Submit to Special Issue Submit Abstract to Special Issue Review for Diagnostics Propose a Special Issue

Journal Menu

Journal Browser

AI-Driven Intelligent Health Care Diagnostic Solutions: A Machine Learning Approach

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 10647

Share This Special Issue

Special Issue Editors

Prof. Dr. Muhammad Ikram Ullah Lali

E-Mail Website
Guest Editor

Information Sciences Department, University of Education Lahore, Jauhrabad Campus, Khushab 41200, Pakistan
Interests: image processing; computer vision; machine learning; evolutionary computing; social network data analysis

Dr. Hafiz Tayyab Rauf

E-Mail Website
Guest Editor

Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
Interests: image processing; computer vision; machine learning; evolutionary computing; social network data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Several approaches have been proposed in recent decades to improve medical decision systems' robustness. The advent of AI-enabled healthcare data analysis may result in the accurate detection and prevention of diseases. Several machine learning and deep learning models have been proven to be responsive to medical image data to detect cancer and severe diseases promptly.

We require scalable, resilient, and efficient AI-enabled healthcare diagnostics systems to assist clinicians in their expert decisions. These systems must be able to make intelligent decisions in detecting a wide range of severe diseases, including cancers. Evolutionary computing (EC) methods are promising to enhance healthcare data analysis and have been frequently employed in medical image analysis. EC methods are inspired by the biological immune system, a strongly distributive adaptive defense system. Using multilevel defense mechanisms, they make rapid, specific, and often very protective responses against many pathogenic microorganisms. Over millions of years, they have grown through comprehensive optimization, tuning, and the redesigning of several AI-driven healthcare systems. Unlike other computational problems, the design of intelligent healthcare diagnostics solutions through EC techniques must be resistant to determine cancer.

This Special Issue's primary purpose is to cover the theoretical applications of several AI algorithms, i.e., machine learning, deep learning, and their advanced variants for medical image processing.

We intend to present a rational platform for researchers and scientists in academia and engineers to present their most recent research results in medical image processing and disease diagnosis.

Topics of interest include, but are not limited to, the following:

Convolutional neural networks;
Deep learning;
Medical image processing;
Artificial intelligence for health care disease diagnostics;
Machine learning;
Health care data analytics;
Optimization in medical image processing;
Evolutionary computing.

Prof. Dr. Muhammad Ikram Ullah Lali
Dr. Hafiz Tayyab Rauf
Guest Editors

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

convolutional neural networks
deep learning
medical image processing
artificial intelligence for health care disease diagnostics
machine learning
health care data analytics
optimization in medical image processing
evolutionary computing

Published Papers (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

17 pages, 6981 KiB

Open AccessArticle

A Preliminary Diagnostic Model for Forward Head Posture among Adolescents Using Forward Neck Tilt Angle and Radiographic Sagittal Alignment Parameters

by Young Jae Moon, Tae Young Ahn, Seung Woo Suh, Kun-Bo Park, Sam Yeol Chang, Do-Kun Yoon, Moo-Sub Kim, Hyeonjoo Kim, Yong Dae Jeon and Jae Hyuk Yang

Diagnostics 2024, 14(4), 394; https://doi.org/10.3390/diagnostics14040394 - 11 Feb 2024

Viewed by 693

Abstract

Despite numerous attempts to correct forward head posture (FHP), definitive evidence-based screening and diagnostic methods remain elusive. This study proposes a preliminary diagnostic methodology for FHP, utilizing a noninvasive body angle measurement system as a screening test for FHP and incorporating radiological parameters for sagittal alignment. We enrolled 145 adolescents for FHP screening. The forward neck tilt angle (FNTA), defined as the angle between the vertical line and the line connecting the participant’s acromion and tragus, was measured using the POM-Checker (a noninvasive depth sensor-based body angle measurement system). A whole-spine standing lateral radiograph was obtained, and eight sagittal alignment parameters were measured. Statistical analyses of the association between the FNTA and eight sagittal alignment parameters were conducted. We used 70% of the participant data to establish a preliminary diagnostic model for FHP based on FNTA and each sagittal alignment parameter. The accuracy of the model was evaluated using the remaining 30% of the participant data. All radiological parameters of sagittal alignment showed weak statistical significance with respect to FNTA (best case: r = 0.16, p = 0.0500; cranial tilt). The proposed preliminary diagnostic model for FHP demonstrated 95.35% agreement. Notably, the model using FNTA without radiological parameters accurately identified (100%) participants who required radiographic scanning for FHP diagnosis. Owing to the weak statistical significance of the association between radiological parameters and external body angle, both factors must be considered for accurate FHP diagnosis. When a clear and severe angle variation is observed in an external body angle check, medical professionals should perform radiographic scanning for an accurate FHP diagnosis. In conclusion, FNTA assessment of FNTA through the proposed preliminary diagnostic model is a significant screening factor for selecting participants who must undergo radiographic scanning so that a diagnosis of FHP can be obtained. Full article

(This article belongs to the Special Issue AI-Driven Intelligent Health Care Diagnostic Solutions: A Machine Learning Approach)

► Show Figures

Figure 1

23 pages, 12360 KiB

Open AccessArticle

A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble

by Qiuyu An, Wei Chen and Wei Shao

Diagnostics 2024, 14(4), 390; https://doi.org/10.3390/diagnostics14040390 - 11 Feb 2024

Cited by 1 | Viewed by 1271

Abstract

In the domain of AI-driven healthcare, deep learning models have markedly advanced pneumonia diagnosis through X-ray image analysis, thus indicating a significant stride in the efficacy of medical decision systems. This paper presents a novel approach utilizing a deep convolutional neural network that effectively amalgamates the strengths of EfficientNetB0 and DenseNet121, and it is enhanced by a suite of attention mechanisms for refined pneumonia image classification. Leveraging pre-trained models, our network employs multi-head, self-attention modules for meticulous feature extraction from X-ray images. The model’s integration and processing efficiency are further augmented by a channel-attention-based feature fusion strategy, one that is complemented by a residual block and an attention-augmented feature enhancement and dynamic pooling strategy. Our used dataset, which comprises a comprehensive collection of chest X-ray images, represents both healthy individuals and those affected by pneumonia, and it serves as the foundation for this research. This study delves deep into the algorithms, architectural details, and operational intricacies of the proposed model. The empirical outcomes of our model are noteworthy, with an exceptional performance marked by an accuracy of 95.19%, a precision of 98.38%, a recall of 93.84%, an F1 score of 96.06%, a specificity of 97.43%, and an AUC of 0.9564 on the test dataset. These results not only affirm the model’s high diagnostic accuracy, but also highlight its promising potential for real-world clinical deployment. Full article

(This article belongs to the Special Issue AI-Driven Intelligent Health Care Diagnostic Solutions: A Machine Learning Approach)

► Show Figures

Figure 1

23 pages, 4285 KiB

Open AccessArticle

Early Prediction Model of Macrosomia Using Machine Learning for Clinical Decision Support

by Md. Shamshuzzoha and Md. Motaharul Islam

Diagnostics 2023, 13(17), 2754; https://doi.org/10.3390/diagnostics13172754 - 25 Aug 2023

Cited by 1 | Viewed by 2119

Abstract

The condition of fetal overgrowth, also known as macrosomia, can cause serious health complications for both the mother and the infant. It is crucial to identify high-risk macrosomia-relevant pregnancies and intervene appropriately. Despite this need, there are several gaps in research related to macrosomia, including limited predictive models, insufficient machine learning applications, ineffective interventions, and inadequate understanding of how to integrate machine learning models into clinical decision-making. To address these gaps, we developed a machine learning-based model that uses maternal characteristics and medical history to predict macrosomia. Three different algorithms, namely logistic regression, support vector machine, and random forest, were used to develop the model. Based on the evaluation metrics, the logistic regression algorithm provided the best results among the three. The logistic regression algorithm was chosen as the final algorithm to predict macrosomia. The hyper parameters of the logistic regression model were tuned using cross-validation to achieve the best possible performance. Our results indicate that machine learning-based models have the potential to improve macrosomia prediction and enable appropriate interventions for high-risk pregnancies, leading to better health outcomes for both mother and fetus. By leveraging machine learning algorithms and addressing research gaps related to macrosomia, we can potentially reduce the health risks associated with this condition and make informed decisions about high-risk pregnancies. Full article

(This article belongs to the Special Issue AI-Driven Intelligent Health Care Diagnostic Solutions: A Machine Learning Approach)

► Show Figures

Figure 1

13 pages, 3022 KiB

Open AccessArticle

Machine Learning Models for Prediction of Severe Pneumocystis carinii Pneumonia after Kidney Transplantation: A Single-Center Retrospective Study

by Yiting Liu, Tao Qiu, Haochong Hu, Chenyang Kong, Yalong Zhang, Tianyu Wang, Jiangqiao Zhou and Jilin Zou

Diagnostics 2023, 13(17), 2735; https://doi.org/10.3390/diagnostics13172735 - 23 Aug 2023

Cited by 1 | Viewed by 1016

Abstract

Background: The objective of this study was to formulate and validate a prognostic model for postoperative severe Pneumocystis carinii pneumonia (SPCP) in kidney transplant recipients utilizing machine learning algorithms, and to compare the performance of various models. Methods: Clinical manifestations and laboratory test results upon admission were gathered as variables for 88 patients who experienced PCP following kidney transplantation. The most discriminative variables were identified, and subsequently, Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), Light Gradient Boosting Machine (LGBM), and eXtreme Gradient Boosting (XGB) models were constructed. Finally, the models’ predictive capabilities were assessed through ROC curves, sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1-scores. The Shapley additive explanations (SHAP) algorithm was employed to elucidate the contributions of the most effective model’s variables. Results: Through lasso regression, five features—hemoglobin (Hb), Procalcitonin (PCT), C-reactive protein (CRP), progressive dyspnea, and Albumin (ALB)—were identified, and six machine learning models were developed using these variables after evaluating their correlation and multicollinearity. In the validation cohort, the RF model demonstrated the highest AUC (0.920 (0.810–1.000), F1-Score (0.8), accuracy (0.885), sensitivity (0.818), PPV (0.667), and NPV (0.913) among the six models, while the XGB and KNN models exhibited the highest specificity (0.909) among the six models. Notably, CRP exerted a significant influence on the models, as revealed by SHAP and feature importance rankings. Conclusions: Machine learning algorithms offer a viable approach for constructing prognostic models to predict the development of severe disease following PCP in kidney transplant recipients, with potential practical applications. Full article

(This article belongs to the Special Issue AI-Driven Intelligent Health Care Diagnostic Solutions: A Machine Learning Approach)

► Show Figures

Figure 1

23 pages, 9869 KiB

Open AccessArticle

Enhancement of Diabetic Retinopathy Prognostication Using Deep Learning, CLAHE, and ESRGAN

by Ghadah Alwakid, Walaa Gouda and Mamoona Humayun

Diagnostics 2023, 13(14), 2375; https://doi.org/10.3390/diagnostics13142375 - 14 Jul 2023

Cited by 2 | Viewed by 1393

Abstract

One of the primary causes of blindness in the diabetic population is diabetic retinopathy (DR). Many people could have their sight saved if only DR were detected and treated in time. Numerous Deep Learning (DL)-based methods have been presented to improve human analysis. Using a DL model with three scenarios, this research classified DR and its severity stages from fundus images using the “APTOS 2019 Blindness Detection” dataset. Following the adoption of the DL model, augmentation methods were implemented to generate a balanced dataset with consistent input parameters across all test scenarios. As a last step in the categorization process, the DenseNet-121 model was employed. Several methods, including Enhanced Super-resolution Generative Adversarial Networks (ESRGAN), Histogram Equalization (HIST), and Contrast Limited Adaptive HIST (CLAHE), have been used to enhance image quality in a variety of contexts. The suggested model detected the DR across all five APTOS 2019 grading process phases with the highest test accuracy of 98.36%, top-2 accuracy of 100%, and top-3 accuracy of 100%. Further evaluation criteria (precision, recall, and F1-score) for gauging the efficacy of the proposed model were established with the help of APTOS 2019. Furthermore, comparing CLAHE + ESRGAN against both state-of-the-art technology and other recommended methods, it was found that its use was more effective in DR classification. Full article

(This article belongs to the Special Issue AI-Driven Intelligent Health Care Diagnostic Solutions: A Machine Learning Approach)

► Show Figures

Figure 1

16 pages, 1052 KiB

Open AccessArticle

Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome

by Chadi S. Barakat, Konstantin Sharafutdinov, Josefine Busch, Sina Saffaran, Declan G. Bates, Jonathan G. Hardman, Andreas Schuppert, Sigurður Brynjólfsson, Sebastian Fritsch and Morris Riedel

Diagnostics 2023, 13(12), 2098; https://doi.org/10.3390/diagnostics13122098 - 17 Jun 2023

Viewed by 1577

Abstract

Acute Respiratory Distress Syndrome (ARDS) is a condition that endangers the lives of many Intensive Care Unit patients through gradual reduction of lung function. Due to its heterogeneity, this condition has been difficult to diagnose and treat, although it has been the subject of continuous research, leading to the development of several tools for modeling disease progression on the one hand, and guidelines for diagnosis on the other, mainly the “Berlin Definition”. This paper describes the development of a deep learning-based surrogate model of one such tool for modeling ARDS onset in a virtual patient: the Nottingham Physiology Simulator. The model-development process takes advantage of current machine learning and data-analysis techniques, as well as efficient hyperparameter-tuning methods, within a high-performance computing-enabled data science platform. The lightweight models developed through this process present comparable accuracy to the original simulator (per-parameter R² > 0.90). The experimental process described herein serves as a proof of concept for the rapid development and dissemination of specialised diagnosis support systems based on pre-existing generalised mechanistic models, making use of supercomputing infrastructure for the development and testing processes and supported by open-source software for streamlined implementation in clinical routines. Full article

(This article belongs to the Special Issue AI-Driven Intelligent Health Care Diagnostic Solutions: A Machine Learning Approach)

► Show Figures

Figure 1

23 pages, 5636 KiB

Open AccessArticle

Human Pathogenic Monkeypox Disease Recognition Using Q-Learning Approach

by Malathi Velu, Rajesh Kumar Dhanaraj, Balamurugan Balusamy, Seifedine Kadry, Yang Yu, Ahmed Nadeem and Hafiz Tayyab Rauf

Diagnostics 2023, 13(8), 1491; https://doi.org/10.3390/diagnostics13081491 - 20 Apr 2023

Cited by 4 | Viewed by 1538

Abstract

While the world is working quietly to repair the damage caused by COVID-19’s widespread transmission, the monkeypox virus threatens to become a global pandemic. There are several nations that report new monkeypox cases daily, despite the virus being less deadly and contagious than COVID-19. Monkeypox disease may be detected using artificial intelligence techniques. This paper suggests two strategies for improving monkeypox image classification precision. Based on reinforcement learning and parameter optimization for multi-layer neural networks, the suggested approaches are based on feature extraction and classification: the Q-learning algorithm determines the rate at which an act occurs in a particular state; Malneural networks are binary hybrid algorithms that improve the parameters of neural networks. The algorithms are evaluated using an openly available dataset. In order to analyze the proposed optimization feature selection for monkeypox classification, interpretation criteria were utilized. In order to evaluate the efficiency, significance, and robustness of the suggested algorithms, a series of numerical tests were conducted. There were 95% precision, 95% recall, and 96% f1 scores for monkeypox disease. As compared to traditional learning methods, this method has a higher accuracy value. The overall macro average was around 0.95, and the overall weighted average was around 0.96. When compared to the benchmark algorithms, DDQN, Policy Gradient, and Actor–Critic, the Malneural network had the highest accuracy (around 0.985). In comparison with traditional methods, the proposed methods were found to be more effective. Clinicians can use this proposal to treat monkeypox patients and administration agencies can use it to observe the origin and current status of the disease. Full article

(This article belongs to the Special Issue AI-Driven Intelligent Health Care Diagnostic Solutions: A Machine Learning Approach)

► Show Figures

Journal Menu

Journal Browser

AI-Driven Intelligent Health Care Diagnostic Solutions: A Machine Learning Approach

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (7 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI