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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 65850

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Special Issue Editor

Dr. Mugahed A. Al-antari

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Department of Artificial Intelligence, College of Software and Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea
Interests: image classification; image segmentation; medical image processing; biomedical optical imaging; medical signal processing; artificial intelligence; deep learning; machine learning; computer-aided diagnosis; explainable artificial intelligence
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Special Issue Information

Dear Colleagues,

A computer-aided diagnosis (CAD) system involves various stages like detection, segmentation, and classification. Over the last few decades, computer-aided diagnosis systems have become a part of clinical practice. They have the potential to assist clinicians in daily diagnostic tasks. The image processing techniques are fast, repeatable, and robust, which helps physicians to detect, classify, segment, and measure various structures. Medical experts rely on the medical imaging modalities such as computed tomography (CT), microscopic blood smear images, Magnetic Resonance Imaging (MRI), X-ray, and ultrasound (US) to diagnose health challenges and assign treatment prescriptions. Researchers and developers are able to deliver smart solutions for medical imaging diagnoses thanks to the AI-based potential functionalities of machine learning and deep learning technologies.

In this Special Issue, “Artificial Intelligence Advances for Medical Computer-Aided Diagnosis”, we will cover original articles, short communication, and reviews related to various deep learning techniques and computer-aided diagnosis for biomedical systems. We invite all potential authors to submit their research contributions to explore possible methodologies and techniques for the healthcare environment.

This Special Issue is dedicated to high-quality, original research papers in the overlapping fields of:

AI-based Medical Image Diagnosis;
Medical deep learning CAD Systems;
XAI-based Medical Imaging;
Medical Image/Bio-Signal analysis;
Medical Image Segmentation;
Medical Image Segmentation;
Hybrid Medical Knowledge Generation;
Deep Reinforcement Learning;
Healthcare systems;
AI-based Prognosis and Recommendations.

Dr. Mugahed A. Al-antari
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

image classification
image segmentation
medical image processing
biomedical optical imaging
medical signal processing
artificial intelligence
deep learning
machine learning
computer-aided diagnosis
explainable artificial intelligence

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Published Papers (13 papers)

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Editorial

Jump to: Research, Review, Other

6 pages, 181 KiB

Open AccessEditorial

Advancements in Artificial Intelligence for Medical Computer-Aided Diagnosis

by Mugahed A. Al-antari

Diagnostics 2024, 14(12), 1265; https://doi.org/10.3390/diagnostics14121265 - 15 Jun 2024

Cited by 2 | Viewed by 4069

Abstract

Rapid advancements in artificial intelligence (AI) and machine learning (ML) are currently transforming the field of diagnostics, enabling unprecedented accuracy and efficiency in disease detection, classification, and treatment planning. This Special Issue, entitled “Artificial Intelligence Advances for Medical Computer-Aided Diagnosis”, presents a curated collection of cutting-edge research that explores the integration of AI and ML technologies into various diagnostic modalities. The contributions presented here highlight innovative algorithms, models, and applications that pave the way for improved diagnostic capabilities across a range of medical fields, including radiology, pathology, genomics, and personalized medicine. By showcasing both theoretical advancements and practical implementations, this Special Issue aims to provide a comprehensive overview of current trends and future directions in AI-driven diagnostics, fostering further research and collaboration in this dynamic and impactful area of healthcare. We have published a total of 12 research articles in this Special Issue, all collected between March 2023 and December 2023, comprising 1 Editorial cover letter, 9 regular research articles, 1 review article, and 1 article categorized as “other”. Full article

(This article belongs to the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis)

3 pages, 179 KiB

Open AccessEditorial

Artificial Intelligence for Medical Diagnostics—Existing and Future AI Technology!

by Mugahed A. Al-Antari

Diagnostics 2023, 13(4), 688; https://doi.org/10.3390/diagnostics13040688 - 12 Feb 2023

Cited by 46 | Viewed by 12097

Abstract

We would like to express our gratitude to all authors who contributed to the Special Issue of “Artificial Intelligence Advances for Medical Computer-Aided Diagnosis” by providing their excellent and recent research findings for AI-based medical diagnosis [...] Full article

(This article belongs to the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis)

Research

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19 pages, 2549 KiB

Open AccessArticle

High-Dimensional Feature Selection for Automatic Classification of Coronary Stenosis Using an Evolutionary Algorithm

by Miguel-Angel Gil-Rios, Ivan Cruz-Aceves, Arturo Hernandez-Aguirre, Ernesto Moya-Albor, Jorge Brieva, Martha-Alicia Hernandez-Gonzalez and Sergio-Eduardo Solorio-Meza

Diagnostics 2024, 14(3), 268; https://doi.org/10.3390/diagnostics14030268 - 26 Jan 2024

Cited by 2 | Viewed by 1387

Abstract

In this paper, a novel strategy to perform high-dimensional feature selection using an evolutionary algorithm for the automatic classification of coronary stenosis is introduced. The method involves a feature extraction stage to form a bank of 473 features considering different types such as [...] Read more.

(2^{n})

and

n = 473

. The proposed evolutionary search strategy was compared in terms of the Jaccard coefficient and accuracy classification with different state-of-the-art methods. The highest feature selection rate, along with the best classification performance, was obtained with a subset of four features, representing a

99 %

discrimination rate. In the last stage, the feature subset was used as input to train a support vector machine using an independent testing set. The classification of coronary stenosis cases involves a binary classification type by considering positive and negative classes. The highest classification performance was obtained with the four-feature subset in terms of accuracy

(0.86)

and Jaccard coefficient

(0.75)

metrics. In addition, a second dataset containing 2788 instances was formed from a public image database, obtaining an accuracy of

0.89

and a Jaccard Coefficient of

0.80

. Finally, based on the performance achieved with the four-feature subset, they can be suitable for use in a clinical decision support system. Full article

(This article belongs to the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis)

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19 pages, 1931 KiB

Open AccessArticle

Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases

by Adedayo Ogunpola, Faisal Saeed, Shadi Basurra, Abdullah M. Albarrak and Sultan Noman Qasem

Diagnostics 2024, 14(2), 144; https://doi.org/10.3390/diagnostics14020144 - 8 Jan 2024

Cited by 19 | Viewed by 11823

Abstract

Cardiovascular diseases present a significant global health challenge that emphasizes the critical need for developing accurate and more effective detection methods. Several studies have contributed valuable insights in this field, but it is still necessary to advance the predictive models and address the gaps in the existing detection approaches. For instance, some of the previous studies have not considered the challenge of imbalanced datasets, which can lead to biased predictions, especially when the datasets include minority classes. This study’s primary focus is the early detection of heart diseases, particularly myocardial infarction, using machine learning techniques. It tackles the challenge of imbalanced datasets by conducting a comprehensive literature review to identify effective strategies. Seven machine learning and deep learning classifiers, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest, were deployed to enhance the accuracy of heart disease predictions. The research explores different classifiers and their performance, providing valuable insights for developing robust prediction models for myocardial infarction. The study’s outcomes emphasize the effectiveness of meticulously fine-tuning an XGBoost model for cardiovascular diseases. This optimization yields remarkable results: 98.50% accuracy, 99.14% precision, 98.29% recall, and a 98.71% F1 score. Such optimization significantly enhances the model’s diagnostic accuracy for heart disease. Full article

(This article belongs to the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis)

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13 pages, 3590 KiB

Open AccessArticle

Validation of a Deep Learning Chest X-ray Interpretation Model: Integrating Large-Scale AI and Large Language Models for Comparative Analysis with ChatGPT

by Kyu Hong Lee, Ro Woon Lee and Ye Eun Kwon

Diagnostics 2024, 14(1), 90; https://doi.org/10.3390/diagnostics14010090 - 30 Dec 2023

Cited by 5 | Viewed by 5949

Abstract

This study evaluates the diagnostic accuracy and clinical utility of two artificial intelligence (AI) techniques: Kakao Brain Artificial Neural Network for Chest X-ray Reading (KARA-CXR), an assistive technology developed using large-scale AI and large language models (LLMs), and ChatGPT, a well-known LLM. The study was conducted to validate the performance of the two technologies in chest X-ray reading and explore their potential applications in the medical imaging diagnosis domain. The study methodology consisted of randomly selecting 2000 chest X-ray images from a single institution’s patient database, and two radiologists evaluated the readings provided by KARA-CXR and ChatGPT. The study used five qualitative factors to evaluate the readings generated by each model: accuracy, false findings, location inaccuracies, count inaccuracies, and hallucinations. Statistical analysis showed that KARA-CXR achieved significantly higher diagnostic accuracy compared to ChatGPT. In the ‘Acceptable’ accuracy category, KARA-CXR was rated at 70.50% and 68.00% by two observers, while ChatGPT achieved 40.50% and 47.00%. Interobserver agreement was moderate for both systems, with KARA at 0.74 and GPT4 at 0.73. For ‘False Findings’, KARA-CXR scored 68.00% and 68.50%, while ChatGPT scored 37.00% for both observers, with high interobserver agreements of 0.96 for KARA and 0.97 for GPT4. In ‘Location Inaccuracy’ and ‘Hallucinations’, KARA-CXR outperformed ChatGPT with significant margins. KARA-CXR demonstrated a non-hallucination rate of 75%, which is significantly higher than ChatGPT’s 38%. The interobserver agreement was high for KARA (0.91) and moderate to high for GPT4 (0.85) in the hallucination category. In conclusion, this study demonstrates the potential of AI and large-scale language models in medical imaging and diagnostics. It also shows that in the chest X-ray domain, KARA-CXR has relatively higher accuracy than ChatGPT. Full article

(This article belongs to the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis)

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14 pages, 1828 KiB

Open AccessArticle

Supervised Machine Learning Methods for Seasonal Influenza Diagnosis

by Edna Marquez, Eira Valeria Barrón-Palma, Katya Rodríguez, Jesus Savage and Ana Laura Sanchez-Sandoval

Diagnostics 2023, 13(21), 3352; https://doi.org/10.3390/diagnostics13213352 - 31 Oct 2023

Cited by 3 | Viewed by 1751

Abstract

Influenza has been a stationary disease in Mexico since 2009, and this causes a high cost for the national public health system, including its detection using RT-qPCR tests, treatments, and absenteeism in the workplace. Despite influenza’s relevance, the main clinical features to detect the disease defined by international institutions like the World Health Organization (WHO) and the United States Centers for Disease Control and Prevention (CDC) do not follow the same pattern in all populations. The aim of this work is to find a machine learning method to facilitate decision making in the clinical differentiation between positive and negative influenza patients, based on their symptoms and demographic features. The research sample consisted of 15480 records, including clinical and demographic data of patients with a positive/negative RT-qPCR influenza tests, from 2010 to 2020 in the public healthcare institutions of Mexico City. The performance of the methods for classifying influenza cases were evaluated with indices like accuracy, specificity, sensitivity, precision, the f1-measure and the area under the curve (AUC). Results indicate that random forest and bagging classifiers were the best supervised methods; they showed promise in supporting clinical diagnosis, especially in places where performing molecular tests might be challenging or not feasible. Full article

(This article belongs to the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis)

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14 pages, 967 KiB

Open AccessArticle

Parkinson’s Disease Detection Using Filter Feature Selection and a Genetic Algorithm with Ensemble Learning

by Abdullah Marish Ali, Farsana Salim and Faisal Saeed

Diagnostics 2023, 13(17), 2816; https://doi.org/10.3390/diagnostics13172816 - 31 Aug 2023

Cited by 7 | Viewed by 1682

Abstract

Parkinson’s disease (PD) is a neurodegenerative disorder marked by motor and non-motor symptoms that have a severe impact on the quality of life of the affected individuals. This study explores the effect of filter feature selection, followed by ensemble learning methods and genetic selection, on the detection of PD patients from attributes extracted from voice clips from both PD patients and healthy patients. Two distinct datasets were employed in this study. Filter feature selection was carried out by eliminating quasi-constant features. Several classification models were then tested on the filtered data. Decision tree, random forest, and XGBoost classifiers produced remarkable results, especially on Dataset 1, where 100% accuracy was achieved by decision tree and random forest. Ensemble learning methods (voting, stacking, and bagging) were then applied to the best-performing models to see whether the results could be enhanced further. Additionally, genetic selection was applied to the filtered data and evaluated using several classification models for their accuracy and precision. It was found that in most cases, the predictions for PD patients showed more precision than those for healthy individuals. The overall performance was also better on Dataset 1 than on Dataset 2, which had a greater number of features. Full article

(This article belongs to the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis)

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23 pages, 3147 KiB

Open AccessArticle

A New Deep-Learning-Based Model for Breast Cancer Diagnosis from Medical Images

by Salman Zakareya, Habib Izadkhah and Jaber Karimpour

Diagnostics 2023, 13(11), 1944; https://doi.org/10.3390/diagnostics13111944 - 1 Jun 2023

Cited by 14 | Viewed by 5449

Abstract

Breast cancer is one of the most prevalent cancers among women worldwide, and early detection of the disease can be lifesaving. Detecting breast cancer early allows for treatment to begin faster, increasing the chances of a successful outcome. Machine learning helps in the early detection of breast cancer even in places where there is no access to a specialist doctor. The rapid advancement of machine learning, and particularly deep learning, leads to an increase in the medical imaging community’s interest in applying these techniques to improve the accuracy of cancer screening. Most of the data related to diseases is scarce. On the other hand, deep-learning models need much data to learn well. For this reason, the existing deep-learning models on medical images cannot work as well as other images. To overcome this limitation and improve breast cancer classification detection, inspired by two state-of-the-art deep networks, GoogLeNet and residual block, and developing several new features, this paper proposes a new deep model to classify breast cancer. Utilizing adopted granular computing, shortcut connection, two learnable activation functions instead of traditional activation functions, and an attention mechanism is expected to improve the accuracy of diagnosis and consequently decrease the load on doctors. Granular computing can improve diagnosis accuracy by capturing more detailed and fine-grained information about cancer images. The proposed model’s superiority is demonstrated by comparing it to several state-of-the-art deep models and existing works using two case studies. The proposed model achieved an accuracy of 93% and 95% on ultrasound images and breast histopathology images, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis)

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17 pages, 2806 KiB

Open AccessArticle

An Adaptive Early Stopping Technique for DenseNet169-Based Knee Osteoarthritis Detection Model

by Bander Ali Saleh Al-rimy, Faisal Saeed, Mohammed Al-Sarem, Abdullah M. Albarrak and Sultan Noman Qasem

Diagnostics 2023, 13(11), 1903; https://doi.org/10.3390/diagnostics13111903 - 29 May 2023

Cited by 8 | Viewed by 2254

Abstract

Knee osteoarthritis (OA) detection is an important area of research in health informatics that aims to improve the accuracy of diagnosing this debilitating condition. In this paper, we investigate the ability of DenseNet169, a deep convolutional neural network architecture, for knee osteoarthritis detection using X-ray images. We focus on the use of the DenseNet169 architecture and propose an adaptive early stopping technique that utilizes gradual cross-entropy loss estimation. The proposed approach allows for the efficient selection of the optimal number of training epochs, thus preventing overfitting. To achieve the goal of this study, the adaptive early stopping mechanism that observes the validation accuracy as a threshold was designed. Then, the gradual cross-entropy (GCE) loss estimation technique was developed and integrated to the epoch training mechanism. Both adaptive early stopping and GCE were incorporated into the DenseNet169 for the OA detection model. The performance of the model was measured using several metrics including accuracy, precision, and recall. The obtained results were compared with those obtained from the existing works. The comparison shows that the proposed model outperformed the existing solutions in terms of accuracy, precision, recall, and loss performance, which indicates that the adaptive early stopping coupled with GCE improved the ability of DenseNet169 to accurately detect knee OA. Full article

(This article belongs to the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis)

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23 pages, 7266 KiB

Open AccessArticle

Detection of Monkeypox Disease from Human Skin Images with a Hybrid Deep Learning Model

by Fatih Uysal

Diagnostics 2023, 13(10), 1772; https://doi.org/10.3390/diagnostics13101772 - 17 May 2023

Cited by 18 | Viewed by 4525

Abstract

Monkeypox, a virus transmitted from animals to humans, is a DNA virus with two distinct genetic lineages in central and eastern Africa. In addition to zootonic transmission through direct contact with the body fluids and blood of infected animals, monkeypox can also be transmitted from person to person through skin lesions and respiratory secretions of an infected person. Various lesions occur on the skin of infected individuals. This study has developed a hybrid artificial intelligence system to detect monkeypox in skin images. An open source image dataset was used for skin images. This dataset has a multi-class structure consisting of chickenpox, measles, monkeypox and normal classes. The data distribution of the classes in the original dataset is unbalanced. Various data augmentation and data preprocessing operations were applied to overcome this imbalance. After these operations, CSPDarkNet, InceptionV4, MnasNet, MobileNetV3, RepVGG, SE-ResNet and Xception, which are state-of-the-art deep learning models, were used for monkeypox detection. In order to improve the classification results obtained in these models, a unique hybrid deep learning model specific to this study was created by using the two highest-performing deep learning models and the long short-term memory (LSTM) model together. In this hybrid artificial intelligence system developed and proposed for monkeypox detection, test accuracy was 87% and Cohen’s kappa score was 0.8222. Full article

(This article belongs to the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis)

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16 pages, 2594 KiB

Open AccessArticle

A Deep Learning Framework for Cardiac MR Under-Sampled Image Reconstruction with a Hybrid Spatial and k-Space Loss Function

by Walid Al-Haidri, Igor Matveev, Mugahed A. Al-antari and Mikhail Zubkov

Diagnostics 2023, 13(6), 1120; https://doi.org/10.3390/diagnostics13061120 - 15 Mar 2023

Cited by 1 | Viewed by 2427

Abstract

Magnetic resonance imaging (MRI) is an efficient, non-invasive diagnostic imaging tool for a variety of disorders. In modern MRI systems, the scanning procedure is time-consuming, which leads to problems with patient comfort and causes motion artifacts. Accelerated or parallel MRI has the potential to minimize patient stress as well as reduce scanning time and medical costs. In this paper, a new deep learning MR image reconstruction framework is proposed to provide more accurate reconstructed MR images when under-sampled or aliased images are generated. The proposed reconstruction model is designed based on the conditional generative adversarial networks (CGANs) where the generator network is designed in a form of an encoder–decoder U-Net network. A hybrid spatial and k-space loss function is also proposed to improve the reconstructed image quality by minimizing the L1-distance considering both spatial and frequency domains simultaneously. The proposed reconstruction framework is directly compared when CGAN and U-Net are adopted and used individually based on the proposed hybrid loss function against the conventional L1-norm. Finally, the proposed reconstruction framework with the extended loss function is evaluated and compared against the traditional SENSE reconstruction technique using the evaluation metrics of structural similarity (SSIM) and peak signal to noise ratio (PSNR). To fine-tune and evaluate the proposed methodology, the public Multi-Coil k-Space OCMR dataset for cardiovascular MR imaging is used. The proposed framework achieves a better image reconstruction quality compared to SENSE in terms of PSNR by 6.84 and 9.57 when U-Net and CGAN are used, respectively. Similarly, it demonstrates SSIM of the reconstructed MR images comparable to the one provided by the SENSE algorithm when U-Net and CGAN are used. Comparing cases where the proposed hybrid loss function is used against the cases with the simple L1-norm, the reconstruction performance can be noticed to improve by 6.84 and 9.57 for U-Net and CGAN, respectively. To conclude this, the proposed framework using CGAN provides the best reconstruction performance compared with U-Net or the conventional SENSE reconstruction techniques. The proposed framework seems to be useful for the practical reconstruction of cardiac images since it can provide better image quality in terms of SSIM and PSNR. Full article

(This article belongs to the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis)

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Review

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25 pages, 1246 KiB

Open AccessReview

Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains

by Suman Bhakar, Deepak Sinwar, Nitesh Pradhan, Vijaypal Singh Dhaka, Ivan Cherrez-Ojeda, Amna Parveen and Muhammad Umair Hassan

Diagnostics 2023, 13(7), 1212; https://doi.org/10.3390/diagnostics13071212 - 23 Mar 2023

Cited by 5 | Viewed by 3230

Abstract

Disease severity identification using computational intelligence-based approaches is gaining popularity nowadays. Artificial intelligence and deep-learning-assisted approaches are proving to be significant in the rapid and accurate diagnosis of several diseases. In addition to disease identification, these approaches have the potential to identify the severity of a disease. The problem of disease severity identification can be considered multi-class classification, where the class labels are the severity levels of the disease. Plenty of computational intelligence-based solutions have been presented by researchers for severity identification. This paper presents a comprehensive review of recent approaches for identifying disease severity levels using computational intelligence-based approaches. We followed the PRISMA guidelines and compiled several works related to the severity identification of multidisciplinary diseases of the last decade from well-known publishers, such as MDPI, Springer, IEEE, Elsevier, etc. This article is devoted toward the severity identification of two main diseases, viz. Parkinson’s Disease and Diabetic Retinopathy. However, severity identification of a few other diseases, such as COVID-19, autonomic nervous system dysfunction, tuberculosis, sepsis, sleep apnea, psychosis, traumatic brain injury, breast cancer, knee osteoarthritis, and Alzheimer’s disease, was also briefly covered. Each work has been carefully examined against its methodology, dataset used, and the type of disease on several performance metrics, accuracy, specificity, etc. In addition to this, we also presented a few public repositories that can be utilized to conduct research on disease severity identification. We hope that this review not only acts as a compendium but also provides insights to the researchers working on disease severity identification using computational intelligence-based approaches. Full article

(This article belongs to the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis)

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Other

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17 pages, 1066 KiB

Open AccessSystematic Review

Computational Intelligence-Based Stuttering Detection: A Systematic Review

by Raghad Alnashwan, Noura Alhakbani, Abeer Al-Nafjan, Abdulaziz Almudhi and Waleed Al-Nuwaiser

Diagnostics 2023, 13(23), 3537; https://doi.org/10.3390/diagnostics13233537 - 27 Nov 2023

Cited by 2 | Viewed by 3677

Abstract

Stuttering is a widespread speech disorder affecting people globally, and it impacts effective communication and quality of life. Recent advancements in artificial intelligence (AI) and computational intelligence have introduced new possibilities for augmenting stuttering detection and treatment procedures. In this systematic review, the latest AI advancements and computational intelligence techniques in the context of stuttering are explored. By examining the existing literature, we investigated the application of AI in accurately determining and classifying stuttering manifestations. Furthermore, we explored how computational intelligence can contribute to developing innovative assessment tools and intervention strategies for persons who stutter (PWS). We reviewed and analyzed 14 refereed journal articles that were indexed on the Web of Science from 2019 onward. The potential of AI and computational intelligence in revolutionizing stuttering assessment and treatment, which can enable personalized and effective approaches, is also highlighted in this review. By elucidating these advancements, we aim to encourage further research and development in this crucial area, enhancing in due course the lives of PWS. Full article

(This article belongs to the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis)

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