at Zhongshan Hospital, Fudan University, were retrospectively reviewed. Using an AI-powered digital pathology platform, Morphogo, 305,019 cell images were collected for training. Morphogo's efficacy in CPC detection was evaluated with additional 184 PB smears (94 from patients with MM and 90 from those with other hematological malignancies) and compared with manual microscopy. Morphogo achieved 99.64% accuracy, 89.03% sensitivity, and 99.68% specificity in classifying CPCs. At a 0.60 threshold, Morphogo achieved a sensitivity of 96.15%, which was approximately twice that of manual microscopy, with a specificity of 78.03%. Patients with CPCs detected by AI scanning had a significantly shorter median progression-free survival compared with those without CPC detection (18months vs. 34months, p< .01). Morphogo is a highly sensitive system for the automated detection of CPCs, with potential applications in initial screening, prognosis prediction, and posttreatment monitoring for MM patients. Diagnosing and monitoring multiple myeloma (MM), a type of blood cancer, requires identifying and quantifying specific cells called circulating plasma cells (CPCs) in the blood. The conventional method for detecting CPCs is manual microscopic examination, which is time-consuming and lacks sensitivity. This study introduces a highly sensitive CPC detection method using an artificial intelligence-based system, Morphogo. It demonstrated remarkable sensitivity and accuracy, surpassing conventional microscopy. This advanced approach suggests that early and accurate CPC detection is achievable by morphology examination, making efficient CPC screening more accessible for patients with MM. This innovative system has the potential to be used in the diagnosis and risk assessment of MM.

Histopathology image classification: highlighting the gap between manual analysis and AI automation.

The field of histopathological image analysis has evolved significantly with the advent of digital pathology, leading to the development of automated models capable of classifying tissues and structures within diverse pathological images. Artificial intelligence algorithms, such as convolutional neural networks, have shown remarkable capabilities in pathology image analysis tasks, including tumor identification, metastasis detection, and patient prognosis assessment. However, traditional manual analysis methods have generally shown low accuracy in diagnosing colorectal cancer using histopathological images. This study investigates the use of AI in image classification and image analytics using histopathological images using the histogram of oriented gradients method. The study develops an AI-based architecture for image classification using histopathological images, aiming to achieve high performance with less complexity through specific parameters and layers. In this study, we investigate the complicated state of histopathological image classification, explicitly focusing on categorizing nine distinct tissue types. Our research used open-source multi-centered image datasets that included records of 100.000 non-overlapping images from 86 patients for training and 7180 non-overlapping images from 50 patients for testing. The study compares two distinct approaches, training artificial intelligence-based algorithms and manual machine learning models, to automate tissue classification. This research comprises two primary classification tasks: binary classification, distinguishing between normal and tumor tissues, and multi-classification, encompassing nine tissue types, including adipose, background, debris, stroma, lymphocytes, mucus, smooth muscle, normal colon mucosa, and tumor. Our findings show that artificial intelligence-based systems can achieve 0.91 and 0.97 accuracy in binary and multi-class classifications. In comparison, the histogram of directed gradient features and the Random Forest classifier achieved accuracy rates of 0.75 and 0.44 in binary and multi-class classifications, respectively. Our artificial intelligence-based methods are generalizable, allowing them to be integrated into histopathology diagnostics procedures and improve diagnostic accuracy and efficiency. The CNN model outperforms existing machine learning techniques, demonstrating its potential to improve the precision and effectiveness of histopathology image analysis. This research emphasizes the importance of maintaining data consistency and applying normalization methods during the data preparation stage for analysis. It particularly highlights the potential of artificial intelligence to assess histopathological images.

Application of an artificial intelligence-based system in the diagnosis of breast ultrasound images obtained using a smartphone

BackgroundBreast ultrasound (US) is useful for dense breasts, and the introduction of artificial intelligence (AI)-assisted diagnoses of breast US images should be considered. However, the implementation of AI-based technologies in clinical practice is problematic because of the costs of introducing such approaches to hospital information systems (HISs) and the security risk of connecting HIS to the Internet to access AI services. To solve these problems, we developed a system that applies AI to the analysis of breast US images captured using a smartphone.MethodsTraining data were prepared using 115 images of benign lesions and 201 images of malignant lesions acquired at the Division of Breast Surgery, Gifu University Hospital. YOLOv3 (object detection models) was used to detect lesions on US images. A graphical user interface (GUI) was developed to predict an AI server. A smartphone application was also developed for capturing US images displayed on the HIS monitor with its camera and displaying the prediction results received from the AI server. The sensitivity and specificity of the prediction performed on the AI server and via the smartphone were calculated using 60 images spared from the training.ResultsThe established AI showed 100% sensitivity and 75% specificity for malignant lesions and took 0.2 s per prediction with the AI sever. Prediction using a smartphone required 2 s per prediction and showed 100% sensitivity and 97.5% specificity for malignant lesions.ConclusionsGood-quality predictions were obtained using the AI server. Moreover, the quality of the prediction via the smartphone was slightly better than that on the AI server, which can be safely and inexpensively introduced into HISs.

Improving the Performance of the General Course Scheduling System at UPN Veteran Jawa Timur through the Application of the IWCFPSO Algorithm

Algorithm selection is the main key in producing a system, especially an artificial intelligence-based system, for example, a course scheduling system that involves many constraints in producing an optimal schedule. This research develops a scheduling system using the ICWFPSO algorithm which is a development from previous research, namely the development of a scheduling system using the MIPSO algorithm. The use of inertia weight in the MIPSO algorithm which is used as a scheduling algorithm in previous research can be optimized by using a combination of inertia weight and constriction factor. Scheduling system development is carried out using the waterfall method with research steps namely problem and needs analysis, data collection and literature study, system design, system implementation, and ends with testing the performance of the algorithms that are applied to the scheduling system that is built. Based on the tests carried out, it can be concluded that the ICWFPSO algorithm provides 2 times better performance compared to the MIPSO algorithm in terms of optimal schedule generation time. Meanwhile, tests carried out on CPU and RAM usage showed that there was no significant impact on these two parameters through the implementation of MIPSO and ICWFPSO in the course scheduling system created.&#x0D; &#x0D; Keywords: best scheduling, ICWFPSO, number of iterations

Artificial Intelligence-based System for Detecting Attention Levels in Students.

The attention level of students in a classroom can be improved through the use of Artificial Intelligence (AI) techniques. By automatically identifying the attention level, teachers can employ strategies to regain students' focus. This can be achieved through various sources of information. One source is to analyze the emotions reflected on students' faces. AI can detect emotions, such as neutral, disgust, surprise, sadness, fear, happiness, and anger. Additionally, the direction of the students' gaze can also potentially indicate their level of attention. Another source is to observe the students' body posture. By using cameras and deep learning techniques, posture can be analyzed to determine the level of attention. For example, students who are slouching or resting their heads on their desks may have a lower level of attention. Smartwatches distributed to the students can provide biometric and other data, including heart rate and inertial measurements, which can also be used as indicators of attention. By combining these sources of information, an AI system can be trained to identify the level of attention in the classroom. However, integrating the different types of data poses a challenge that requires creating a labeled dataset. Expert input and existing studies are consulted for accurate labeling. In this paper, we propose the integration of such measurements and the creation of a dataset and a potential attention classifier. To provide feedback to the teacher, we explore various methods, such as smartwatches or direct computers. Once the teacher becomes aware of attention issues, they can adjust their teaching approach to re-engage and motivate the students. In summary, AI techniques can automatically identify the students' attention level by analyzing their emotions, gaze direction, body posture, and biometric data. This information can assist teachers in optimizing the teaching-learning process.

A comprehensive survey of automatic dysarthric speech recognition

The need for automated speech recognition has expanded as a result of significant industrial expansion for a variety of automation and human-machine interface applications. The speech impairment brought on by communication disorders, neurogenic speech disorders, or psychological speech disorders limits the performance of different artificial intelligence-based systems. The dysarthric condition is a neurogenic speech disease that restricts the capacity of the human voice to articulate. This article presents a comprehensive survey of the recent advances in the automatic dysarthric speech recognition (DSR) using machine learning (ML) and deep learning (DL) paradigms. It focuses on the methodology, database, evaluation metrics, and major findings from the study of previous approaches. From the literature survey it provides the gaps between exiting work and previous work on DSR and provides the future direction for improvement of DSR. The performance of the various machine and DL schemes is evaluated for the DSR on UASpeech dataset based on accuracy, precision, recall, and F1-score. It is observed that the DL based DSR schems outperforms the ML based DSR schemes.

EPH113 Assessment of the Quality of Life in Patients With Acute Ischemic Stroke Diagnosed With Brainomix Artificial Intelligence-Based System, Under Different Therapeutic Approaches (Standard Treatment, Thrombolysis, Thrombectomy)

EPH113 Assessment of the Quality of Life in Patients With Acute Ischemic Stroke Diagnosed With Brainomix Artificial Intelligence-Based System, Under Different Therapeutic Approaches (Standard Treatment, Thrombolysis, Thrombectomy)

Survey of Automatic Dysarthric Speech Recognition

The need for automated speech recognition has expanded as a result of significant industrial expansion for a variety of automation and human-machine interface applications. The speech impairment brought on by communication disorders, neurogenic speech disorders, or psychological speech disorders limits the performance of different artificial intelligence-based systems. The dysarthric condition is a neurogenic speech disease that restricts the capacity of the human voice to articulate. This article presents a comprehensive survey of the recent advances in the automatic Dysarthric Speech Recognition (DSR) using machine learning and deep learning paradigms. It focuses on the methodology, database, evaluation metrics and major findings from the study of previous approaches. From the literature survey it provides the gaps between exiting work and previous work on DSR and provides the future direction for improvement of DSR.